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1. Bidirectional Dijkstra's Algorithm is Instance-Optimal

Author: Haeupler, Bernhard, Hladík, Richard, Rozhon, Vaclav, Tarjan, Robert E., and Tětek, Jakub
Subjects: Computer Science - Data Structures and Algorithms
Abstract: While Dijkstra's algorithm has near-optimal time complexity for the problem of finding the shortest $st$-path, in practice, other algorithms are often superior on huge graphs. A prominent such example is the bidirectional search, which executes Dijkstra's algorithm from both endpoints in parallel and stops when these executions meet. In this paper, we give a strong theoretical justification for the use of such bidirectional search algorithms. We prove that for weighted multigraphs, both directed and undirected, a careful implementation of bidirectional search is instance-optimal with respect to the number of edges it explores. That is, we prove that no correct algorithm can outperform our implementation of bidirectional search on any single instance by more than a constant factor. For unweighted graphs, we show that bidirectional search is instace-optimal up to a factor of $O(\Delta)$ where $\Delta$ is the maximum degree of the graph. We also show that this is the best possible., Comment: To appear at SOSA 2025
Published: 2024

2. Jet modification via $\pi^0$-hadron correlations in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Apadula, N., Aphecetche, L., Asai, J., Asano, H., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Bandara, N. S., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Baumann, C., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Bickley, A. A., Blankenship, B., Blau, D. S., Boissevain, J. G., Bok, J. S., Borel, H., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Chang, B. S., Chang, W. C., Charvet, J. L., Chen, C. -H., Chen, D., Chernichenko, S., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Choudhury, R. K., Chujo, T., Chung, P., Churyn, A., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Corliss, R., Csanád, M., Csörgő, T., d'Enterria, D., Dahms, T., Dairaku, S., Danley, T. W., Das, K., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Diss, P. B., Donadelli, M., Doomra, V., Do, J. H., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Efremenko, Y. V., Ellinghaus, F., En'yo, H., Engelmore, T., Enokizono, A., Esha, R., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gallus, P., Gal, C., Garg, P., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, R., Han, S. Y., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., Heffner, M., Hemmick, T. K., Hester, T., He, X., Hill, J. C., Hodges, A., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Jezghani, M., Jiang, X., Jin, J., Ji, Z., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kang, J. H., Kapustinsky, J., Kawall, D., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kimelman, B., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, M., Kim, S. H., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Komkov, B., Konno, M., Koster, J., Kotov, D., Kovacs, L., Kozlov, A., Kravitz, A., Král, A., Kunde, G. J., Kurgyis, B., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lai, Y. S., Lajoie, J. G., Layton, D., Lebedev, A., Lee, D. M., Lee, K. B., Lee, S., Lee, S. H., Lee, T., Leitch, M. J., Leite, M. A. L., Lenzi, B., Liebing, P., Lim, S. H., Litvinenko, A., Liu, H., Liu, M. X., Liška, T., Li, X., Lokos, S., Loomis, D. A., Love, B., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Matathias, F., Mašek, L., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Moukhanova, T. V., Mukhopadhyay, D., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagamiya, S., Nagashima, K., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, T., Nakano, K., Nattrass, C., Netrakanti, P. K., Newby, J., Nguyen, M., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Okada, K., Oka, M., Onuki, Y., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, J., Park, J. S., Park, S., Park, W. J., Patel, M., Pate, S. F., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Purschke, M. L., Purwar, A. K., Qu, H., Rakotozafindrabe, A., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Rembeczki, S., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Takagui, E. M., Taketani, A., Tanabe, R., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tomita, Y., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Veicht, A., Velkovska, J., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Vértesi, R., Wang, X. R., Watanabe, Y., Watanabe, Y. S., Wei, F., Wessels, J., White, A. S., White, S. N., Winter, D., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoon, I., Yoo, J. H., Young, G. R., Younus, I., Yushmanov, I. E., Yu, H., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zhou, S., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 535 authors from 84 institutions, 12 pages, 8 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024
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3. Fast and Simple Sorting Using Partial Information

Author: Haeupler, Bernhard, Hladík, Richard, Iacono, John, Rozhon, Vaclav, Tarjan, Robert, and Tětek, Jakub
Subjects: Computer Science - Data Structures and Algorithms, F.2.2, G.2.2
Abstract: We consider the problem of sorting $n$ items, given the outcomes of $m$ pre-existing comparisons. We present a simple, natural deterministic algorithm that runs in $O(m+\log T)$ time and does $O(\log T)$ comparisons, where $T$ is the number of total orders consistent with the pre-existing comparisons. Our running time and comparison bounds are best possible up to constant factors, thus resolving a problem that has been studied intensely since 1976 (Fredman, Theoretical Computer Science). The best previous algorithm with a bound of $O(\lg T)$ on the number of comparisons has a time bound of $O(n^{2.5})$ and is more complicated. (A recent independent and concurrent work by Van der Hoog and Rutschmann implies an $O(n^{2.371552})$-time algorithm with $O(\log T)$ comparisons.) Our algorithm combines three classic algorithms: topological sort, heapsort with the right kind of heap, and efficient insertion into a sorted list. It outputs the items in sorted order one by one. As a result, it can be modified to solve the important and more general top-$k$ sorting problem: Given $k$ and the outcomes of some pre-existing comparisons, output the smallest $k$ items in sorted order. The modified algorithm solves the top-$k$ sorting problem in minimum time and comparisons, to within constant factors.
Published: 2024

4. Minimum-cost paths for electric cars

Author: Dorfman, Dani, Kaplan, Haim, Tarjan, Robert E., Thorup, Mikkel, and Zwick, Uri
Subjects: Computer Science - Data Structures and Algorithms, 68W05, F.2.2
Abstract: An electric car equipped with a battery of a finite capacity travels on a road network with an infrastructure of charging stations. Each charging station has a possibly different cost per unit of energy. Traversing a given road segment requires a specified amount of energy that may be positive, zero or negative. The car can only traverse a road segment if it has enough charge to do so (the charge cannot drop below zero), and it cannot charge its battery beyond its capacity. To travel from one point to another the car needs to choose a \emph{travel plan} consisting of a path in the network and a recharging schedule that specifies how much energy to charge at each charging station on the path, making sure of having enough energy to reach the next charging station or the destination. The cost of the plan is the total charging cost along the chosen path. We reduce the problem of computing plans between every two junctions of the network to two problems: Finding optimal energetic paths when no charging is allowed and finding standard shortest paths. When there are no negative cycles in the network, we obtain an $O(n^3)$-time algorithm for computing all-pairs travel plans, where~$n$ is the number of junctions in the network. We obtain slightly faster algorithms under some further assumptions. We also consider the case in which a bound is placed on the number of rechargings allowed., Comment: 9 pages, 2 figures, SOSA 2024
Published: 2024

5. Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Author: Nawrot, Piotr, Łańcucki, Adrian, Chochowski, Marcin, Tarjan, David, and Ponti, Edoardo M.
Subjects: Computer Science - Computation and Language
Abstract: Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value representations for past tokens, whose size scales linearly with the input sequence length and batch size. As a solution, we propose Dynamic Memory Compression (DMC), a method for online key-value cache compression at inference time. Most importantly, the model learns to apply different compression ratios in different heads and layers. We retrofit pre-trained LLMs such as Llama 2 (7B, 13B and 70B) into DMC Transformers, achieving up to 7x throughput increase during auto-regressive inference on an NVIDIA H100 GPU. DMC is applied via continued pre-training on a negligible percentage of the original data without adding any extra parameters. DMC preserves the original downstream performance with up to 4x cache compression, outperforming up-trained grouped-query attention (GQA) and key-value eviction policies (H$_2$O, TOVA). GQA and DMC can be even combined to obtain compounded gains. Hence, DMC can serve as a drop-in replacement for KV caching in existing LLMs to fit longer contexts and larger batches within any given memory budget.
Published: 2024

6. Universal Optimality of Dijkstra via Beyond-Worst-Case Heaps

Author: Haeupler, Bernhard, Hladík, Richard, Rozhoň, Václav, Tarjan, Robert, and Tětek, Jakub
Subjects: Computer Science - Data Structures and Algorithms, F.2.2, G.2.2
Abstract: This paper proves that Dijkstra's shortest-path algorithm is universally optimal in both its running time and number of comparisons when combined with a sufficiently efficient heap data structure. Universal optimality is a powerful beyond-worst-case performance guarantee for graph algorithms that informally states that a single algorithm performs as well as possible for every single graph topology. We give the first application of this notion to any sequential algorithm. We design a new heap data structure with a working-set property guaranteeing that the heap takes advantage of locality in heap operations. Our heap matches the optimal (worst-case) bounds of Fibonacci heaps but also provides the beyond-worst-case guarantee that the cost of extracting the minimum element is merely logarithmic in the number of elements inserted after it instead of logarithmic in the number of all elements in the heap. This makes the extraction of recently added elements cheaper. We prove that this working-set property guarantees universal optimality for the problem of ordering vertices by their distance from the source vertex: The sequence of heap operations generated by any run of Dijkstra's algorithm on a fixed graph possesses enough locality that one can couple the number of comparisons performed by any heap with our working-set bound to the minimum number of comparisons required to solve the distance ordering problem on this graph for a worst-case choice of arc lengths.
Published: 2023

7. Zip-zip Trees: Making Zip Trees More Balanced, Biased, Compact, or Persistent

Author: Gila, Ofek, Goodrich, Michael T., and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms, E.1
Abstract: We define simple variants of zip trees, called zip-zip trees, which provide several advantages over zip trees, including overcoming a bias that favors smaller keys over larger ones. We analyze zip-zip trees theoretically and empirically, showing, e.g., that the expected depth of a node in an $n$-node zip-zip tree is at most $1.3863\log n-1+o(1)$, which matches the expected depth of treaps and binary search trees built by uniformly random insertions. Unlike these other data structures, however, zip-zip trees achieve their bounds using only $O(\log\log n)$ bits of metadata per node, w.h.p., as compared to the $\Theta(\log n)$ bits per node required by treaps. In fact, we even describe a ``just-in-time'' zip-zip tree variant, which needs just an expected $O(1)$ number of bits of metadata per node. Moreover, we can define zip-zip trees to be strongly history independent, whereas treaps are generally only weakly history independent. We also introduce \emph{biased zip-zip trees}, which have an explicit bias based on key weights, so the expected depth of a key, $k$, with weight, $w_k$, is $O(\log (W/w_k))$, where $W$ is the weight of all keys in the weighted zip-zip tree. Finally, we show that one can easily make zip-zip trees partially persistent with only $O(n)$ space overhead w.h.p., Comment: v2 to appear in the journal Algorithmica, 24 pages, 9 figures
Published: 2023

8. Efficiency of Self-Adjusting Heaps

Author: Sinnamon, Corwin and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: Since the invention of the pairing heap by Fredman et al., it has been an open question whether this or any other simple "self-adjusting" heap supports decrease-key operations on $n$-item heaps in $O(\log\log n)$ time. Using powerful new techniques, we answer this question in the affirmative. We prove that both slim and smooth heaps, recently introduced self-adjusting heaps, support heap operations on an $n$-item heap in the following amortized time bounds: $O(\log n)$ for delete-min and delete, $O(\log\log n)$ for decrease-key, and $O(1)$ for all other heap operations, including insert and meld. We also analyze the multipass pairing heap, a variant of pairing heaps. For this heap implementation, we obtain the same bounds except for decrease-key, for which our bound is $O(\log\log n \log\log\log n)$. Our bounds significantly improve the best previously known bounds for all three data structures. For slim and smooth heaps our bounds are tight, since they match lower bounds of Iacono and Ozkan; for multipass pairing heaps our bounds are tight except for decrease-key, which by the lower bounds of Fredman and Iacono and \"Ozkan must take $O(\log\log n)$ amortized time if delete-min takes $O(\log n)$ time.
Published: 2023

9. Optimal energetic paths for electric cars

Author: Dorfman, Dani, Kaplan, Haim, Tarjan, Robert E., and Zwick, Uri
Subjects: Computer Science - Data Structures and Algorithms, 68W05, F.2.2
Abstract: A weighted directed graph $G=(V,A,c)$, where $A\subseteq V\times V$ and $c:A\to R$, describes a road network in which an electric car can roam. An arc $uv$ models a road segment connecting the two vertices $u$ and $v$. The cost $c(uv)$ of an arc $uv$ is the amount of energy the car needs to traverse the arc. This amount may be positive, zero or negative. To make the problem realistic, we assume there are no negative cycles. The car has a battery that can store up to $B$ units of energy. It can traverse an arc $uv\in A$ only if it is at $u$ and the charge $b$ in its battery satisfies $b\ge c(uv)$. If it traverses the arc, it reaches $v$ with a charge of $\min(b-c(uv),B)$. Arcs with positive costs deplete the battery, arcs with negative costs charge the battery, but not above its capacity of $B$. Given $s,t\in V$, can the car travel from $s$ to $t$, starting at $s$ with an initial charge $b$, where $0\le b\le B$? If so, what is the maximum charge with which the car can reach $t$? Equivalently, what is the smallest $\delta_{B,b}(s,t)$ such that the car can reach $t$ with a charge of $b-\delta_{B,b}(s,t)$, and which path should the car follow to achieve this? We refer to $\delta_{B,b}(s,t)$ as the energetic cost of traveling from $s$ to $t$. We let $\delta_{B,b}(s,t)=\infty$ if the car cannot travel from $s$ to $t$ starting with an initial charge of $b$. The problem of computing energetic costs is a strict generalization of the standard shortest paths problem. We show that the single-source minimum energetic paths problem can be solved using simple, but subtle, adaptations of the Bellman-Ford and Dijkstra algorithms. To make Dijkstra's algorithm work in the presence of negative arcs, but no negative cycles, we use a variant of the $A^*$ search heuristic. These results are explicit or implicit in some previous papers. We provide a simpler and unified description of these algorithms., Comment: 11 pages
Published: 2023

