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305,093 results on '"Hoffman, A."'

1. Educator Well-Being as a Pathway toward Investing in Education Staff: Insights from the Transformative SEL (TSEL) Adult Learning Series Pilot. The BELE Learning Series

Author

Collaborative for Academic, Social, and Emotional Learning (CASEL), Building Equitable Learning Environments (BELE) Network, Brittney V. Williams, Ally Skoog-Hoffman, Rob Jagers, and Bloodine Barthelus

Abstract

The Building Equitable Learning Environments (BELE) network is a diverse collaborative of organizations working together to advance equity in education. The BELE Network research and learning team, and are responsible for documenting the network learnings, which are grounded in seven "Essential Actions" and reflected in shared annual aims. This brief shares learnings from BELE district partnerships around the fifth Essential Action: invest in staff. To support this Essential Action, Collaborative for Academic, Social, and Emotional Learning (CASEL) developed the Transformative (tSEL) Adult Learning Series, a set of six interactive workshops that build an opportunity for adults themselves to deepen their self-awareness and discover how they can curate equitable learning environments for young people by leveraging the five focal constructs (identity, agency, belonging, collaborative problem-solving, and curiosity). Through this series, education leaders are making investments for in-service professional learning that supports educator well-being and promotes equitable learning environments for adults and young people. Investing in staff actualizes the value of continuous improvement, which fosters a culture of expansion, growth, and development among educators.

Published
2024

2. Physical Therapy Student Learning Perspectives in a Volunteer Interprofessional Interuniversity Service-Learning Opportunity: A Case Study Report

Author

Roberto Cantu, Abigail Gaines, Jessica Hall, Kelsey Wortman, Zachary Young, Stacey J. Hoffman, and Tamara Buck

Abstract

Interdisciplinary service-learning (IDSL) has become a staple of healthcare education over the last two decades as a mechanism of training students to provide cohesive, team-based health care in a complicated and sometimes fragmented health care system. This case study describes the perceived learning of Doctor of Physical Therapy (DPT) students from [Blinded] University who attended a week-long interdisciplinary, interuniversity service-learning trip in rural [Blinded]. Qualitative data from two consecutive year teams (n = 26) were collected via surveys and focus groups and thematically analyzed for motivation to attend the trip and perceived learning. Intrinsic motivation to "give back" and desire to hone professional skills were the primary drivers for desiring to attend the trip. Three themes of perceived learning that emerged were: 1) impact on core values, 2) perceived improvement in para-clinical (soft) skills including interdisciplinary fluency and cultural competence, and 3) improvement of clinical (hard) skills. The dominant theme was the impact the trip had on students' core values. The learning occurred predominantly in Bloom's affective domain and aligned with the physical therapy profession's Core Values and Code of Ethics.

Published
2024

3. A Physical Layer Analysis of Entropy in Delay-Based PUFs Implemented on FPGAs

Author

Plusquellic, Jim, Howard, Jennifer, MacKinnon, Ross, Hoffman, Kristianna, Tsiropoulou, Eirini Eleni, and Chan, Calvin

Subjects
Computer Science - Cryptography and Security
Abstract

Physical Unclonable Functions (PUFs) leverage signal variations that occur within the device as a source of entropy. On-chip instrumentation is utilized by some PUF architectures to measure and digitize these variations, which are then processed into bitstrings and secret keys for use in security functions such as authentication and encryption. In many cases, the variations in the measured signals are introduced by a sequence of components in the circuit structure defined by the PUF architecture. In particular, the Hardware-Embedded deLay PUF (HELP) measures delay variations that occur in combinational logic paths on Field Programmable Gate Arrays (FPGAs), which are composed of a set of interconnecting wires (nodes) and look-up tables (LUTs). Previous investigations of variations in these path delays show that it is possible to derive high quality bitstrings, i.e., those which exhibit high levels of uniqueness and randomness across the device population. However, the underlying source and level of variations associated with the constituent components of the paths remain unknown. In this paper, we apply statistical averaging and differencing techniques to derive estimates for the delay variation associated with an FPGA's basic components, namely LUTs and nodes, as a means of fully characterizing the PUF's source of entropy. The analysis is carried out on a set of 50,015 path delay measurements collected from a set of 20 Xilinx Zynq 7020 SoC-class FPGAs, on which 25 identical instances of a functional unit are instantiated, for a total of 500 instances.

Published
2024

4. Axion Dark Matter eXperiment around 3.3 {\mu}eV with Dine-Fischler-Srednicki-Zhitnitsky Discovery Ability

Author

Bartram, C., Boutan, C., Braine, T., Buckley, J. H., Caligiure, T. J., Carosi, G., Chou, A. S., Cisneros, C., Clarke, John, Daw, E. J., Du, N., Duffy, L. D., Dyson, T. A., Gaikwad, C., Gleason, J. R., Goodman, C., Goryachev, M., Guzzetti, M., Hanretty, C., Hartman, E., Hipp, A. T., Hoffman, J., Hollister, M., Khatiwada, R., Knirck, S., Kuo, C. L., Lentz, E., McAllister, B. T., Mostyn, C., Murch, K., Oblath, N. S., Perry, M. G., Quiskamp, A., Robertson, N., Rosenberg, L. J, Ruppert, S., Rybka, G., Siddiqi, I., Sikivie, P., Sinnis, J., Solano, M. E., Sonnenschein, A., Sullivan, N. S., Tanner, D. B., Taubman, M. S., Tobar, M. E., Withers, M. O., Woollett, N., and Zhang, D.

Subjects
High Energy Physics - Experiment
Abstract

We report the results of a QCD axion dark matter search with discovery ability for Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axions using an axion haloscope. Sub-Kelvin noise temperatures are reached with an ultra low-noise Josephson parametric amplifier cooled by a dilution refrigerator. This work excludes (with a 90% confidence level) DFSZ axions with masses between 3.27 to 3.34 {\mu}eV, assuming a standard halo model with a local energy density of 0.45 GeV/cc made up 100% of axions.

Published
2024

5. Machine Learning with Physics Knowledge for Prediction: A Survey

Author

Watson, Joe, Song, Chen, Weeger, Oliver, Gruner, Theo, Le, An T., Hansel, Kay, Hendawy, Ahmed, Arenz, Oleg, Trojak, Will, Cranmer, Miles, D'Eramo, Carlo, Bülow, Fabian, Goyal, Tanmay, Peters, Jan, and Hoffman, Martin W.

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis, Physics - Computational Physics
Abstract

This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning., Comment: 56 pages, 8 figures, 2 tables

Published
2024

6. QM abelian varieties, hypergeometric character sums and modular forms

Author

Hoffman, Jerome William and Tu, Fang-Ting

Subjects
Mathematics - Number Theory, 11T23, 11T24, 11S40, 11F80, 11F85, 33C05, 33C65
Abstract

This is a report on recent work, with Wen-Ching Winnie Li and Ling Long. In that work explicit formulas are given, involving hypergeometric character sums, for the traces of Hecke operators $T_p$ acting spaces of cusp forms $S_k(\Gamma)$ of weight $k$ for certain arithmetically defined Fuchsian subgroups $\Gamma\subset \mathrm{SL}_2 (\mathbf{R})$. In particular we consider the groups attached to the quaternion division algebra $B_6$ over $\mathbf{Q}$ of discriminant 6., Comment: An overview of the background of the joint paper with Wen-Ching W. Li and Ling Long on "Traces of Hecke Operators via Hypergeometric Character Sums" (arXiv: 2408.02918)

Published
2024

7. Statistical Quality Comparison of the Bitstrings Generated by a Physical Unclonable Function across Xilinx, Altera and Microsemi Devices

Author

Jao, Jenilee, Hoffman, Kristi, Reid, Cheryl, Thomson, Ryan, Thompson, Michael, and Plusquellic, Jim

Subjects
Computer Science - Cryptography and Security
Abstract

Entropy or randomness represents a foundational security property in security-related operations, such as key generation. Key generation in turn is central to security protocols such as authentication and encryption. Physical unclonable functions (PUF) are hardware-based primitives that can serve as key generation engines in modern microelectronic devices and applications. PUFs derive entropy from manufacturing variations that exist naturally within and across otherwise identical copies of a device. However, the levels of random variations that represent entropy, which are strongly correlated to the quality of the PUF-generated bitstrings, vary from one manufacturer to another. In this paper, we evaluate entropy across a set of devices manufactured by three mainstream FPGA vendors, Xilinx, Altera and Microsemi. The devices selected for evaluation are considered low-end commercial devices to make the analysis relevant to IoT applications. The SiRF PUF is used in the evaluation, and is constructed nearly identically across the three vendor devices, setting aside minor differences that exist in certain logic element primitives used within the PUF architecture, and which have only a minor impact on our comparative analysis. The SiRF PUF uses a high-resolution time-to-digital converter (TDC) crafted from high-speed carry-chain logic embedded within each device to measure path delays in an engineered netlist of logic gates as a source of entropy. Therefore, our analysis includes an evaluation of actual path delay variation as it exists across the three device classes, as well as a statistical evaluation of the PUF-generated bitstrings. A reliablity analysis is also provided using data collected in industrial-standard temperature experiments to round out the evaluation of important statistical properties of the PUF., Comment: 15 pages, 22 figures, IEEE journal

Published
2024

8. Traces of Hecke Operators via Hypergeometric Character Sums

Author

Hoffman, Jerome W., Li, Wen-Ching Winnie, Long, Ling, and Tu, Fang-Ting

Subjects
Mathematics - Number Theory, 11F11, 11F80, 11L05, 33C20
Abstract

In this paper we obtain explicit formulas for the traces of Hecke operators on spaces of cusp forms in certain instances related to arithmetic triangle groups. These expressions are in terms of hypergeometric character sums over finite fields, a theory developed largely by Greene, Katz, Beukers-Cohen-Mellit, and Fuselier-Long-Ramakrishna-Swisher-Tu. Our approach, in contrast to the previous works, is uniform and more geometric, and it works equally well for forms on elliptic modular curves and Shimura curves. The same method can be applied to obtain eigenvalues of Hecke operators as well.

