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1. From MCAS to College: Educational Milestones and Postsecondary Success in Massachusetts. White Paper No. 275

Author

Pioneer Institute for Public Policy Research and Kerry L. Donahue

Abstract

This paper examines the long-term impact of the 1993 Massachusetts Education Reform Act (MERA) and the Massachusetts Comprehensive Assessment System (MCAS) on postsecondary education outcomes, with a focus on historically underserved students. MERA aimed to improve educational standards and close achievement gaps through the introduction of MCAS, a statewide assessment system, and the high school Competency Determination (CD) requirement for graduation. More than two decades later, questions remain about how these reforms have influenced students' readiness for and success in postsecondary education. This analysis addresses three key areas: (1) Changes in 10th-grade MCAS performance over time; (2) Student participation and success in key college readiness benchmarks such as advanced coursework, SAT, and high school graduation; and (3) Postsecondary enrollment and degree completion rates.

Published
2024

2. Cold Gas and Star Formation in the Phoenix Cluster with JWST

Author

Reefe, Michael, McDonald, Michael, Chatzikos, Marios, Seebeck, Jerome, Mushotzky, Richard, Veilleux, Sylvain, Allen, Steven, Bayliss, Matthew, Calzadilla, Michael, Canning, Rebecca, Donahue, Megan, Floyd, Benjamin, Gaspari, Massimo, Hlavacek-Larrondo, Julie, McNamara, Brian, Russell, Helen, Sarkar, Arnab, Sharon, Keren, and Somboonpanyakul, Taweewat

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present integral field unit observations of the Phoenix Cluster with the JWST Mid-infrared Instrument's Medium Resolution Spectrometer (MIRI/MRS). We focus this study on the molecular gas, dust, and star formation in the brightest cluster galaxy (BCG). We use precise spectral modeling to produce maps of the silicate dust, molecular gas, and polycyclic aromatic hydrocarbons (PAHs) in the inner $\sim$50 kpc of the cluster. We have developed a novel method for measuring the optical depth from silicates by comparing the observed H$_2$ line ratios to those predicted by excitation models. We provide updated measurements of the total molecular gas mass of $2.2^{+0.4}_{-0.1} \times 10^{10}$ ${\rm M}_\odot$, which agrees with CO-based estimates, providing an estimate of the CO-to-H$_2$ conversion factor of $\alpha_{\rm CO} = 0.9 \pm 0.2\,{\rm M}_{\odot}\,{\rm pc}^{-2}\,({\rm K}\,{\rm km}\,{\rm s}^{-1})^{-1}$; an updated stellar mass of $M_* = 2.6 \pm 0.5 \times 10^{10}$ ${\rm M}_\odot$; and star formation rates averaged over 10 and 100 Myr of $\langle{\rm SFR}\rangle_{\rm 10} = 1340 \pm 100$ ${\rm M}_\odot\,{\rm yr}^{-1}$ and $\langle{\rm SFR}\rangle_{\rm 100} = 740 \pm 80$ ${\rm M}_\odot\,{\rm yr}^{-1}$, respectively. The H$_2$ emission seems to be powered predominantly by star formation within the central $\sim 20$ kpc, with no need for an extra particle heating component as is seen in other BCGs. Additionally, we find nearly an order of magnitude drop in the star formation rates estimated by PAH fluxes in cool core BCGs compared to field galaxies, suggesting that hot particles from the intracluster medium are destroying PAH grains even in the centralmost 10s of kpc., Comment: 21 pages, 13 figures, 2 tables. Submitted to ApJ. Comments welcome!

Published
2025

3. Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations

Author

Kim, Yewon, Lee, Sung-Ju, and Donahue, Chris

Subjects
Computer Science - Human-Computer Interaction
Abstract

Songwriting is often driven by multimodal inspirations, such as imagery, narratives, or existing music, yet songwriters remain unsupported by current music AI systems in incorporating these multimodal inputs into their creative processes. We introduce Amuse, a songwriting assistant that transforms multimodal (image, text, or audio) inputs into chord progressions that can be seamlessly incorporated into songwriters' creative processes. A key feature of Amuse is its novel method for generating coherent chords that are relevant to music keywords in the absence of datasets with paired examples of multimodal inputs and chords. Specifically, we propose a method that leverages multimodal large language models (LLMs) to convert multimodal inputs into noisy chord suggestions and uses a unimodal chord model to filter the suggestions. A user study with songwriters shows that Amuse effectively supports transforming multimodal ideas into coherent musical suggestions, enhancing users' agency and creativity throughout the songwriting process., Comment: Preprint. Project page: https://yewon-kim.com/amuse

Published
2024

4. VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Music

Author

Shi, Jiatong, Shim, Hye-jin, Tian, Jinchuan, Arora, Siddhant, Wu, Haibin, Petermann, Darius, Yip, Jia Qi, Zhang, You, Tang, Yuxun, Zhang, Wangyou, Alharthi, Dareen Safar, Huang, Yichen, Saito, Koichi, Han, Jionghao, Zhao, Yiwen, Donahue, Chris, and Watanabe, Shinji

Subjects
Computer Science - Sound, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In this work, we introduce VERSA, a unified and standardized evaluation toolkit designed for various speech, audio, and music signals. The toolkit features a Pythonic interface with flexible configuration and dependency control, making it user-friendly and efficient. With full installation, VERSA offers 63 metrics with 711 metric variations based on different configurations. These metrics encompass evaluations utilizing diverse external resources, including matching and non-matching reference audio, text transcriptions, and text captions. As a lightweight yet comprehensive toolkit, VERSA is versatile to support the evaluation of a wide range of downstream scenarios. To demonstrate its capabilities, this work highlights example use cases for VERSA, including audio coding, speech synthesis, speech enhancement, singing synthesis, and music generation. The toolkit is available at https://github.com/shinjiwlab/versa.