10. Adoption status of integrated pest management (IPM) practices among vegetable growers of Lamjung district of Nepal

Author: Sukraraj Shrestha, Lal Prasad Amgain, Prem Pandey, Tarjan Bhandari, and Sudip Khatiwada
Subjects: Bio-pesticides, Logistic regression, Sustainable agriculture, Synthetic pesticides, Vegetable cultivation, Science (General), Q1-390, Social sciences (General), H1-99
Abstract: The widespread use of highly toxic pesticides for agricultural purposes has raised concerns about their hazardous impact on both human health and the environment. Integrated pest management (IPM) is a strategy designed to tackle pest problems and reduce pesticide use, with the aim of protecting both human health and the environment. This study was conducted in Besishahar, Sundarbazar, Rainas, and Madhyenepal municipalities of the Lamjung district of Nepal in the year 2023 with a sample of 100 vegetable-growing farmers to assess the adoption of IPM practices in vegetable cultivation. Descriptive and inferential statistics were used to analyze the data, and the logit model was used to identify the factors affecting the adoption of IPM practices in vegetable cultivation among farmers. Respondents were grouped into two categories, adopter and non-adopter, based on the extent of adoption of IPM practices. The findings revealed that only 37 % of the total respondents adopted IPM practices for vegetable cultivation. The easy availability of chemical pesticides and lack of bio-pesticides were the major constraints for the adoption of IPM practices in the study area. The output of the binary logit model indicated that greater participation in training, higher education levels, and increased contact with extension agents significantly influence the adoption of IPM practices in vegetable cultivation. The findings could be used to formulate better policies towards increasing the adoption of sustainable approaches in agriculture and regulation.
Published: 2024
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11. Optimal resizable arrays

Author: Tarjan, Robert E. and Zwick, Uri
Subjects: Computer Science - Data Structures and Algorithms, 68P05, 68Q25, F.2.2, E.2
Abstract: A \emph{resizable array} is an array that can \emph{grow} and \emph{shrink} by the addition or removal of items from its end, or both its ends, while still supporting constant-time \emph{access} to each item stored in the array given its \emph{index}. Since the size of an array, i.e., the number of items in it, varies over time, space-efficient maintenance of a resizable array requires dynamic memory management. A standard doubling technique allows the maintenance of an array of size~$N$ using only $O(N)$ space, with $O(1)$ amortized time, or even $O(1)$ worst-case time, per operation. Sitarski and Brodnik et al.\ describe much better solutions that maintain a resizable array of size~$N$ using only $N+O(\sqrt{N})$ space, still with $O(1)$ time per operation. Brodnik et al.\ give a simple proof that this is best possible. We distinguish between the space needed for \emph{storing} a resizable array, and accessing its items, and the \emph{temporary} space that may be needed while growing or shrinking the array. For every integer $r\ge 2$, we show that $N+O(N^{1/r})$ space is sufficient for storing and accessing an array of size~$N$, if $N+O(N^{1-1/r})$ space can be used briefly during grow and shrink operations. Accessing an item by index takes $O(1)$ worst-case time while grow and shrink operations take $O(r)$ amortized time. Using an exact analysis of a \emph{growth game}, we show that for any data structure from a wide class of data structures that uses only $N+O(N^{1/r})$ space to store the array, the amortized cost of grow is $\Omega(r)$, even if only grow and access operations are allowed. The time for grow and shrink operations cannot be made worst-case, unless $r=2$., Comment: Preliminary version appeared in SOSA 2023. Section 6.3 revised
Published: 2022

12. A Simpler Proof that Pairing Heaps Take O(1) Amortized Time per Insertion

Author: Sinnamon, Corwin and Tarjan, Robert
Subjects: Computer Science - Data Structures and Algorithms, 68P05 (Primary) 68W40 (Secondary), F.2.2, E.1, E.2
Abstract: The pairing heap is a simple "self-adjusting" implementation of a heap (priority queue). Inserting an item into a pairing heap or decreasing the key of an item takes O(1) time worst-case, as does melding two heaps. But deleting an item of minimum key can take time linear in the heap size in the worst case. The paper that introduced the pairing heap proved an O(log n) amortized time bound for each heap operation, where n is the number of items in the heap or heaps involved in the operation, by charging all but O(log n) of the time for each deletion to non-deletion operations, O(log n) to each. Later Iacono found a way to reduce the amortized time per insertion to O(1) and that of meld to zero while preserving the O(log n) amortized time bound for the other update operations. We give a simpler proof of Iacono's result with significantly smaller constant factors. Our analysis uses the natural representation of pairing heaps instead of the conversion to a binary tree used in the original analysis and in Iacono's., Comment: 13 pages, 1 figure, submitted to SOSA 2023
Published: 2022

13. Habitat Use by Breeding Waterbirds in Relation to Tidal Marsh Restoration in the San Francisco Bay Estuary

Author: Schacter, Carley R., Hartman, C. Alex, Herzog, Mark P., Peterson, Sarah H., Tarjan, L. Max, Wang, Yiwei, Strong, Cheryl, Tertes, Rachel, Warnock, Nils, and Ackerman, Joshua T.
Subjects: American Avocet, behavior, Black-necked Stilt, Black Skimmer, Caspian Tern, Forster’s Tern, managed ponds, micro-habitat, nesting islands, tidal marsh restoration
Abstract: The South Bay Salt Pond Restoration Project aims to restore many former salt production ponds, now managed for wildlife and water quality, to tidal marsh. However, because managed ponds support large densities of breeding waterbirds, reduction of pond habitat may influence breeding waterbird distribution and abundance. We investigated habitat use associated with breeding, feeding, and roosting behaviors during the breeding season for American Avocets (Recurvirostra americana), Black-necked Stilts (Himantopus mexicanus), Forster’s Terns (Sterna forsteri), and Caspian Terns (Hydroprogne caspia) in south San Francisco Bay in 2019 after substantial tidal marsh restoration, and compared results to a 2001 survey (before restoration). In 2019, managed ponds (26% of currently available habitat) were selected by waterbirds engaged in breeding behaviors (> 39% of observations), foraging (> 42%), and roosting (> 73%). Waterbirds avoided tidal habitats (43% of available habitat), comprising
Published: 2023

14. Transforming an Academic into a Leader: Providing a Framework and Behaviors for Leading Teams in the Workplace

Author: Tarjan, L. Max, Raschke, Lynne, and Hunter, Lisa
Subjects: leadership, management, professional development, teams
Abstract: In many organizations (e.g., higher education, non-profits, small companies), individuals are called upon to lead small groups of people to complete one or more tasks both in formal roles and in informal settings. For example, department heads, committee chairs, project leads, and program managers are all roles that require an individual to utilize leadership skills to lead their team to the successful completion of the tasks at hand. However, in many science, technology, engineering, and math (STEM) fields and their associated jobs, training and support in leadership development are often lacking. To meet this need, the Institute for Scientist and Engineer Educators (ISEE) at the University of California - Santa Cruz (UCSC) made supporting and mentoring leadership de-velopment a key component of the Professional Development Program (PDP) for graduate stu-dents and postdoctoral scholars in STEM, which ran for over 20 years. Building off of the ISEE leadership development model (ISEE 2020), this workshop is designed to give professionals an opportunity to learn about and practice important leadership skills that can be used in their organi-zations. In this workshop, participants learn to apply three elements of effective leadership that are useful in practice and inclusive of multiple perspectives on leadership. Participants apply actiona-ble leadership practices to their own challenges at work and develop the language to discuss their own leadership skills. Workshop duration: 15 minutes individual reading, 2 hours in-person work-shop, 15 minutes follow up.
Published: 2022

15. The Value of Teaching Leadership Skills to STEM Graduate Students and Postdocs

Author: Strubbe, Linda, Bosinger, Mia, Stauffer, Heidi L, and Tarjan, L. Max
Subjects: equity & inclusion, leadership, professional development
Abstract: To create and achieve awesome things in the world together, STEM (science, technology, engineering and mathematics) professionals need to be able to lead effectively. Leadership can be thought of as “a process of social influence through which an individual enlists and mobilizes the aid of others in the attainment of a collective goal” (Chemers, 2001). In the Institute for Scientist & Engineers Educators’ Professional Development Program (PDP), STEM graduate students and postdocs learned, practiced, and reflected on leadership skills and strategies explicitly. Design Team Leaders (DTLs) practiced leading their teams, all participants facilitated inquiry (led their students in learning), and some (in later years) learned through the inclusive leadership PDP strand. In this panel paper, we reflect on what we learned from these experiences and discuss how we apply PDP leadership training daily in our work beyond the PDP. We review key principles about inclusive leadership, such as building an image as a credible leader; how to lead meetings; and how to build feelings of motivation, belonging, trust, and shared ownership among team members. We also share case studies of our experiences applying PDP leadership training in roles as co-director for an African summer school, facilitator for a physics equity project, middle/high school math and science teacher, mentor for new teachers, teaching professor and online curriculum designer, and project manager for a non-profit. Last, we offer recommendations for stakeholders who want to support STEM graduate students’ and postdocs’ development as inclusive leaders.
Published: 2022

16. BEA-Base: A Benchmark for ASR of Spontaneous Hungarian

Author: Mihajlik, P., Balog, A., Gráczi, T. E., Kohári, A., Tarján, B., and Mády, K.
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Sound
Abstract: Hungarian is spoken by 15 million people, still, easily accessible Automatic Speech Recognition (ASR) benchmark datasets - especially for spontaneous speech - have been practically unavailable. In this paper, we introduce BEA-Base, a subset of the BEA spoken Hungarian database comprising mostly spontaneous speech of 140 speakers. It is built specifically to assess ASR, primarily for conversational AI applications. After defining the speech recognition subsets and task, several baselines - including classic HMM-DNN hybrid and end-to-end approaches augmented by cross-language transfer learning - are developed using open-source toolkits. The best results obtained are based on multilingual self-supervised pretraining, achieving a 45% recognition error rate reduction as compared to the classical approach - without the application of an external language model or additional supervised data. The results show the feasibility of using BEA-Base for training and evaluation of Hungarian speech recognition systems., Comment: Submitted to LREC 2022
Published: 2022

17. Finding Strong Components Using Depth-First Search

Author: Tarjan, Robert E. and Zwick, Uri
Subjects: Computer Science - Data Structures and Algorithms, 68Q25, F.2.2
Abstract: We survey three algorithms that use depth-first search to find the strong components of a directed graph in linear time: (1) Tarjan's algorithm; (2) a cycle-finding algorithm; and (3) a bidirectional search algorithm., Comment: 27 pages. In memory of Pierre Rosenstiehl. A slightly revised version
Published: 2022

18. Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia

Author: Tarjan Laslo, Šenk Ivana, Pracner Doni, Štrbac Ljuba, Šaran Momčilo, Ivković Mirko, and Dedović Nebojša
Subjects: assessment of milk yield, machine learning, cattle, prediction of breeding values, Agriculture
Abstract: This paper presents a deep neural network (DNN) approach designed to estimate the milk yield of Holstein-Friesian cattle. The DNN comprised stacked dense (fully connected) layers, each hidden layer followed by a dropout layer. Various configurations of the DNN were tested, incorporating 2 and 3 hidden layers containing 8 to 54 neurons. The experiment involved testing the DNN with different activation functions such as the sigmoid, tanh, and rectified linear unit (ReLU). The dropout rates ranging from 0 to 0.3 were employed, with the output layer using a linear activation function. The DNN models were trained using the Adam, SGD, and RMSprop optimizers, with the root mean square error serving as the loss metric. The training dataset comprised information from a unique database containing records of dairy cows in the Republic of Serbia, totaling 3,406 cows. The input parameters (a total of 27) for the DNN included breeding and milk yield data from the cow’s mother, as well as the father’s ID, whereas the output parameters (a total of 8) consisted of milk yield parameters (a total of 3) and breeding parameters of the cow (a total of 5). Training iterations were conducted using a batch size of 8 over 500, 1000, and 5000 epochs.
Published: 2023
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19. A Tight Analysis of Slim Heaps and Smooth Heaps

Author: Sinnamon, Corwin and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: The smooth heap and the closely related slim heap are recently invented self-adjusting implementations of the heap (priority queue) data structure. We analyze the efficiency of these data structures. We obtain the following amortized bounds on the time per operation: $O(1)$ for make-heap, insert, find-min, and meld; $O(\log\log n)$ for decrease-key; and $O(\log n)$ for delete-min and delete, where $n$ is the current number of items in the heap. These bounds are tight not only for smooth and slim heaps but for any heap implementation in Iacono and \"{O}zkan's pure heap model, intended to capture all possible "self-adjusting" heap implementations. Slim and smooth heaps are the first known data structures to match Iacono and \"{O}zkan's lower bounds and to satisfy the constraints of their model. Our analysis builds on Pettie's insights into the efficiency of pairing heaps, a classical self-adjusting heap implementation., Comment: This paper was withdrawn by the authors due to a nontrivial error in Lemma 13. The proof does not adequately account for phantom links
Published: 2021

20. Analysis of Smooth Heaps and Slim Heaps

Author: Hartmann, Maria, Kozma, László, Sinnamon, Corwin, and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: The smooth heap is a recently introduced self-adjusting heap [Kozma, Saranurak, 2018] similar to the pairing heap [Fredman, Sedgewick, Sleator, Tarjan, 1986]. The smooth heap was obtained as a heap-counterpart of Greedy BST, a binary search tree updating strategy conjectured to be \emph{instance-optimal} [Lucas, 1988], [Munro, 2000]. Several adaptive properties of smooth heaps follow from this connection; moreover, the smooth heap itself has been conjectured to be instance-optimal within a certain class of heaps. Nevertheless, no general analysis of smooth heaps has existed until now, the only previous analysis showing that, when used in \emph{sorting mode} ($n$ insertions followed by $n$ delete-min operations), smooth heaps sort $n$ numbers in $O(n\lg n)$ time. In this paper we describe a simpler variant of the smooth heap we call the \emph{slim heap}. We give a new, self-contained analysis of smooth heaps and slim heaps in unrestricted operation, obtaining amortized bounds that match the best bounds known for self-adjusting heaps. Previous experimental work has found the pairing heap to dominate other data structures in this class in various settings. Our tests show that smooth heaps and slim heaps are competitive with pairing heaps, outperforming them in some cases, while being comparably easy to implement., Comment: 22 pages, 9 figures
Published: 2021

21. Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR

Author: Tarján, Balázs, Szaszák, György, Fegyó, Tibor, and Mihajlik, Péter
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Computation and Language
Abstract: Recently Deep Transformer models have proven to be particularly powerful in language modeling tasks for ASR. Their high complexity, however, makes them very difficult to apply in the first (single) pass of an online system. Recent studies showed that a considerable part of the knowledge of neural network Language Models (LM) can be transferred to traditional n-grams by using neural text generation based data augmentation. In our paper, we pre-train a GPT-2 Transformer LM on a general text corpus and fine-tune it on our Hungarian conversational call center ASR task. We show that although data augmentation with Transformer-generated text works well for isolating languages, it causes a vocabulary explosion in a morphologically rich language. Therefore, we propose a new method called subword-based neural text augmentation, where we retokenize the generated text into statistically derived subwords. We compare Morfessor and BPE statistical subword tokenizers and show that both methods can significantly improve the WER while greatly reducing vocabulary size and memory requirements. Finally, we also demonstrate that subword-based neural text augmentation outperforms the word-based approach not only in terms of overall WER but also in recognition of OOV words., Comment: 7 pages, 4 figures
Published: 2020

22. On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech

Author: Tarján, Balázs, Szaszák, György, Fegyó, Tibor, and Mihajlik, Péter
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Computation and Language, Computer Science - Sound
Abstract: Advanced neural network models have penetrated Automatic Speech Recognition (ASR) in recent years, however, in language modeling many systems still rely on traditional Back-off N-gram Language Models (BNLM) partly or entirely. The reason for this are the high cost and complexity of training and using neural language models, mostly possible by adding a second decoding pass (rescoring). In our recent work we have significantly improved the online performance of a conversational speech transcription system by transferring knowledge from a Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with text generation based data augmentation. In the present paper we analyze the amount of transferable knowledge and demonstrate that the neural augmented LM (RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by dropping the second decoding pass and making the system real-time capable. We also systematically compare word and subword LMs and show that subword-based neural text augmentation can be especially beneficial in under-resourced conditions. In addition, we show that using the RNN-BNLM in the first pass followed by a neural second pass, offline ASR results can be even significantly improved., Comment: 8 pages, 2 figures, accepted for publication at TSD 2020
Published: 2020
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23. Measurement of jet-medium interactions via direct photon-hadron correlations in Au$+$Au and $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: Acharya, U., Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Al-Ta'ani, H., Angerami, A., Aoki, K., Apadula, N., Aramaki, Y., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Bannier, B., Barish, K. N., Bassalleck, B., Basye, A. T., Bathe, S., Baublis, V., Baumann, C., Baumgart, S., Bazilevsky, A., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bhom, J. H., Bichon, L., Bickley, A. A., Black, D., Blankenship, B., Blau, D. S., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Roman, V. Canoa, Caringi, A., Castera, P., Chen, C. -H., Chi, C. Y., Chiu, M., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Chvala, O., Cianciolo, V., Citron, Z., Cole, B. A., del Valle, Z. Conesa, Connors, M., Constantin, P., Cronin, N., Crossette, N., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Das, K., Datta, A., Daugherity, M. S., David, G., Dayananda, M. K., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Do, J. H., Donadelli, M., D'Orazio, L., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., Dutta, D., Edwards, S., Efremenko, Y. V., Ellinghaus, F., Engelmore, T., Enokizono, A., En'yo, H., Esha, R., Esumi, S., Eyser, K. O., Fadem, B., Fan, W., Fields, D. E., Finger, M., Finger, Jr., M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gainey, K., Gal, C., Garg, P., Garishvili, A., Garishvili, I., Giordano, F., Glenn, A., Gong, H., Gong, X., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Grim, G., Perdekamp, M. Grosse, Gu, Y., Gunji, T., Guo, L., Gustafsson, H. -Å., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Han, R., Han, S. Y., Hanks, J., Hartouni, E. P., Hasegawa, S., Hashimoto, K., Haslum, E., Hayano, R., Hayashi, S., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hill, J. C., Hodges, A., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Ide, J., Iinuma, H., Ikeda, Y., Imai, K., Imazu, Y., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isinhue, A., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Iwanaga, Y., Jacak, B. V., Javani, M., Ji, Z., Jia, J., Jiang, X., Jin, J., Johnson, B. M., Jones, T., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kapustinsky, J., Karatsu, K., Kasai, M., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khandai, P. K., Khanzadeev, A., Khatiwada, A., Kijima, K. M., Kikuchi, J., Kim, A., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, H. J., Kim, K. -B., Kim, S. H., Kim, Y. -J., Kim, Y. K., Kincses, D., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Klatsky, J., Kleinjan, D., Kline, P., Kochenda, L., Komatsu, Y., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Krizek, F., Kunde, G. J., Kurgyis, B., Kurita, K., Kurosawa, M., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Larionova, D., Larionova, M., Lebedev, A., Lee, B., Lee, D. M., Lee, J., Lee, K., Lee, K. B., Lee, K. S., Lee, S. H., Lee, S. R., Leitch, M. J., Leite, M. A. L., Leitgab, M., Leitner, E., Lenzi, B., Lewis, B., Lewis, N. A., Li, X., Lichtenwalner, P., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Lökös, S., Love, B., Luechtenborg, R., Lynch, D., Maguire, C. F., Majoros, T., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Masumoto, S., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Metzger, W. J., Miake, Y., Mibe, T., Midori, J., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitrankov, Iu., Miyachi, Y., Miyasaka, S., Mohanty, A. K., Mohapatra, S., Moon, H. J., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Morrow, S. I., Moskowitz, M., Motschwiller, S., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nam, S., Nattrass, C., Nederlof, A., Nelson, S., Netrakanti, P. K., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Nouicer, R., Novitzky, N., Nukariya, A., Nyanin, A. S., Oakley, C., Obayashi, H., O'Brien, E., Oda, S. X., Ogilvie, C. A., Oka, M., Okada, K., Onuki, Y., Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Patel, M., Pei, H., Peng, J. -C., Peng, W., Pereira, H., Perepelitsa, D. V., Peresedov, V., Peressounko, D. Yu., PerezLara, C. E., Petti, R., Pinkenburg, C., Pisani, R. P., Proissl, M., Pun, A., Purschke, M. L., Purwar, A. K., Qu, H., Radzevich, P. V., Rak, J., Rakotozafindrabe, A., Ramasubramanian, N., Ravinovich, I., Read, K. F., Rembeczki, S., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Richford, D., Rinn, T., Riveli, N., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rukoyatkin, P., Runchey, J., Ružička, P., Ryu, M. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakashita, K., Sako, H., Samsonov, V., Sano, M., Sano, S., Sarsour, M., Sato, S., Sato, T., Sawada, S., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Sen, A., Seto, R., Sett, P., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skolnik, M., Slunečka, M., Smith, K. L., Solano, S., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Sparks, N. A., Stankus, P. W., Steinberg, P., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Sukhanov, A., Sun, J., Sun, X., Sun, Z., Sziklai, J., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, D., Thomas, T. L., Todoroki, T., Togawa, M., Toia, A., Tomášek, L., Tomášek, M., Torii, H., Towell, R. S., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Ueda, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Voas, B., Vossen, A., Vrba, V., Vznuzdaev, E., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., Whitaker, S., White, S. N., Winter, D., Wolin, S., Wong, C. P., Wood, J. P., Woody, C. L., Wright, R. M., Wu, Y., Wysocki, M., Xia, B., Xie, W., Xu, Q., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoon, I., You, Z., Young, G. R., Younus, I., Yushmanov, I. E., Zajc, W. A., Zelenski, A., Zhai, Y., Zhang, C., Zharko, S., Zhou, S., Zolin, L., and Zou, L.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: We present direct photon-hadron correlations in 200 GeV/A Au$+$Au, $d$$+$Au and $p$$+$$p$ collisions, for direct photon $p_T$ from 5--12 GeV/$c$, collected by the PHENIX Collaboration in the years from 2006 to 2011. We observe no significant modification of jet fragmentation in $d$$+$Au collisions, indicating that cold nuclear matter effects are small or absent. Hadrons carrying a large fraction of the quark's momentum are suppressed in Au$+$Au compared to $p$$+$$p$ and $d$$+$Au. As the momentum fraction decreases, the yield of hadrons in Au$+$Au increases to an excess over the yield in $p$$+$$p$ collisions. The excess is at large angles and at low hadron $p_T$ and is most pronounced for hadrons associated with lower momentum direct photons. Comparison to theoretical calculations suggests that the hadron excess arises from medium response to energy deposited by jets., Comment: 578 authors from 80 institutions, 11 pages, 7 figures, data from 2007, 2008, 2010, and 2011. v2 is version accepted for publication in Physical Review C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2020
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24. Concurrent Disjoint Set Union

Author: Jayanti, Siddhartha V. and Tarjan, Robert E.
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Data Structures and Algorithms
Abstract: We develop and analyze concurrent algorithms for the disjoint set union (union-find) problem in the shared memory, asynchronous multiprocessor model of computation, with CAS (compare and swap) or DCAS (double compare and swap) as the synchronization primitive. We give a deterministic bounded wait-free algorithm that uses DCAS and has a total work bound of $O(m \cdot (\log(np/m + 1) + \alpha(n, m/(np)))$ for a problem with $n$ elements and $m$ operations solved by $p$ processes, where $\alpha$ is a functional inverse of Ackermann's function. We give two randomized algorithms that use only CAS and have the same work bound in expectation. The analysis of the second randomized algorithm is valid even if the scheduler is adversarial. Our DCAS and randomized algorithms take $O(\log n)$ steps per operation, worst-case for the DCAS algorithm, high-probability for the randomized algorithms. Our work and step bounds grow only logarithmically with $p$, making our algorithms truly scalable. We prove that for a class of symmetric algorithms that includes ours, no better step or work bound is possible., Comment: 40 pages, combines ideas in two previous PODC papers
Published: 2020

25. Connected Components on a PRAM in Log Diameter Time

Author: Liu, S. Cliff, Tarjan, Robert E., and Zhong, Peilin
Subjects: Computer Science - Data Structures and Algorithms, Computer Science - Distributed, Parallel, and Cluster Computing, F.2.2
Abstract: We present an $O(\log d + \log\log_{m/n} n)$-time randomized PRAM algorithm for computing the connected components of an $n$-vertex, $m$-edge undirected graph with maximum component diameter $d$. The algorithm runs on an ARBITRARY CRCW (concurrent-read, concurrent-write with arbitrary write resolution) PRAM using $O(m)$ processors. The time bound holds with good probability. Our algorithm is based on the breakthrough results of Andoni et al. [FOCS'18] and Behnezhad et al. [FOCS'19]. Their algorithms run on the more powerful MPC model and rely on sorting and computing prefix sums in $O(1)$ time, tasks that take $\Omega(\log n / \log\log n)$ time on a CRCW PRAM with $\text{poly}(n)$ processors. Our simpler algorithm uses limited-collision hashing and does not sort or do prefix sums. It matches the time and space bounds of the algorithm of Behnezhad et al., who improved the time bound of Andoni et al. It is widely believed that the larger private memory per processor and unbounded local computation of the MPC model admit algorithms faster than that on a PRAM. Our result suggests that such additional power might not be necessary, at least for fundamental graph problems like connected components and spanning forest.
Published: 2020

26. Breeding Waterbird Populations Have Declined in South San Francisco Bay: An Assessment Over Two Decades

Author: Hartman, C. Alex, Ackerman, Joshua T., Schacter, Carley R., Herzog, Mark P., Tarjan, L. Max, Wang, Yiwei, Strong, Cheryl, Tertes, Rachel, and Warnock, Nils
Subjects: American Avocet, Black-necked Stilt, California Gull, Forster’s Tern, habitat use, nesting islands, managed ponds, population change, tidal marsh restoration
Abstract: In south San Francisco Bay, former salt ponds now managed as wildlife habitat support large populations of breeding waterbirds. In 2006, the South Bay Salt Pond Restoration Project began the process of converting 50% to 90% of these managed pond habitats into tidal marsh. We compared American Avocet (Recurvirostra americana) and Black-necked Stilt (Himantopus mexicanus) abundance in south San Francisco Bay before (2001) and after approximately 1,300 ha of managed ponds were breached to tidal action to begin tidal marsh restoration (2019). Over the 18-year period, American Avocet abundance declined 13.5% (2,765 in 2001 vs. 2,391 in 2019), and Black-necked Stilt abundance declined 30.0% (1,184 in 2001 vs. 828 in 2019). Forster’s Tern (Sterna forsteri) abundance was 2,675 birds in 2019. In 2019, managed ponds accounted for only 25.8% of suitable habitats, yet contained 53.9%, 38.6%, and 65.6% American Avocet, Black-necked Stilt, and Forster’s Tern observations, respectively. Conversely, tidal marsh and tidal mudflats accounted for 42.9% of suitable habitats, yet contained only 18.4%, 10.3%, and 19.8% of American Avocet, Black-necked Stilt, and Forster’s Tern observations, respectively. Using a separate nest-monitoring data set, we found that nest abundance in south San Francisco Bay declined for all three species from 2005–2019. Average annual nest abundance during 2017–2019 declined 53%, 71%, and 36%, for American Avocets, Back-necked Stilts, and Forster’s Terns, respectively, compared to 2005–2007. Loss of island nesting habitat as a result of tidal marsh conversion and an increasing population of predatory California Gulls (Larus californicus) are two potential causes of these declines. All three species established nesting colonies on newly constructed islands within remaining managed ponds; however, these new colonies did not make up for the steep declines observed at other historical nesting sites. For future wetland restoration, retaining more managed ponds that contain islands suitable for nesting may help to limit further declines in breeding waterbird populations.
Published: 2021

27. Investigation on N-gram Approximated RNNLMs for Recognition of Morphologically Rich Speech

Author: Tarján, Balázs, Szaszák, György, Fegyó, Tibor, and Mihajlik, Péter
Subjects: Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Recognition of Hungarian conversational telephone speech is challenging due to the informal style and morphological richness of the language. Recurrent Neural Network Language Model (RNNLM) can provide remedy for the high perplexity of the task; however, two-pass decoding introduces a considerable processing delay. In order to eliminate this delay we investigate approaches aiming at the complexity reduction of RNNLM, while preserving its accuracy. We compare the performance of conventional back-off n-gram language models (BNLM), BNLM approximation of RNNLMs (RNN-BNLM) and RNN n-grams in terms of perplexity and word error rate (WER). Morphological richness is often addressed by using statistically derived subwords - morphs - in the language models, hence our investigations are extended to morph-based models, as well. We found that using RNN-BNLMs 40% of the RNNLM perplexity reduction can be recovered, which is roughly equal to the performance of a RNN 4-gram model. Combining morph-based modeling and approximation of RNNLM, we were able to achieve 8% relative WER reduction and preserve real-time operation of our conversational telephone speech recognition system., Comment: 12 pages, 2 figures, accepted for publication at SLSP 2019
Published: 2019
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28. A Foundation for Proving Splay is Dynamically Optimal

