Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Kyle Knoepfel"'
Autor:
Michael Wang, Tingjun Yang, Maria Acosta Flechas, Philip Harris, Benjamin Hawks, Burt Holzman, Kyle Knoepfel, Jeffrey Krupa, Kevin Pedro, Nhan Tran
Publikováno v:
Frontiers in Big Data, Vol 3 (2021)
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volu
Externí odkaz:
https://doaj.org/article/d3f7984ea362452e9070d98c67655b65
The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31db837b5f230787410f73768329fd4a
http://arxiv.org/abs/2203.02479
http://arxiv.org/abs/2203.02479
Autor:
Sophie Berkman, Giuseppe Cerati, Kyle Knoepfel, Marc Mengel, Allison Reinsvold Hall, Michael Wang, Brian Gravelle, Boyana Norris
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3865e0ce9055d76dace92081aee4aef
http://arxiv.org/abs/2107.00812
http://arxiv.org/abs/2107.00812
Autor:
Kyle Knoepfel
Publikováno v:
Proceedings of The Xth Nicola Cabibbo International Conference on Heavy Quarks and Leptons — PoS(HQL 2010).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::485113aee0026e53a99cd0300d5f4d21
https://doi.org/10.22323/1.128.0018
https://doi.org/10.22323/1.128.0018