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Machine learning and the physical sciences
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Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences. This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.
Forward citations
Cited by 8 Pith papers
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Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
Simplex demixing recovers T mutually irreducible jet-flavor topics from M mixed samples via the (T−1)-simplex geometry of a multi-category classifier, demonstrated on Pythia dijets.
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NNStar: An end-to-end AI agent for nuclear matter and neutron star physics
NNStar packages the RMF-to-neutron-star pipeline as a portable LLM-agent skill, validated on TM1/NL3/FSU-δ6.7 and demonstrated by an autonomous σ6-extended TM1 fit.
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Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Weighting furthest-point sampling distances by force norms yields training sets that reduce kernel-ridge energy errors and error variance on MD17 molecules.
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Solving two and three-body systems with deep neural networks
A deep neural network with energy as the loss function solves the two-body deuteron and a three-channel triton model, matching analytic and Gaussian-expansion benchmarks.
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Shedding Light on Dark Matter at the LHC with Machine Learning
A machine-learned LHC analysis projects 5-sigma sensitivity to singlino-dominated NMSSM dark matter via radiative higgsino decays to photons, covering higgsino masses up to 225 GeV.
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Toward Supporting Narrative-Driven Data Exploration: Barriers and Design Opportunities
Clustering particles by mass, spin, lifetime and decay modes with conventional tools reproduces known Standard Model groupings, but the dataset and algorithm choices quietly encode the theory being 'rediscovered'.
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Vector Boson Fusion Signatures of Superheavy Majorana Neutrinos at Muon Colliders
Future muon colliders could probe heavy Majorana neutrino masses via t-channel vector boson fusion, with projected exclusions in the (mass, mixing) plane from cut-based and BDT analyses.
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How to set up your first machine learning project in astronomy
A review that collects best-practice recommendations for designing, validating, and communicating astronomy machine learning projects, with no new empirical results.
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