Zobrazeno 1 - 10
of 500
pro vyhledávání: '"Gregor, Kasieczka"'
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 84, Iss 9, Pp 1-10 (2024)
Abstract In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely o
Externí odkaz:
https://doaj.org/article/252ff79fd37144de9e72d5e535a4fcbd
Autor:
Tobias Golling, Gregor Kasieczka, Claudius Krause, Radha Mastandrea, Benjamin Nachman, John Andrew Raine, Debajyoti Sengupta, David Shih, Manuel Sommerhalder
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 84, Iss 3, Pp 1-21 (2024)
Abstract Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly
Externí odkaz:
https://doaj.org/article/7bb2652dadb34dc8a8cda8fe8534fcdf
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035031 (2024)
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a
Externí odkaz:
https://doaj.org/article/6e78acce49e84723950d934705722f10
Autor:
Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper, Thea K Årrestad
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035017 (2024)
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the inpu
Externí odkaz:
https://doaj.org/article/4541642a69904f4eb05a8f8a4651282e
Publikováno v:
SciPost Physics, Vol 15, Iss 4, p 130 (2023)
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far th
Externí odkaz:
https://doaj.org/article/7c6869bfed924b0181eb48bc27e01095
Publikováno v:
Journal of High Energy Physics, Vol 2021, Iss 12, Pp 1-29 (2021)
Abstract Confining dark sectors with pseudo-conformal dynamics produce SUEPs, or Soft Unclustered Energy Patterns, at colliders: isotropic dark hadrons with soft and democratic energies. We target the experimental nightmare scenario, SUEPs in exotic
Externí odkaz:
https://doaj.org/article/0b89a0cb5e2d4c0da1edf0a017661d06
Publikováno v:
SciPost Physics, Vol 14, Iss 4, p 079 (2023)
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applic
Externí odkaz:
https://doaj.org/article/3479be73aa7242cb9b86f165b65d5d9a
Publikováno v:
Journal of High Energy Physics, Vol 2020, Iss 9, Pp 1-32 (2020)
Abstract The past few years have seen a rapid development of machine-learning algorithms. While surely augmenting performance, these complex tools are often treated as black-boxes and may impair our understanding of the physical processes under study
Externí odkaz:
https://doaj.org/article/9be79886536543a89f18e27064860f09
Autor:
Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger, Peter McKeown, Lennart Rustige
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035044 (2023)
The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accura
Externí odkaz:
https://doaj.org/article/4cd6694d671545718317a9b070e3b528
Publikováno v:
SciPost Physics, Vol 13, Iss 4, p 087 (2022)
The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to l
Externí odkaz:
https://doaj.org/article/87a212624a3a40f4bcca175803259cf7