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Physics opportunities with a future soft particle tracker in LHCb
Cesar Luis Da Silva
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Quantum sensing for particle physics
Nature Reviews Physics, 2023Quantum sensing uses properties of quantum mechanics to go beyond what is possible with traditional measurement techniques. In particle physics, key problems in which quantum sensing can have a vital role include neutrino properties, tests of fundamental
S. Bass, M. Doser
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Lessons on interpretable machine learning from particle physics
Nature Reviews Physics, 2022Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust.
C. Grojean +3 more
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Introduction to Elementary Particle Physics
The third edition of this successful textbook has been redesigned to reflect the progress of the field in the last decade, including the latest studies of the Higgs boson, quark–gluon plasma, progress in flavour and neutrino physics and the discovery of ...
A. Bettini
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MadMiner: Machine Learning-Based Inference for Particle Physics
Computing and Software for Big Science, 2019Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods.
J. Brehmer +3 more
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FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing
Computing and Software for Big Science, 2019Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable ...
Javier Mauricio Duarte +22 more
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The Geometer of Particle Physics
Scientific American, 2006This article profiles French mathematician Alain Connes, focusing on his contribution to mathematics with the development of "noncommutative geometry." This form of geometry places emphasis on the order of events in a sequence, rather than the location of objects. Connes hopes that further research in this field will aid in modelling spacetime.
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Computing and Software for Big Science, 2017
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images—2D ...
Luke de Oliveira +2 more
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We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images—2D ...
Luke de Oliveira +2 more
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1972
Time and space are shown to have very different properties, both from each other and from that of the macroscopic world, when they are investigated at distances of the size of elementary particles; even the definition of time is in some doubt in such a case. Space reflection is violated maximally, but time reversal is not.
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Time and space are shown to have very different properties, both from each other and from that of the macroscopic world, when they are investigated at distances of the size of elementary particles; even the definition of time is in some doubt in such a case. Space reflection is violated maximally, but time reversal is not.
openaire +2 more sources