Published
2024

5. Vision Language Models Are Few-Shot Audio Spectrogram Classifiers

Author

Dixit, Satvik, Heller, Laurie M., and Donahue, Chris

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We demonstrate that vision language models (VLMs) are capable of recognizing the content in audio recordings when given corresponding spectrogram images. Specifically, we instruct VLMs to perform audio classification tasks in a few-shot setting by prompting them to classify a spectrogram image given example spectrogram images of each class. By carefully designing the spectrogram image representation and selecting good few-shot examples, we show that GPT-4o can achieve 59.00% cross-validated accuracy on the ESC-10 environmental sound classification dataset. Moreover, we demonstrate that VLMs currently outperform the only available commercial audio language model with audio understanding capabilities (Gemini-1.5) on the equivalent audio classification task (59.00% vs. 49.62%), and even perform slightly better than human experts on visual spectrogram classification (73.75% vs. 72.50% on first fold). We envision two potential use cases for these findings: (1) combining the spectrogram and language understanding capabilities of VLMs for audio caption augmentation, and (2) posing visual spectrogram classification as a challenge task for VLMs.

Published
2024

6. Local deployment of large-scale music AI models on commodity hardware

Author

Zhou, Xun, Ruan, Charlie, Zhao, Zihe, Chen, Tianqi, and Donahue, Chris

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We present the MIDInfinite, a web application capable of generating symbolic music using a large-scale generative AI model locally on commodity hardware. Creating this demo involved porting the Anticipatory Music Transformer, a large language model (LLM) pre-trained on the Lakh MIDI dataset, to the Machine Learning Compilation (MLC) framework. Once the model is ported, MLC facilitates inference on a variety of runtimes including C++, mobile, and the browser. We envision that MLC has the potential to bridge the gap between the landscape of increasingly capable music AI models and technology more familiar to music software developers. As a proof of concept, we build a web application that allows users to generate endless streams of multi-instrumental MIDI in the browser, either from scratch or conditioned on a prompt. On commodity hardware (an M3 Macbook Pro), our demo can generate 51 notes per second, which is faster than real-time playback for 72.9% of generations, and increases to 86.3% with 2 seconds of upfront buffering., Comment: 2 pages

Published
2024

7. Just Label the Repeats for In-The-Wild Audio-to-Score Alignment

Author

Bukey, Irmak, Feffer, Michael, and Donahue, Chris

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We propose an efficient workflow for high-quality offline alignment of in-the-wild performance audio and corresponding sheet music scans (images). Recent work on audio-to-score alignment extends dynamic time warping (DTW) to be theoretically able to handle jumps in sheet music induced by repeat signs-this method requires no human annotations, but we show that it often yields low-quality alignments. As an alternative, we propose a workflow and interface that allows users to quickly annotate jumps (by clicking on repeat signs), requiring a small amount of human supervision but yielding much higher quality alignments on average. Additionally, we refine audio and score feature representations to improve alignment quality by: (1) integrating measure detection into the score feature representation, and (2) using raw onset prediction probabilities from a music transcription model instead of piano roll. We propose an evaluation protocol for audio-to-score alignment that computes the distance between the estimated and ground truth alignment in units of measures. Under this evaluation, we find that our proposed jump annotation workflow and improved feature representations together improve alignment accuracy by 150% relative to prior work (33% to 82%)., Comment: 25th International Society for Music Information Retrieval Conference, San Francisco, 2024

Published
2024

8. Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Optimization

Author

Liu, Meitong, Zhang, Xiaoyuan, Xie, Chulin, Donahue, Kate, and Zhao, Han

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The goal of multi-objective optimization (MOO) is to learn under multiple, potentially conflicting, objectives. One widely used technique to tackle MOO is through linear scalarization, where one fixed preference vector is used to combine the objectives into a single scalar value for optimization. However, recent work (Hu et al., 2024) has shown linear scalarization often fails to capture the non-convex regions of the Pareto Front, failing to recover the complete set of Pareto optimal solutions. In light of the above limitations, this paper focuses on Tchebycheff scalarization that optimizes for the worst-case objective. In particular, we propose an online mirror descent algorithm for Tchebycheff scalarization, which we call OMD-TCH. We show that OMD-TCH enjoys a convergence rate of $O(\sqrt{\log m/T})$ where $m$ is the number of objectives and $T$ is the number of iteration rounds. We also propose a novel adaptive online-to-batch conversion scheme that significantly improves the practical performance of OMD-TCH while maintaining the same convergence guarantees. We demonstrate the effectiveness of OMD-TCH and the adaptive conversion scheme on both synthetic problems and federated learning tasks under fairness constraints, showing state-of-the-art performance., Comment: 26 pages, 7 figures, 2 tables

Published
2024

9. DAXA: Traversing the X-ray desert by Democratising Archival X-ray Astronomy

Author

Turner, David J., Pilling, Jessica E., Donahue, Megan, Giles, Paul A., Romer, Kathy, Gupta, Agrim, Wallage, Toby, and Wang, Ray

