Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
CP-analyses with symbolic regression
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CMBolic supplies analytic emulators for CMB lensing spectra achieving 0.27-0.32% mean fractional error, validated against CLASS on ACT DR6 and Planck lensing data.
AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
citing papers explorer
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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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CMBolic: Symbolic emulators for the Cosmic Microwave Background. I. Lensing
CMBolic supplies analytic emulators for CMB lensing spectra achieving 0.27-0.32% mean fractional error, validated against CLASS on ACT DR6 and Planck lensing data.
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AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements
AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.