Published
2024

9. Gemma 2: Improving Open Language Models at a Practical Size

Author

Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, and Andreev, Alek

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Published
2024

10. BOND: Aligning LLMs with Best-of-N Distillation

Author

Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Vieillard, Nino, Ramé, Alexandre, Shariari, Bobak, Perrin, Sarah, Friesen, Abe, Cideron, Geoffrey, Girgin, Sertan, Stanczyk, Piotr, Michi, Andrea, Sinopalnikov, Danila, Ramos, Sabela, Héliou, Amélie, Severyn, Aliaksei, Hoffman, Matt, Momchev, Nikola, and Bachem, Olivier

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates. In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We use the Jeffreys divergence (a linear combination of forward and backward KL) to balance between mode-covering and mode-seeking behavior, and derive an iterative formulation that utilizes a moving anchor for efficiency. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models. Aligning Gemma policies with BOND outperforms other RLHF algorithms by improving results on several benchmarks.

Published
2024

11. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Zapaishchykova, Anna, Pavaine, Julija, Shah, Lubdha M., Jones, Blaise V., Sheth, Nakul, Prabhu, Sanjay P., McAllister, Aaron S., Tu, Wenxin, Nandolia, Khanak K., Rodriguez, Andres F., Shaikh, Ibraheem Salman, Montano, Mariana Sanchez, Lai, Hollie Anne, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anderson, Hannah, Anwar, Syed Muhammed, Aristizabal, Alejandro, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Eddy, James, Ezhov, Ivan, Familiar, Ariana M., Farahani, Keyvan, Gandhi, Deep, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Elaine, Elaine, Karargyris, Alexandros, Kassem, Hasan, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei B., Maleki, Nazanin, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W., Pati, Sarthak, Piraud, Marie, Poussaint, Tina, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Sheller, MIcah, Shinohara, Russell Takeshi, Viswanathan, Karthik, Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter F., Davatzikos, Christos, Storm, Phillip B., Bornhorst, Miriam, Packer, Roger, Hummel, Trent, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Ware, Jeffrey B., Kann, Benjamin H., Rood, Brian, Resnick, Adam, Bakas, Spyridon, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Published
2024

12. MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization

Author

Ahia, Orevaoghene, Kumar, Sachin, Gonen, Hila, Hoffman, Valentin, Limisiewicz, Tomasz, Tsvetkov, Yulia, and Smith, Noah A.

Subjects
Computer Science - Computation and Language
Abstract

In multilingual settings, non-Latin scripts and low-resource languages are usually disadvantaged in terms of language models' utility, efficiency, and cost. Specifically, previous studies have reported multiple modeling biases that the current tokenization algorithms introduce to non-Latin script languages, the main one being over-segmentation. In this work, we propose MAGNET; multilingual adaptive gradient-based tokenization to reduce over-segmentation via adaptive gradient-based subword tokenization. MAGNET learns to predict segment boundaries between byte tokens in a sequence via sub-modules within the model, which act as internal boundary predictors (tokenizers). Previous gradient-based tokenization methods aimed for uniform compression across sequences by integrating a single boundary predictor during training and optimizing it end-to-end through stochastic reparameterization alongside the next token prediction objective. However, this approach still results in over-segmentation for non-Latin script languages in multilingual settings. In contrast, MAGNET offers a customizable architecture where byte-level sequences are routed through language-script-specific predictors, each optimized for its respective language script. This modularity enforces equitable segmentation granularity across different language scripts compared to previous methods. Through extensive experiments, we demonstrate that in addition to reducing segmentation disparities, MAGNET also enables faster language modelling and improves downstream utility.

Published
2024

13. Single-Neutron Adding on $^{34}$S

Author

Kuchera, A. N., Hoffman, C. R., Ryan, G., D'Amato, I. B., Guarinello, O. M., Kielb, P. S., Aggarwal, R., Ajayi, S., Conley, A. L., Conroy, I., Cottle, P. D., Esparza, J. C., Genty, S., Hanselman, K., Heinze, M., Houlihan, D., Kelly, B., Khawaja, M. I., Lopez-Saavedra, E., McCann, G. W., Morelock, A. B., Riley, L. A., Sandrik, A., Sitaraman, V., Spieker, M., Temanson, E., Wibisono, C., and Wiedenhover, I.

Subjects
Nuclear Experiment
Abstract

Purpose: Single-neutron adding data was collected in order to determine the distribution of the single-neutron strength of the $0f_{7/2}$, $1p_{3/2}$, $1p_{1/2}$ and $0f_{5/2}$ orbitals outside of $Z=16, N=18$, $^{34}$S. Methods: The $^{34}$S($d$,$p$)$^{35}$S reaction has been measured at 8 MeV/u to investigate cross sections to excited states in $^{35}$S. Outgoing proton yields and momenta were analyzed by the Super-Enge Split-Pole Spectrograph in conjunction with the CeBrA demonstrator located at the John D. Fox Laboratory at Florida State University. Angular distributions were compared with Distorted Wave Born Approximation calculations in order to extract single-neutron spectroscopic overlaps. Results: Spectroscopic overlaps and strengths were determined for states in $^{35}$S up through 6 MeV in excitation energy. Each orbital was observed to have fragmented strength where a single level carried the majority. The single-neutron centroids of the $0f_{7/2}$, $1p_{3/2}$, $1p_{1/2}$ and $0f_{5/2}$ orbitals were determined to be $2360^{+90}_{-40}$ keV, $3280^{+80}_{-50}$ keV, $4780^{+60}_{-40}$ keV, and $\gtrsim7500$ keV, respectively. Conclusion: A previous discrepancy in the literature with respect to distribution of the neutron $1p_{1/2}$ strength was resolved. The integration of the normalized spectroscopic strengths, up to 5.1 MeV in excitation energy, revealed fully-vacant occupancies for the $0f_{7/2}$, $1p_{3/2}$, and $1p_{1/2}$ orbitals, as expected. The spacing in the single-neutron energies highlighted a reduction in the traditional $N=28$ shell-gap, relative to both the $1p$ spin-orbit energy difference ($N=32$) and the lower limit on the $N=34$ shell spacing., Comment: Updated draft

Published
2024

14. An Upper Limit on the Photoproduction Cross Section of the Spin-Exotic $\pi_1(1600)$

Author

Afzal, F., Akondi, C. S., Albrecht, M., Amaryan, M., Arrigo, S., Arroyave, V., Asaturyan, A., Austregesilo, A., Baldwin, Z., Barbosa, F., Barlow, J., Barriga, E., Barsotti, R., Barton, D., Baturin, V., Berdnikov, V. V., Black, T., Boeglin, W., Boer, M., Briscoe, W. J., Britton, T., Cao, S., Chudakov, E., Chung, G., Cole, P. L., Cortes, O., Crede, V., Dalton, M. M., Darulis, D., Deur, A., Dobbs, S., Dolgolenko, A., Dugger, M., Dzhygadlo, R., Ebersole, D., Edo, M., Egiyan, H., Erbora, T., Eugenio, P., Fabrizi, A., Fanelli, C., Fang, S., Fitches, J., Foda, A. M., Furletov, S., Gan, L., Gao, H., Gardner, A., Gasparian, A., Glazier, D., Gleason, C., Goryachev, V. S., Grube, B., Guo, J., Guo, L., Hernandez, J., Hernandez, K., Hoffman, N. D., Hornidge, D., Hou, G., Hurck, P., Hurley, A., Imoehl, W., Ireland, D. G., Ito, M. M., Jaegle, I., Jarvis, N. S., Jeske, T., Jing, M., Jones, R. T., Kakoyan, V., Kalicy, G., Khachatryan, V., Kourkoumelis, C., LaDuke, A., Larin, I., Lawrence, D., Lersch, D. I., Li, H., Liu, B., Livingston, K., Lolos, G. J., Lorenti, L., Lyubovitskij, V., Ma, R., Mack, D., Mahmood, A., Marukyan, H., Matveev, V., McCaughan, M., McCracken, M., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mizutani, K., Neelamana, V., Ng, L., Nissen, E., Orešić, S., Ostrovidov, A. I., Papandreou, Z., Paudel, C., Pedroni, R., Pentchev, L., Peters, K. J., Prather, E., Rakshit, S., Reinhold, J., Remington, A., Ritchie, B. G., Ritman, J., Rodriguez, G., Romanov, D., Saldana, K., Salgado, C., Schadmand, S., Schertz, A. M., Scheuer, K., Schick, A., Schmidt, A., Schumacher, R. A., Schwiening, J., Septian, N., Sharp, P., Shen, X., Shepherd, M. R., Sikes, J., Smith, A., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Stevens, J. R., Strakovsky, I. I., Sumner, B., Suresh, K., Tarasov, V. V., Taylor, S., Teymurazyan, A., Thiel, A., Viducic, T., Whitlatch, T., Wickramaarachchi, N., Wunderlich, Y., Yu, B., Zarling, J., Zhang, Z., Zhou, X., and Zihlmann, B.