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We introduce a new, open-source, Python module for the acquisition and processing of archival data from many X-ray telescopes - Democratising Archival X-ray Astronomy (hereafter referred to as DAXA). Our software is built to increase access to, and use of, large archives of X-ray astronomy data; providing a unified, easy-to-use, Python interface to the disparate archives and processing tools. We provide this interface for the majority of X-ray telescopes launched within the last 30 years. This module enables much greater access to X-ray data for non-specialists, while preserving low-level control of processing for X-ray experts. It is useful for identifying relevant observations of a single object of interest but it excels at creating multi-mission datasets for serendipitous or targeted studies of large samples of X-ray emitting objects. The management and organization of datasets is also made easier; DAXA archives can be version controlled and updated if new data become available. Once relevant observations are identified, the raw data can be downloaded (and optionally processed) through DAXA, or pre-processed event lists, images, and exposure maps can be downloaded if they are available. X-ray observations are perfectly suited to serendipitous discoveries and archival analyses, and with a decade-long `X-ray desert' potentially on the horizon archival data will take on even greater importance; enhanced access to those archives will be vital to the continuation of X-ray astronomy., Comment: 5 pages, 1 figure, submitted to JOSS; GitHub repository - https://github.com/DavidT3/DAXA; Documentation - https://daxa.readthedocs.io/

Published
2024

10. Detection of a space capsule entering Earth's atmosphere with distributed acoustic sensing (DAS)

Author

Carr, Chris G., Donahue, Carly M., Viens, Loic, Beardslee, Luke B., McGhee, Elisa A., and Danielson, Lisa R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Physics - Geophysics
Abstract

On 24 September 2023, the Origins, Spectral Interpretation, Resource Identification, and Security Regolith Explorer (OSIRIS-REx) Sample Return Capsule entered the Earth's atmosphere after successfully collecting samples from an asteroid. The known trajectory and timing of this return provided a rare opportunity to strategically instrument sites to record geophysical signals produced by the capsule as it traveled at hypersonic speeds through the atmosphere. We deployed two optical-fiber distributed acoustic sensing (DAS) interrogators to sample over 12 km of surface-draped, fiber-optic cables along with six co-located seismometer-infrasound sensor pairs, spread across two sites near Eureka, NV. This campaign-style rapid deployment is the first reported recording of a sample return capsule entry with any distributed fiber optic sensing technology. The DAS interrogators recorded an impulsive arrival with an extended coda which had features that were similar to recordings from both the seismometers and infrasound sensors. While the signal-to-noise of the DAS data was lower than the seismic-infrasound data, the extremely dense spacing of fiber-optic sensors allowed for more phases to be clearly distinguished and the continuous transformation of the wavefront as it impacted the ground could be visualized. Unexpectedly, the DAS recordings contain less low-frequency content than is present in both the seismic and infrasound data. The deployment conditions strongly affected the recorded DAS data, in particular, we observed that fiber selection and placement exert strong controls on data quality.

Published
2024

11. Do Music Generation Models Encode Music Theory?

Author

Wei, Megan, Freeman, Michael, Donahue, Chris, and Sun, Chen

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Music foundation models possess impressive music generation capabilities. When people compose music, they may infuse their understanding of music into their work, by using notes and intervals to craft melodies, chords to build progressions, and tempo to create a rhythmic feel. To what extent is this true of music generation models? More specifically, are fundamental Western music theory concepts observable within the "inner workings" of these models? Recent work proposed leveraging latent audio representations from music generation models towards music information retrieval tasks (e.g. genre classification, emotion recognition), which suggests that high-level musical characteristics are encoded within these models. However, probing individual music theory concepts (e.g. tempo, pitch class, chord quality) remains under-explored. Thus, we introduce SynTheory, a synthetic MIDI and audio music theory dataset, consisting of tempos, time signatures, notes, intervals, scales, chords, and chord progressions concepts. We then propose a framework to probe for these music theory concepts in music foundation models (Jukebox and MusicGen) and assess how strongly they encode these concepts within their internal representations. Our findings suggest that music theory concepts are discernible within foundation models and that the degree to which they are detectable varies by model size and layer., Comment: Accepted at ISMIR 2024. Dataset: https://huggingface.co/datasets/meganwei/syntheory Code: https://github.com/brown-palm/syntheory Website: https://brown-palm.github.io/music-theory

Published
2024

12. The Impact of Element Ordering on LM Agent Performance

Author

Chi, Wayne, Talwalkar, Ameet, and Donahue, Chris

Subjects
Computer Science - Machine Learning
Abstract

There has been a surge of interest in language model agents that can navigate virtual environments such as the web or desktop. To navigate such environments, agents benefit from information on the various elements (e.g., buttons, text, or images) present. It remains unclear which element attributes have the greatest impact on agent performance, especially in environments that only provide a graphical representation (i.e., pixels). Here we find that the ordering in which elements are presented to the language model is surprisingly impactful--randomizing element ordering in a webpage degrades agent performance comparably to removing all visible text from an agent's state representation. While a webpage provides a hierarchical ordering of elements, there is no such ordering when parsing elements directly from pixels. Moreover, as tasks become more challenging and models more sophisticated, our experiments suggest that the impact of ordering increases. Finding an effective ordering is non-trivial. We investigate the impact of various element ordering methods in web and desktop environments. We find that dimensionality reduction provides a viable ordering for pixel-only environments. We train a UI element detection model to derive elements from pixels and apply our findings to an agent benchmark--OmniACT--where we only have access to pixels. Our method completes more than two times as many tasks on average relative to the previous state-of-the-art.

