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pro vyhledávání: '"Rankin , Dylan"'
The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulat
Externí odkaz:
http://arxiv.org/abs/2410.13947
Autor:
Marx, Ethan, Benoit, William, Gunny, Alec, Omer, Rafia, Chatterjee, Deep, Venterea, Ricco C., Wills, Lauren, Saleem, Muhammed, Moreno, Eric, Raikman, Ryan, Govorkova, Ekaterina, Rankin, Dylan, Coughlin, Michael W., Harris, Philip, Katsavounidis, Erik
The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks h
Externí odkaz:
http://arxiv.org/abs/2403.18661
Autor:
Zhao, Haoran, Naylor, Andrew, Hsu, Shih-Chieh, Calafiura, Paolo, Farrell, Steven, Feng, Yongbing, Harris, Philip Coleman, Khoda, Elham E, Mccormack, William Patrick, Rankin, Dylan Sheldon, Ju, Xiangyang
Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelera
Externí odkaz:
http://arxiv.org/abs/2402.09633
Autor:
Raikman, Ryan, Moreno, Eric A., Govorkova, Ekaterina, Marx, Ethan J, Gunny, Alec, Benoit, William, Chatterjee, Deep, Omer, Rafia, Saleem, Muhammed, Rankin, Dylan S, Coughlin, Michael W, Harris, Philip C, Katsavounidis, Erik
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescen
Externí odkaz:
http://arxiv.org/abs/2309.11537
Autor:
Saleem, Muhammed, Gunny, Alec, Chou, Chia-Jui, Yang, Li-Cheng, Yeh, Shu-Wei, Chen, Andy H. Y., Magee, Ryan, Benoit, William, Nguyen, Tri, Fan, Pinchen, Chatterjee, Deep, Marx, Ethan, Moreno, Eric, Omer, Rafia, Raikman, Ryan, Rankin, Dylan, Sharma, Ritwik, Coughlin, Michael, Harris, Philip, Katsavounidis, Erik
Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of sig
Externí odkaz:
http://arxiv.org/abs/2306.11366
Autor:
Khoda, Elham E, Rankin, Dylan, de Lima, Rafael Teixeira, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of impl
Externí odkaz:
http://arxiv.org/abs/2207.00559
Autor:
Carini, Gabriella, Deptuch, Grzegorz, Dickinson, Jennet, Doering, Dionisio, Dragone, Angelo, Fahim, Farah, Harris, Philip, Herbst, Ryan, Herwig, Christian, Huang, Jin, Mandal, Soumyajit, Suarez, Cristina Mantilla, Deiana, Allison McCarn, Miryala, Sandeep, Newcomer, F. Mitchell, Parpillon, Benjamin, Radeka, Veljko, Rankin, Dylan, Ren, Yihui, Rota, Lorenzo, Ruckman, Larry, Tran, Nhan
Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated
Externí odkaz:
http://arxiv.org/abs/2204.13223
Autor:
Harris, Philip, Katsavounidis, Erik, McCormack, William Patrick, Rankin, Dylan, Feng, Yongbin, Gandrakota, Abhijith, Herwig, Christian, Holzman, Burt, Pedro, Kevin, Tran, Nhan, Yang, Tingjun, Ngadiuba, Jennifer, Coughlin, Michael, Hauck, Scott, Hsu, Shih-Chieh, Khoda, Elham E, Chen, Deming, Neubauer, Mark, Duarte, Javier, Karagiorgi, Georgia, Liu, Mia
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML
Externí odkaz:
http://arxiv.org/abs/2203.16255
Autor:
Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E, Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia
Publikováno v:
Front. Big Data 5, 787421 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Externí odkaz:
http://arxiv.org/abs/2110.13041
Autor:
Gunny, Alec, Rankin, Dylan, Krupa, Jeffrey, Saleem, Muhammed, Nguyen, Tri, Coughlin, Michael, Harris, Philip, Katsavounidis, Erik, Timm, Steven, Holzman, Burt
The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers,
Externí odkaz:
http://arxiv.org/abs/2108.12430