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

The spin-exotic hybrid meson $\pi_{1}(1600)$ is predicted to have a large decay rate to the $\omega\pi\pi$ final state. Using 76.6~pb$^{-1}$ of data collected with the GlueX detector, we measure the cross sections for the reactions $\gamma p \to \omega \pi^+ \pi^- p$, $\gamma p \to \omega \pi^0 \pi^0 p$, and $\gamma p\to\omega\pi^-\pi^0\Delta^{++}$ in the range $E_\gamma =$ 8-10 GeV. Using isospin conservation, we set the first upper limits on the photoproduction cross sections of the $\pi^{0}_{1}(1600)$ and $\pi^{-}_{1}(1600)$. We combine these limits with lattice calculations of decay widths and find that photoproduction of $\eta'\pi$ is the most sensitive two-body system to search for the $\pi_1(1600)$., Comment: 6 pages, 3 figures plus supplemental materials

Published
2024

15. A Pattern Language for Machine Learning Tasks

Author

Rodatz, Benjamin, Fan, Ian, Laakkonen, Tuomas, Ortega, Neil John, Hoffman, Thomas, and Wang-Mascianica, Vincent

Subjects
Computer Science - Machine Learning, Mathematics - Category Theory, 18M30, 68T01, I.2.6
Abstract

Idealised as universal approximators, learners such as neural networks can be viewed as "variable functions" that may become one of a range of concrete functions after training. In the same way that equations constrain the possible values of variables in algebra, we may view objective functions as constraints on the behaviour of learners. We extract the equivalences perfectly optimised objective functions impose, calling them "tasks". For these tasks, we develop a formal graphical language that allows us to: (1) separate the core tasks of a behaviour from its implementation details; (2) reason about and design behaviours model-agnostically; and (3) simply describe and unify approaches in machine learning across domains. As proof-of-concept, we design a novel task that enables converting classifiers into generative models we call "manipulators", which we implement by directly translating task specifications into code. The resulting models exhibit capabilities such as style transfer and interpretable latent-space editing, without the need for custom architectures, adversarial training or random sampling. We formally relate the behaviour of manipulators to GANs, and empirically demonstrate their competitive performance with VAEs. We report on experiments across vision and language domains aiming to characterise manipulators as approximate Bayesian inversions of discriminative classifiers.

Published
2024

16. Probing the connection between IceCube neutrinos and MOJAVE AGN

Author

Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

Published
2024

17. Search for a light sterile neutrino with 7.5 years of IceCube DeepCore data

Author

Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
High Energy Physics - Experiment
Abstract

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. To be submitted to Physical Review D

Published
2024

18. Mixed-platonic 3-manifolds

Author

Chesebro, Eric, Chu, Michelle, DeBlois, Jason, Hoffman, Neil R., Mondal, Priyadip, and Walsh, Genevieve S.

Subjects
Mathematics - Geometric Topology, 57K32, 57K10
Abstract

We introduce a class of cusped hyperbolic $3$-manifolds that we call mixed-platonic, composed of regular ideal hyperbolic polyhedra of more than one type, which includes certain previously-known examples. We establish basic facts about mixed-platonic manifolds which allow us to conclude, among other things, that there is no mixed-platonic hyperbolic knot complement with hidden symmetries., Comment: 36 pages, 17 figures. Main update to v2: ancillary file is more easily accessible

Published
2024

19. Precipitating Change: Integrating Computational Thinking in Middle School Weather Forecasting

Author

Nanette I. Marcum-Dietrich, Meredith Bruozas, Rachel Becker-Klein, Emily Hoffman, and Carolyn Staudt

Abstract

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13-14) students in rural indigenous communities in the Arctic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference.

Published
2024
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20. Dependability and Usability of the DASS-21 as a Progress-Monitoring Tool for Youth

Author

Aberdine R. Dwight, Amy M. Briesch, Jessica A. Hoffman, and Christopher Rutt

Abstract

Despite widespread evidence-based treatments for addressing internalizing concerns, many youth do not demonstrate reliable or clinically meaningful improvement. Regular progress monitoring, consisting of measurement and feedback, offers the opportunity to improve outcomes in real time. The 21-item Depression Anxiety and Stress Scales (DASS-21; Lovibond & Lovibond, 1995) has potential as a progress-monitoring tool for internalizing concerns in youth; however, limited psychometric data are available to support this use. The purpose of this study was to investigate the dependability of data obtained from the DASS-21 when completed by adolescents in a clinical setting. This study also aimed to understand the feasibility and utility of using the DASS-21 as a progress-monitoring tool from youth and clinicians' perspectives. Generalizability and dependability analyses were conducted to determine the number of ratings needed to obtain a dependable estimate of youth functioning within 1 week. Whereas two daily ratings were needed to dependably estimate total distress, results for the depression, anxiety, and stress subscales indicated that two to five data points would be needed over the course of the week. Finally, results demonstrate the usability of the DASS-21 from both youth and clinician perspectives when used in a progress-monitoring context.

Published
2024
Full Text
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21. Integrated Learning, Integrated Lives: Highlighting Opportunities for Transformative SEL within Academic Instruction. Social and Emotional Learning Innovations Series

Author

Collaborative for Academic, Social, and Emotional Learning (CASEL), Heather N. Schwartz, Ally Skoog-Hoffman, Joe Polman, Olivia Kelly, Josefina Bañales, and Rob Jagers

Abstract

The SEL Innovations series aims to help the field imagine new, more expansive and equitable approaches to social and emotional learning (SEL) and wellness to ensure that all children, adolescents, and adults feel safe, supported, and seen so that they can thrive. This is the second report in a series exploring innovations in SEL. The purpose of the report is to highlight the importance of systemic, integrated SEL in classrooms, where the goal is to foster supportive classroom environments wherein educators teach explicit SEL and integrate SEL throughout academic instruction by weaving deep academic learning with opportunities for students to understand their own emotions, empathize with diverse perspectives, solve problems constructively, and make decisions while considering the needs of others. The authors briefly explore the Collaborative for Academic, Social, and Emotional Learning (CASEL) model for SEL before critically examining the literature around inquiry-based learning opportunities. The focus is on two inquiry-based approaches to learning, Project-Based Learning (PBL) and Youth Participatory Action Research (YPAR), which show promise as examples of integrated, systemic approaches to SEL by allowing students to be authors and co-designers in their own learning. Next, conditions necessary to enact inquiry-based approaches in the classroom are explored. Lastly, they share a set of case studies illustrating what inquiry-based learning opportunities, specifically PBL and YPAR, can look like in practice. In this way, the goal is to provide both inspiration and practical application for educators, program providers, and researchers looking to continue to move the conversation forward, integrate new strategies into their programs or practices, and expand their research agendas.

Published
2023

22. Targeting Patient-Derived Orthotopic Gastric Cancers with a Fluorescent Humanized Anti-CEA Antibody

Author

Cox, Kristin E, Turner, Michael A, Lwin, Thinzar M, Amirfakhri, Siamak, Kelly, Kaitlyn J, Hosseini, Mojgan, Ghosh, Pradipta, Obonyo, Marygorret, Hoffman, Robert M, Yazaki, Paul J, and Bouvet, Michael