Published
2024

13. Relational Reactive Programming: miniKanren for the Web

Author

Donahue, Evan

Subjects
Computer Science - Programming Languages, D.3.3, D.1, D.2.11
Abstract

Over the past decade, reactive frameworks and languages have become the dominant programming paradigm in front-end web development. In this paradigm, user actions change application state, and those changes propagate reactively to derived state and to the display, reducing the likelihood that various parts of the data model and user-facing view will become out of sync due to programmer error. In this paper, we explore the application of relational programming to the specification and synchronized evolution of model and view across time in response to user input. To that end, we present a reactive Javascript implementation of miniKanren and an integrated reactive programming model oriented towards the challenges of front-end web development., Comment: 20 pages, 4 figures

Published
2024

14. Foundation Models for Music: A Survey

Author

Ma, Yinghao, Øland, Anders, Ragni, Anton, Del Sette, Bleiz MacSen, Saitis, Charalampos, Donahue, Chris, Lin, Chenghua, Plachouras, Christos, Benetos, Emmanouil, Shatri, Elona, Morreale, Fabio, Zhang, Ge, Fazekas, György, Xia, Gus, Zhang, Huan, Manco, Ilaria, Huang, Jiawen, Guinot, Julien, Lin, Liwei, Marinelli, Luca, Lam, Max W. Y., Sharma, Megha, Kong, Qiuqiang, Dannenberg, Roger B., Yuan, Ruibin, Wu, Shangda, Wu, Shih-Lun, Dai, Shuqi, Lei, Shun, Kang, Shiyin, Dixon, Simon, Chen, Wenhu, Huang, Wenhao, Du, Xingjian, Qu, Xingwei, Tan, Xu, Li, Yizhi, Tian, Zeyue, Wu, Zhiyong, Wu, Zhizheng, Ma, Ziyang, and Wang, Ziyu

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.

Published
2024

15. Star Formation, Nebulae, and Active Galactic Nuclei in CLASH Brightest Cluster Galaxies. I. Dependence on Core Entropy of Intracluster Medium

Author

Levitskiy, Arsen, Lim, Jeremy, Ohyama, Youichi, Li, Juno, and Donahue, Megan

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We set the stage for reassessing how star formation, emission-line nebulae, and active galactic nuclei (AGNs) in brightest cluster galaxies (BCGs) depend on the thermodynamics of the intracluster medium (ICM). Our work is based on the 25 clusters observed in the CLASH program for which the aforementioned attributes in their BCGs can be well scrutinized, as has the thermodynamics of their ICM. Nine of these BCGs display complex UV morphologies tracing recent star formation, whereas the remaining 16 are characterized by a relatively compact central UV enhancement. Here, we show definitively that three of the latter BCGs also display star formation, whereas the diffuse UV of the remaining 13 is entirely consistent with old low-mass stars. The overall results support the previously established dependence of star formation and nebulae in BCGs on an "excess core entropy," K$_{0}$, for the ICM: all 11 clusters with K$_{0}$ $\leq$ 24 keV cm$^{2}$ (but only one of 14 clusters with K$_{0}$ $\geq$ 42 keV cm$^{2}$) host star-forming BCGs that almost if not always possess nebulae. Instead of an entropy floor, we show that K$_{0}$ reflects the degree to which the radial entropy profile decreases inward within $\sim$100 kpc rather than (except perhaps at large K$_{0}$) actually flattening: clusters with lower ICM entropies and hence shorter cooling times at their cores preferentially host BCGs displaying star formation, nebulae, and more radio-luminous AGNs. Nearly all BCGs possess detectable AGNs, however, indicating multiple pathways for fuelling their AGNs., Comment: 39 Pages, 18 Figures

Published
2024
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16. Magnetodynamics of few-nanoparticle chains

Author

Bui, Thinh Q., Oberdick, Samuel D., Abel, Frank M., Donahue, Michael J., Quelhas, Klaus N., Dennis, Cindi L., Cleveland, Thomas, Liu, Yanxin, and Woods, Solomon I.

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics, Physics - Classical Physics
Abstract

In recent years, there has been increasing interest in the understanding and application of nanoparticle assemblies driven by external fields. Although these systems can exhibit marked transitions in behavior compared to non-interacting counterparts, it has often proven challenging to connect their dynamics with underlying physical mechanisms or even to verifiably establish their structure under realistic experimental conditions. We have studied colloidal iron oxide nanoparticles that assemble into ordered, few-particle linear chains under the influence of oscillating and pulsed magnetic fields. Cryo-EM has been used to flash freeze and image the structures formed by oscillatory drive fields, and magnetic relaxometry has been used to extract the multiple time constants associated with magnetic switching of the short chains. Armed with the physical structure from cryo-EM and the field-dependent switching times from magnetic measurements, we have conducted extensive micromagnetic simulations, revealing probable mechanisms for each time constant regime spanning $10^{9}$ in time and how switching develops from individual particles to entire chains. These types of magnetic nanomaterials have great potential for biomedical technologies, particularly magnetic particle imaging and hyperthermia, and rigorous elucidation of their physics will hasten their optimization.

