Moment Unfolding
arXiv:2407.11284 · doi:10.1103/PhysRevD.110.116013
Abstract
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our Moment Unfolding technique uses machine learning and is inspired by Generative Adversarial Networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our Moment Unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.
16 pages, 7 figures, 1 table. v2. Added momentum fraction with SoftDrop () as an example of a non-gaussian variable. Made other minor changes to match published version
References in corpus (46)
- Array Programming with NumPy
- PYTHIA 6.4 Physics and Manual
- The anti-k_t jet clustering algorithm
- An Introduction to PYTHIA 8.2
- A Brief Introduction to PYTHIA 8.1
- FastJet user manual
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- Herwig++ Physics and Manual
- Dispelling the N^3 myth for the Kt jet-finder
- Herwig 7.0 / Herwig++ 3.0 Release Note
- Particle-flow reconstruction and global event description with the CMS detector
- Soft Drop
- Towards an understanding of jet substructure
- Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
- Jet Substructure at the Large Hadron Collider: Experimental Review
- Learning New Physics from a Machine
- Sudakov Safety in Perturbative QCD
- OmniFold: A Method to Simultaneously Unfold All Observables
- TUnfold: an algorithm for correcting migration effects in high energy physics
- Jet Charge at the LHC
- Studies of Hadronic Event Structure in e+e- Annihilation from 30 GeV to 209 GeV with the L3 Detector
- Measurement of event shape distributions and moments in e+e- -> hadrons at 91-209 GeV and a determination of alpha_s
- A study of the energy evolution of event shape distributions and their means with the DELPHI detector at LEP
- Calculating the Charge of a Jet
- Precision Thrust Cumulant Moments at N^3LL
- Invertible Networks or Partons to Detector and Back Again
- How to GAN away Detector Effects
- Measurement of jet charge in dijet events from sqrt(s)=8 TeV pp collisions with the ATLAS detector
- Jet Charge: A Flavor Prism for Spin Asymmetries at the EIC
- Presenting Unbinned Differential Cross Section Results
- Learning to Simulate High Energy Particle Collisions from Unlabeled Data
- E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
- Measurements of jet charge with dijet events in pp collisions at sqrt(s) = 8 TeV
- The measurement of alpha_s from event shapes with the DELPHI detector at the highest LEP energies
- Jet charge modification in dense QCD matter
- Measurement of quark- and gluon-like jet fractions using jet charge in PbPb and pp collisions at 5.02 TeV
- Disentangling Quarks and Gluons with CMS Open Data
- Improving Generative Model-based Unfolding with Schrödinger Bridges
- Study of moments of event shapes and a determination of \as using \epem annihilation data from \Jade
- Multidifferential study of identified charged hadron distributions in -tagged jets in proton-proton collisions at 13 TeV
- Unbinned Deep Learning Jet Substructure Measurement in High ep collisions at HERA
- Dynamic Jet Charge
- Unbinned Profiled Unfolding
- Towards a data-driven model of hadronization using normalizing flows
- Robust and Provably Monotonic Networks
- Safe but Incalculable: Energy-weighting is not all you need