Uncertainty quantification for neural networks in particle physics - Anja BUTTER

Item: Uncertainty quantification for neural networks in particle physics - Anja BUTTER
Rating: 4
Author: PostSingularity Institute

by Anja Butter

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQNNeural networks and fuzzy systems

🎙 Anja Butter 👥 5K 📅 October 9, 2025 ⏱ 63 min 👁 101 📄 expert opinion 🧭 2026-08-16

Available in: English (current) Français

Keywords

uncertaintyneural networksparticle physicscalibrationBayesian

Summary

Anja Butter presents an overview of uncertainty quantification techniques for neural networks used in particle physics. She begins by clarifying terminology, distinguishing between statistical and systematic uncertainties in physics versus aleatoric and epistemic uncertainties in machine learning. She emphasizes the importance of calibrated uncertainties, where predicted intervals match actual coverage. The talk covers three main applications: amplitude regression, event generation, and unfolding. For amplitude regression, she discusses heteroscedastic loss, Bayesian neural networks, and repulsive ensembles, showing how they recover known noise and achieve calibrated uncertainties. She highlights the trade-off between statistical and systematic uncertainties and the need for large datasets and appropriate network architectures. For event generation, she explains the role of Monte Carlo techniques and the challenges of integrating neural networks into the simulation chain. Finally, she touches on unfolding as an inverse problem, where bias must be overcome. Throughout, she stresses the importance of communication between physicists and machine learning experts to develop a common language for uncertainty.

160 words

Critical Evaluation

Value of the Information & Strength of the Argument

The talk provides valuable insights into the practical application of uncertainty quantification in high-energy physics. Butter presents concrete examples and results from her group’s research, demonstrating the effectiveness of various methods. She argues for the importance of calibrated uncertainties over mere accuracy and precision, and supports her claims with empirical evidence. The argumentation is solid, with clear explanations of the methods and their limitations.

Scientific Rigor, Source Quality, Title Accuracy

The talk is scientifically rigorous, referencing multiple papers from her group and collaborators. She clearly distinguishes between established results and ongoing work. The title accurately reflects the content, and the presentation is well-structured. The sources cited are relevant and credible, though specific URLs are not provided in the description.

128 words

Title / Content Match

The title accurately reflects the content, focusing on uncertainty quantification for neural networks applied to particle physics.

Quality & Reliability

8/10

Talk by a recognized researcher in the field, presenting methods and results from peer-reviewed papers, with clear explanations and references to specific works.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Contribution & Novelties

The talk synthesizes recent advances in uncertainty quantification for neural networks in particle physics, highlighting practical challenges and solutions. It emphasizes the need for calibrated uncertainties and presents methods like heteroscedastic loss, Bayesian networks, and repulsive ensembles. The discussion on the interplay between statistical and systematic uncertainties is particularly insightful.

Pour aller plus loin :

Bayesian neural networks — Overview of Bayesian networks, relevant to the discussed methods.
Uncertainty quantification — General concepts of uncertainty quantification.
Monte Carlo method — Foundational for event generation in particle physics.

87 words

Radar Profile

The radar profile shows high scores in information quantity, quality, and reliability, with a slightly lower technical level, indicating a talk that is informative and credible but accessible to a broader audience.

Reliability 8/10