Author: Levy, Caleb C. and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: Consider the task of performing a sequence of searches in a binary search tree. After each search, we allow an algorithm to arbitrarily restructure the tree. The cost of executing the task is the sum of the time spent searching and the time spent optimizing the searches with restructuring operations. Sleator and Tarjan introduced this notion in 1985, along with an algorithm and a conjecture. The algorithm, Splay, is an elegant procedure for performing adjustments that move searched items to the top of the tree. The conjecture, called dynamic optimality, is that the cost of splaying is always within a constant factor of the optimal algorithm for performing searches. We lay a foundation for proving the dynamic optimality conjecture. Central to our method is approximate monotonicity. Approximately monotone algorithms are those whose cost does not increase by more than a fixed multiple after removing searches from the sequence. As we shall see, Splay is dynamically optimal if and only if it is approximately monotone. This result extends to a weaker form of approximate monotonicity as well as insertion, deletion, and related algorithms. We prove that a lower bound on optimal execution cost is approximately monotone and outline how to adapt this proof from the lower bound to Splay, and how to overcome the remaining barriers to establishing dynamic optimality., Comment: V1 was titled "New Paths from Splay to Dynamic Optimality." V2 is a major revision. V3 adds refinements. This work is based on the first author's Ph.D. thesis. It shares expository material with arXiv:1907.06309. We presented an earlier version at the Symposium on Discrete Algorithms. 29 pages, 7 figures
Published: 2019

29. Splaying Preorders and Postorders

Author: Levy, Caleb C. and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: Let $T$ be a binary search tree. We prove two results about the behavior of the Splay algorithm (Sleator and Tarjan 1985). Our first result is that inserting keys into an empty binary search tree via splaying in the order of either $T$'s preorder or $T$'s postorder takes linear time. Our proof uses the fact that preorders and postorders are pattern-avoiding: i.e. they contain no subsequences that are order-isomorphic to $(2,3,1)$ and $(3,1,2)$, respectively. Pattern-avoidance implies certain constraints on the manner in which items are inserted. We exploit this structure with a simple potential function that counts inserted nodes lying on access paths to uninserted nodes. Our methods can likely be extended to permutations that avoid more general patterns. Second, if $T'$ is any other binary search tree with the same keys as $T$ and $T$ is weight-balanced (Nievergelt and Reingold 1973), then splaying $T$'s preorder sequence or $T$'s postorder sequence starting from $T'$ takes linear time. To prove this, we demonstrate that preorders and postorders of balanced search trees do not contain many large "jumps" in symmetric order, and exploit this fact by using the dynamic finger theorem (Cole et al. 2000). Both of our results provide further evidence in favor of the elusive "dynamic optimality conjecture."
Published: 2019

30. Simple Concurrent Labeling Algorithms for Connected Components

Author: Liu, S. Cliff and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms, F.2.2
Abstract: We study a class of simple algorithms for concurrently computing the connected components of an $n$-vertex, $m$-edge graph. Our algorithms are easy to implement in either the COMBINING CRCW PRAM or the MPC computing model. For two related algorithms in this class, we obtain $\Theta(\lg n)$ step and $\Theta(m \lg n)$ work bounds. For two others, we obtain $O(\lg^2 n)$ step and $O(m \lg^2 n)$ work bounds, which are tight for one of them. All our algorithms are simpler than related algorithms in the literature. We also point out some gaps and errors in the analysis of previous algorithms. Our results show that even a basic problem like connected components still has secrets to reveal., Comment: 24 pages
Published: 2018

31. SDCNet: Video Prediction Using Spatially-Displaced Convolution

Author: Reda, Fitsum A., Liu, Guilin, Shih, Kevin J., Kirby, Robert, Barker, Jon, Tarjan, David, Tao, Andrew, and Catanzaro, Bryan
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows. Previous approaches rely on resampling past frames, guided by a learned future optical flow, or on direct generation of pixels. Resampling based on flow is insufficient because it cannot deal with disocclusions. Generative models currently lead to blurry results. Recent approaches synthesis a pixel by convolving input patches with a predicted kernel. However, their memory requirement increases with kernel size. Here, we spatially-displaced convolution (SDC) module for video frame prediction. We learn a motion vector and a kernel for each pixel and synthesize a pixel by applying the kernel at a displaced location in the source image, defined by the predicted motion vector. Our approach inherits the merits of both vector-based and kernel-based approaches, while ameliorating their respective disadvantages. We train our model on 428K unlabelled 1080p video game frames. Our approach produces state-of-the-art results, achieving an SSIM score of 0.904 on high-definition YouTube-8M videos, 0.918 on Caltech Pedestrian videos. Our model handles large motion effectively and synthesizes crisp frames with consistent motion., Comment: Published in ECCV 2018. Codes available at https://github.com/NVIDIA/semantic-segmentation/tree/sdcnet/sdcnet. Project page available at https://nv-adlr.github.io/publication/2018-SDCNet
Published: 2018

32. Zip Trees

Author: Tarjan, Robert E., Levy, Caleb C., and Timmel, Stephen
Subjects: Computer Science - Data Structures and Algorithms
Abstract: We introduce the zip tree, a form of randomized binary search tree that integrates previous ideas into one practical, performant, and pleasant-to-implement package. A zip tree is a binary search tree in which each node has a numeric rank and the tree is (max)-heap-ordered with respect to ranks, with rank ties broken in favor of smaller keys. Zip trees are essentially treaps (Seidel and Aragon 1996), except that ranks are drawn from a geometric distribution instead of a uniform distribution, and we allow rank ties. These changes enable us to use fewer random bits per node. We perform insertions and deletions by unmerging and merging paths ("unzipping" and "zipping") rather than by doing rotations, which avoids some pointer changes and improves efficiency. The methods of zipping and unzipping take inspiration from previous top-down approaches to insertion and deletion (Stephenson 1980; Mart\'inez and Roura 1998; Sprugnoli 1980). From a theoretical standpoint, this work provides two main results. First, zip trees require only $O(\log \log n)$ bits (with high probability) to represent the largest rank in an $n$-node binary search tree; previous data structures require $O(\log n)$ bits for the largest rank. Second, zip trees are naturally isomorphic to skip lists (Pugh 1990), and simplify the mapping of (Dean and Jones 2007) between skip lists and binary search trees., Comment: V5 is the final published version. V4 appeared in the Workshop on Algorithms and Data Structures in 2019. V1 was presented at Highlights of Algorithms in 2018. 14 pages, 3 figures
Published: 2018
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33. Beam-energy and centrality dependence of direct-photon emission from ultra-relativistic heavy-ion collisions

