ML4Jets2025

Session

Uncertainties & Interpretability

Aug 19, 2025, 2:00 PM

CHEN100 (California Institute of Technology)

Conveners

Uncertainties & Interpretability

• Mariel Pettee (University of Wisconsin--Madison)

19. Physics-guided Machine Learning in Cosmology [remote]

Leonora KARDUM

8/19/25, 2:00 PM

Today, many physics experiments rely on Machine Learning (ML) methods to support their data analysis pipelines. Although ML has revolutionized science, most models are still difficult to interpret and lack clarity of the process with which they calculate results and the way they utilize information from used datasets. In this work, we introduce physics-guided ML methods that keep the...

52. Fair Universe HiggsML Uncertainty Challenge: Benchmark for Uncertainty-Aware Machine Learning in High Energy Physics

Po-Wen Chang

8/19/25, 2:20 PM

Measurements and observations in particle physics fundamentally depend on one's ability to quantify their uncertainty and, thereby, their significance. Therefore, as machine learning (ML) methods become more prevalent in high energy physics, being able to determine the uncertainties of an ML method becomes more important. A wide range of possible approaches has been proposed, however, there...

55. Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties

Dr Claudius Krause (HEPHY Vienna (ÖAW))

8/19/25, 2:40 PM

We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approach incorporates known analytic dependencies on parameters of interest, including signal strengths and nuisance parameters. When these dependencies are unknown, as is frequently the case for systematic uncertainties,...

74. Tackling interpretability with physical baselines for Integrated Gradients [remote]

Jai Bardhan

8/19/25, 3:00 PM

Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these "black box" models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find that the zero-vector baseline does not...