A numerical polology sampler maps ghost- and tachyon-free regions of bosonic EFT coupling spaces and finds new particle branches, including a spin-one mode in general rank-2 tensor theories.
AbdusSalam et al., Simple and statistically sound recommendations for analysing physical theories, Rept
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Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.
Precision study of dark sector phase transitions finds PTA-favored parameters near EFT breakdown with disfavored GW signals after higher-order corrections.
BSMArt version 2 adds new scanning algorithms including Affine MC, MLScanner, and CMA-ES variants to simplify and accelerate parameter space exploration in new physics models, demonstrated on soft lepton excess searches at the LHC.
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
citing papers explorer
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Numerical polology: towards next-generation model-building for cosmology
A numerical polology sampler maps ghost- and tachyon-free regions of bosonic EFT coupling spaces and finds new particle branches, including a spin-one mode in general rank-2 tensor theories.
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Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
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Bring the noise: exact inference from noisy simulations in collider physics
Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.
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A critical look at low-scale cosmological phase transitions in the PTA era
Precision study of dark sector phase transitions finds PTA-favored parameters near EFT breakdown with disfavored GW signals after higher-order corrections.
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BSMArt 2: simpler and faster parameter space scans
BSMArt version 2 adds new scanning algorithms including Affine MC, MLScanner, and CMA-ES variants to simplify and accelerate parameter space exploration in new physics models, demonstrated on soft lepton excess searches at the LHC.
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Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.