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Alfred, M., Al-Jamel, A., Al-Ta'ani, H., Angerami, A., Aoki, K., Apadula, N., Aphecetche, L., Aramaki, Y., Armendariz, R., Aronson, S. H., Asai, J., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bagoly, A., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Bauer, F., Baumann, C., Baumgart, S., Bazilevsky, A., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bhom, J. H., Bickley, A. A., Bjorndal, M. T., Blau, D. S., Boer, M., Boissevain, J. G., Bok, J. S., Borel, H., Boyle, K., Brooks, M. L., Brown, D. S., Bryslawskyj, J., Bucher, D., Buesching, H., Bumazhnov, V., Bunce, G., Burward-Hoy, J. M., Butsyk, S., Camacho, C. M., Campbell, S., Roman, V. Canoa, Caringi, A., Castera, P., Chai, J. -S., Chang, B. S., Chang, W. C., Charvet, J. -L., Chen, C. -H., Chernichenko, S., Chi, C. Y., Chiba, J., Chiu, M., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Churyn, A., Chvala, O., Cianciolo, V., Citron, Z., Cleven, C. R., Cobigo, Y., Cole, B. A., Comets, M. P., del Valle, Z. Conesa, Connors, M., Constantin, P., Csanád, M., Csörgö, T., Dahms, T., Dairaku, S., Danchev, I., Danley, T. W., Das, K., Datta, A., Daugherity, M. S., David, G., Dayananda, M. K., Deaton, M. B., Dehmelt, K., Delagrange, H., Denisov, A., d'Enterria, D., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Do, J. H., Donadelli, M., D'Orazio, L., Drachenberg, J. L., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Edwards, S., Efremenko, Y. V., Egdemir, J., Ellinghaus, F., Emam, W. S., Engelmore, T., Enokizono, A., En'yo, H., Espagnon, B., Esumi, S., Eyser, K. O., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Forestier, B., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fung, S. -Y., Fusayasu, T., Gadrat, S., Gainey, K., Gal, C., Gallus, P., Garg, P., Garishvili, A., Garishvili, I., Gastineau, F., Ge, H., Germain, M., Glenn, A., Gong, H., Gong, X., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Grim, G., Perdekamp, M. Grosse, Gunji, T., Guo, L., Gustafsson, H. Å., Hachiya, T., Henni, A. Hadj, Haegemann, C., Haggerty, J. S., Hagiwara, M. N., Hahn, K. I., Hamagaki, H., Hamblen, J., Han, R., Hanks, J., Harada, H., Hartouni, E. P., Haruna, K., Harvey, M., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hasuko, K., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Heuser, J. M., Hiejima, H., Hill, J. C., Hill, K., Hobbs, R., Hodges, A., Hohlmann, M., Hollis, R. S., Holmes, M., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Hotvedt, N., Huang, J., Huang, S., Hur, M. G., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Inoue, Y., Iordanova, A., Isenhower, D., Isenhower, L., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Iwanaga, Y., Jacak, B. V., Javani, M., Ji, Z., Jia, J., Jiang, X., Jin, J., Jinnouchi, O., Johnson, B. M., Jones, T., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kaneta, M., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kanou, H., Kapustinsky, J., Karatsu, K., Kasai, M., Kawagishi, T., Kawall, D., Kawashima, M., Kazantsev, A. V., Kelly, S., Kempel, T., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kim, A., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, H. J., Kim, K. -B., Kim, M., Kim, S. H., Kim, Y. -J., Kim, Y. K., Kim, Y. -S., Kincses, D., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kiyomichi, A., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Kochenda, L., Kochetkov, V., Komatsu, Y., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Krizek, F., Kroon, P. J., Kubart, J., Kunde, G. J., Kurgyis, B., Kurihara, N., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Layton, D., Lebedev, A., Bornec, Y. Le, Leckey, S., Lee, B., Lee, D. M., Lee, J., Lee, K. B., Lee, K. S., Lee, M. K., Lee, S. H., Lee, S. R., Lee, T., Leitch, M. J., Leite, M. A. L., Leitgab, M., Lenzi, B., Leung, Y. H., Lewis, B., Lewis, N. A., Li, X., Li, X. H., Lichtenwalner, P., Liebing, P., Lim, H., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Lökös, S., Love, B., Lynch, D., Maguire, C. F., Majoros, T., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Mašek, L., Masui, H., Masumoto, S., Matathias, F., McCain, M. C., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Mihalik, D. E., Mikeš, P., Miki, K., Miller, T. E., Milov, A., Mioduszewski, S., Mishra, D. K., Mishra, G. C., Mishra, M., Mitchell, J. T., Mitrovski, M., Mitsuka, G., Miyachi, Y., Miyasaka, S., Mohanty, A. K., Mohapatra, S., Moon, H. J., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Morrow, S. I., Moss, J. M., Motschwiller, S., Moukhanova, T. V., Mukhopadhyay, D., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagashima, K., Nagata, Y., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nam, S., Nattrass, C., Nederlof, A., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Norman, B. E., Nouicer, R., Novák, T., Novitzky, N., Nyanin, A. S., Nystrand, J., Oakley, C., O'Brien, E., Oda, S. X., Ogilvie, C. A., Ohnishi, H., Ojha, I. D., Oka, M., Okada, K., Omiwade, O. O., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Otterlund, I., Ouchida, M., Ozawa, K., Pak, R., Pal, D., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Patel, M., Pei, H., Peng, J. -C., Peng, W., Pereira, H., Perepelitsa, D. V., Peresedov, V., Peressounko, D. Yu., PerezLara, C. E., Petti, R., Pinkenburg, C., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Radzevich, P. V., Rak, J., Rakotozafindrabe, A., Ravinovich, I., Read, K. F., Rembeczki, S., Reuter, M., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Richford, D., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Romana, A., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rukoyatkin, P., Runchey, J., Ružička, P., Rykov, V. L., Ryu, S. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sakata, H., Sako, H., Samsonov, V., Sano, M., Sano, S., Sarsour, M., Sato, H. D., Sato, S., Sato, T., Sawada, S., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sharma, D., Shea, T. K., Shein, I., Shevel, A., Shibata, T. -A., Shigaki, K., Shimomura, M., Shohjoh, T., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skoby, M. J., Skutnik, S., Slunečka, M., Smith, W. C., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Sullivan, J. P., Sun, J., Sun, Z., Sziklai, J., Tabaru, T., Takagi, S., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, K. H., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, D., Thomas, T. L., Tieulent, R., Todoroki, T., Togawa, M., Toia, A., Tojo, J., Tomášek, L., Tomášek, M., Tomita, Y., Torii, H., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Tuli, S. K., Tydesjö, H., Tyurin, N., Ueda, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vossen, A., Vrba, V., Vznuzdaev, E., Wagner, M., Walker, D., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., White, S. N., Willis, N., Winter, D., Wolin, S., Wong, C. P., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Xu, C., Xu, Q., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Yasin, Z., Ying, J., Yokkaichi, S., Yoo, J. H., You, Z., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zharko, S., Zhou, S., Zimamyi, J., Zolin, L., and Zou, L.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: The PHENIX collaboration presents first measurements of low-momentum ($0.4$1\,GeV/$c$) direct-photon yield $dN_{\gamma}^{\rm dir}/d\eta$ is a smooth function of $dN_{\rm ch}/d\eta$ and can be well described as proportional to $(dN_{\rm ch}/d\eta)^\alpha$ with $\alpha{\approx}1.25$. This scaling behavior holds for a wide range of beam energies at the Relativistic Heavy Ion Collider and the Large Hadron Collider, for centrality selected samples, as well as for different, $A$$+$$A$ collision systems. At a given beam energy the scaling also holds for high $p_T$ ($>5$\,GeV/$c$) but when results from different collision energies are compared, an additional $\sqrt{s_{_{NN}}}$-dependent multiplicative factor is needed to describe the integrated-direct-photon yield., Comment: 673 authors from 82 institutions, 10 pages, 4 figures. v2 is version accepted for publication in Physical Review Letters. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2018
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34. Low-momentum direct photon measurement in Cu$+$Cu collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Aphecetche, L., Armendariz, R., Aronson, S. H., Asai, J., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bagoly, A., Baksay, G., Baksay, L., Baldisseri, A., Barish, K. N., Barnes, P. D., Bassalleck, B., Bathe, S., Batsouli, S., Baublis, V., Bazilevsky, A., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Boer, M., Boissevain, J. G., Bok, J. S., Borel, H., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Campbell, S., Roman, V. Canoa, Chang, B. S., Charvet, J. -L., Chernichenko, S., Chi, C. Y., Chiba, J., Chiu, M., Choi, I. J., Chujo, T., Chung, P., Churyn, A., Cianciolo, V., Cleven, C. R., Cole, B. A., Comets, M. P., Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Danley, T. W., Das, K., David, G., Deaton, M. B., Dehmelt, K., Delagrange, H., Denisov, A., d'Enterria, D., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Do, J. H., Donadelli, M., Drapier, O., Drees, A., Dubey, A. K., Durham, J. M., Durum, A., Dzhordzhadze, V., Efremenko, Y. V., Egdemir, J., Ellinghaus, F., Emam, W. S., Enokizono, A., En'yo, H., Esumi, S., Eyser, K. O., Fan, W., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gadrat, S., Gallus, P., Garg, P., Garishvili, I., Ge, H., Glenn, A., Gong, H., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haegemann, C., Haggerty, J. S., Hamagaki, H., Han, R., Harada, H., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hiejima, H., Hill, J. C., Hill, K., Hobbs, R., Hodges, A., Hohlmann, M., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Ichihara, T., Iinuma, H., Imai, K., Inaba, M., Inoue, Y., Isenhower, D., Isenhower, L., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Ji, Z., Jia, J., Jin, J., Jinnouchi, O., Johnson, B. M., Joo, K. S., Jouan, D., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kaneta, M., Kang, J. H., Kanou, H., Kawall, D., Kazantsev, A. V., Khachatryan, V., Khanzadeev, A., Kikuchi, J., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, M., Kincses, D., Kinney, E., Kiss, Á., Kistenev, E., Kiyomichi, A., Klay, J., Klein-Boesing, C., Kochenda, L., Kochetkov, V., Komkov, B., Konno, M., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Kubart, J., Kunde, G. J., Kurihara, N., Kurita, K., Kweon, M. J., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, D. M., Lee, M. K., Lee, S. H., Lee, T., Leitch, M. J., Leite, M. A. L., Lenzi, B., Leung, Y. H., Lewis, N. A., Li, X., Lim, S. H., Liška, T., Litvinenko, A., Liu, M. X., Lökös, S., Love, B., Lynch, D., Maguire, C. F., Majoros, T., Makdisi, Y. I., Malakhov, A., Malik, M. D., Manko, V. I., Mao, Y., Mašek, L., Masui, H., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., Miake, Y., Mignerey, A. C., Mihalik, D. E., Mikeš, P., Miki, K., Miller, T. E., Milov, A., Mioduszewski, S., Mishra, M., Mitchell, J. T., Mitrovski, M., Mitsuka, G., Moon, T., Morreale, A., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mukhopadhyay, D., Murata, J., Nagamiya, S., Nagashima, K., Nagata, Y., Nagle, J. L., Naglis, M., Nakagawa, I., Nakamiya, Y., Nakamura, T., Nakano, K., Newby, J., Nguyen, M., Norman, B. E., Nouicer, R., Novák, T., Novitzky, N., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Ohnishi, H., Oka, M., Okada, K., Omiwade, O. O., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pal, D., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, J., Park, S., Park, W. J., Pate, S. F., Patel, M., Pei, H., Peng, J. -C., Peng, W., Pereira, H., Perepelitsa, D. V., Peresedov, V., Peressounko, D. Yu., PerezLara, C. E., Pinkenburg, C., Purschke, M. L., Purwar, A. K., Qu, H., Radzevich, P. V., Rak, J., Rakotozafindrabe, A., Ravinovich, I., Read, K. F., Rembeczki, S., Reuter, M., Reygers, K., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Roche, G., Romana, A., Rosati, M., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rukoyatkin, P., Runchey, J., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakata, H., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Sawada, S., Schmoll, B. K., Seele, J., Seidl, R., Semenov, V., Seto, R., Sharma, D., Shein, I., Shevel, A., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, C. P., Singh, V., Skoby, M. J., Skutnik, S., Slunečka, M., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sun, Z, Sun, Z., Sziklai, J., Tabaru, T., Takagi, S., Takagui, E. M., Taketani, A., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Taranenko, A., Tarján, P., Thomas, T. L., Tieulent, R., Togawa, M., Toia, A., Tojo, J., Tomášek, L., Torii, H., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Wagner, M., Walker, D., Wang, X. R., Watanabe, Y., Wei, F., Wessels, J., White, S. N., Winter, D., Wong, C. P., Woody, C. L., Wysocki, M., Xie, W., Xu, C., Xu, Q., Yamaguchi, Y. L., Yanovich, A., Yasin, Z., Ying, J., Yokkaichi, S., Yoo, J. H., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zaudtke, O., Zhang, C., Zharko, S., Zhou, S., Zimamyi, J., Zolin, L., and Zou, L.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: We have measured direct photons for $p_T<5~$GeV/$c$ in minimum bias and 0\%--40\% most central events at midrapidity for Cu$+$Cu collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The $e^{+}e^{-}$ contribution from quasi-real direct virtual photons has been determined as an excess over the known hadronic contributions in the $e^{+}e^{-}$ mass distribution. A clear enhancement of photons over the binary scaled $p$$+$$p$ fit is observed for $p_T<4$ GeV/$c$ in Cu$+$Cu data. The $p_T$ spectra are consistent with the Au$+$Au data covering a similar number of participants. The inverse slopes of the exponential fits to the excess after subtraction of the $p$$+$$p$ baseline are 285$\pm$53(stat)$\pm$57(syst)~MeV/$c$ and 333$\pm$72(stat)$\pm$45(syst)~MeV/$c$ for minimum bias and 0\%--40\% most central events, respectively. The rapidity density, $dN/dy$, of photons demonstrates the same power law as a function of $dN_{\rm ch}/d\eta$ observed in Au$+$Au at the same collision energy., Comment: 453 authors from 74 institutions, 12 pages, 8 figures, 2 tables. v3 is version accepted for publication in Physical Review C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2018
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35. Measurement of two-particle correlations with respect to second- and third-order event planes in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Aramaki, Y., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bagoly, A., Bai, M., Baksay, G., Baksay, L., Barish, K. N., Bassalleck, B., Basye, A. T., Bathe, S., Baublis, V., Baumann, C., Bazilevsky, A., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Boer, M., Bok, J. S., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chi, C. Y., Chiu, M., Choi, I. J., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Chvala, O., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Danley, T. W., Das, K., Datta, A., David, G., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Do, J. H., Donadelli, M., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., Dutta, D., Edwards, S., Efremenko, Y. V., Ellinghaus, F., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gallus, P., Garg, P., Garishvili, I., Ge, H., Glenn, A., Gong, H., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Gustafsson, H. -Å., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Han, R., Hanks, J., Hartouni, E. P., Hasegawa, S., Haseler, T. O. S., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hill, J. C., Hill, K., Hodges, A., Hohlmann, M., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hotvedt, N., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Ide, J., Ikeda, Y., Imai, K., Inaba, M., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Ji, Z., Jia, J., Jin, J., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kang, J. H., Kapustinsky, J., Karatsu, K., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kim, B. I., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, M., Kim, S. H., Kim, Y. -J., Kincses, D., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kochenda, L., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Kunde, G. J., Kurgyis, B., Kurita, K., Kurosawa, M., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, D. M., Lee, J., Lee, K., Lee, K. B., Lee, K. S., Lee, S. H., Leitch, M. J., Leite, M. A. L., Leitner, E., Lenzi, B., Leung, Y. H., Lewis, N. A., Li, X., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Lökös, S., Love, B., Luechtenborg, R., Lynch, D., Maguire, C. F., Majoros, T., Makdisi, Y. I., Malakhov, A., Malik, M. D., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., Means, N., Meredith, B., Miake, Y., Mignerey, A. C., Mihalik, D. E., Mikeš, P., Miki, K., Milov, A., Mishra, M., Mitchell, J. T., Mitsuka, G., Mizuno, S., Mohanty, A. K., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Murata, J., Nagamiya, S., Nagashima, K., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakamiya, Y., Nakamura, T., Nakano, K., Nattrass, C., Newby, J., Nguyen, M., Niida, T., Nouicer, R., Novák, T., Novitzky, N., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Oka, M., Okada, K., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, I. H., Park, J., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, M., Pei, H., Peng, J. -C., Peng, W., Pereira, H., Perepelitsa, D. V., Peresedov, V., Peressounko, D. Yu., PerezLara, C. E., Pinkenburg, C., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Radzevich, P. V., Rak, J., Rakotozafindrabe, A., Ravinovich, I., Read, K. F., Reygers, K., Riabov, V., Riabov, Y., Richardson, E., Richford, D., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rukoyatkin, P., Runchey, J., Ružička, P., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakashita, K., Sako, H., Samsonov, V., Sano, S., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Seto, R., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skoby, M. J., Slunečka, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Sparks, N. A., Stankus, P. W., Stenlund, E., Stoll, S. P., Sugitate, T., Sukhanov, A., Sun, Z., Sziklai, J., Takagui, E. M., Taketani, A., Tanabe, R., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Themann, H., Thomas, T. L., Tieulent, R., Todoroki, T., Togawa, M., Toia, A., Tomášek, L., Torii, H., Towell, R. S., Tserruya, I., Tsuchimoto, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Wei, F., Wei, R., Wessels, J., White, S. N., Winter, D., Wong, C. P., Wood, J. P., Woody, C. L., Wright, R. M., Wysocki, M., Xie, W., Xu, C., Xu, Q., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoo, J. H., You, Z., Young, G. R., Younus, I., Yu, H., Ueda, Y., Yushmanov, I. E., Zajc, W. A., Zhang, C., Zharko, S., Zhou, S., Zolin, L., and Zou, L.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: We present measurements of azimuthal correlations of charged hadron pairs in $\sqrt{s_{_{NN}}}=200$ GeV Au$+$Au collisions for the trigger and associated particle transverse-momentum ranges of $14$~GeV/$c$) correlations is suppressed compared to that of correlations measured in $p$$+$$p$ collisions. At the lowest associated particle $p_T$ ($0.5
Published: 2018
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36. Measurements of $e^+e^-$ pairs from open heavy flavor in $p$+$p$ and $d$+$A$ collisions at $\sqrt{s_{NN}}=200$ GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Apadula, N., Aphecetche, L., Asai, J., Atomssa, E. T., Averbeck, R., Awes, T. C., Ayuso, C., Azmoun, B., Babintsev, V., Bagoly, A., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Baumann, C., Bazilevsky, A., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Blau, D. S., Boer, M., Boissevain, J. G., Bok, J. S., Borel, H., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butler, C., Butsyk, S., Camacho, C. M., Campbell, S., Roman, V. Canoa, Chang, B. S., Chang, W. C., Charvet, J. -L., Chernichenko, S., Chi, C. Y., Chiu, M., Choi, I. J., Choudhury, R. K., Chujo, T., Chung, P., Churyn, A., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danley, T. W., Das, K., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., d'Enterria, D., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Do, J. H., Donadelli, M., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Dumancic, M., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Efremenko, Y. V., Elder, T., Ellinghaus, F., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Eyser, K. O., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, M., Finger Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fukuda, Y., Fusayasu, T., Gal, C., Garg, P., Garishvili, I., Ge, H., Glenn, A., Gong, H., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haggerty, J. S., Hahn, K. I., Hamagaki, H., Han, R., Han, S. Y., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hill, J. C., Hill, K., Hohlmann, M., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ito, Y., Ivanishchev, D., Jacak, B. V., Ji, Z., Jia, J., Jin, J., Johnson, B. M., Joo, K. S., Jorjadze, V., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kang, J. H., Kapukchyan, D., Kapustinsky, J., Karthas, S., Kawall, D., Kazantsev, A. V., Kempel, T., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, M., Kim, S. H., Kincses, D., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Klay, J., Klein-Boesing, C., Koblesky, T., Kochenda, L., Komkov, B., Konno, M., Koster, J., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Kudo, S., Kunde, G. J., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lallow, E. O., Layton, D., Lebedev, A., Lee, D. M., Lee, K. B., Lee, T., Leitch, M. J., Leite, M. A. L., Lenzi, B., Leung, Y. H., Lewis, N. A., Li, X., Liebing, P., Lim, S. H., Liška, T., Litvinenko, A., Liu, H., Liu, L. D., Liu, M. X., Loggins, V. -R., Lokos, S., Love, B., Lynch, D., Maguire, C. F., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Malakhov, A., Malik, M. D., Manko, V. I., Mannel, E., Mao, Y., Mašek, L., Masuda, H., Masui, H., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., Means, N., Meredith, B., Metzger, W. J., Miake, Y., Mignerey, A. C., Mihalik, D. E., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitsuka, G., Mohanty, A. K., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Morrow, S. I. M., Moukhanova, T. V., Mukhopadhyay, D., Murakami, T., Murata, J., Nagai, K., Nagamiya, S., Nagashima, K., Nagashima, T., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, T., Nakano, K., Nattrass, C., Newby, J., Nguyen, M., Niida, T., Nouicer, R., Novák, T., Novitzky, N., Novotny, R., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Oka, M., Okada, K., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, J., Park, J. S., Park, S., Park, W. J., Pate, S. F., Patel, M., Pei, H., Peng, J. -C., Peng, W., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., PerezLara, C. E., Petti, R., Phipps, M., Pinkenburg, C., Pun, A., Purschke, M. L., Purwar, A. K., Qu, H., Radzevich, P. V., Rak, J., Rakotozafindrabe, A., Ravinovich, I., Read, K. F., Rembeczki, S., Reygers, K., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rukoyatkin, P., Runchey, J., Ružička, P., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K, Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skoby, M. J., Slunečka, M., Smith, K. L., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Syed, S., Sziklai, J., Takagui, E. M., Takeda, A, Taketani, A., Tanabe, R., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tarnai, G., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Togawa, M., Toia, A., Tomášek, L., Tomášek, M., Tomita, Y., Torii, H., Towell, C. L., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Ueda, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Vazquez-Carson, S., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Wang, X. R., Wang, Z., Watanabe, Y., Wei, F., Wessels, J., White, S. N., Winter, D., Wong, C. P., Woody, C. L., Wysocki, M., Xie, W., Xu, C., Xu, Q., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Yin, P., Ying, J., Yokkaichi, S., Yoo, J. H., Yoon, I., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zaudtke, O., Zhang, C., Zharko, S., Zhou, S., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: We report a measurement of $e^+e^-$ pairs from semileptonic heavy-flavor decays in $p$+$p$ collisions at $\sqrt{s_{NN}}=200$~GeV. The $e^+e^-$ pair yield from $b\bar{b}$ and $c\bar{c}$ is separated by exploiting a double differential fit done simultaneously in dielectron invariant mass and $p_T$. We used three different event generators, {\sc pythia}, {\sc mc@nlo}, and {\sc powheg}, to simulate the $e^+e^-$ spectra from $c\bar{c}$ and $b\bar{b}$ production. The data can be well described by all three generators within the detector acceptance. However, when using the generators to extrapolate to $4\pi$, significant differences are observed for the total cross section. These difference are less pronounced for $b\bar{b}$ than for $c\bar{c}$. The same model dependence was observed in already published $d$+$A$ data. The $p$+$p$ data are also directly compared with $d$+$A$ data in mass and $p_T$, and within the statistical accuracy no nuclear modification is seen., Comment: 510 authors, 19 pages, 16 figures, 7 tables, 2006 data. v2 is version accepted for publication by Phys. Rev. C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2017
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37. A Randomized Concurrent Algorithm for Disjoint Set Union

