5 papers
Agentic Re-Casting using Agentic Re-Simulations
Sascha Diefenbacher, Tilman Plehn, Daniel Schiller +1
Analysis re-casting at the LHC is highly standardized and nevertheless requires resources, time, and physics input. Building on the new MadAgents.v3, we show how a global SFitter a…
One Generator, Any Process: LLM-Conditioning for the LHC
Henning Bahl, Tilman Plehn, Daniel Schiller +1
Neural network training for LHC event generation should, ideally, benefit from common high-level patterns in different processes. We propose novel conditioning schemes for continuo…
MadAgents
Tilman Plehn, Daniel Schiller, Nikita Schmal
We uncover an effective and communicative set of agents working with MadGraph. Agentic installation, learning-by-doing training, and user support provide easy access to state-of-th…
Large Language Models -- the Future of Fundamental Physics?
Caroline Heneka, Florian Nieser, Ayodele Ore +2
For many fundamental physics applications, transformers, as the state of the art in learning complex correlations, benefit from pretraining on quasi-out-of-domain data. The obvious…
Localized Gravity, de Sitter, and the Horizon Criterion
Bjoern Hassfeld, Arthur Hebecker, Daniel Schiller
Realizing de Sitter-like solutions in string theory remains challenging, prompting speculation about which specific feature might be responsible for their inconsistency in quantum…