17 citations · 44 across the 13 of their papers we have counts for
6 papers · 1 filter
Virtues and Vices of Equivariant Transformers
Luigi Favaro, Tilman Plehn, Huilin Qu +1
We study for the first time the benefit of Lorentz-equivariant transformers for large-size jet tagging and flavor tagging. To control their computing demands, we optimize all imple…
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…
MadNIS at NLO
Giovanni De Crescenzo, Javier Mariño Villadamigo, Nina Elmer +4
We combine fast amplitude surrogates with neural importance sampling to accelerate NLO calculations. For virtual corrections, a learned ratio to the Born matrix element with calibr…
Explicit or Implicit? Encoding Physics at the Precision Frontier
Victor Breso-Pla, Kevin Greif, Vinicius Mikuni +4
High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the st…
Unfolding without Iterations, Adversaries, or Surrogates
Ayodele Ore, Tilman Plehn
Correcting measurements for detector effects and constructing appropriate public data representations is a pressing problem in LHC physics. Current methods solve this inverse probl…
Economical Jet Taggers -- Equivariant, Slim, and Quantized
Antoine Petitjean, Tilman Plehn, Jonas Spinner +1
Modern machine learning is transforming jet tagging at the LHC, but the leading transformer architectures are large, not particularly fast, and training-intensive. We present a sli…