Published
2024

17. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run

Author

Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. 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C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Sergeev, A., Serra, M., Servignat, G., Setyawati, Y., Shaffer, T., Shah, U. S., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Shen, B., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.

Subjects
Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Published
2024

18. Geophysical Observations of the 24 September 2023 OSIRIS-REx Sample Return Capsule Re-Entry

Author

Silber, Elizabeth A., Bowman, Daniel C., Carr, Chris G., Eisenberg, David P., Elbing, Brian R., Fernando, Benjamin, Garcés, Milton A., Haaser, Robert, Krishnamoorthy, Siddharth, Langston, Charles A., Nishikawa, Yasuhiro, Webster, Jeremy, Anderson, Jacob F., Arrowsmith, Stephen, Bazargan, Sonia, Beardslee, Luke, Beck, Brant, Bishop, Jordan W., Blom, Philip, Bracht, Grant, Chichester, David L., Christe, Anthony, Clarke, Jacob, Cummins, Kenneth, Cutts, James, Danielson, Lisa, Donahue, Carly, Eack, Kenneth, Fleigle, Michael, Fox, Douglas, Goel, Ashish, Green, David, Hasumi, Yuta, Hayward, Chris, Hicks, Dan, Hix, Jay, Horton, Stephen, Hough, Emalee, Huber, David P., Hunt, Madeline A., Inman, Jennifer, Islam, S. M. Ariful, Izraelevitz, Jacob, Jacob, Jamey D., Johnson, James, KC, Real J., Komjathy, Attila, Lam, Eric, LaPierre, Justin, Lewis, Kevin, Lewis, Richard D., Liu, Patrick, Martire, Léo, McCleary, Meaghan, McGhee, Elisa A., Mitra, Ipsita, Nag, Amitabh, Giraldo, Luis Ocampo, Pearson, Karen, Plaisir, Mathieu, Popenhagen, Sarah K., Rassoul, Hamid, Giannone, Miro Ronac, Samnani, Mirza, Schmerr, Nicholas, Spillman, Kate, Srinivas, Girish, Takazawa, Samuel K., Tempert, Alex, Turley, Reagan, Van Beek, Cory, Viens, Loïc, Walsh, Owen A., Weinstein, Nathan, White, Robert, Williams, Brian, Wilson, Trevor C., Wyckoff, Shirin, Yamamoto, Masa-yuki, Yap, Zachary, Yoshiyama, Tyler, and Zeiler, Cleat

Subjects
Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Geophysics
Abstract

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

Published
2024
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19. Equilibrium States of Galactic Atmospheres II: Interpretation and Implications

Author

Voit, G. M., Carr, C., Fielding, D. B., Pandya, V., Bryan, G. L., Donahue, M., Oppenheimer, B. D., and Somerville, R. S.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The scaling of galaxy properties with halo mass suggests that feedback loops regulate star formation, but there is no consensus yet about how those feedback loops work. To help clarify discussions of galaxy-scale feedback, Paper I presented a very simple model for supernova feedback that it called the minimalist regulator model. This followup paper interprets that model and discusses its implications. The model itself is an accounting system that tracks all of the mass and energy associated with a halo's circumgalactic baryons--the central galaxy's atmosphere. Algebraic solutions for the equilibrium states of that model reveal that star formation in low-mass halos self-regulates primarily by expanding the atmospheres of those halos, ultimately resulting in stellar masses that are insensitive to the mass-loading properties of galactic winds. What matters most is the proportion of supernova energy that couples with circumgalactic gas. However, supernova feedback alone fails to expand galactic atmospheres in higher-mass halos. According to the minimalist regulator model, an atmospheric contraction crisis ensues, which may be what triggers strong black-hole feedback. The model also predicts that circumgalactic medium properties emerging from cosmological simulations should depend largely on the specific energy of the outflows they produce, and we interpret the qualitative properties of several numerical simulations in light of that prediction., Comment: 15 pages, 3 figures, Submitted to ApJ

Published
2024

20. Equilibrium States of Galactic Atmospheres I: The Flip Side of Mass Loading

Author

Voit, G. M., Pandya, V., Fielding, D. B., Bryan, G. L., Carr, C., Donahue, M., Oppenheimer, B. D., and Somerville, R. S.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

This paper presents a new framework for understanding the relationship between a galaxy and its circumgalactic medium (CGM). It focuses on how imbalances between heating and cooling cause either expansion or contraction of the CGM. It does this by tracking \textit{all} of the mass and energy associated with a halo's baryons, including their gravitational potential energy, even if feedback has pushed some of those baryons beyond the halo's virial radius. We show how a star-forming galaxy's equilibrium state can be algebraically derived within the context of this framework, and we analyze how the equilibrium star formation rate depends on supernova feedback. We consider the consequences of varying the mass loading parameter etaM = Mdot_wind / Mdot_* relating a galaxy's gas mass outflow rate (Mdot_wind) to its star formation rate (Mdot_*) and obtain results that challenge common assumptions. In particular, we find that equilibrium star formation rates in low-mass galaxies are generally insensitive to mass loading, and when mass loading does matter, increasing it actually results in \textit{more} star formation because more supernova energy is needed to resist atmospheric contraction., Comment: 18 pages, 5 figures, submitted to ApJ

Published
2024

24. Clinicogenomic landscape of pancreatic adenocarcinoma identifies KRAS mutant dosage as prognostic of overall survival