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Orphan Drug, Rare Diseases, Digestive Diseases, 5.1 Pharmaceuticals, Stomach Neoplasms, Animals, Humans, Mice, Carcinoembryonic Antigen, Adenocarcinoma, Xenograft Model Antitumor Assays, Antibodies, Monoclonal, Humanized, Fluorescent Dyes, Tumor Cells, Cultured, Female, Indoles, Optical Imaging, Gastrectomy, Mice, Nude, Cell Line, Tumor, Gastric cancer, Patient-derived orthotopic xenograft, PDOX, Fluorescence, Fluorescent antibody, CEA, Tumor targeting, Tumor labeling, Gastric cancer, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundGastric cancer poses a major diagnostic and therapeutic challenge as surgical resection provides the only opportunity for a cure. Specific labeling of gastric cancer could distinguish resectable and nonresectable disease and facilitate an R0 resection, which could improve survival.MethodsTwo patient-derived gastric cancer lines, KG8 and KG10, were established from surgical specimens of two patients who underwent gastrectomy for gastric adenocarcinoma. Harvested tumor fragments were implanted into the greater curvature of the stomach to establish patient-derived orthotopic xenograft (PDOX) models. M5A (humanized anti-CEA antibody) or IgG control antibodies were conjugated with the near-infrared dye IRDye800CW. Mice received 50 µg of M5A-IR800 or 50 µg of IgG-IR800 intravenously and were imaged after 72 hr. Fluorescence imaging was performed by using the LI-COR Pearl Imaging System. A tumor-to-background ratio (TBR) was calculated by dividing the mean fluorescence intensity of the tumor versus adjacent stomach tissue.ResultsM5A-IR800 administration resulted in bright labeling of both KG8 and K10 tumors. In the KG8 PDOX models, the TBR for M5A-IR800 was 5.85 (SE ± 1.64) compared with IgG-IR800 at 0.70 (SE ± 0.17). The K10 PDOX models had a TBR of 3.71 (SE ± 0.73) for M5A-IR800 compared with 0.66 (SE ± 0.12) for IgG-IR800.ConclusionsHumanized anti-CEA (M5A) antibodies conjugated to fluorescent dyes provide bright and specific labeling of gastric cancer PDOX models. This tumor-specific fluorescent antibody is a promising potential clinical tool to detect the extent of disease for the determination of resectability as well as to visualize tumor margins during gastric cancer resection.

Published
2024

23. Search for leptoquark pair production decaying into te-t¯e+ or tμ-t¯μ+ in multi-lepton final states in pp collisions at s=13TeV with the ATLAS detector

Author

Aad, G, Abbott, B, Abbott, DC, Abeling, K, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, AC Abusleme, Acharya, BS, Achkar, B, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Adelman, J, Adiguzel, A, Adorni, S, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Aguilar-Saavedra, JA, Ahmad, A, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Khoury, K Al, Alberghi, GL, Albert, J, Albicocco, P, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, A, Alfonsi, F, Alhroob, M, Ali, B, Ali, S, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Camelia, E Alunno, Estevez, M Alvarez, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Santos, SP Amor Dos, Amoroso, S, Amos, KR, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Antrim, DJA, Anulli, F, Aoki, M, Aoki, T, Pozo, JA Aparisi, Aparo, MA, Bella, L Aperio, Appelt, C, Aranzabal, N, Ferraz, V Araujo, Arcangeletti, C, Arce, ATH, and Arena, E

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Quantum Physics, Nuclear & Particles Physics, Astronomical sciences, Atomic, molecular and optical physics, Particle and high energy physics
Abstract

A search for leptoquark pair production decaying into te-t¯e+ or tμ-t¯μ+ in final states with multiple leptons is presented. The search is based on a dataset of pp collisions at s=13TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb-1. Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from a b-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into te- (tμ-), the corresponding lower limit on the scalar mixed-generation leptoquark mass mLQmixd is at 1.58 (1.59) TeV and on the vector leptoquark mass mU~1 at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang–Mills scenario.

Published
2024

24. Early Cold Stored Platelet Transfusion Following Severe Injury: A Randomized Clinical Trial.

Author

Sperry, Jason, Guyette, Francis, Rosario-Rivera, Bedda, Kutcher, Matthew, Kornblith, Lucy, Cotton, Bryan, Wilson, Chad, Inaba, Kenji, Zadorozny, Eva, Vincent, Laura, Harner, Ashley, Love, Emily, Doherty, Joseph, Cuschieri, Joseph, Kornblith, Aaron, Fox, Erin, Bai, Yu, Hoffman, Marcus, Seger, Catherine, Hudgins, Jay, Mallett-Smith, Sheila, Neal, Matthew, Leeper, Christine, Spinella, Philip, Yazer, Mark, and Wisniewski, Stephen

Subjects
Humans, Platelet Transfusion, Male, Female, Adult, Middle Aged, Shock, Hemorrhagic, Blood Preservation, Feasibility Studies, Wounds and Injuries, Treatment Outcome, Resuscitation, Cold Temperature
Abstract

OBJECTIVE: To determine the feasibility, efficacy, and safety of early cold stored platelet transfusion compared with standard care resuscitation in patients with hemorrhagic shock. BACKGROUND: Data demonstrating the safety and efficacy of early cold stored platelet transfusion are lacking following severe injury. METHODS: A phase 2, multicenter, randomized, open label, clinical trial was performed at 5 US trauma centers. Injured patients at risk of large volume blood transfusion and the need for hemorrhage control procedures were enrolled and randomized. The intervention was the early transfusion of a single apheresis cold stored platelet unit, stored for up to 14 days versus standard care resuscitation. The primary outcome was feasibility and the principal clinical outcome for efficacy and safety was 24-hour mortality. RESULTS: Mortality at 24 hours was 5.9% in patients who were randomized to early cold stored platelet transfusion compared with 10.2% in the standard care arm (difference, -4.3%; 95% CI, -12.8% to 3.5%; P =0.26). No significant differences were found for any of the prespecified ancillary outcomes. Rates of arterial and/or venous thromboembolism and adverse events did not differ across treatment groups. CONCLUSIONS AND RELEVANCE: In severely injured patients, early cold stored platelet transfusion is feasible, safe and did not result in a significant lower rate of 24-hour mortality. Early cold stored platelet transfusion did not result in a higher incidence of arterial and/or venous thrombotic complications or adverse events. The storage age of the cold stored platelet product was not associated with significant outcome differences. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04667468.

Published
2024

25. Trust in health workers and patient-centeredness of care were strongest factors associated with vaccination for Kenyan children born between 2017-2022.

Author

Moucheraud, Corrina, Ochieng, Eric, Ogutu, Vitalis, Sudhinaraset, May, Szilagyi, Peter, Hoffman, Risa, Glenn, Beth, Golub, Ginger, and Njomo, Doris

Subjects
Immunization, Kenya, Preventive health services, Primary prevention, Public health, Vaccination
Abstract

OBJECTIVE: Although vaccination confidence is declining globally, there is little detailed information from low- and middle-income countries about factors influencing routine vaccination behavior in these contexts. METHODS: In mid-2022, we surveyed people who gave birth in Kenya between 2017-2022, and asked them about their childrens vaccination history and about hypothesized correlates of vaccination per the Behavioural and Social Drivers of Vaccination model. RESULTS: Of 873 children in this sample, 117 (13%) were under-vaccinated (i.e., delayed or missing vaccine dose(s)) - and under-vaccination was more common among births during the COVID-19 pandemic (2020-2022) versus pre-pandemic (2017-2019). In multi-level multivariable models, children of respondents who expressed concerns about serious side effects from vaccines had significantly higher odds of missed vaccine dose(s) (aOR 2.06, 95 % CI 1.14-3.72), and there was a strong association between having more safety concerns now versus before the COVID-19 pandemic (aOR missed dose(s) 4.44, 95 % CI 1.71-11.51; aOR under-vaccination 3.03, 95 % CI 1.28-7.19). People with greater trust in health workers had lower odds of having a child with missed vaccine dose(s) (aOR 0.85, 95 % CI 0.75-0.97). People who reported higher patient-centered quality of vaccination care had much lower odds of having children with delayed or missed vaccine dose(s) (aOR missed dose(s) 0.14, 95 % CI 0.04-0.58; aOR under-vaccination 0.27, 95 % CI 0.10-0.79). CONCLUSIONS: These findings highlight potential strategies to improve vaccine coverage: greater focus on patient-centered quality of care, training healthcare workers on how to address safety concerns about vaccines, and building trust in the health care system and in health workers.

Published
2024

26. Search for pair-produced higgsinos decaying via Higgs or Z bosons to final states containing a pair of photons and a pair of b-jets with the ATLAS detector

Author

Aad, G, Abbott, B, Abeling, K, Abicht, NJ, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, AC Abusleme, Acharya, BS, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmad, A, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Tamlihat, M Ait, Aitbenchikh, B, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Al Khoury, K, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, A, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Flores, CA Allendes, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Estevez, M Alvarez, Fernandez, A Alvarez, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Dos Santos, SP Amor, Amos, KR, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Antrim, DJA, Anulli, F, Aoki, M, Aoki, T, Pozo, JA Aparisi, and Aparo, MA

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Mathematical Physics, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Nuclear & Particles Physics, Mathematical sciences, Physical sciences
Published
2024

27. Engineering programmable material-to-cell pathways via synthetic notch receptors to spatially control differentiation in multicellular constructs.

