High-dimensional and permutation invariant anomaly detection [PDF]
SciPost Physics, 2023 Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as
Vinicius Mikuni, Benjamin Nachman
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PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics [PDF]
arXiv.org, 2022 Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools.
Alexander Bogatskiy +3 more
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Model-Independent Searches for New Physics in Multi-Body Invariant Masses [PDF]
Universe, 2021 Model-independent searches for physics beyond the Standard Model typically focus on invariant masses of two objects (jets, leptons or photons). In this study, we explore opportunities for similar model-agnostic searches in multi-body invariant masses. In
Sergei Chekanov +4 more
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Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models [PDF]
Physical Review Research, 2023 Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics. We develop a general approach to constructing gauge-invariant or anyonic-symmetric autoregressive neural networks, including a wide range of ...
Di Luo +5 more
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Configurational temperature in active matter. I. Lines of invariant physics in the phase diagram of the Ornstein-Uhlenbeck model [PDF]
Physical Review E, 2022 This paper shows that the configurational temperature of liquid-state theory, T_{conf}, defines an energy scale, which can be used for adjusting model parameters of active Ornstein-Uhlenbeck particle (AOUP) models in order to achieve approximately ...
Shibu Saw +2 more
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Invariant Set Theory: Violating Measurement Independence without Fine Tuning, Conspiracy, Constraints on Free Will or Retrocausality [PDF]
Electronic Proceedings in Theoretical Computer Science, 2015 Invariant Set (IS) theory is a locally causal ontic theory of physics based on the Cosmological Invariant Set postulate that the universe U can be considered a deterministic dynamical system evolving precisely on a (suitably constructed) fractal ...
Tim Palmer
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Parton distributions and new physics searches: the Drell–Yan forward–backward asymmetry as a case study [PDF]
European Physical Journal C: Particles and Fields, 2022 We discuss the sensitivity of theoretical predictions of observables used in searches for new physics to parton distributions (PDFs) at large momentum fraction x.
Richard D. Ball +6 more
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A signature invariant geometric algebra framework for spacetime physics and its applications in relativistic dynamics of a massive particle and gyroscopic precession [PDF]
Scientific Reports, 2022 A signature invariant geometric algebra framework for spacetime physics is formulated. By following the original idea of David Hestenes in the spacetime algebra of signature $$(+,-,-,-)$$ ( + , - , - , - ) , the techniques related to relative vector and ...
Bofeng Wu
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Lattice gauge tensor networks [PDF]
New Journal of Physics, 2014 We present a unified framework to describe lattice gauge theories by means of tensor networks: this framework is efficient as it exploits the high local symmetry content native to these systems by describing only the gauge invariant subspace. Compared to
Pietro Silvi +3 more
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Invariant representation of physical stability in the human brain [PDF]
eLife, 2021 Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future.
Michael A. Cohen +4 more
openaire +5 more sources