FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Albouy et al., Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report , Eur
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A generalized parton shower for arbitrary gauge groups plus a Mamba network on Lund jet planes can distinguish dark gauge symmetries even when non-perturbative hadronization details are unknown.
Energy correlators can convert scaling violations into angular bump hunting for new physics, yielding projected competitive LHC sensitivity for a light hadrophilic Z'.
Neutrinos disintegrate into dark jets in a composite sterile sector, producing enhanced neutral-to-charged current ratios and displaced vertices that probe compositeness scales at facilities like DUNE and FCC-ee.
Dark pions stabilized by U(1) flavor symmetry in an SU(3)/SO(3) dark sector obtain the correct thermal relic density through up-scatterings to heavier mesons and dark eta decays, producing LHC signals from long-lived particle showers.
Neural-network regression reconstructs the semi-visible-jet invisible fraction r_inv more precisely than prior analytical methods in ISR-boosted photon-associated events.
Recast LHC searches yield a ~1.2 TeV lower bound on long-lived charged dark mesons and show that anomaly-driven diboson resonances can reconstruct UV parameters like dark flavor and color numbers from IR measurements.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Disentangling Dark Gauge Symmetries with Deep Learning on the Lund Jet Plane
A generalized parton shower for arbitrary gauge groups plus a Mamba network on Lund jet planes can distinguish dark gauge symmetries even when non-perturbative hadronization details are unknown.
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Bump Hunting Inside Jets with Energy Correlators
Energy correlators can convert scaling violations into angular bump hunting for new physics, yielding projected competitive LHC sensitivity for a light hadrophilic Z'.
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Probing Neutrino Compositeness with Invisible and Displaced Signals
Neutrinos disintegrate into dark jets in a composite sterile sector, producing enhanced neutral-to-charged current ratios and displaced vertices that probe compositeness scales at facilities like DUNE and FCC-ee.
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Dark Matter on a Slide
Dark pions stabilized by U(1) flavor symmetry in an SU(3)/SO(3) dark sector obtain the correct thermal relic density through up-scatterings to heavier mesons and dark eta decays, producing LHC signals from long-lived particle showers.
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Reconstructing the Invisible Fraction of Semi-visible Jets in ISR-Boosted Events via Neural Network Regression
Neural-network regression reconstructs the semi-visible-jet invisible fraction r_inv more precisely than prior analytical methods in ISR-boosted photon-associated events.
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Stopping Dark Mesons in Their Tracks with Long-Lived Particle and Resonant Signatures
Recast LHC searches yield a ~1.2 TeV lower bound on long-lived charged dark mesons and show that anomaly-driven diboson resonances can reconstruct UV parameters like dark flavor and color numbers from IR measurements.