Author

Garibyan, Mher, Hoffman, Tyler, Makaske, Thijs, Do, Stephanie, Wu, Yifan, Williams, Brian, March, Alexander, Cho, Nathan, Pedroncelli, Nicolas, Lima, Ricardo, Soto, Jennifer, Jackson, Brooke, Santoso, Jeffrey, Khademhosseini, Ali, Thomson, Matt, Li, Song, McCain, Megan, and Morsut, Leonardo

Subjects
Receptors, Notch, Cell Differentiation, Tissue Engineering, Animals, Humans, Signal Transduction, Mice, Extracellular Matrix, Fibroblasts, Extracellular Matrix Proteins, Ligands, Tissue Scaffolds, Muscle, Skeletal, Endothelial Cells, HEK293 Cells
Abstract

Synthetic Notch (synNotch) receptors are genetically encoded, modular synthetic receptors that enable mammalian cells to detect environmental signals and respond by activating user-prescribed transcriptional programs. Although some materials have been modified to present synNotch ligands with coarse spatial control, applications in tissue engineering generally require extracellular matrix (ECM)-derived scaffolds and/or finer spatial positioning of multiple ligands. Thus, we develop here a suite of materials that activate synNotch receptors for generalizable engineering of material-to-cell signaling. We genetically and chemically fuse functional synNotch ligands to ECM proteins and ECM-derived materials. We also generate tissues with microscale precision over four distinct reporter phenotypes by culturing cells with two orthogonal synNotch programs on surfaces microcontact-printed with two synNotch ligands. Finally, we showcase applications in tissue engineering by co-transdifferentiating fibroblasts into skeletal muscle or endothelial cell precursors in user-defined micropatterns. These technologies provide avenues for spatially controlling cellular phenotypes in mammalian tissues.

Published
2024

28. Recurrence and tumor-related death after resection of hepatocellular carcinoma in patients with metabolic syndrome.

Author

Berardi, Giammauro, Cucchetti, Alessandro, Sposito, Carlo, Ratti, Francesca, Nebbia, Martina, DSouza, Daniel, Pascual, Franco, Dogeas, Epameinondas, Tohme, Samer, Vitale, Alessandro, DAmico, Francesco, Alessandris, Remo, Panetta, Valentina, Simonelli, Ilaria, Colasanti, Marco, Russolillo, Nadia, Moro, Amika, Fiorentini, Guido, Serenari, Matteo, Rotellar, Fernando, Zimitti, Giuseppe, Famularo, Simone, Ivanics, Tommy, Donando, Felipe, Hoffman, Daniel, Onkendi, Edwin, Essaji, Yasmin, Giuliani, Tommaso, Lopez Ben, Santiago, Caula, Celia, Rompianesi, Gianluca, Chopra, Asmita, Abu Hilal, Mohammed, Sapisochin, Gonzalo, Torzilli, Guido, Corvera, Carlos, Alseidi, Adnan, Helton, Scott, Troisi, Roberto, Simo, Kerri, Conrad, Claudius, Cescon, Matteo, Cleary, Sean, Kwon, David, Ferrero, Alessandro, Ettorre, Giuseppe, Cillo, Umberto, Geller, David, Cherqui, Daniel, Serrano, Pablo, Ferrone, Cristina, Aldrighetti, Luca, Kingham, T, and Mazzaferro, Vincenzo

Subjects
Hepatocellular carcinoma, Metabolic syndrome, Metabolic-associated fatty liver disease, Nonalcoholic fatty liver disease, Obesity, Recurrence, Steatosis
Abstract

BACKGROUND & AIMS: Metabolic syndrome (MS) is a growing epidemic and a risk factor for the development of hepatocellular carcinoma (HCC). This study investigated the long-term outcomes of liver resection (LR) for HCC in patients with MS. Rates, timing, patterns, and treatment of recurrences were investigated, and cancer-specific survivals were assessed. METHODS: Between 2001 and 2021, data from 24 clinical centers were collected. Overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival were analyzed as well as recurrence patterns and treatment. The analysis was conducted using a competing-risk framework. The trajectory of the risk of recurrence over time was applied to a competing risk analysis. For post-recurrence survival, death resulting from tumor progression was the primary endpoint, whereas deaths with recurrence relating to other causes were considered as competing events. RESULTS: In total, 813 patients were included in the study. Median OS was 81.4 months (range 28.1-157.0 months), and recurrence occurred in 48.3% of patients, with a median RFS of 39.8 months (range 15.7-174.7 months). Cause-specific hazard of recurrence showed a first peak 6 months (0.027), and a second peak 24 months (0.021) after surgery. The later the recurrence, the higher the chance of receiving curative intent approaches (p = 0.001). Size >5 cm, multiple tumors, microvascular invasion, and cirrhosis were independent predictors of recurrence showing a cause-specific hazard over time. RFS was associated with death for recurrence (hazard ratio: 0.985, 95% CI: 0.977-0.995; p = 0.002). CONCLUSIONS: Patients with MS undergoing LR for HCC have good long-term survival. Recurrence occurs in 48% of patients with a double-peak incidence and time-specific hazards depending on tumor-related factors and underlying disease. The timing of recurrence significantly impacts survival. Surveillance after resection should be adjusted over time depending on risk factors. IMPACT AND IMPLICATIONS: Metabolic syndrome (MS) is a growing epidemic and a significant risk factor for the development of hepatocellular carcinoma (HCC). The present study demonstrated that patients who undergo surgical resection for HCC on MS have a good long-term survival and that recurrence occurs in almost half of the cases with a double peak incidence and time-specific hazards depending on tumor-related factors and underlying liver disease. Also, the timing of recurrence significantly impacts survival. Clinicians should therefore adjust follow-up after surgery accordingly, considering timing of recurrence and specific risk factors. Also, the results of the present study might help design future trials on the use of adjuvant therapy following resection.

Published
2024

29. Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data

Author

Aad, G, Abbott, B, Abeling, K, Abicht, NJ, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Abusleme Hoffman, AC, Acharya, BS, Adam Bourdarios, C, Adamczyk, L, Adamek, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmad, A, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aikot, A, Ait Tamlihat, M, Aitbenchikh, B, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Al Khoury, K, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Allendes Flores, CA, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Alvarez Estevez, M, Alvarez Fernandez, A, Alves Cardoso, M, Alviggi, MG, Aly, M, Amaral Coutinho, Y, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Amor Dos Santos, SP, Amos, KR, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Anulli, F, Aoki, M, Aoki, T, Aparisi Pozo, JA, and Aparo, MA

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Clinical Research, ATLAS Collaboration, Beyond Standard Model, Dark matter, High-energy physics, Proton-proton
Abstract

Results from a wide range of searches targeting different experimental signatures with and without missing transverse momentum (ETmiss) are used to constrain a Two-Higgs-Doublet Model (2HDM) with an additional pseudo-scalar mediating the interaction between ordinary and dark matter (2HDM+a). The analyses use up to 139 fb-1 of proton-proton collision data at a centre-of-mass energy s=13 TeV recorded with the ATLAS detector at the Large Hadron Collider during 2015-2018. The results from three of the most sensitive searches are combined statistically. These searches target signatures with large ETmiss and a leptonically decaying Z boson; large ETmiss and a Higgs boson decaying to bottom quarks; and production of charged Higgs bosons in final states with top and bottom quarks, respectively. Constraints are derived for several common and new benchmark scenarios in the 2HDM+a.

Published
2024

30. Uncertainty-enabled machine learning for emulation of regional sea-level change caused by the Antarctic Ice Sheet

Author

Yoo, Myungsoo, Gopalan, Giri, Hoffman, Matthew J., Coulson, Sophie, Han, Holly Kyeore, Wikle, Christopher K., and Hillebrand, Trevor

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Projecting sea-level change in various climate-change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice mass change, which requires high computational cost and time. Here we build neural-network emulators of sea-level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are based on datasets produced using a numerical solver for the static sea-level equation and published ISMIP6-2100 ice-sheet model simulations referenced in the IPCC AR6 report. We show that the neural-network emulators have an accuracy that is competitive with baseline machine learning emulators. In order to quantify uncertainty, we derive well-calibrated prediction intervals for simulated sea-level change via a linear regression postprocessing technique that uses (nonlinear) machine learning model outputs, a technique that has previously been applied to numerical climate models. We also demonstrate substantial gains in computational efficiency: a feedforward neural-network emulator exhibits on the order of 100 times speedup in comparison to the numerical sea-level equation solver that is used for training.

