Pith. sign in

REVIEW 7 cited by

Comparison of unfolding methods using RooFitUnfold

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

arxiv 1910.14654 v2 pith:CPCZNXI7 submitted 2019-10-31 physics.data-an hep-ex

Comparison of unfolding methods using RooFitUnfold

classification physics.data-an hep-ex
keywords methodsunfoldingcommonroofitunfoldinterfacepackageperformanceproblems
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

In this paper we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson-Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, Bin-by-bin and inversion methods on several example problems.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

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

  1. Probing jet evolution with charged energy correlators in small systems

    nucl-ex 2026-06 unverdicted novelty 7.0

    Charged energy correlators measured in jets at the LHC show like-sign pairs sensitive to parton shower models and unlike-sign pairs sensitive to hadronization, with charge-independent cold nuclear matter effects in p-...

  2. Reweighting Adversarial Networks for Unbinned Unfolding

    hep-ph 2026-06 unverdicted novelty 7.0

    RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.

  3. Measurements of jet quenching with semi-inclusive hadron-jet correlations in Ru+Ru and Zr+Zr collisions at $\sqrt{s_\mathrm{NN}}=200$ GeV

    nucl-ex 2026-05 conditional novelty 6.0

    First semi-inclusive hadron-jet measurement in Ru+Ru and Zr+Zr at 200 GeV finds recoil-jet suppression and intra-jet broadening in central relative to peripheral collisions.

  4. Explicit or Implicit? Encoding Physics at the Precision Frontier

    hep-ph 2026-03 conditional novelty 6.0

    On three precision classification tasks — reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection — a Lorentz-equivariant transformer and a pretrained foundation model perform...

  5. Measurements of jet quenching with semi-inclusive hadron-jet correlations in Ru+Ru and Zr+Zr collisions at $\sqrt{s_\mathrm{NN}}=200$ GeV

    nucl-ex 2026-05 unverdicted novelty 5.0

    Suppression of recoil jet yields and intra-jet broadening is observed in central Ru+Ru and Zr+Zr collisions, indicating medium-induced partonic energy loss.

  6. Measurement of $\pi^0$ Production in $\bar{\nu}_{\mu}$ Charged-Current Interactions in the NOvA Near Detector

    hep-ex 2025-11 unverdicted novelty 5.0

    The most precise measurement to date of antineutrino-induced neutral pion production shows agreement with the GENIE model but indicates that other models underestimate the cross section in the Delta(1232) resonance region.

  7. Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum

    astro-ph.CO 2026-06 unverdicted novelty 4.0

    A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.