Author: Jayanti, Siddhartha V. and Tarjan, Robert E.
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Data Structures and Algorithms
Abstract: The disjoint set union problem is a basic problem in data structures with a wide variety of applications. We extend a known efficient sequential algorithm for this problem to obtain a simple and efficient concurrent wait-free algorithm running on an asynchronous parallel random access machine (APRAM). Crucial to our result is the use of randomization. Under a certain independence assumption, for a problem instance in which there are n elements, m operations, and p processes, our algorithm does Theta(m (alpha(n, m/(np)) + log(np/m + 1))) expected work, where the expectation is over the random choices made by the algorithm and alpha is a functional inverse of Ackermann's function. In addition, each operation takes O(log n) steps with high probability. Our algorithm is significantly simpler and more efficient than previous algorithms proposed by Anderson and Woll. Under our independence assumption, our algorithm achieves almost-linear speed-up for applications in which all or most of the processes can be kept busy.
Published: 2016

38. Body Dysmorphic and Narcissistic Personality Disorder in Cosmetic Rhinoplasty Candidates

Author: Sahraian, Ali, Janipour, Masoud, Tarjan, Aida, Zareizadeh, Zohre, Habibi, Pardis, and Babaei, Amirhossein
Published: 2022
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39. Concurrent disjoint set union

Author: Jayanti, Siddhartha V. and Tarjan, Robert E.
Published: 2021
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40. Rare fibrolipoma of attached gingiva: A case report and review of the literature

Author: Fahimeh Rezazadeh, Zohreh Jaafari‐Ashkavandi, Aylar Afshari, and Armaghan Tarjan
Subjects: adipocyte, adipose tissue, gingiva, lipoma, neoplasm, Medicine, Medicine (General), R5-920
Abstract: Abstract Lipoma is a rare benign tumor. Fibrolipoma, a variant of lipoma, is relatively uncommon in mouth tissues. Among all benign lesions of oral cavity, the approximate incidence rate of lipoma is 1%–4%, with a prevalence rate of about 0.0002%. Fibrolipoma is an extremely rare subtype of lipoma, especially in the attached gingiva. This subtype accounts for 1.6% of all facial lipomas, with a prevalence rate of 1/5000 adults in the oral and oropharyngeal region. It is diagnosed by histopathologic and immunohistochemistry evaluation and treated by total excision. Due to the adhesion to the surrounding tissues and pseudo‐infiltrating characteristics of this lesion, it can get mistaken with malignant lesions; therefore, a histological examination is mandatory. In this paper, a case of a patient with attached gingiva fibrolipoma treated by surgical excision is reported. This case report is about a 26‐year‐old female patient in Shiraz School of Dentistry with a fibrolipoma of attached gingiva. Because of fibrolipoma's growth tendency, adhesion to the surrounding tissues, and specific histological characteristics, its differential diagnosis with malignant infiltrating tumors is important. Surgical excision is the gold standard for treatment. Accurate differential diagnosis, careful histological examination, and periodic follow‐up are essential.
Published: 2022
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41. Protected area use by two sympatric marine predators repopulating their historical range

Author: M Salton, M Carr, LM Tarjan, J Clarke, R Kirkwood, D Slip, and R Harcourt
Subjects: Zoology, QL1-991, Botany, QK1-989
Abstract: As large carnivores recover from over-exploitation, managers often lack evidence-based information on species habitat requirements and the efficacy of management practices, particularly where species repopulate areas from which they have long been extirpated. We investigated the movement and habitat use by 2 semi-aquatic carnivores (Australian fur seals Arctocephalus pusillus doriferus and New Zealand fur seals A. forsteri) at the northern end of their distributions in Australia, where after a long absence both are recolonising their historic range. We also assessed male fur seal habitat use overlap with terrestrial and marine protected areas (PAs). While at the margin of the range during winter and early spring, the males remained inshore close to terrestrial sites and where interactions with humans often occur. From early spring, the males from the range margin showed uniform movement toward colonies in the core of the species’ range prior to their breeding seasons. This contrasts with males tracked from the core of the species’ range that returned periodically to colonies during the year, and highlights the importance of range-wide monitoring of a species to inform conservation planning. Habitat use by some males included over 90% of a marine PA at the margin of the species’ range. Most terrestrial haul-outs used were within terrestrial PAs, while sites not protected were on the margin of the range. Despite wide-ranging habits, their dependence on coastal sites, where human access and activities can be regulated and more readily enforced, suggests that terrestrial and marine PAs will continue to play an important role in managing the recovery of these fur seals.
Published: 2021
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42. A Note on Fault Tolerant Reachability for Directed Graphs

Author: Georgiadis, Loukas and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: In this note we describe an application of low-high orders in fault-tolerant network design. Baswana et al. [DISC 2015] study the following reachability problem. We are given a flow graph $G = (V, A)$ with start vertex $s$, and a spanning tree $T =(V, A_T)$ rooted at $s$. We call a set of arcs $A'$ valid if the subgraph $G' = (V, A_T \cup A')$ of $G$ has the same dominators as $G$. The goal is to find a valid set of minimum size. Baswana et al. gave an $O(m \log{n})$-time algorithm to compute a minimum-size valid set in $O(m \log{n})$ time, where $n = |V|$ and $m = |A|$. Here we provide a simple $O(m)$-time algorithm that uses the dominator tree $D$ of $G$ and a low-high order of it.
Published: 2015

43. Hollow Heaps

Author: Hansen, Thomas Dueholm, Kaplan, Haim, Tarjan, Robert E., and Zwick, Uri
Subjects: Computer Science - Data Structures and Algorithms
Abstract: We introduce the hollow heap, a very simple data structure with the same amortized efficiency as the classical Fibonacci heap. All heap operations except delete and delete-min take $O(1)$ time, worst case as well as amortized; delete and delete-min take $O(\log n)$ amortized time on a heap of $n$ items. Hollow heaps are by far the simplest structure to achieve this. Hollow heaps combine two novel ideas: the use of lazy deletion and re-insertion to do decrease-key operations, and the use of a dag (directed acyclic graph) instead of a tree or set of trees to represent a heap. Lazy deletion produces hollow nodes (nodes without items), giving the data structure its name., Comment: 27 pages, 7 figures, preliminary version appeared in ICALP 2015
Published: 2015