Published
2024

31. Measurement of Spin-Density Matrix Elements in $\Delta^{++}(1232)$ photoproduction

Author

Afzal, F., Akondi, C. S., Albrecht, M., Amaryan, M., Arrigo, S., Arroyave, V., Asaturyan, A., Austregesilo, A., Baldwin, Z., Barbosa, F., Barlow, J., Barriga, E., Barsotti, R., Barton, D., Baturin, V., Berdnikov, V. V., Black, T., Boeglin, W., Boer, M., Briscoe, W. J., Britton, T., Cao, S., Chudakov, E., Chung, G., Cole, P. L., Cortes, O., Crede, V., Dalton, M. M., Darulis, D., Deur, A., Dobbs, S., Dolgolenko, A., Dugger, M., Dzhygadlo, R., Ebersole, D., Edo, M., Egiyan, H., Erbora, T., Eugenio, P., Fabrizi, A., Fanelli, C., Fang, S., Fitches, J., Foda, A. M., Furletov, S., Gan, L., Gao, H., Gardner, A., Gasparian, A., Glazier, D. I., Gleason, C., Goryachev, V. S., Grube, B., Guo, J., Guo, L., Hernandez, J., Hernandez, K., Hoffman, N. D., Hornidge, D., Hou, G., Hurck, P., Hurley, A., Imoehl, W., Ireland, D. G., Ito, M. M., Jaegle, I., Jarvis, N. S., Jeske, T., Jing, M., Jones, R. T., Kakoyan, V., Kalicy, G., Khachatryan, V., Kourkoumelis, C., LaDuke, A., Larin, I., Lawrence, D., Lersch, D. I., Li, H., Liu, B., Livingston, K., Lolos, G. J., Lorenti, L., Lyubovitskij, V., Ma, R., Mack, D., Mahmood, A., Marukyan, H., Matveev, V., McCaughan, M., McCracken, M., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mizutani, K., Neelamana, V., Ng, L., Nissen, E., Orešić, S., Ostrovidov, A. I., Papandreou, Z., Paudel, C., Pedroni, R., Pentchev, L., Peters, K. J., Prather, E., Rakshit, S., Reinhold, J., Remington, A., Ritchie, B. G., Ritman, J., Rodriguez, G., Romanov, D., Saldana, K., Salgado, C., Schadmand, S., Schertz, A. M., Scheuer, K., Schick, A., Schmidt, A., Schumacher, R. A., Schwiening, J., Septian, N., Sharp, P., Shen, X., Shepherd, M. R., Sikes, J., Smith, A., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Stevens, J. R., Strakovsky, I. I., Sumner, B., Suresh, K., Tarasov, V. V., Taylor, S., Teymurazyan, A., Thiel, A., Viducic, T., Whitlatch, T., Wickramaarachchi, N., Wunderlich, Y., Yu, B., Zarling, J., Zhang, Z., Zhou, X., and Zihlmann, B.

Subjects
Nuclear Experiment
Abstract

We measure the spin-density matrix elements (SDMEs) of the $\Delta^{++}(1232)$ in the photoproduction reaction $\gamma p \to \pi^-\Delta^{++}(1232)$ with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly--polarized photon beam with energies from $8.2$ to $8.8$~GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below $1.4$ GeV$^2$. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural--exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region.

Published
2024

32. IceCube Search for Neutrino Emission from X-ray Bright Seyfert Galaxies

Author

Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glauch, T., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Q. R., Liu, Y. T., Liubarska, M., Lohfink, E., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract

The recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGN) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV $\gamma$-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to $\gamma$-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy $\gamma$-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays, as they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the BAT AGN Spectroscopic Survey (BASS). Besides the generic single power-law, we evaluate the spectra predicted by the disk-corona model. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the Northern Hemisphere, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7$\sigma$ significance with respect to background expectations., Comment: 17 pages, 9 figures

Published
2024

33. Search for neutrino emission from hard X-ray AGN with IceCube

Author

Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Privon, G. C., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Active Galactic Nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos since they provide environments rich in matter and photon targets where cosmic ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the $\textit{Swift}$-BAT Spectroscopic Survey (BASS) catalog of hard X-ray sources and 12 years of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $4.02_{-1.52}^{+1.58} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV, with power-law spectral index, $\gamma$ = 3.10$^{+0.26}_{-0.22}$, consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a post-trial significance of 2.9$\sigma$. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $1.51_{-0.81}^{+0.99} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV and $\gamma$ = 2.83$^{+0.35}_{-0.28}$.

Published
2024

34. The computational power of random quantum circuits in arbitrary geometries

Author

DeCross, Matthew, Haghshenas, Reza, Liu, Minzhao, Rinaldi, Enrico, Gray, Johnnie, Alexeev, Yuri, Baldwin, Charles H., Bartolotta, John P., Bohn, Matthew, Chertkov, Eli, Cline, Julia, Colina, Jonhas, DelVento, Davide, Dreiling, Joan M., Foltz, Cameron, Gaebler, John P., Gatterman, Thomas M., Gilbreth, Christopher N., Giles, Joshua, Gresh, Dan, Hall, Alex, Hankin, Aaron, Hansen, Azure, Hewitt, Nathan, Hoffman, Ian, Holliman, Craig, Hutson, Ross B., Jacobs, Trent, Johansen, Jacob, Lee, Patricia J., Lehman, Elliot, Lucchetti, Dominic, Lykov, Danylo, Madjarov, Ivaylo S., Mathewson, Brian, Mayer, Karl, Mills, Michael, Niroula, Pradeep, Pino, Juan M., Roman, Conrad, Schecter, Michael, Siegfried, Peter E., Tiemann, Bruce G., Volin, Curtis, Walker, James, Shaydulin, Ruslan, Pistoia, Marco, Moses, Steven. A., Hayes, David, Neyenhuis, Brian, Stutz, Russell P., and Foss-Feig, Michael

Subjects
Quantum Physics
Abstract

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of $\sim 50$ qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and therefore how much infidelity is suffered) in generating highly-entangled states. Here, we describe recent hardware upgrades to Quantinuum's H2 quantum computer enabling it to operate on up to $56$ qubits with arbitrary connectivity and $99.843(5)\%$ two-qubit gate fidelity. Utilizing the flexible connectivity of H2, we present data from random circuit sampling in highly connected geometries, doing so at unprecedented fidelities and a scale that appears to be beyond the capabilities of state-of-the-art classical algorithms. The considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the QCCD architecture as continued progress is made towards building larger machines., Comment: Includes minor updates to the text and an updated author list to include researchers who made technical contributions in upgrading the machine to 56 qubits but were left off the original version by mistake

Published
2024

35. The density conjecture for activated random walk

Author

Hoffman, Christopher, Johnson, Tobias, and Junge, Matthew

Subjects
Mathematics - Probability, Mathematical Physics, 60K35, 82C22
Abstract

Bak, Tang, and Wiesenfeld developed their theory of self-organized criticality in the late 1980s to explain why many real-life processes exhibit signs of critical behavior despite the absence of a tuning parameter. A decade later, Dickman, Mu\~noz, Vespignani, and Zapperi explained self-organized criticality as an external force pushing a hidden parameter toward the critical value of a traditional absorbing-state phase transition. As evidence, they observed empirically that for various sandpile models, the particle density in a finite box under driven-dissipative dynamics converges to the critical density of an infinite-volume version of the model. We give the first proof of this well-known density conjecture in any setting by establishing it for activated random walk in one dimension. We prove that two other natural versions of the model have the same critical value, further establishing activated random walk as a universal model of self-organized criticality., Comment: 77 pages; split Section 3 into two sections, improved the presentation in various ways, and added new expository figures

Published
2024

36. Exploration of mass splitting and muon/tau mixing parameters for an eV-scale sterile neutrino with IceCube

Author

Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
High Energy Physics - Experiment
Abstract

We present the first three-parameter fit to a 3+1 sterile neutrino model using 7.634 years of data from the IceCube Neutrino Observatory on $\nu_\mu+\overline{\nu}_\mu$ charged-current interactions in the energy range 500-9976 GeV. Our analysis is sensitive to the mass-squared splitting between the heaviest and lightest mass state ($\Delta m_{41}^2$), the mixing matrix element connecting muon flavor to the fourth mass state ($|U_{\mu4}|^2$), and the element connecting tau flavor to the fourth mass state ($|U_{\tau4}|^2$). Predicted propagation effects in matter enhance the signature through a resonance as atmospheric neutrinos from the Northern Hemisphere traverse the Earth to the IceCube detector at the South Pole. The result is consistent with the no-sterile neutrino hypothesis with a probability of 4.3 %. Profiling the likelihood of each parameter yields the 90 % confidence levels: $ 2.4\,\mathrm{eV}^{2} < \Delta m_{41}^2 <9.6\,\mathrm{eV}^{2} $ , $0.0081 < |U_{\mu4}|^2 < 0.10$ , and $|U_{\tau4}|^2< 0.035$, which narrows the allowed parameter-space for $|U_{\tau4}|^2$. However, the primary result of this analysis is the first map of the 3+1 parameter space exploring the interdependence of $\Delta m_{41}^2$, $|U_{\mu4}|^2$, and $|U_{\tau4}|^2$.

Published
2024

37. Modeling the refractive index profile n(z) of polar ice for ultra-high energy neutrino experiments

Author

Ali, S., Allison, P., Archambault, S., Beatty, J. J., Besson, D. Z., Bishop, A., Chen, P., Chen, Y. C., Clark, B. A., Clay, W., Connolly, A., Couberly, K., Cremonesi, L., Cummings, A., Dasgupta, P., Debolt, R., de Kockere, S., de Vries, K. D., Deaconu, C., DuVernois, M. A., Flaherty, J., Friedman, E., Gaior, R., Giri, P., Hanson, J., Harty, N., Hoffman, K. D., Huang, J. J., Huang, M. -H., Hughes, K., Ishihara, A., Karle, A., Kelley, J. L., Kim, K. -C., Kim, M. -C., Kravchenko, I., Krebs, R., Kuo, C. Y., Kurusu, K., Latif, U. A., Liu, C. H, Liu, T. C., Luszczak, W., Mase, K., Muzio, M. S., Nam, J., Nichol, R. J., Novikov, A., Nozdrina, A., Oberla, E., Pan, Y., Pfendner, C., Punsuebsay, N., Roth, J., Salcedo-Gomez, A., Seckel, D., Seikh, M. F. H., Shaio, Y. -S., Smith, D., Toscano, S., Torres, J., Touart, J., van Eijndhoven, N., Vieregg, A., Wang, M. -Z., Wang, S. -H., Wissel, S. A., Xie, C., Yoshida, S., and Young, R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We develop an in-situ index of refraction profile using the transit time of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted path (R); the measured D vs. R (dt(D,R)) timing differences provide constraints on the index of refraction profile near South Pole, where the Askaryan Radio Array (ARA) neutrino observatory is located. We constrain the refractive index profile by simulating D and R ray paths via ray tracing and comparing those to measured dt(D,R) signals. Using previous ice density data as a proxy for n(z), we demonstrate that our data strongly favors a glaciologically-motivated three-phase densification model rather than a single exponential scale height model. Simulations show that the single exponential model overestimates ARA neutrino sensitivity compared to the three-phase model.

