Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Ojalvo Isobel"'
Autor:
Tsoi Ho Fung, Pol Adrian Alan, Loncar Vladimir, Govorkova Ekaterina, Cranmer Miles, Dasu Sridhara, Elmer Peter, Harris Philip, Ojalvo Isobel, Pierini Maurizio
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09036 (2024)
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this cont
Externí odkaz:
https://doaj.org/article/817d76a9edcc44f6a31f1c9c7616cb98
Autor:
Pol, Adrian Alan, Govorkova, Ekaterina, Gronroos, Sonja, Chernyavskaya, Nadezda, Harris, Philip, Pierini, Maurizio, Ojalvo, Isobel, Elmer, Peter
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model size. However, the size is often a limiting factor for the deployment on
Externí odkaz:
http://arxiv.org/abs/2310.06047
Autor:
Tsoi, Ho Fung, Pol, Adrian Alan, Loncar, Vladimir, Govorkova, Ekaterina, Cranmer, Miles, Dasu, Sridhara, Elmer, Peter, Harris, Philip, Ojalvo, Isobel, Pierini, Maurizio
Publikováno v:
EPJ Web of Conferences 295, 09036 (2024)
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this cont
Externí odkaz:
http://arxiv.org/abs/2305.04099
Autor:
Narain, Meenakshi, Reina, Laura, Tricoli, Alessandro, Begel, Michael, Belloni, Alberto, Bose, Tulika, Boveia, Antonio, Dawson, Sally, Doglioni, Caterina, Freitas, Ayres, Hirschauer, James, Hoeche, Stefan, Lee, Yen-Jie, Lin, Huey-Wen, Lipeles, Elliot, Liu, Zhen, Meade, Patrick, Mukherjee, Swagato, Nadolsky, Pavel, Ojalvo, Isobel, Griso, Simone Pagan, Royon, Christophe, Schmitt, Michael, Schwienhorst, Reinhard, Shah, Nausheen, Tian, Junping, Vernieri, Caterina, Wackeroth, Doreen, Wang, Lian-Tao, Denisov, Dmitri, Gleyzer, Sergey, Onyisi, Peter, Sevilla, Manuel Franco, Titov, Maksym, Whiteson, Daniel
This report, as part of the 2021 Snowmass Process, summarizes the current status of collider physics at the Energy Frontier, the broad and exciting future prospects identified for the Energy Frontier, the challenges and needs of future experiments, a
Externí odkaz:
http://arxiv.org/abs/2211.11084
Report of the Topical Group on Higgs Physics for Snowmass 2021: The Case for Precision Higgs Physics
Autor:
Dawson, Sally, Meade, Patrick, Ojalvo, Isobel, Vernieri, Caterina, Adhikari, S., Abu-Ajamieh, F., Alberta, A., Bahl, H., Barman, R., Basso, M., Beniwal, A., Bozovi-Jelisav, I., Bright-Thonney, S., Cairo, V., Celiberto, F., Chang, S., Chen, M., Damerell, C., Davis, J., de Blas, J., Dekens, W., Duarte, J., Egana-Ugrinovic, D., Einhaus, U., Gao, Y., Goncalves, D., Gritsan, A., Haber, H., Heintz, U., Homiller, S., Hsu, S. C., Jean, D., Kawada, S., Khoda, E., Kong, K., Konstantinidis, N., Korytov, A., Kyriacou, S., Lane, S., Lewis, I. M., Li, K., Li, S., Liu, Z., Luo, J., Mandacar-Guerra, L., Mantel, C., Monroy, J., Narain, M., Orr, R., Pan, R., Papaefstathiou, A., Peskin, M., Prim, M. T., Rajec, F., Ramsey-Musolf, M., Reichert, J., Reina, L., Robens, T., Roskes, J., Ryd, A., Schwartzman, A., Scott, P., Strube, J., Dong, Su, Su, W., Sullivan, M., Tanabe, T., Tian, J., Tricoli, A., Usai, E., Vavra, J., Wang, Z., White, G., White, M., Williams, A. G., Woodcock, A., Wu, Y., Young, C., Zhang, Y., Zhu, X., Zou, R.