44. Azimuthally anisotropic emission of low-momentum direct photons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Alfred, M., Al-Ta'ani, H., Angerami, A., Aoki, K., Apadula, N., Aramaki, Y., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Bandara, N. S., Bannier, B., Barish, K. N., Bassalleck, B., Basye, A. T., Bathe, S., Baublis, V., Baumann, C., Baumgart, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Blau, D. S., Bok, J. S., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Castera, P., Chen, C. -H., Chi, C. Y., Chiu, M., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Chvala, O., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Danley, D., Das, K., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Diss, P. B., Do, J. H., Donadelli, M., D'Orazio, L., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., Dutta, D., Edwards, S., Efremenko, Y. V., Ellinghaus, F., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gainey, K., Gal, C., Gallus, P., Garg, P., Garishvili, A., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gong, X., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, L., Gustafsson, H. -Å., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Hamilton, H. F., Han, R., Han, S. Y., Hanks, J., Hartouni, E. P., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hill, J. C., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Ide, J., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Javani, M., Jezghani, M., Jia, J., Jiang, X., Jin, J., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kapustinsky, J., Karatsu, K., Kasai, M., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, H. J., Kim, K. -B., Kim, M., Kim, S. H., Kim, Y. -J., Kim, Y. K., Kimelman, B., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Komatsu, Y., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Krizek, F., Kunde, G. J., Kurita, K., Kurosawa, M., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, B., Lee, D. M., Lee, J., Lee, K., Lee, K. B., Lee, K. S., Lee, S, Lee, S. H., Lee, S. R., Leitch, M. J., Leite, M. A. L., Leitgab, M., Leitner, E., Lenzi, B., Lewis, B., Li, X., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Love, B., Luechtenborg, R., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Masumoto, S., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Miyachi, Y., Miyasaka, S., Mizuno, S., Mohanty, A. K., Mohapatra, S., Montuenga, P., Moon, H. J., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Motschwiller, S., Moukhanova, T. V., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagashima, K., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nattrass, C., Nederlof, A., Netrakanti, P. K., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Nishimura, S., Nouicer, R., Novak, T., Novitzky, N., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Oka, M., Okada, K., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, J. S., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Patel, M., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Rak, J., Rakotozafindrabe, A., Ramson, B. J., Ravinovich, I., Read, K. F., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakashita, K., Sako, H., Samsonov, V., Sano, M., Sano, S., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Sparks, N. A., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sziklai, J., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Vale, C., Valle, H., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vossen, A., Vrba, V., Vznuzdaev, E., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., White, A. S., White, S. N., Winter, D., Wolin, S., Wood, J. P., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoo, J. H., Yoon, I., You, Z., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zelenski, A., Zhang, C., Zhou, S., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: The PHENIX experiment at the Relativistic Heavy Ion Collider has measured 2nd and 3rd order Fourier coefficients of the azimuthal distributions of direct photons emitted at midrapidity in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV for various collision centralities. Combining two different analysis techniques, results were obtained in the transverse momentum range of $0.4
Published: 2015
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45. Scaling properties of fractional momentum loss of high-pT hadrons in nucleus-nucleus collisions at $\sqrt{s_{_{NN}}}$ from 62.4 GeV to 2.76 TeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Alfred, M., Al-Ta'ani, H., Angerami, A., Aoki, K., Apadula, N., Aphecetche, L., Aramaki, Y., Armendariz, R., Aronson, S. H., Asai, J., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Bandara, N. S., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Baumann, C., Baumgart, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Blau, D. S., Boissevain, J. G., Bok, J. S., Borel, H., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Castera, P., Chang, B. S., Charvet, J. -L., Chen, C. -H., Chernichenko, S., Chi, C. Y., Chiba, J., Chiu, M., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Churyn, A., Chvala, O., Cianciolo, V., Citron, Z., Cleven, C. R., Cole, B. A., Comets, M. P., Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Danley, D., Das, K., Datta, A., Daugherity, M. S., David, G., Deaton, M. B., DeBlasio, K., Dehmelt, K., Delagrange, H., Denisov, A., d'Enterria, D., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Diss, P. B., Do, J. H., Donadelli, M., D'Orazio, L., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Edwards, S., Efremenko, Y. V., Egdemir, J., Ellinghaus, F., Emam, W. S., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gadrat, S., Gainey, K., Gal, C., Gallus, P., Garg, P., Garishvili, A., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gong, X., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, L., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haegemann, C., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Hamilton, H. F., Han, R., Han, S. Y., Hanks, J., Harada, H., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hiejima, H., Hill, J. C., Hobbs, R., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Ide, J., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Inoue, Y., Iordanova, A., Isenhower, D., Isenhower, L., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Javani, M., Jezghani, M., Jia, J., Jiang, X., Jin, J., Jinnouchi, O., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kaneta, M., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kanou, H., Kapustinsky, J., Karatsu, K., Kasai, M., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, H. J., Kim, K. -B., Kim, M., Kim, S. H., Kim, Y. -J., Kim, Y. K., Kimelman, B., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Kiyomichi, A., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Kochetkov, V., Komatsu, Y., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Krizek, F., Kubart, J., Kunde, G. J., Kurihara, N., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, B., Lee, D. M., Lee, J., Lee, K., Lee, K. B., Lee, K. S., Lee, M. K., Lee, S, Lee, S. H., Lee, S. R., Lee, T., Leitch, M. J., Leite, M. A. L., Leitgab, M., Leitner, E., Lenzi, B., Lewis, B., Li, X., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Love, B., Luechtenborg, R., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Mašek, L., Masui, H., Masumoto, S., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Mikeš, P., Miki, K., Miller, T. E., Milov, A., Mioduszewski, S., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitrovski, M., Miyachi, Y., Miyasaka, S., Mizuno, S., Mohanty, A. K., Mohapatra, S., Montuenga, P., Moon, H. J., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Motschwiller, S., Moukhanova, T. V., Mukhopadhyay, D., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagashima, K., Nagata, Y., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nattrass, C., Nederlof, A., Netrakanti, P. K., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Nishimura, S., Norman, B. E., Nouicer, R., Novak, T., Novitzky, N., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Ohnishi, H., Oka, M., Okada, K., Omiwade, O. O., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pal, D., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, J. S., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Patel, M., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Rak, J., Rakotozafindrabe, A., Ramson, B. J., Ravinovich, I., Read, K. F., Rembeczki, S., Reuter, M., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Romana, A., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sakata, H., Sako, H., Samsonov, V., Sano, M., Sano, S., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shevel, A., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skutnik, S., Slunečka, M., Snowball, M., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Sparks, N. A., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Sumita, T., Sun, J., Sziklai, J., Tabaru, T., Takagi, S., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tojo, J., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Vale, C., Valle, H., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vossen, A., Vrba, V., Vznuzdaev, E., Wagner, M., Walker, D., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., White, A. S., White, S. N., Winter, D., Wolin, S., Wood, J. P., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Yasin, Z., Ying, J., Yokkaichi, S., Yoo, J. H., Yoon, I., You, Z., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zhou, S., Zimamyi, J., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: Measurements of the fractional momentum loss ($S_{\rm loss}\equiv{\delta}p_T/p_T$) of high-transverse-momentum-identified hadrons in heavy ion collisions are presented. Using $\pi^0$ in Au$+$Au and Cu$+$Cu collisions at $\sqrt{s_{_{NN}}}=62.4$ and 200 GeV measured by the PHENIX experiment at the Relativistic Heavy Ion Collider and and charged hadrons in Pb$+$Pb collisions measured by the ALICE experiment at the Large Hadron Collider, we studied the scaling properties of $S_{\rm loss}$ as a function of a number of variables: the number of participants, $N_{\rm part}$, the number of quark participants, $N_{\rm qp}$, the charged-particle density, $dN_{\rm ch}/d\eta$, and the Bjorken energy density times the equilibration time, $\varepsilon_{\rm Bj}\tau_{0}$. We find that the $p_T$ where $S_{\rm loss}$ has its maximum, varies both with centrality and collision energy. Above the maximum, $S_{\rm loss}$ tends to follow a power-law function with all four scaling variables. The data at $\sqrt{s_{_{NN}}}$=200 GeV and 2.76 TeV, for sufficiently high particle densities, have a common scaling of $S_{\rm loss}$ with $dN_{\rm ch}/d\eta$ and $\varepsilon_{\rm Bj}\tau_{0}$, lending insight on the physics of parton energy loss., Comment: 630 authors, 21 pages, 16 figures, 7 tables. v2 is version accepted by Phys. Rev. C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2015
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46. Transverse energy production and charged-particle multiplicity at midrapidity in various systems from $\sqrt{s_{NN}}=7.7$ to 200 GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Alfred, M., Al-Jamel, A., Al-Ta'ani, H., Angerami, A., Aoki, K., Apadula, N., Aphecetche, L., Aramaki, Y., Armendariz, R., Aronson, S. H., Asai, J., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Bai, X., Baksay, G., Baksay, L., Baldisseri, A., Bandara, N. S., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Bauer, F., Baumann, C., Baumgart, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bhom, J. H., Bickley, A. A., Bjorndal, M. T., Black, D., Blau, D. S., Boissevain, J. G., Bok, J. S., Borel, H., Boyle, K., Brooks, M. L., Brown, D. S., Bryslawskyj, J., Bucher, D., Buesching, H., Bumazhnov, V., Bunce, G., Burward-Hoy, J. M., Butsyk, S., Campbell, S., Caringi, A., Castera, P., Chai, J. -S., Chang, B. S., Charvet, J. -L., Chen, C. -H., Chernichenko, S., Chi, C. Y., Chiba, J., Chiu, M., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Churyn, A., Chvala, O., Cianciolo, V., Citron, Z., Cleven, C. R., Cobigo, Y., Cole, B. A., Comets, M. P., del Valle, Z. Conesa, Connors, M., Constantin, P., Cronin, N., Crossette, N., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Danley, D., Das, K., Datta, A., Daugherity, M. S., David, G., Dayananda, M. K., Deaton, M. B., DeBlasio, K., Dehmelt, K., Delagrange, H., Denisov, A., d'Enterria, D., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Diss, P. B., Do, J. H., Donadelli, M., D'Orazio, L., Drachenberg, J. L., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Edwards, S., Efremenko, Y. V., Egdemir, J., Ellinghaus, F., Emam, W. S., Engelmore, T., Enokizono, A., En'yo, H., Espagnon, B., Esumi, S., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, M., Finger Jr., M., Fleuret, F., Fokin, S. L., Forestier, B., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fung, S. -Y., Fusayasu, T., Gadrat, S., Gainey, K., Gal, C., Gallus, P., Garg, P., Garishvili, A., Garishvili, I., Gastineau, F., Ge, H., Germain, M., Giordano, F., Glenn, A., Gong, H., Gong, X., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Grim, G., Perdekamp, M. Grosse, Gu, Y., Gunji, T., Guo, L., Guragain, H., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haegemann, C., Haggerty, J. S., Hagiwara, M. N., Hahn, K. I., Hamagaki, H., Hamblen, J., Hamilton, H. F., Han, R., Han, S. Y., Hanks, J., Harada, H., Hartouni, E. P., Haruna, K., Harvey, M., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hasuko, K., Hayano, R., Hayashi, S., He, X., Heffner, M., Hemmick, T. K., Hester, T., Heuser, J. M., Hiejima, H., Hill, J. C., Hobbs, R., Hohlmann, M., Hollis, R. S., Holmes, M., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Hur, M. G., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imazu, Y., Imrek, J., Inaba, M., Inoue, Y., Iordanova, A., Isenhower, D., Isenhower, L., Ishihara, M., Isinhue, A., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Iwanaga, Y., Jacak, B. V., Javani, M., Jeon, S. J., Jezghani, M., Jia, J., Jiang, X., Jin, J., Jinnouchi, O., Johnson, B. M., Jones, T., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kaneta, M., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kanou, H., Kapustinsky, J., Karatsu, K., Kasai, M., Kawagishi, T., Kawall, D., Kawashima, M., Kazantsev, A. V., Kelly, S., Kempel, T., Key, J. A., Khachatryan, V., Khandai, P. K., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kim, A., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, H. J., Kim, K. -B., Kim, M., Kim, Y. -J., Kim, Y. K., Kim, Y. -S., Kimelman, B., Kinney, E., Kiss, Á., Kistenev, E., Kitamura, R., Kiyomichi, A., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Kochetkov, V., Kofarago, M., Komatsu, Y., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Krizek, F., Kroon, P. J., Kubart, J., Kunde, G. J., Kurihara, N., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Bornec, Y. Le, Leckey, S., Lee, B., Lee, D. M., Lee, G. H., Lee, J., Lee, K. B., Lee, K. S., Lee, M. K., Lee, S, Lee, S. H., Lee, S. R., Lee, T., Leitch, M. J., Leite, M. A. L., Leitgab, M., Lenzi, B., Lewis, B., Li, X., Li, X. H., Lichtenwalner, P., Liebing, P., Lim, H., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Love, B., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Maruyama, T., Mašek, L., Masui, H., Masumoto, S., Matathias, F., McCain, M. C., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Midori, J., Mignerey, A. C., Mikeš, P., Miki, K., Miller, T. E., Milov, A., Mioduszewski, S., Mishra, D. K., Mishra, G. C., Mishra, M., Mitchell, J. T., Mitrovski, M., Miyachi, Y., Miyasaka, S., Mizuno, S., Mohanty, A. K., Mohapatra, S., Montuenga, P., Moon, H. J., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Moskowitz, M., Moss, J. M., Motschwiller, S., Moukhanova, T. V., Mukhopadhyay, D., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagashima, K., Nagata, Y., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nam, S., Nattrass, C., Nederlof, A., Netrakanti, P. K., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Nishimura, S., Norman, B. E., Nouicer, R., Novak, T., Novitzky, N., Nukariya, A., Nyanin, A. S., Nystrand, J., Oakley, C., Obayashi, H., O'Brien, E., Oda, S. X., Ogilvie, C. A., Ohnishi, H., Oide, H., Ojha, I. D., Oka, M., Okada, K., Omiwade, O. O., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Otterlund, I., Ouchida, M., Ozawa, K., Pak, R., Pal, D., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, J. S., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Patel, M., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Rak, J., Rakotozafindrabe, A., Ramson, B. J., Ravinovich, I., Read, K. F., Rembeczki, S., Reuter, M., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Rinn, T., Riveli, N., Roach, D., Roche, G., Rolnick, S. D., Romana, A., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Rykov, V. L., Ryu, M. S., Ryu, S. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sakata, H., Sako, H., Samsonov, V., Sano, M., Sano, S., Sarsour, M., Sato, H. D., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Sekiguchi, Y., Semenov, V., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shaver, A., Shea, T. K., Shein, I., Shevel, A., Shibata, T. -A., Shigaki, K., Shimomura, M., Shohjoh, T., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skolnik, M., Skutnik, S., Slunečka, M., Smith, W. C., Snowball, M., Solano, S., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Steinberg, P., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Stone, M. R., Sugitate, T., Suire, C., Sukhanov, A., Sullivan, J. P., Sumita, T., Sun, J., Sziklai, J., Tabaru, T., Takagi, S., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, K. H., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, D., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tojo, J., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Tuli, S. K., Tydesjö, H., Tyurin, N., Vale, C., Valle, H., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Voas, B., Vossen, A., Vrba, V., Vznuzdaev, E., Wagner, M., Walker, D., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., Whitaker, S., White, A. S., White, S. N., Willis, N., Winter, D., Wolin, S., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Yasin, Z., Ying, J., Yokkaichi, S., Yoo, J. H., Yoon, I., You, Z., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zhou, S., Zimamyi, J., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: Measurements of midrapidity charged particle multiplicity distributions, $dN_{\rm ch}/d\eta$, and midrapidity transverse-energy distributions, $dE_T/d\eta$, are presented for a variety of collision systems and energies. Included are distributions for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$, 130, 62.4, 39, 27, 19.6, 14.5, and 7.7 GeV, Cu$+$Cu collisions at $\sqrt{s_{_{NN}}}=200$ and 62.4 GeV, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV, U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV, $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV, $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV, and $p$$+$$p$ collisions at $\sqrt{s_{_{NN}}}=200$ GeV. Centrality-dependent distributions at midrapidity are presented in terms of the number of nucleon participants, $N_{\rm part}$, and the number of constituent quark participants, $N_{q{\rm p}}$. For all $A$$+$$A$ collisions down to $\sqrt{s_{_{NN}}}=7.7$ GeV, it is observed that the midrapidity data are better described by scaling with $N_{q{\rm p}}$ than scaling with $N_{\rm part}$. Also presented are estimates of the Bjorken energy density, $\varepsilon_{\rm BJ}$, and the ratio of $dE_T/d\eta$ to $dN_{\rm ch}/d\eta$, the latter of which is seen to be constant as a function of centrality for all systems., Comment: 706 authors, 32 pages, 20 figures, 34 tables, 2004, 2005, 2008, 2010, 2011, and 2012 data. v2 is version accepted for publication in Phys. Rev. C
Published: 2015
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47. Measurements of elliptic and triangular flow in high-multiplicity $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Alfred, M., Al-Ta'ani, H., Andrews, K. R., Angerami, A., Aoki, K., Apadula, N., Aphecetche, L., Appelt, E., Aramaki, Y., Armendariz, R., Aronson, S. H., Asai, J., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Bandara, N. S., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Baumann, C., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Ben-Benjamin, J., Bennett, R., Berdnikov, A., Berdnikov, Y., Bhom, J. H., Bickley, A. A., Blau, D. S., Boissevain, J. G., Bok, J. S., Borel, H., Boyle, K., Brooks, M. L., Broxmeyer, D., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Caringi, A., Castera, P., Chang, B. S., Chang, W. C., Charvet, J. -L., Chen, C. -H., Chernichenko, S., Chi, C. Y., Chiba, J., Chiu, M., Choi, I. J., Choi, J. B., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Churyn, A., Chvala, O., Cianciolo, V., Citron, Z., Cleven, C. R., Cole, B. A., Comets, M. P., del Valle, Z. Conesa, Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Danley, D., Das, K., Datta, A., Daugherity, M. S., David, G., Dayananda, M. K., Deaton, M. B., DeBlasio, K., Dehmelt, K., Delagrange, H., Denisov, A., d'Enterria, D., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Dion, A., Diss, P. B., Do, J. H., Donadelli, M., D'Orazio, L., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Edwards, S., Efremenko, Y. V., Egdemir, J., Ellinghaus, F., Emam, W. S., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gadrat, S., Gal, C., Gallus, P., Garg, P., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gong, X., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Grim, G., Perdekamp, M. Grosse, Gu, Y., Gunji, T., Guo, L., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haegemann, C., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Hamilton, H. F., Han, R., Han, S. Y., Hanks, J., Harada, H., Harper, C., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hiejima, H., Hill, J. C., Hobbs, R., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Inoue, Y., Iordanova, A., Isenhower, D., Isenhower, L., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Iwanaga, Y., Jacak, B. V., Jezghani, M., Jia, J., Jiang, X., Jin, J., Jinnouchi, O., John, D., Johnson, B. M., Jones, T., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kaneta, M., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kanou, H., Kapustinsky, J., Karatsu, K., Kasai, M., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kim, A., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, M., Kim, S. H., Kim, Y. -J., Kim, Y. K., Kimelman, B., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Kiyomichi, A., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Kochetkov, V., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Kubart, J., Kunde, G. J., Kurihara, N., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Layton, D., Lebedev, A., Lee, D. M., Lee, J., Lee, K. B., Lee, K. S., Lee, M. K., Lee, S, Lee, S. H., Lee, S. R., Lee, T., Leitch, M. J., Leite, M. A. L., Lenzi, B., Li, X., Lichtenwalner, P., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Love, B., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Mašek, L., Masui, H., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Mikeš, P., Miki, K., Miller, T. E., Milov, A., Mioduszewski, S., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitrovski, M., Miyachi, Y., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, H. J., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Motschwiller, S., Moukhanova, T. V., Mukhopadhyay, D., Murakami, T., Murata, J., Mwai, A., Nagamiya, S., Nagashima, K., Nagata, Y., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nam, S., Nattrass, C., Netrakanti, P. K., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Nishimura, S., Norman, B. E., Nouicer, R., Novak, T., Novitzky, N., Nyanin, A. S., Oakley, C., O'Brien, E., Oda, S. X., Ogilvie, C. A., Ohnishi, H., Oka, M., Okada, K., Omiwade, O. O., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pal, D., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, J. S., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Patel, M., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Rak, J., Rakotozafindrabe, A., Ramson, B. J., Ravinovich, I., Read, K. F., Rembeczki, S., Reuter, M., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Romana, A., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sakata, H., Sako, H., Samsonov, V., Sano, S., Sarsour, M., Sato, S., Sato, T., Savastio, M., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shevel, A., Shibata, T. -A., Shigaki, K., Shim, H. H., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skutnik, S., Slunečka, M., Snowball, M., Sodre, T., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Sumita, T., Sun, J., Sziklai, J., Tabaru, T., Takagi, S., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, D., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tojo, J., Tomášek, L., Tomášek, M., Tomita, Y., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Utsunomiya, K., Vale, C., Valle, H., van Hecke, H. W., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vossen, A., Vrba, V., Vznuzdaev, E., Wagner, M., Walker, D., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., White, A. S., White, S. N., Winter, D., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Yasin, Z., Ying, J., Yokkaichi, S., Yoo, J. H., Yoo, J. S., Yoon, I., You, Z., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zhou, S., Zimamyi, J., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment, High Energy Physics - Experiment
Abstract: We present the first measurement of elliptic ($v_2$) and triangular ($v_3$) flow in high-multiplicity $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. Two-particle correlations, where the particles have a large separation in pseudorapidity, are compared in $^{3}$He$+$Au and in $p$$+$$p$ collisions and indicate that collective effects dominate the second and third Fourier components for the correlations observed in the $^{3}$He$+$Au system. The collective behavior is quantified in terms of elliptic $v_2$ and triangular $v_3$ anisotropy coefficients measured with respect to their corresponding event planes. The $v_2$ values are comparable to those previously measured in $d$$+$Au collisions at the same nucleon-nucleon center-of-mass energy. Comparison with various theoretical predictions are made, including to models where the hot spots created by the impact of the three $^{3}$He nucleons on the Au nucleus expand hydrodynamically to generate the triangular flow. The agreement of these models with data may indicate the formation of low-viscosity quark-gluon plasma even in these small collision systems., Comment: 630 authors, 9 pages, 4 figures, 2 tables. v2 is the version accepted for publication by Physical Review Letters. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2015
Full Text: View/download PDF

