Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

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

Strange quark as a probe for new physics in the Higgs sector

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

Decoding Dark Matter Substructure without Supervision

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

Deep Learning the Morphology of Dark Matter Substructure

Autor: Alexander, Stephon, Gleyzer, Sergei, McDonough, Evan, Toomey, Michael W., Usai, Emanuele

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

Machine Learning in High Energy Physics Community White Paper

Autor: Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Anderson, John, Andrews, Michael, Espinosa, Juan Pedro Araque, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Burkle, Bjorn, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Chen, Yi-fan, Childers, Taylor, Coadou, Yann, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, De Simone, Andrea, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Heintz, Ulrich, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meenakshi, Narain, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Neubauer, Mark, Newman, Harvey, Otten, Sydney, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Sun, Wei, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vasel, Justin, Vallecorsa, Sofia, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Yang, Kun, Zapata, Omar

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and r

Externí odkaz: http://arxiv.org/abs/1807.02876