Zobrazeno 1 - 10
of 2 325
pro vyhledávání: '"Heintz Ulrich"'
The U.S. CMS collaboration has designed a novel internship program for undergraduates to enhance the participation of students from under-represented populations, including those at minority serving institutions, in High Energy Physics (HEP). These s
Externí odkaz:
http://arxiv.org/abs/2401.16217
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
Autor:
Llatas, Maria Chamizo, Dasu, Sridhara, Heintz, Ulrich, Nanni, Emilio A., Power, John, Wagner, Stephen
A summary of the Snowmass 2021 e$^+$e$^-$-Collider Forum discussions, white papers submitted to the Snowmass 2021 community study, submissions of the Energy Frontier (EF) subgroups and the Accelerator Frontier (AF) Integrated Task Force (ITF) are pre
Externí odkaz:
http://arxiv.org/abs/2209.03472
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:
Heintz Ulrich
Publikováno v:
EPJ Web of Conferences, Vol 28, p 09011 (2012)
In this paper I review some recent searches for physics beyond the standard model from the CDF and D0 experiments at the Fermilab Tevatron collider based on an integrated luminosity of 5 to 7 fb−1 from pp¯ $par p$ collisions at 1.96 TeV. I present
Externí odkaz:
https://doaj.org/article/7264cecbb50f4d618f127dc55722be1e
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:
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
Autor:
Heintz, Ulrich, Bortoletto, Daniela, Hohlmann, Marcus, LeCompte, Thomas, Lipton, Ron, Narain, Meenakshi, White, Andrew
The Instrumentation Frontier was set up as a part of the Snowmass 2013 Community Summer Study to examine the instrumentation R&D needed to support particle physics research over the coming decade. This report summarizes the findings of the Energy Fro
Externí odkaz:
http://arxiv.org/abs/1309.0162