48. Measurement of higher cumulants of net-charge multiplicity distributions in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=7.7-200$ GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Bataineh, H., Alexander, J., Al-Ta'ani, H., Angerami, A., Aoki, K., Apadula, N., Aramaki, Y., Asano, H., Aschenauer, E. C., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Bannier, B., Barish, K. N., Bassalleck, B., Basye, A. T., Bathe, S., Baublis, V., Baumann, C., Baumgart, S., Bazilevsky, A., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Black, D., Blau, D. S., Bok, J. S., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Castera, P., Chen, C. -H., Chi, C. Y., Chiu, M., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Chvala, O., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Cronin, N., Crossette, N., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Das, K., Datta, A., Daugherity, M. S., David, G., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Do, J. H., Donadelli, M., D'Orazio, L., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., Dutta, D., Edwards, S., Efremenko, Y. V., Ellinghaus, F., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Eyser, K. O., Fadem, B., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gainey, K., Gal, C., Garg, P., Garishvili, A., Garishvili, I., Giordano, F., Glenn, A., Gong, H., Gong, X., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gu, Y., Gunji, T., Guo, L., Gustafsson, H. -Å., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Han, R., Hanks, J., Hartouni, E. P., Hashimoto, K., Haslum, E., Hayano, R., Hayashi, S., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hill, J. C., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Hornback, D., Huang, S., Ichihara, T., Ichimiya, R., Ide, J., Iinuma, H., Ikeda, Y., Imai, K., Imazu, Y., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isinhue, A., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Javani, M., Jia, J., Jiang, X., Jin, J., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kapustinsky, J., Karatsu, K., Kasai, M., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Key, J. A., Khandai, P. K., Khanzadeev, A., Kijima, K. M., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, H. J., Kim, K. -B., Kim, S. H., Kim, Y. -J., Kim, Y. K., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Klatsky, J., Kleinjan, D., Kline, P., Kochenda, L., Komatsu, Y., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Krizek, F., Kunde, G. J., Kurita, K., Kurosawa, M., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, B., Lee, D. M., Lee, J., Lee, K., Lee, K. B., Lee, K. S., Lee, S. H., Lee, S. R., Leitch, M. J., Leite, M. A. L., Leitgab, M., Leitner, E., Lenzi, B., Lewis, B., Li, X., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Love, B., Luechtenborg, R., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Maruyama, T., Masui, H., Masumoto, S., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Midori, J., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Miyachi, Y., Miyasaka, S., Mohanty, A. K., Mohapatra, S., Moon, H. J., Morino, Y., Morreale, A., Morrison, D. P., Moskowitz, M., Motschwiller, S., Moukhanova, T. V., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nattrass, C., Nederlof, A., Netrakanti, P. K., Newby, J., Nguyen, M., Nihashi, M., Niida, T., Nouicer, R., Novitzky, N., Nukariya, A., Nyanin, A. S., Obayashi, H., O'Brien, E., Oda, S. X., Ogilvie, C. A., Oka, M., Okada, K., Onuki, Y., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, L., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Peresedov, V., Peressounko, D. Yu., Petti, R., Pinkenburg, C., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Rak, J., Rakotozafindrabe, A., Ravinovich, I., Read, K. F., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Riveli, N., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rukoyatkin, P., Ružička, P., Ryu, M. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakashita, K., Sako, H., Samsonov, V., Sano, M., Sano, S., Sarsour, M., Sato, S., Sato, T., Sawada, S., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Sen, A., Seto, R., Sett, P., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Skolnik, M., Slunečka, M., Solano, S., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Sparks, N. A., Stankus, P. W., Steinberg, P., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Sukhanov, A., Sun, J., Sziklai, J., Takagui, E. M., Takahara, A., Taketani, A., Tanabe, R., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Tennant, E., Themann, H., Thomas, T. L., Todoroki, T., Togawa, M., Toia, A., Tomášek, L., Tomášek, M., Torii, H., Towell, R. S., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Vale, C., Valle, H., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Voas, B., Vossen, A., Vrba, V., Vznuzdaev, E., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., Whitaker, S., White, S. N., Winter, D., Wolin, S., Wood, J. P., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., You, Z., Young, G. R., Younus, I., Yushmanov, I. E., Zajc, W. A., Zelenski, A., Zhang, C., Zhou, S., and Zolin, L.
Subjects: Nuclear Experiment
Abstract: We report the measurement of cumulants ($C_n, n=1\ldots4$) of the net-charge distributions measured within pseudorapidity ($|\eta|<0.35$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=7.7-200$ GeV with the PHENIX experiment at the Relativistic Heavy Ion Collider. The ratios of cumulants (e.g. $C_1/C_2$, $C_3/C_1$) of the net-charge distributions, which can be related to volume independent susceptibility ratios, are studied as a function of centrality and energy. These quantities are important to understand the quantum-chromodynamics phase diagram and possible existence of a critical end point. The measured values are very well described by expectation from negative binomial distributions. We do not observe any nonmonotonic behavior in the ratios of the cumulants as a function of collision energy. The measured values of $C_1/C_2 = \mu/\sigma^2$ and $C_3/C_1 = S\sigma^3/\mu$ can be directly compared to lattice quantum-chromodynamics calculations and thus allow extraction of both the chemical freeze-out temperature and the baryon chemical potential at each center-of-mass energy., Comment: 512 authors, 8 pages, 4 figures, 1 table. v2 is version accepted for publication in Phys. Rev. C as a Rapid Communication. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2015
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49. Amortized Rotation Cost in AVL Trees

Author: Amani, Mahdi, Lai, Kevin A., and Tarjan, Robert E.
Subjects: Computer Science - Data Structures and Algorithms
Abstract: An AVL tree is the original type of balanced binary search tree. An insertion in an $n$-node AVL tree takes at most two rotations, but a deletion in an $n$-node AVL tree can take $\Theta(\log n)$. A natural question is whether deletions can take many rotations not only in the worst case but in the amortized case as well. A sequence of $n$ successive deletions in an $n$-node tree takes $O(n)$ rotations, but what happens when insertions are intermixed with deletions? Heaupler, Sen, and Tarjan conjectured that alternating insertions and deletions in an $n$-node AVL tree can cause each deletion to do $\Omega(\log n)$ rotations, but they provided no construction to justify their claim. We provide such a construction: we show that, for infinitely many $n$, there is a set $E$ of {\it expensive} $n$-node AVL trees with the property that, given any tree in $E$, deleting a certain leaf and then reinserting it produces a tree in $E$, with the deletion having done $\Theta(\log n)$ rotations. One can do an arbitrary number of such expensive deletion-insertion pairs. The difficulty in obtaining such a construction is that in general the tree produced by an expensive deletion-insertion pair is not the original tree. Indeed, if the trees in $E$ have even height $k$, $2^{k/2}$ deletion-insertion pairs are required to reproduce the original tree.
Published: 2015

50. Systematic study of charged-pion and kaon femtoscopy in Au$+$Au collisions at $\sqrt{s_{_{NN}}}$=200 GeV

Author: Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Apadula, N., Aramaki, Y., Asano, H., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Bandara, N. S., Bannier, B., Barish, K. N., Bassalleck, B., Basye, A. T., Bathe, S., Baublis, V., Baumann, C., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bickley, A. A., Blau, D. S., Bok, J. S., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Chen, C. -H., Chi, C. Y., Chiu, M., Choi, I. J., Choi, J. B., Choudhury, R. K., Christiansen, P., Chujo, T., Chung, P., Chvala, O., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Csanád, M., Csörgő, T., Dahms, T., Dairaku, S., Danchev, I., Danley, D., Das, K., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Diss, P. B., Do, J. H., Donadelli, M., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., Dutta, D., Edwards, S., Efremenko, Y. V., Ellinghaus, F., Engelmore, T., Enokizono, A., En'yo, H., Esumi, S., Fadem, B., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gal, C., Gallus, P., Garg, P., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Gustafsson, H. -Å., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamblen, J., Hamilton, H. F., Han, R., Han, S. Y., Hanks, J., Hartouni, E. P., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., He, X., Heffner, M., Hemmick, T. K., Hester, T., Hill, J. C., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Ichihara, T., Ichimiya, R., Ide, J., Ikeda, Y., Imai, K., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Jezghani, M., Jia, J., Jiang, X., Jin, J., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kang, J. H., Kapustinsky, J., Karatsu, K., Kawall, D., Kawashima, M., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, M., Kim, S. H., Kim, Y. -J., Kimelman, B., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Komkov, B., Konno, M., Koster, J., Kotchetkov, D., Kotov, D., Kozlov, A., Král, A., Kravitz, A., Kunde, G. J., Kurita, K., Kurosawa, M., Kwon, Y., Kyle, G. S., Lacey, R., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, D. M., Lee, J., Lee, K., Lee, K. B., Lee, K. S., Lee, S, Lee, S. H., Leitch, M. J., Leite, M. A. L., Leitner, E., Lenzi, B., Li, X., Liebing, P., Lim, S. H., Levy, L. A. Linden, Liška, T., Litvinenko, A., Liu, H., Liu, M. X., Love, B., Luechtenborg, R., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Matathias, F., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Moukhanova, T. V., Murakami, T., Murata, J., Mwai, A., Nagamiya, S., Nagashima, K., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, T., Nakano, K., Nattrass, C., Netrakanti, P. K., Newby, J., Nguyen, M., Niida, T., Nishimura, S., Nouicer, R., Novak, T., Novitzky, N., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Oka, M., Okada, K., Onuki, Y., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, I. H., Park, J., Park, J. S., Park, S., Park, S. K., Park, W. J., Pate, S. F., Patel, M., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Proissl, M., Purschke, M. L., Purwar, A. K., Qu, H., Rak, J., Rakotozafindrabe, A., Ramson, B. J., Ravinovich, I., Read, K. F., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosen, C. A., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakashita, K., Sako, H., Samsonov, V., Sano, S., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Sparks, N. A., Stankus, P. W., Stenlund, E., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sziklai, J., Takagui, E. M., Taketani, A., Tanabe, R., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Tsuchimoto, Y., Vale, C., Valle, H., van Hecke, H. W., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Vértesi, R., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., Wessels, J., White, A. S., White, S. N., Winter, D., Wood, J. P., Woody, C. L., Wright, R. M., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoo, J. H., Yoon, I., You, Z., Young, G. R., Younus, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zelenski, A., Zhang, C., Zhou, S., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: We present a systematic study of charged pion and kaon interferometry in Au$+$Au collisions at $\sqrt{s_{_{NN}}}$=200 GeV. The kaon mean source radii are found to be larger than pion radii in the outward and longitudinal directions for the same transverse mass; this difference increases for more central collisions. The azimuthal-angle dependence of the radii was measured with respect to the second-order event plane and similar oscillations of the source radii were found for pions and kaons. Hydrodynamic models qualitatively describe the similar oscillations of the mean source radii for pions and kaons, but they do not fully describe the transverse-mass dependence of the oscillations., Comment: 499 authors, 27 pages, 13 figures, and 11 tables. v2 is the version accepted for publication in Phys. Rev. C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2015
Full Text: View/download PDF

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