Published
2024

38. Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models

Author

Hoffman, Kentaro, Salerno, Stephen, Leek, Jeff, and McCormick, Tyler

Subjects
Statistics - Methodology, Economics - Econometrics
Abstract

Large-scale prediction models (typically using tools from artificial intelligence, AI, or machine learning, ML) are increasingly ubiquitous across a variety of industries and scientific domains. Such methods are often paired with detailed data from sources such as electronic health records, wearable sensors, and omics data (high-throughput technology used to understand biology). Despite their utility, implementing AI and ML tools at the scale necessary to work with this data introduces two major challenges. First, it can cost tens of thousands of dollars to train a modern AI/ML model at scale. Second, once the model is trained, its predictions may become less relevant as patient and provider behavior change, and predictions made for one geographical area may be less accurate for another. These two challenges raise a fundamental question: how often should you refit the AI/ML model to optimally trade-off between cost and relevance? Our work provides a framework for making decisions about when to {\it refit} AI/ML models when the goal is to maintain valid statistical inference (e.g. estimating a treatment effect in a clinical trial). Drawing on portfolio optimization theory, we treat the decision of {\it recalibrating} versus {\it refitting} the model as a choice between ''investing'' in one of two ''assets.'' One asset, recalibrating the model based on another model, is quick and relatively inexpensive but bears uncertainty from sampling and the possibility that the other model is not relevant to current circumstances. The other asset, {\it refitting} the model, is costly but removes the irrelevance concern (though not the risk of sampling error). We explore the balancing act between these two potential investments in this paper.

Published
2024

39. Methods and stability tests associated with the sterile neutrino search using improved high-energy $\nu_\mu$ event reconstruction in IceCube

Author

IceCube Collaboration, Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Lohfink, E., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Sponsler, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

We provide supporting details for the search for a 3+1 sterile neutrino using data collected over eleven years at the IceCube Neutrino Observatory. The analysis uses atmospheric muon-flavored neutrinos from 0.5 to 100\, TeV that traverse the Earth to reach the IceCube detector, and finds a best-fit point at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$ with a goodness-of-fit p-value of 12\% and consistency with the null hypothesis of no oscillations to sterile neutrinos with a p-value of 3.1\%. Several improvements were made over past analyses, which are reviewed in this article, including upgrades to the reconstruction and the study of sources of systematic uncertainty. We provide details of the fit quality and discuss stability tests that split the data for separate samples, comparing results. We find that the fits are consistent between split data sets., Comment: 18 pages, 17 figures, 2 tables. This long-form paper is a companion to the letter "A search for an eV-scale sterile neutrino using improved high-energy {\nu}{\mu} event reconstruction in IceCube."

Published
2024

40. A search for an eV-scale sterile neutrino using improved high-energy $\nu_\mu$ event reconstruction in IceCube

Author

IceCube Collaboration, Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Lohfink, E., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Sponsler, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This Letter presents the result of a 3+1 sterile neutrino search using 10.7 years of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and through-going events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$, which agrees with previous iterations of this study. The result is consistent with the null hypothesis of no sterile neutrinos with a p-value of 3.1\%., Comment: 9 pages, 3 figures. This letter is supported by the long-form paper "Methods and stability tests associated with the sterile neutrino search using improved high-energy $\nu_\mu$ event reconstruction in IceCube," also appearing on arXiv

Published
2024

41. Search for joint multimessenger signals from potential Galactic PeVatrons with HAWC and IceCube

Author

Alfaro, R., Alvarez, C., Arteaga-Velázquez, J. C., Rojas, D. Avila, Solares, H. A. Ayala, Babu, R., Belmont-Moreno, E., Caballero-Mora, K. S., Capistrán, T., Carramiñana, A., Casanova, S., Cotti, U., Cotzomi, J., de León, S. Coutiño, De la Fuente, E., Depaoli, D., Di Lalla, N., Hernandez, R. Diaz, Díaz-Vélez, J. C., Engel, K., Ergin, T., Fan, K. L., Fang, K., Fraija, N., Fraija, S., García-González, J. A., Garfias, F., González, M. M., Goodman, J. A., Groetsch, S., Harding, J. P., Hernández-Cadena, S., Herzog, I., Huang, D., Hueyotl-Zahuantitla, F., Hüntemeyer, P., Iriarte, A., Kaufmann, S., Lee, J., Vargas, H. León, Longinotti, A. L., Luis-Raya, G., Malone, K., Martínez-Castro, J., Matthews, J. A., Miranda-Romagnoli, P., Montes, J. A., Moreno, E., Mostafá, M., Nellen, L., Omodei, N., Osorio, M., Araujo, Y. Pérez, Pérez-Pérez, E. G., Rho, C. D., Rosa-González, D., Salazar, H., Salazar-Gallegos, D., Sandoval, A., Schneider, M., Serna-Franco, J., Smith, A. J., Son, Y., Tibolla, O., Tollefson, K., Torres, I., Torres-Escobedo, R., Turner, R., Ureña-Mena, F., Wang, X., Watson, I. J., Whitaker, K., Willox, E., Wu, H., Yun-Cárcamo, S., Zhou, H., de León, C., Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Lohfink, E., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Galactic PeVatrons are sources that can accelerate cosmic rays to PeV energies. The high-energy cosmic rays are expected to interact with the surrounding ambient material or radiation, resulting in the production of gamma rays and neutrinos. To optimize for the detection of such associated production of gamma rays and neutrinos for a given source morphology and spectrum, a multi-messenger analysis that combines gamma rays and neutrinos is required. In this study, we use the Multi-Mission Maximum Likelihood framework (3ML) with IceCube Maximum Likelihood Analysis software (i3mla) and HAWC Accelerated Likelihood (HAL) to search for a correlation between 22 known gamma-ray sources from the third HAWC gamma-ray catalog and 14 years of IceCube track-like data. No significant neutrino emission from the direction of the HAWC sources was found. We report the best-fit gamma-ray model and 90% CL neutrino flux limit from the 22 sources. From the neutrino flux limit, we conclude that the gamma-ray emission from five of the sources can not be produced purely from hadronic interactions. We report the limit for the fraction of gamma rays produced by hadronic interactions for these five sources.

Published
2024

42. Automated, Reliable, and Efficient Continental-Scale Replication of 7.3 Petabytes of Climate Simulation Data: A Case Study

Author

Lacinski, Lukasz, Liming, Lee, Turoscy, Steven, Harr, Cameron, Chard, Kyle, Dart, Eli, Durack, Paul, Ames, Sasha, Hoffman, Forrest M., and Foster, Ian T.

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) climate simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee. This movement of some 29 million files, twice, undertaken in order to establish new ESGF nodes at ANL and ORNL, was performed largely automatically by a simple replication tool, a script that invoked Globus to transfer large bundles of files while tracking progress in a database. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure.

