REVIEW 9 cited by
The Landscape of Unfolding with Machine Learning
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
read the original abstract
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.
Forward citations
Cited by 9 Pith papers
-
Neural Control Variates at LO and NLO
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.
-
Agentic Re-Casting using Agentic Re-Simulations
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.
-
Explicit or Implicit? Encoding Physics at the Precision Frontier
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...
-
Optimization-based Unfolding in High-Energy Physics
Unfolding is recast as a QUBO optimization problem solvable on quantum annealers, implemented in open-source QUnfold and benchmarked competitively against RooUnfold methods on synthetic data.
-
Neural Posterior Unfolding
A normalizing-flow-based Bayesian unfolding method (NPU) plus a modern Python implementation of Fully Bayesian Unfolding (FBU) are introduced and validated on Gaussian and simulated LHC jet data.
-
Simulation-Prior Independent Neural Unfolding Procedure
SPINUP is a neural-unfolding method that fits a parton-level generative model directly to detector-level data through a learned forward simulator, aiming to remove the simulation-prior bias.
-
High-Dimensional Unfolding in Large Backgrounds
OmniFold-HI, an ML unfolding algorithm that handles large backgrounds and high-dimensional auxiliary observables, is derived, shown equivalent to iterative Bayesian unfolding, and demonstrated to improve jet-substruct...
-
Generator Based Inference (GBI)
Generator Based Inference uses data-derived background generators to turn resonant anomaly detection into parameter estimation, reaching 0.1 sigma signal sensitivity on the LHCO benchmark.
-
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
A structured introduction to ML-based simulation-based inference, contrasting Bayesian and frequentist frameworks and covering parameter inference, unfolding, and validation.
Discussion (0). Sign in to comment.