REVIEW 4 cited by
Accelerating Resonance Searches via Signature-Oriented Pre-training
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.
Forward citations
Cited by 4 Pith papers
-
JetCoRD: Reliability-Aware Cross-Experiment Distillation of Jet Taggers with Adaptive Corrective Representation
A tiny 82k-parameter student jointly distills two experiments' jet taggers and uses a per-sample reliability gate to beat the teachers at several flavor-tagging working points.
-
Search for Higgs boson production at high transverse momentum in the WW decay channel in proton-proton collisions at $\sqrt{s}$ = 13 TeV
A first dedicated search for highly Lorentz-boosted H->WW decays at the LHC finds mu = -0.19 +0.48/-0.46, consistent with no signal above background.
-
Enhancing generalization in high energy physics using white-box adversarial attacks
Adversarial training reduces loss sharpness and improves cross-Monte-Carlo generalization for Higgs-jet classifiers, with projected gradient descent giving the largest gains.
-
Improving the performance of weak supervision searches using data augmentation
Physics-inspired data augmentation halves the signal data requirement for CWoLa weak supervision searches, cutting the practical sensitivity threshold from roughly 6 sigma to roughly 3 sigma.
Discussion (0). Continue with ORCID to comment.