Published
2024

43. Acceptance Tests of more than 10 000 Photomultiplier Tubes for the multi-PMT Digital Optical Modules of the IceCube Upgrade

Author

Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Ausborm, L., Axani, S. N., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., Benning, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Coleman, A., Collin, G. H., Connolly, A., Conrad, J. M., Coppin, P., Corley, R., Correa, P., Cowen, D. F., Dave, P., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Gries, O., Griffin, S., Griswold, S., Groth, K. M., Günther, C., Gutjahr, P., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Janik, O., Jansson, M., Japaridze, G. S., Jeong, M., Jin, M., Jones, B. J. P., Joppe, R., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Kossatz, M., Koundal, P., Kovacevich, M., Kowalski, M., Kozynets, T., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Latseva, S., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Lohfink, E., Love, C., Mariscal, C. J. Lozano, Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Micallef, J., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Mukherjee, T., Naab, R., Nagai, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Oeyen, B., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Pandya, H., Park, N., Parker, G. K., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Philippen, S., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Raab, C., Rack-Helleis, J., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Reichherzer, P., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Sclafani, S., Seckel, D., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sulanke, K. H., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Twagirayezu, J. P., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, A., Wolf, M., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zhang, Z., Zhelnin, P., Zilberman, P., and Zimmerman, M.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

More than 10,000 photomultiplier tubes (PMTs) with a diameter of 80 mm will be installed in multi-PMT Digital Optical Modules (mDOMs) of the IceCube Upgrade. These have been tested and pre-calibrated at two sites. A throughput of more than 1000 PMTs per week with both sites was achieved with a modular design of the testing facilities and highly automated testing procedures. The testing facilities can easily be adapted to other PMTs, such that they can, e.g., be re-used for testing the PMTs for IceCube-Gen2. Single photoelectron response, high voltage dependence, time resolution, prepulse, late pulse, afterpulse probabilities, and dark rates were measured for each PMT. We describe the design of the testing facilities, the testing procedures, and the results of the acceptance tests., Comment: 24 pages, 19 figures, 2 tables, submitted to JINST

Published
2024

44. The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Gandhi, Deep, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Franson, Andrea, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Sheth, Nakul, Tu, Wenxin, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Zapaishchykova, Anna, Bornhorst, Miriam, Deutsch, Michelle, Fouladi, Maryam, Lazow, Margot, Mikael, Leonie, Hummel, Trent, Kann, Benjamin, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Packer, Roger, Bakas, Spyridon, Resnick, Adam, Rood, Brian, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Published
2024

45. Theta oscillons in behaving rats

Author

Zobaer, M. S., Lotfi, N., Domenico, C. M., Hoffman, C., Perotti, L., Ji, D., and Dabaghian, Y.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Recently discovered constituents of the brain waves -- the oscillons -- provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional theta-waves. We show that the resemblances between theta-oscillons and the conventional theta-waves apply to the ballpark characteristics -- mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the theta-rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion., Comment: 12 pages, 6 figures

Published
2024

46. Associations between pain-management treatments and opioid use disorder risk among Medicaid patients

Author

Rudolph, Kara E., Williams, Nicholas T., Diaz, Ivan, Forrest, Sarah, Hoffman, Katherine L., Samples, Hillary, Olfson, Mark, Doan, Lisa, Cerda, Magdalena, and Ross, Rachael

Subjects
Statistics - Applications
Abstract

Introduction: Chronic pain patients are at increased risk of opioid-misuse. Less is known about the unique risk conferred by each pain-management treatment, as treatments are typically implemented together, confounding their independent effects. We estimated the extent to which pain-management strategies were associated with risk of incident opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain-management treatments at their observed levels. Methods: We used data from two chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: 1) those with a chronic pain condition co-morbid with physical disability (N=6,133) or 2) those with chronic pain without disability (N=67,438). We considered 9 pain-management treatments: prescription opioid i) dose and ii) duration; iii) number of opioid prescribers; opioid co-prescription with iv) benzodiazepines, v) muscle relaxants, and vi) gabapentinoids; vii) non-opioid pain prescription, viii) physical therapy, and ix) other pain treatment modality. Our outcome was incident OUD. Results: Having an opioid and gabapentin co-prescription or an opioid and benzodiazepine co-prescription was statistically significantly associated with a 16-46% increased risk of OUD. Opioid dose and duration also were significantly associated with increased risk of OUD. Physical therapy was significantly associated with an 11% decreased risk of OUD in the subgroup with chronic pain but no disability. Conclusions: Co-prescription of opioids with either gabapentin or benzodiazepines may substantially increase risk of OUD. More positively, physical therapy may be a relatively accessible and safe pain-management strategy.

Published
2024

47. QGen: On the Ability to Generalize in Quantization Aware Training

Author

AskariHemmat, MohammadHossein, Jeddi, Ahmadreza, Hemmat, Reyhane Askari, Lazarevich, Ivan, Hoffman, Alexander, Sah, Sudhakar, Saboori, Ehsan, Savaria, Yvon, and David, Jean-Pierre

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we develop a theoretical model for quantization in neural networks and demonstrate how quantization functions as a form of regularization. Second, motivated by recent work connecting the sharpness of the loss landscape and generalization, we derive an approximate bound for the generalization of quantized models conditioned on the amount of quantization noise. We then validate our hypothesis by experimenting with over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets on convolutional and transformer-based models.

Published
2024

48. Evolution of the nuclear spin-orbit splitting explored via the $^{32}$Si($d$,$p$)$^{33}$Si reaction using SOLARIS

Author

Chen, J., Kay, B. P., Hoffman, C. R., Tang, T. L., Tolstukhin, I. A., Bazin, D., Lubna, R. S., Ayyad, Y., Beceiro-Novo, S., Coombes, B. J., Freeman, S. J., Gaffney, L. P., Garg, R., Jayatissa, H., Kuchera, A. N., MacGregor, P., Mitchell, A. J., Mittig, W., Monteagudo, B., Munoz-Ramos, A., Müller-Gatermann, C., Recchia, F., Rijal, N., Santamaria, C., Serikow, M. Z., Sharp, D. K., Smith, J., Stecenko, J. K., Wilson, G. L., Wuosmaa, A. H., Yuan, C. X., Zamora, J. C., and Zhang, Y. N.

Subjects
Nuclear Experiment
Abstract

The spin-orbit splitting between neutron 1$p$ orbitals at $^{33}$Si has been deduced using the single-neutron-adding ($d$,$p$) reaction in inverse kinematics with a beam of $^{32}$Si, a long-lived radioisotope. Reaction products were analyzed by the newly implemented SOLARIS spectrometer at the reaccelerated-beam facility at the National Superconducting Cyclotron Laboratory. The measurements show reasonable agreement with shell-model calculations that incorporate modern cross-shell interactions, but they contradict the prediction of proton density depletion based on relativistic mean-field theory. The evolution of the neutron 1$p$-shell orbitals is systematically studied using the present and existing data in the isotonic chains of $N=17$, 19, and 21. In each case, a smooth decrease in the separation of the $1p_{3/2}$-$1p_{1/2}$ orbitals is seen as the respective $p$-orbitals approach zero binding, suggesting that the finite nuclear potential strongly influences the evolution of nuclear structure in this region., Comment: 10 pages, 5 figures

Published
2024

49. GP-MoLFormer: A Foundation Model For Molecular Generation

Author

Ross, Jerret, Belgodere, Brian, Hoffman, Samuel C., Chenthamarakshan, Vijil, Mroueh, Youssef, and Das, Payel

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Physics - Chemical Physics
Abstract

Transformer-based models trained on large and general purpose datasets consisting of molecular strings have recently emerged as a powerful tool for successfully modeling various structure-property relations. Inspired by this success, we extend the paradigm of training chemical language transformers on large-scale chemical datasets to generative tasks in this work. Specifically, we propose GP-MoLFormer, an autoregressive molecular string generator that is trained on more than 1.1B chemical SMILES. GP-MoLFormer uses a 46.8M parameter transformer decoder model with linear attention and rotary positional encodings as the base architecture. We explore the utility of GP-MoLFormer in generating novel, valid, and unique SMILES. Impressively, we find GP-MoLFormer is able to generate a significant fraction of novel, valid, and unique SMILES even when the number of generated molecules is in the 10 billion range and the reference set is over a billion. We also find strong memorization of training data in GP-MoLFormer generations, which has so far remained unexplored for chemical language models. Our analyses reveal that training data memorization and novelty in generations are impacted by the quality of the training data; duplication bias in training data can enhance memorization at the cost of lowering novelty. We evaluate GP-MoLFormer's utility and compare it with that of existing baselines on three different tasks: de novo generation, scaffold-constrained molecular decoration, and unconstrained property-guided optimization. While the first two are handled with no additional training, we propose a parameter-efficient fine-tuning method for the last task, which uses property-ordered molecular pairs as input. We call this new approach pair-tuning. Our results show GP-MoLFormer performs better or comparable with baselines across all three tasks, demonstrating its general utility.

Published
2024

50. From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives

Author

Fan, Shuxian, Visokay, Adam, Hoffman, Kentaro, Salerno, Stephen, Liu, Li, Leek, Jeffrey T., and McCormick, Tyler H.

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a surviving caregiver or relative that are used to predict the decedent's COD. Turning VAs into actionable insights for researchers and policymakers requires two steps (i) predicting likely COD using the VA interview and (ii) performing inference with predicted CODs (e.g. modeling the breakdown of causes by demographic factors using a sample of deaths). In this paper, we develop a method for valid inference using outcomes (in our case COD) predicted from free-form text using state-of-the-art NLP techniques. This method, which we call multiPPI++, extends recent work in "prediction-powered inference" to multinomial classification. We leverage a suite of NLP techniques for COD prediction and, through empirical analysis of VA data, demonstrate the effectiveness of our approach in handling transportability issues. multiPPI++ recovers ground truth estimates, regardless of which NLP model produced predictions and regardless of whether they were produced by a more accurate predictor like GPT-4-32k or a less accurate predictor like KNN. Our findings demonstrate the practical importance of inference correction for public health decision-making and suggests that if inference tasks are the end goal, having a small amount of contextually relevant, high quality labeled data is essential regardless of the NLP algorithm., Comment: 12 pages, 7 figures

Published
2024

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