Author

Varghese, Anna M., Perry, Maria A., Chou, Joanne F., Nandakumar, Subhiksha, Muldoon, Daniel, Erakky, Amanda, Zucker, Amanda, Fong, Christopher, Mehine, Miika, Nguyen, Bastien, Basturk, Olca, Balogun, Fiyinfolu, Kelsen, David P., Brannon, A. Rose, Mandelker, Diana, Vakiani, Efsevia, Park, Wungki, Yu, Kenneth H., Stadler, Zsofia K., Schattner, Mark A., Jarnagin, William R., Wei, Alice C., Chakravarty, Debyani, Capanu, Marinela, Schultz, Nikolaus, Berger, Michael F., Iacobuzio-Donahue, Christine A., Bandlamudi, Chaitanya, and O’Reilly, Eileen M.

Published
2025
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26. New particle formation from isoprene under upper-tropospheric conditions

Author

Shen, Jiali, Russell, Douglas M., DeVivo, Jenna, Kunkler, Felix, Baalbaki, Rima, Mentler, Bernhard, Scholz, Wiebke, Yu, Wenjuan, Caudillo-Plath, Lucía, Sommer, Eva, Ahongshangbam, Emelda, Alfaouri, Dina, Almeida, João, Amorim, Antonio, Beck, Lisa J., Beckmann, Hannah, Berntheusel, Moritz, Bhattacharyya, Nirvan, Canagaratna, Manjula R., Chassaing, Anouck, Cruz-Simbron, Romulo, Dada, Lubna, Duplissy, Jonathan, Gordon, Hamish, Granzin, Manuel, Große Schute, Lena, Heinritzi, Martin, Iyer, Siddharth, Klebach, Hannah, Krüger, Timm, Kürten, Andreas, Lampimäki, Markus, Liu, Lu, Lopez, Brandon, Martinez, Monica, Morawiec, Aleksandra, Onnela, Antti, Peltola, Maija, Rato, Pedro, Reza, Mago, Richter, Sarah, Rörup, Birte, Sebastian, Milin Kaniyodical, Simon, Mario, Surdu, Mihnea, Tamme, Kalju, Thakur, Roseline C., Tomé, António, Tong, Yandong, Top, Jens, Umo, Nsikanabasi Silas, Unfer, Gabriela, Vettikkat, Lejish, Weissbacher, Jakob, Xenofontos, Christos, Yang, Boxing, Zauner-Wieczorek, Marcel, Zhang, Jiangyi, Zheng, Zhensen, Baltensperger, Urs, Christoudias, Theodoros, Flagan, Richard C., El Haddad, Imad, Junninen, Heikki, Möhler, Ottmar, Riipinen, Ilona, Rohner, Urs, Schobesberger, Siegfried, Volkamer, Rainer, Winkler, Paul M., Hansel, Armin, Lehtipalo, Katrianne, Donahue, Neil M., Lelieveld, Jos, Harder, Hartwig, Kulmala, Markku, Worsnop, Doug R., Kirkby, Jasper, Curtius, Joachim, and He, Xu-Cheng

Published
2024
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29. An Abundance of Katherines: The Game Theory of Baby Naming

Author

Blumer, Katy, Donahue, Kate, Fritz, Katie, Ivanovich, Kate, Lee, Katherine, Luo, Katie, Meng, Cathy, and Van Koevering, Katie

Subjects
Computer Science - Computer Science and Game Theory, Computer Science - Computers and Society
Abstract

In this paper, we study the highly competitive arena of baby naming. Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeable agents who pick a name based solely on its uniqueness), we create a model which is not only tractable and clean, but also perfectly captures the real world. We then extend our investigation with numerical experiments, as well as analysis of large language model tools. We conclude by discussing avenues for future research., Comment: Accepted at SIGBOVIK 2024

Published
2024

30. Ultralight vector dark matter search using data from the KAGRA O3GK run

Author

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boumerdassi, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, R. -J., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, K. H., Chen, X., Chen, Yi-Ru, Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Chia, H. 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S., Ricci, F., Ricci, M., Richards, D., Richardson, C. J., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sako, T., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Sergeev, A., Serra, M., Servignat, G., Setyawati, Y., Shaffer, T., Shah, U. S., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Shen, B., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Southgate, A., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sullivan, A. G., Sullivan, K. D., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, D., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, L. -C., Yang, Y., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zucker, M. E., Zweizig, J., Fujimori, T., Fujimoto, H., Fujita, T., Manita, Y., Obata, I., and Takidera, H.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for $U(1)_{B-L}$ gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the $U(1)_{B-L}$ gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM., Comment: 20 pages, 5 figures

Published
2024

37. Oncogenic pathway signatures predict the risk of progression and recurrence in well-differentiated pancreatic neuroendocrine tumors.

