Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jawahar, Pratik"'
Synergies between MAchine learning, Real-Time analysis and Hybrid architectures for efficient Event Processing and decision-making (SMARTHEP) is a European Training Network, training a new generation of Early Stage Researchers (ESRs) to advance real-
Externí odkaz:
http://arxiv.org/abs/2401.13484
Autor:
Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, Yeh, Shu-Wei
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient w
Externí odkaz:
http://arxiv.org/abs/2306.08106
Autor:
Bengtsson, Fritjof, Doglioni, Caterina, Ekman, Per Alexander, Gallén, Axel, Jawahar, Pratik, Orucevic-Alagic, Alma, Santasmasas, Marta Camps, Skidmore, Nicola, Woolland, Oliver
Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific di
Externí odkaz:
http://arxiv.org/abs/2305.02283
Autor:
Jawahar, Pratik, Aarrestad, Thea, Chernyavskaya, Nadezda, Pierini, Maurizio, Wozniak, Kinga A., Ngadiuba, Jennifer, Duarte, Javier, Tsan, Steven
Publikováno v:
Front. Big Data 5, 803685 (2022)
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show
Externí odkaz:
http://arxiv.org/abs/2110.08508
Autor:
Bengtsson Folkesson Fritjof, Doglioni Caterina, Ekman Per Alexander, Gallén Axel, Jawahar Pratik, Camps Santasmasas Marta, Skidmore Nicola
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09023 (2024)
A common and growing issue in scientific research and industry is that of storing and sharing ever-increasing datasets. In this paper we document the development and applications of Baler - a Machine Learning based tool for tailored compression of da
Externí odkaz:
https://doaj.org/article/deceb653a21646909c114457c9cf8635
Autor:
Aarrestad, Thea, van Beekveld, Melissa, Bona, Marcella, Boveia, Antonio, Caron, Sascha, Davies, Joe, De Simone, Andrea, Doglioni, Caterina, Duarte, Javier M., Farbin, Amir, Gupta, Honey, Hendriks, Luc, Heinrich, Lukas, Howarth, James, Jawahar, Pratik, Jueid, Adil, Lastow, Jessica, Leinweber, Adam, Mamuzic, Judita, Merényi, Erzsébet
Publikováno v:
SciPost Physics, 12 (1)
We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the La
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17c058f4e91e9471706c988695bc0a37
Akademický článek
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Autor:
Jawahar P; Experimental Physics Department, European Center for Nuclear Research (CERN), Geneva, Switzerland., Aarrestad T; Experimental Physics Department, European Center for Nuclear Research (CERN), Geneva, Switzerland., Chernyavskaya N; Experimental Physics Department, European Center for Nuclear Research (CERN), Geneva, Switzerland., Pierini M; Experimental Physics Department, European Center for Nuclear Research (CERN), Geneva, Switzerland., Wozniak KA; Experimental Physics Department, European Center for Nuclear Research (CERN), Geneva, Switzerland.; Faculty of Computer Science, University of Vienna, Vienna, Austria., Ngadiuba J; Particle Physics Division, Fermi National Accelerator Laboratory (FNAL), Batavia, IL, United States.; Lauritsen Laboratory of High Energy Physics, California Institute of Technology, Pasadena, CA, United States., Duarte J; Department of Physics, University of California, San Diego, San Diego, CA, United States., Tsan S; Department of Physics, University of California, San Diego, San Diego, CA, United States.
Publikováno v:
Frontiers in big data [Front Big Data] 2022 Feb 28; Vol. 5, pp. 803685. Date of Electronic Publication: 2022 Feb 28 (Print Publication: 2022).