Zobrazeno 1 - 10
of 210
pro vyhledávání: '"Andrews, Michael P."'
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hin
Externí odkaz:
http://arxiv.org/abs/2308.16659
We show that the Laser Interferometer Gravitational Wave Observatory (LIGO) is a powerful instrument in the Search for Extraterrestrial Intelligence (SETI). LIGO's ability to detect gravitational waves (GWs) from astrophysical sources, such as binary
Externí odkaz:
http://arxiv.org/abs/2212.02065
Autor:
Andrews, Michael, Paulini, Manfred, Sellers, Luke, Bobrick, Alexey, Martire, Gianni, Vestal, Haydn
All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipe
Externí odkaz:
http://arxiv.org/abs/2207.04749
Autor:
Uboldi, Lorenzo, Ruth, David, Andrews, Michael, Wang, Michael H. L. S., Wenzel, Hans-Joachim, Wu, Wanwei, Yang, Tingjun
Publikováno v:
Nucl.Instrum.Meth.A1028:166371,2022
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time pr
Externí odkaz:
http://arxiv.org/abs/2106.09911
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:
Kourkchi, Ehsan, Tully, R. Brent, Eftekharzadeh, Sarah, Llop, Jordan, Courtois, Helene M., Guinet, Daniel, Dupuy, Alexandra, Neill, James D., Seibert, Mark, Andrews, Michael, Chuang, Juana, Danesh, Arash, Gonzalez, Randy, Holthaus, Alexandria, Mokelke, Amber, Schoen, Devin, Urasaki, Chase
Publikováno v:
2020, ApJ, 902:145
We present the distances of 9792 spiral galaxies lying within 15,000 km/s using the relation between luminosity and rotation rate of spiral galaxies. The sample is dominantly, but not exclusively, drawn from galaxies detected in the course of the ALF
Externí odkaz:
http://arxiv.org/abs/2009.00733
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
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
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