Pith. sign in

REVIEW 2 cited by

Interpretable machine learning in Physics

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 2203.08021 v3 pith:NRBIFW2M submitted 2022-03-11 hep-ph cs.LG

Christophe Grojean , Ayan Paul , Zhuoni Qian , Inga Strümke This is my paper · ORCID
classification hep-phcs.LG
keywords addingbringingclaritycomplexcorrelationscreatesdegreedynamics
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Step Toward Interpretability: Smearing the Likelihood

    hep-ph 2025-01 conditional novelty 6.0 of 10

    Smearing the likelihood over an energy metric reveals the physical scales used by a jet classifier, and the needed smearing radius follows a power-law scaling with dataset size.

  2. Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis

    hep-ph 2026-08 reject novelty 5.0 of 10

    Within the Inert Doublet Model, machine-learning selection could make charged Higgs pair production at a 10-14 TeV muon collider a 5-sigma probe of dark matter for several benchmark points.

Pith tools