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Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure

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arxiv 1603.00027 v3 pith:GPSHNSWT submitted 2016-02-29 hep-ph hep-ex

James Dolen , Philip Harris , Simone Marzani , Salvatore Rappoccio , Nhan Tran This is my paper · ORCID
classification hep-phhep-ex
keywords observablescorrelationsexistingsubstructureadvantagesbackgroundbeyondconsiderations
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abstract

We explore the scale-dependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentz-boosted objects. Modified observables are designed to remove correlations from existing theoretically well-understood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in $W$ jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies.

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Cited by 4 Pith papers

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

  1. Measurement of the jet mass in hadronic decays of boosted W bosons at 13 TeV and extraction of the W boson mass

    hep-ex 2026-03 accept novelty 7.0 of 10

    Unfolded double-differential W+jets cross section versus jet p_T and soft-drop mass yields m_W = 80.83 ± 0.55 GeV, the most precise all-jets extraction at a hadron collider.

  2. 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.

  3. Mass Agnostic Jet Taggers

    hep-ph 2019-08 conditional novelty 6.0 of 10

    A systematic comparison shows that data-augmentation jet taggers (planing and PCA scaling) achieve background-preserving performance similar to adversarial networks and uBoost, with much lower training cost.

  4. Exploring the Space of Jets with CMS Open Data

    hep-ph 2019-08 accept novelty 6.0 of 10

    The authors apply the energy mover's distance to 1.69 million jets from CMS open data and show that track-based jet studies, including visualizations and anomaly scoring, work on real collider data.

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