Author

Mederos, Michael, Court, Colin, Dipardo, Benjamin, Pisegna, Joseph, Dawson, David, Joe Hines, O, Donahue, Timothy, Graeber, Thomas, Girgis, Mark, and Tomlinson, James

Subjects
genomic biomarker, pancreatic neuroendocrine tumor, prognosis, recurrence, whole‐exome sequencing
Abstract

BACKGROUND: Pancreatic neuroendocrine tumors (pNETs) are genomically diverse tumors. The management of newly diagnosed well-differentiated pNETs is limited by a lack of sensitivity of existing biomarkers for prognostication. Our goal was to investigate the potential utility of genetic markers as a predictor of progression-free survival (PFS) and recurrence-free survival (RFS). METHODS: Whole-exome sequencing of resected well-differentiated, low and intermediate-grade (G1 and G2) pNETs and normal adjacent tissue from patients who underwent resection from 2005 to 2015 was performed. Genetic alterations were classified using pan-genomic and oncogenic pathway classifications. Additional samples with genetic and clinicopathologic data available were obtained from the publicly available International Cancer Genome Consortium (ICGC) database and included in the analysis. The prognostic relevance of these genomic signatures on PFS and RFS was analyzed. RESULTS: Thirty-one patients who underwent resection for pNET were identified. Genomic analysis of mutational, copy number, cytogenetic, and complex phenomena revealed similar patterns to prior studies of pNETs with relatively few somatic gene mutations but numerous instances of copy number changes. Analysis of genomic and clinicopathologic outcomes using the combined data from our study as well as the ICGC pNET cohort (n = 124 patients) revealed that the recurrent pattern of whole chromosome loss (RPCL) and metastatic disease were independently associated with disease progression. When evaluating patients with local disease at the time of resection, RPCL and alterations in the TGFβ oncogenic pathway were independently associated with the risk of recurrence. CONCLUSIONS: Well-differentiated pNETs are genomically diverse tumors. Pathway signatures may be prognostic for predicting disease progression and recurrence.

Published
2024

38. Disparities in neoadjuvant chemotherapy for pancreatic adenocarcinoma with vascular involvement.

Author

Chervu, Nikhil, Kim, Shineui, Sakowitz, Sara, Le, Nguyen, Mallick, Saad, Lee, Hanjoo, Benharash, Peyman, and Donahue, Timothy

Subjects
Borderline resectable, Disparities, Locally advanced, Neoadjuvant therapy, Pancreatic adenocarcinoma, Pancreatic cancer
Abstract

BACKGROUND: Multiagent neoadjuvant chemotherapy (NAT) has been linked with improved survival for locally advanced (LA) or borderline resectable (BR) pancreatic ductal adenocarcinoma (PDAC). However, the existence of disparities in its utilization remains to be elucidated. METHODS: All adults with PDAC were tabulated from the 2011-2017 Nationwide Cancer Database. Tumor vascular involvement was determined using the clinical T stage and CS_EXTENSION variables. The significance of temporal trends was calculated using Cuzicks non-parametric test. A Cox proportional hazard model was used to assess the impact of NAT utilization on hazard of two-year mortality. A logistic regression model was developed to determine factors associated with receipt of NAT. RESULTS: Of 3811 patients meeting inclusion criteria, 50.8 % received NAT. NAT utilization significantly increased over the study period, from 31.7 % in 2011 to 81.1 % in 2017 (p

Published
2024

39. High baseline perivascular space volume in basal ganglia is associated with attention and executive function decline in Parkinsons disease.

Author

Foreman, Ryan, Donahue, Erin, Duran, Jared, Schiehser, Dawn, Petkus, Andrew, ONeill, Joseph, Holschneider, Daniel, Choupan, Jeiran, Van Horn, John, Bayram, Ece, Litvan, Irene, Jakowec, Michael, and Petzinger, Giselle

Subjects
Virchow–Robinson space, Humans, Parkinson Disease, Basal Ganglia, Executive Function, Female, Male, Aged, Middle Aged, Magnetic Resonance Imaging, Attention, Cognitive Dysfunction, Glymphatic System, Neuropsychological Tests, White Matter
Abstract

BACKGROUND: Pathologic perivascular spaces (PVS), the fluid-filled compartments surrounding brain vasculature, may underlie cognitive decline in Parkinsons disease (PD). However, whether this impacts specific cognitive domains has not been investigated. OBJECTIVES: This study examined the relationship of PVS volume at baseline with domain-specific and global cognitive change over 2 years in PD individuals. METHODS: A total of 39 individuals with PD underwent 3T T1w magnetic resonance imaging to determine PVS volume fraction (PVS volume normalized to total regional volume) within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, and superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of cognitive domains and global cognitive function at baseline and after 2 years. RESULTS: Higher basal ganglia PVS at baseline was associated with greater decline in attention, executive function, and global cognition scores. CONCLUSIONS: While previous reports have associated elevated PVS volume in the basal ganglia with decline in global cognition in PD, our findings show such decline may affect the attention and executive function domains.

Published
2024

40. Intense formation of secondary ultrafine particles from Amazonian vegetation fires and their invigoration of deep clouds and precipitation

Author

Shrivastava, Manish, Fan, Jiwen, Zhang, Yuwei, Rasool, Quazi Z, Zhao, Bin, Shen, Jiewen, Pierce, Jeffrey R, Jathar, Shantanu H, Akherati, Ali, Zhang, Jie, Zaveri, Rahul A, Gaudet, Brian, Liu, Ying, Andreae, Meinrat O, Pöhlker, Mira L, Donahue, Neil M, Wang, Yuan, and Seinfeld, John H

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Earth sciences, Environmental sciences
Published
2024

