ML4Jets2025

Session

Unfolding & Inference

20 Aug 2025, 14:00

CHEN100 (California Institute of Technology)

Conveners

Unfolding & Inference

• Eilam Gross (Weizmann Institute of Science (IL))

Unfolding & Inference

• Aishik Ghosh (University of California Irvine (US))

13. Data-Driven High Dimensional Statistical Inference with Generative Models

Oz Amram (Fermi National Accelerator Lab. (US))

20/08/2025, 14:00

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds.
Since the rise of machine learning in the last decade, it is now standard for analyses to employ multivariate classifiers trained on simulation to distinguish signal and background. Such classifiers significantly increase the statistical power...

30. A High-Dimensional, Unbinned Standard Model Measurement with the ATLAS Experiment

Mariel Pettee (Lawrence Berkeley National Lab. (US))

20/08/2025, 14:20

Traditional approaches to precise Standard Model (SM) measurements of fundamental particles at the LHC generally restrict the format of these results to just one or two properties at a time in predetermined histogram bins. The ATLAS Experiment recently published such a measurement in a notable new format for LHC experiments: high-dimensional and unbinned datasets that can be used for a wide...

31. Higgs Signal Strength Estimation with a Dual-Branch GNN under Systematic Uncertainties [remote]

Daohan Wang (HEPHY ÖAW)

20/08/2025, 14:40

We present a graph-neural-network (GNN) framework that delivers precise extractions of the Higgs-boson signal strength while coherently propagating systematic uncertainties. The architecture combines a deterministic branch, which processes kinematic features immune to nuisance parameters, with an uncertainty-aware branch that ingests systematics-modulated inputs and applies gated-attention...

49. wifi Ensembles for Simulation-Based Inference with Systematic Uncertainties

Sean Benevedes (Massachusetts Institute of Technology)

20/08/2025, 15:00

Neural ratio estimation provides a means of performing frequentist simulation-based inference (SBI), but uncertainties on the estimated ratios of probability densities must be taken into account in order to yield reliable confidence intervals on the inferred parameters. We examine the role of these uncertainties on estimated density ratios in the context of the FAIR Universe HiggsML...

54. Forward folding versus unfolding in the age of ML

Kevin Thomas Greif (University of California Irvine (US))

21/08/2025, 14:10

In most measurements in particle physics, correcting for the imperfect resolution of the detector used to observe the events is a necessary step to extract a parameter of interest. This can be done through forward folding, in which the theoretical predictions are adjusted by running a simulation of the detector, or through unfolding, in which detector effects are removed from the experimental...

57. Discriminative versus Generative Approaches to Simulation-based Inference

Sascha Diefenbacher (Lawrence Berkeley National Lab. (US))

21/08/2025, 14:30

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through...

60. On focusing statistical power for searches and measurements in particle physics

James Carzon (Carnegie Mellon University)

21/08/2025, 14:50

Particle physics experiments rely on the (generalised) likelihood ratio test (LRT) for searches and measurements. This is not guaranteed to be optimal for composite hypothesis tests, as the Neyman-Pearson lemma pertains only to simple hypothesis tests. An improvement in the core statistical testing methodology would have widespread ramifications across experiments. We discuss an alternate test...

75. Generator Based Inference (GBI)

Alkaid Cheng (University of Wisconsin Madison (US))

21/08/2025, 15:10

Statistical inference in physics is often based on samples (from a `forward model') that emulate experimental data and depend on parameters of the underlying theory. Modern machine learning has supercharged this workflow to enable high-dimensional and unbinned analyses to utilize much more information than ever before. We propose a general framework for describing the integration of machine...