5 papers
Machine Learning is Good for Physics - and Vice Versa
Michael Krämer, Tilman Plehn
Scientific AI is rapidly transforming fundamental physics research and challenging defining aspects of the fundamental physics methodology. We discuss opportunities and dangers of…
Toward a Comprehensive Exploration of Flavored Dark Matter Models
Benedetta Belfatto, Monika Blanke, Jan Heisig +3
We present a comprehensive framework for the study of flavored dark matter models, combining relic density calculations with direct and indirect detection limits, collider constrai…
SECRET: Stochasticity Emulator for Cosmic Ray Electrons
Nikolas Frediani, Michael Krämer, Philipp Mertsch +1
The spectrum of cosmic-ray electrons depends sensitively on the history and spatial distribution of nearby sources. Given our limited observational handle on cosmic-ray sources, an…
Semi-visible jets, energy-based models, and self-supervision
Luigi Favaro, Michael Krämer, Tanmoy Modak +2
We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approx…
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
Kristian G. Barman, Sascha Caron, Emily Sullivan +19
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs).…