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Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows

https://doi.org/10.1103/PhysRevLett.133.011402

Shih, David; Freytsis, Marat; Taylor, Stephen_R; Dror, Jeff_A; Smyth, Nolan (July 2024, Physical Review Letters)
Machine learning in the search for new fundamental physics

https://doi.org/10.1038/s42254-022-00455-1

Karagiorgi, Georgia; Kasieczka, Gregor; Kravitz, Scott; Nachman, Benjamin; Shih, David (June 2022, Nature Reviews Physics)

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Machine learning and LHC event generation

https://doi.org/10.21468/SciPostPhys.14.4.079

Butter, Anja; Plehn, Tilman; Schumann, Steffen; Badger, Simon; Caron, Sascha; Cranmer, Kyle; Di_Bello, Francesco Armando; Dreyer, Etienne; Forte, Stefano; Ganguly, Sanmay; et al (January 2023, SciPost Physics)

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 applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
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The Machine Learning landscape of top taggers

https://doi.org/10.21468/SciPostPhys.7.1.014

Kasieczka, Gregor; Plehn, Tilman; Butter, Anja; Cranmer, Kyle; Debnath, Dipsikha; Dillon, Barry M.; Fairbairn, Malcolm; Faroughy, Darius A.; Fedorko, Wojtek; Gay, Christophe; et al (January 2019, SciPost Physics)

Based on the established task of identifying boosted, hadronicallydecaying top quarks, we compare a wide range of modern machine learningapproaches. Unlike most established methods they rely on low-levelinput, for instance calorimeter output. While their networkarchitectures are vastly different, their performance is comparativelysimilar. In general, we find that these new approaches are extremelypowerful and great fun.
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