Results 21 to 30 of about 1,246,469 (173)
Evaluating query languages and systems for high-energy physics data [PDF]
Proceedings of the VLDB Endowment, 2021 In the domain of high-energy physics (HEP), general-purpose query languages have found little adoption in analysis. This is surprising regarding SQL-based systems, as HEP data analysis matches SQL’s processing model well: the data is fully structured and
D. Graur +5 more
semanticscholar +1 more source
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments [PDF]
International Conference on Systems, 2023 This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider (HL-LHC).
Shruti R. Kulkarni +14 more
semanticscholar +1 more source
Physics at the LHC -- From Standard Model measurements to Searches for New Physics [PDF]
, 2012 The successful operation of the {\em Large Hadron Collider} (LHC) during the past two years allowed to explore particle interaction in a new energy regime. Measurements of important Standard Model processes like the production of high-\pt\ jets, $W$ and $
Jakobs, Karl
core +1 more source
Finetuning foundation models for joint analysis optimization in High Energy Physics [PDF]
Machine Learning: Science and TechnologyIn this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components.
M. Vigl, N. Hartman, L. Heinrich
semanticscholar +1 more source
Very High Energy Physics and Astronomy with Tau and Photon Probes
Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022), 2022 Very-high energy physics (VHEP) is the development of a higher energy frontier complementary to accelerator-based HEP to investigate interactions in space caused by fundamental particles and to study the structure and fundamental interactions of ...
M. Sasaki
semanticscholar +1 more source
Journal of Physics: Conference Series, 2022 The state acquisition system of high energy physics experimental device has very high requirements for the clock synchronization accuracy of each node. The White Rabbit (WR) distributed synchronous timing technology can realize multi node sub nanosecond ...
Zhanfei Yang +3 more
semanticscholar +1 more source
Quantum machine learning in high energy physics [PDF]
Machine Learning: Science and Technology, 2020 Machine learning has been used in high energy physics (HEP) for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable ...
W. Guan +6 more
semanticscholar +1 more source
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis [PDF]
Physical Review Research, 2020 This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment.
Samuel Yen-Chi Chen +4 more
semanticscholar +1 more source
Journal of High Energy Physics, 2021 We provide a prescription called ThickBrick to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high energy physics (HEP) experiments, as quantified by any of
Konstantin T. Matchev +1 more
semanticscholar +2 more sources
Sizes and Distances in High Energy Physics [PDF]
, 2016 This is a critical discussion of physical relevance of some space-time characteristics which are in current use in high energy physics.
arxiv +1 more source