no code implementations • 4 Dec 2021 • Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler
Advances in machine learning methods provide tools that have broad applicability in scientific research.
no code implementations • 25 Oct 2021 • Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng
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.
no code implementations • 23 Feb 2021 • John Arrington, Carlos Ayerbe Gayoso, Patrick C Barry, Vladimir Berdnikov, Daniele Binosi, Lei Chang, Markus Diefenthaler, Minghui Ding, Rolf Ent, Tobias Frederico, Yulia Furletova, Tim J Hobbs, Tanja Horn, Garth M Huber, Stephen JD Kay, Cynthia Keppel, Huy-Wen Lin, Cedric Mezrag, Rachel Montgomery, Ian L Pegg, Khepani Raya, Paul Reimer, David G Richards, Craig D Roberts, Jose Rodriguez-Quintero, Dmitry Romanov, Giovanni Salme, Nobuo Sato, Jorge Segovia, Petr Stepanov, Arun S Tadepalli, Richard L Trotta
How the bulk of the Universe's visible mass emerges and how it is manifest in the existence and properties of hadrons are profound questions that probe into the heart of strongly interacting matter.
Nuclear Experiment High Energy Physics - Phenomenology Nuclear Theory
no code implementations • 17 Dec 2020 • Andrei Afanasev, Jaseer Ahmed, Igor Akushevich, Jan C. Bernauer, Peter G. Blunden, Andrea Bressan, Duane Byer, Ethan Cline, Markus Diefenthaler, Jan M. Friedrich, Haiyan Gao, Alexandr Ilyichev, Ulrich D. Jentschura, Vladimir Khachatryan, Lin Li, Wally Melnitchouk, Richard Milner, Fred Myhrer, Chao Peng, Jianwei Qiu, Udit Raha, Axel Schmidt, Vanamali C. Shastry, Hubert Spiesberger, Stan Srednyak, Steffen Strauch, Pulak Talukdar, Weizhi Xiong
Current precision scattering experiments and even more so many experiments planed for the Electron Ion Collider will be limited by systematics.
Nuclear Theory