Zobrazeno 1 - 10
of 813
pro vyhledávání: '"Maurizio Pierini"'
Autor:
Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belina von Krosigk, Shen Wang, Thomas K. Warburton
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Externí odkaz:
https://doaj.org/article/fb1f7518123f425abdb4bab7ccb196f6
Autor:
Marco Letizia, Gianvito Losapio, Marco Rando, Gaia Grosso, Andrea Wulzer, Maurizio Pierini, Marco Zanetti, Lorenzo Rosasco
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 82, Iss 10, Pp 1-16 (2022)
Abstract We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any con
Externí odkaz:
https://doaj.org/article/9eead3fe67e14b359bc63153338d8557
Autor:
Shah Rukh Qasim, Nadezda Chernyavskaya, Jan Kieseler, Kenneth Long, Oleksandr Viazlo, Maurizio Pierini, Raheel Nawaz
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 82, Iss 8, Pp 1-15 (2022)
Abstract We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a
Externí odkaz:
https://doaj.org/article/60ff6221e4bd498fb980ca4017e6f044
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 82, Iss 3, Pp 1-37 (2022)
Abstract We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of
Externí odkaz:
https://doaj.org/article/1a2e59e31cf04ec38f0f3854b6493efc
Autor:
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad, Maurizio Pierini, Kinga Anna Woźniak, Jennifer Ngadiuba
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-7 (2022)
Measurement(s) Simulations of LHC collisions in real-time processing data format Technology Type(s) PYTHIA, DELPHES, private python code Factor Type(s) phi • eta • pT
Externí odkaz:
https://doaj.org/article/b95e0e99cc1a43d8844cea16c5ad9c6b
Autor:
Florencia Canelli, Annapaola de Cosa, Luc Le Pottier, Jeremi Niedziela, Kevin Pedro, Maurizio Pierini
Publikováno v:
Journal of High Energy Physics, Vol 2022, Iss 2, Pp 1-17 (2022)
Abstract The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in proton-proton collisions could result in semivisible jets of
Externí odkaz:
https://doaj.org/article/785d3e934f6b47c599441adc6c78f19c
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 81, Iss 5, Pp 1-14 (2021)
Abstract In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detec
Externí odkaz:
https://doaj.org/article/bc8d269b19b843b8b28c84f860997996
Autor:
Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belina von Krosigk, Shen Wang, Thomas K. Warburton
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery
Externí odkaz:
https://doaj.org/article/9e1a26011d554b649834394385e92870
Autor:
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya, Maurizio Pierini, Kinga A. Wozniak, Jennifer Ngadiuba, Javier Duarte, Steven Tsan
Publikováno v:
Frontiers in Big Data, Vol 5 (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:
https://doaj.org/article/bae1cafd2de14da4a85504bf90665db5
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 81, Iss 1, Pp 1-21 (2021)
Abstract We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. It is desig
Externí odkaz:
https://doaj.org/article/0bb109204eb54063b7b4adcf997c4c80