Zobrazeno 1 - 10
of 2 165
pro vyhledávání: '"Usai Emanuele"'
Autor:
Asres, Mulugeta Weldezgina, Omlin, Christian Walter, Wang, Long, Yu, David, Parygin, Pavel, Dittmann, Jay, Karapostoli, Georgia, Seidel, Markus, Venditti, Rosamaria, Lambrecht, Luka, Usai, Emanuele, Ahmad, Muhammad, Menendez, Javier Fernandez, Maeshima, Kaori, Collaboration, the CMS-HCAL
The compact muon solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the large hadron collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acqui
Externí odkaz:
http://arxiv.org/abs/2311.04190
Autor:
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, Di Croce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele
Publikováno v:
EPJ Web of Conferences, Vol 251, p 03057 (2021)
Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments ben
Externí odkaz:
https://doaj.org/article/45d5fcb893f94bdaa449c946063df085
Autor:
Andrews Michael, Burkle Bjorn, Chaudhari Shravan, DiCroce Davide, Gleyzer Sergei, Heintz Ulrich, Narain Meenakshi, Paulini Manfred, Usai Emanuele
Publikováno v:
EPJ Web of Conferences, Vol 251, p 04030 (2021)
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combine
Externí odkaz:
https://doaj.org/article/68f4b42e2190438b8f61ecf2484f33d6
Publikováno v:
Universe 2022, 8(12), 638
The production of four top quarks is a rare process in the Standard Model that provides unique opportunities and sensitivity to Standard Model observables including potential enhancement from many popular new physics extensions. This article summaris
Externí odkaz:
http://arxiv.org/abs/2208.04085
Autor:
Albert, Alexander, Basso, Matthew J., Bright-Thonney, Samuel K., Cairo, Valentina M. M., Damerell, Chris, Egana-Ugrinovic, Daniel, Einhaus, Ulrich, Heintz, Ulrich, Homiller, Samuel, Kawada, Shin-ichi, Luo, Jingyu, Mantel, Chester, Meade, Patrick, Monroy, Jose, Narain, Meenakshi, Orr, Robert S., Reichert, Joseph, Ryd, Anders, Strube, Jan, Su, Dong, Schwartzman, Ariel G., Tanabe, Tomohiko, Tian, Junping, Usai, Emanuele, Va'vra, Jerry, Vernieri, Caterina, Young, Charles C., Zou, Rui
This paper describes a novel algorithm for tagging jets originating from the hadronisation of strange quarks (strange-tagging) with the future International Large Detector (ILD) at the International Linear Collider (ILC). It also presents the first a
Externí odkaz:
http://arxiv.org/abs/2203.07535
Autor:
Andrews, Michael, Burkle, Bjorn, Chen, Yi-fan, DiCroce, Davide, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, Pervan, Nikolas, Shafi, Yusef, Sun, Wei, Usai, Emanuele, Yang, Kun
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combine
Externí odkaz:
http://arxiv.org/abs/2104.14659
Autor:
Alexander, Stephon, Gleyzer, Sergei, Parul, Hanna, Reddy, Pranath, Toomey, Michael W., Usai, Emanuele, Von Klar, Ryker
The identity of dark matter remains one of the most pressing questions in physics today. While many promising dark matter candidates have been put forth over the last half-century, to date the true identity of dark matter remains elusive. While it is
Externí odkaz:
http://arxiv.org/abs/2008.12731
Autor:
Alison, John, An, Sitong, Andrews, Michael, Bryant, Patrick, Burkle, Bjorn, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, Poczos, Barnabas, Usai, Emanuele
From particle identification to the discovery of the Higgs boson, deep learning algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider (LHC). We present an innovative end-to-end deep learning approach for
Externí odkaz:
http://arxiv.org/abs/1910.07029
Publikováno v:
Astrophys. J. 893 (2020) 15
Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP dark matter from other well motivated m
Externí odkaz:
http://arxiv.org/abs/1909.07346
Autor:
Andrews, Michael, Alison, John, An, Sitong, Bryant, Patrick, Burkle, Bjorn, Gleyzer, Sergei, Narain, Meenakshi, Paulini, Manfred, Poczos, Barnabas, Usai, Emanuele
Publikováno v:
Nucl. Instrum. Methods Phys. Res. A 977, 164304 (2020)
We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial inf
Externí odkaz:
http://arxiv.org/abs/1902.08276