42. Campylobacteriosis Outbreak Linked to Municipal Water, Nebraska, USA, 2021

Author

Jansen, Lauren, Birn, Rachael, Koirala, Samir, Oppegard, Sadie, Loeck, Brianna, Hamik, Jeff, Wyckoff, Elizabeth, Spindola, Dana, Dempsey, Sue, Bartling, Amanda, Roundtree, Alexis, Kahler, Amy, Lane, Charlotte, Hogan, Nancy, Strockbine, Nancy, McKeel, Haley, Yoder, Jonathan, Mattioli, Mia, Donahue, Matthew, and Buss, Bryan

Subjects
Epidemics -- Social aspects -- United States, Medical research, Medicine, Experimental, Municipal water supply -- Health aspects -- United States, Campylobacter infections -- Physiological aspects -- Social aspects -- Risk factors, Health
Abstract

Campylobacter is a gram-negative, microaerophilic, flagellated, helical bacterium (1). Campylobacter infection, or campylobacteriosis, is the most common bacterial cause of diarrhea in the United States, producing [approximately equal to]1.5 million [...]

Published
2024
Full Text
View/download PDF

43. Evidence of Lineage 1 and 3 West Nile Virus in Person with Neuroinvasive Disease, Nebraska, USA, 2023

Author

Davis, Emily, Velez, Jason, Hamik, Jeff, Fitzpatrick, Kelly, Haley, Jacki, Eschliman, Jeremy, Panella, Amanda, Staples, J. Erin, Lambert, Amy, Donahue, Matthew, Brault, Aaron C., and Hughes, Holly R.

Subjects
Medical research, Medicine, Experimental, West Nile virus -- Physiological aspects -- Case studies -- Genetic aspects, Central nervous system diseases -- Physiological aspects -- Case studies -- Risk factors, Health
Abstract

West Nile virus (WNV) is a flavivirus within the family Flaviviridae. Since WNV was identified in New York, USA, in 1999, it has become the leading cause of arboviral disease [...]

Published
2024
Full Text
View/download PDF

47. Impact of Decentralized Learning on Player Utilities in Stackelberg Games

Author

Donahue, Kate, Immorlica, Nicole, Jagadeesan, Meena, Lucier, Brendan, and Slivkins, Aleksandrs

Subjects
Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory
Abstract

When deployed in the world, a learning agent such as a recommender system or a chatbot often repeatedly interacts with another learning agent (such as a user) over time. In many such two-agent systems, each agent learns separately and the rewards of the two agents are not perfectly aligned. To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective. We model these systems as Stackelberg games with decentralized learning and show that standard regret benchmarks (such as Stackelberg equilibrium payoffs) result in worst-case linear regret for at least one player. To better capture these systems, we construct a relaxed regret benchmark that is tolerant to small learning errors by agents. We show that standard learning algorithms fail to provide sublinear regret, and we develop algorithms to achieve near-optimal $O(T^{2/3})$ regret for both players with respect to these benchmarks. We further design relaxed environments under which faster learning ($O(\sqrt{T})$) is possible. Altogether, our results take a step towards assessing how two-agent interactions in sequential and decentralized learning environments affect the utility of both agents., Comment: To appear at ICML 2024; this is the full version

Published
2024

48. LoVoCCS. II. Weak Lensing Mass Distributions, Red-Sequence Galaxy Distributions, and Their Alignment with the Brightest Cluster Galaxy in 58 Nearby X-ray-Luminous Galaxy Clusters

Author

Fu, Shenming, Dell'Antonio, Ian, Escalante, Zacharias, Nelson, Jessica, Englert, Anthony, Helhoski, Søren, Shinde, Rahul, Brockland, Julia, LaDuca, Philip, Larkin, Christelyn, Paris, Lucca, Weiner, Shane, Black, William K., Chary, Ranga-Ram, Clowe, Douglas, Cooper, M. C., Donahue, Megan, Evrard, August, Lacy, Mark, Lauer, Tod, Liu, Binyang, McCleary, Jacqueline, Meneghetti, Massimo, Miyatake, Hironao, Montes, Mireia, Natarajan, Priyamvada, Ntampaka, Michelle, Pierpaoli, Elena, Postman, Marc, Sohn, Jubee, Turner, David, Umetsu, Keiichi, Utsumi, Yousuke, and Wilson, Gillian

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The Local Volume Complete Cluster Survey (LoVoCCS) is an on-going program to observe nearly a hundred low-redshift X-ray-luminous galaxy clusters (redshifts $0.0310^{44}$ erg/s) with the Dark Energy Camera (DECam), capturing data in $u,g,r,i,z$ bands with a $5\sigma$ point source depth of approximately 25-26th AB magnitudes. Here, we map the aperture masses in 58 galaxy cluster fields using weak gravitational lensing. These clusters span a variety of dynamical states, from nearly relaxed to merging systems, and approximately half of them have not been subject to detailed weak lensing analysis before. In each cluster field, we analyze the alignment between the 2D mass distribution described by the aperture mass map, the 2D red-sequence (RS) galaxy distribution, and the brightest cluster galaxy (BCG). We find that the orientations of the BCG and the RS distribution are strongly aligned throughout the interiors of the clusters: the median misalignment angle is 19 deg within 2 Mpc. We also observe the alignment between the orientations of the RS distribution and the overall cluster mass distribution (by a median difference of 32 deg within 1 Mpc), although this is constrained by galaxy shape noise and the limitations of our cluster sample size. These types of alignment suggest long-term dynamical evolution within the clusters over cosmic timescales., Comment: 40 pages, 16 figures, 5 tables; revised and accepted for publication in ApJ

Published
2024

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