Conveners
Mixed contributions
- Gregor Kasieczka (Hamburg University (DE))
Mixed contributions
- Anja Butter (Centre National de la Recherche Scientifique (FR))
Mixed contributions
- Corentin Allaire (IJCLab, Université Paris-Saclay, CNRS/IN2P3)
Mixed contributions
- There are no conveners in this block
How can one fully harness the power of physics encoded in relativistic $N$-body phase space? Topologically, phase space is isomorphic to the product space of a simplex and a hypersphere and can be equipped with explicit coordinates and a Riemannian metric. This natural structure that scaffolds the space on which all collider physics events live opens up new directions for machine learning...
Quantum Generative Models are emerging as a promising tool for modelling complex physical phenomena. In this work, we explore the application of Quantum Boltzmann Machines and Quantum Generative Adversarial Networks to the intricate task of jet substructure modelling in high-energy physics. Specifically, we use these quantum frameworks to model the kinematics and corrections of the leading...
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 is evaluated on two benchmark datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex...
The Fair Universe project is organising the HiggsML Uncertainty Challenge, which has been running from Sep 2024 to 14th March 2025. It is a NeurIPS 2024 competition.
This HEP and Machine Learning competition is the first to strongly emphasise uncertainties: mastering uncertainties in the input training dataset and outputting credible confidence intervals.
The context is the measurement...
A calibration of the ATLAS flavor-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the $b$-, $c$-, and light flavor classification probabilities from jet tagging algorithms in simulation are derived for $b$-jets using $t\bar t \to b \bar b e \mu \nu \nu$ events. After application of the derived...
I report the final results of the Fast Calorimeter Challenge 2022: 23 collaborations submitted 59 samples across all 4 datasets. I will show how these rank regarding various metrics judging shower quality, generation time, and other properties. From these results, I present the current, state-of-the-art, Pareto fronts for using deep generative models on high-dimensional datasets in high-energy...
The high-luminosity era of the LHC will pose unprecedented challenges to the detectors. To meet these challenges, the CMS detector will undergo several upgrades, including the replacement the current endcap calorimeters with a novel High-Granularity Calorimeter (HGCAL). To make optimal use of this innovative detector, novel algorithms have to be invented. A dedicated reconstruction framework,...
Weakly supervised anomaly detection has been shown to have great potential for improving traditional resonance searches. We demonstrate that weak supervision offers a unique opportunity to turn a resonance search into a simple cut-and-count experiment, where the potential problem of background sculpting in a traditional bump hunt is absent. Moreover, the cut-and-count setting allows working...
