Pith. sign in

REVIEW 4 cited by

Per-Object Systematics using Deep-Learned Calibration

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 2003.11099 v2 pith:QC2UMS3S submitted 2020-03-24 hep-ph

Gregor Kasieczka , Michel Luchmann , Florian Otterpohl , Tilman Plehn This is my paper · ORCID
classification hep-ph
keywords networksuncertaintiesbayesiancalibrationsystematicanalyzebarsboosted
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertainties on the momenta of boosted top quarks forming fat jets. Next, we propose a novel calibration procedure by training on labels and their error bars. Again, the network cleanly separates the different uncertainties. As a technical side effect, we show how Bayesian networks can be extended to describe non-Gaussian features.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Neural Control Variates at LO and NLO

    hep-ph 2026-07 accept novelty 7.0 of 10

    Signed neural control variates from normalizing flows, combined with neural importance sampling, reduce weight ranges and negative weights for LO and NLO phase-space integration and event generation.

  2. Agentic Re-Casting using Agentic Re-Simulations

    hep-ph 2026-07 conditional novelty 6.0 of 10

    An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.

  3. Forecasting Generative Amplification

    hep-ph 2025-09 conditional novelty 6.0 of 10

    A KS-test-based differential method and a Bayesian averaging method estimate generative amplification without holdout datasets and find amplification in selected LHC phase-space regions.

  4. Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties

    hep-ph 2025-08 conditional novelty 6.0 of 10

    SAGE, a dual-branch GNN trained under nuisance fluctuations, estimates the Higgs signal strength with near-nominal coverage (0.662-0.683) but wider intervals than the top FAIR-HUC leaderboard methods.

Pith tools