A future Higgs Factory will provide improved precision on measurements of Higgs couplings beyond those obtained by the LHC, and will enable a broad range of investigations across the fields of fundamental physics, including the mechanism of electrowe
Externí odkaz:
http://arxiv.org/abs/2209.07510
Autor:
Dasu, Sridhara, Nanni, Emilio A., Peskin, Michael E., Vernieri, Caterina, Barklow, Tim, Bartoldus, Rainer, Bhat, Pushpalatha C., Black, Kevin, Brau, Jim, Breidenbach, Martin, Craig, Nathaniel, Denisov, Dmitri, Gray, Lindsey, Harris, Philip C., Kagan, Michael, Liu, Zhen, Meade, Patrick, Majernik, Nathan, Nagaitsev, Sergei, Ojalvo, Isobel, Paus, Christoph, Schroeder, Carl, Schwartzman, Ariel G., Strube, Jan, Dong, Su, Tantawi, Sami, Wang, LianTao, White, Andy, Wilson, Graham W.
A program to build a lepton-collider Higgs factory, to precisely measure the couplings of the Higgs boson to other particles, followed by a higher energy run to establish the Higgs self-coupling and expand the new physics reach, is widely recognized
Externí odkaz:
http://arxiv.org/abs/2203.07646
Autor:
Elabd, Abdelrahman, Razavimaleki, Vesal, Huang, Shi-Yu, Duarte, Javier, Atkinson, Markus, DeZoort, Gage, Elmer, Peter, Hauck, Scott, Hu, Jin-Xuan, Hsu, Shih-Chieh, Lai, Bo-Cheng, Neubauer, Mark, Ojalvo, Isobel, Thais, Savannah, Trahms, Matthew
Publikováno v:
Front. Big Data 5 (2022) 828666
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase o
Externí odkaz:
http://arxiv.org/abs/2112.02048
Autor:
DeZoort, Gage, Thais, Savannah, Duarte, Javier, Razavimaleki, Vesal, Atkinson, Markus, Ojalvo, Isobel, Neubauer, Mark, Elmer, Peter
Publikováno v:
Comput. Softw. Big Sci. 5, 26 (2021)
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics. In particular, particle tracking data is naturally
Externí odkaz:
http://arxiv.org/abs/2103.16701
Autor:
Ali, Hind Al, Arkani-Hamed, Nima, Banta, Ian, Benevedes, Sean, Buttazzo, Dario, Cai, Tianji, Cheng, Junyi, Cohen, Timothy, Craig, Nathaniel, Ekhterachian, Majid, Fan, JiJi, Forslund, Matthew, Garcia, Isabel Garcia, Homiller, Samuel, Koren, Seth, Koszegi, Giacomo, Liu, Zhen, Lu, Qianshu, Lyu, Kun-Feng, Mariotti, Alberto, McCune, Amara, Meade, Patrick, Ojalvo, Isobel, Oktem, Umut, Redigolo, Diego, Reece, Matthew, Sala, Filippo, Sundrum, Raman, Sutherland, Dave, Tesi, Andrea, Trott, Timothy, Tully, Chris, Wang, Lian-Tao, Wang, Menghang
We lay out a comprehensive physics case for a future high-energy muon collider, exploring a range of collision energies (from 1 to 100 TeV) and luminosities. We highlight the advantages of such a collider over proposed alternatives. We show how one c
Externí odkaz:
http://arxiv.org/abs/2103.14043
Autor:
Heintz, Aneesh, Razavimaleki, Vesal, Duarte, Javier, DeZoort, Gage, Ojalvo, Isobel, Thais, Savannah, Atkinson, Markus, Neubauer, Mark, Gray, Lindsey, Jindariani, Sergo, Tran, Nhan, Harris, Philip, Rankin, Dylan, Aarrestad, Thea, Loncar, Vladimir, Pierini, Maurizio, Summers, Sioni, Ngadiuba, Jennifer, Liu, Mia, Kreinar, Edward, Wu, Zhenbin
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms
Externí odkaz:
http://arxiv.org/abs/2012.01563