26 citations · 100 across the 13 of their papers we have counts for
14 papers
Neural Boltzmann Equations
Jonas Spinner, Jack D. Shergold, Jack Shergold
The dynamics of particles in the early universe are described by Boltzmann equations, which involve high-dimensional phase-space integrals. Classical approaches use quadrature inte…
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…
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…
Generative Unfolding of Jets and Their Substructure
Antoine Petitjean, Anja Butter, Kevin Greif +4
Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding…
Forecasting Generative Amplification
Henning Bahl, Sascha Diefenbacher, Nina Elmer +2
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is import…
Lorentz-Equivariance without Limitations
Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht +5
Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts…
The SN 1987A Cooling Bound on Dark Matter Absorption in Electron Targets
Claudio Andrea Manzari, Jorge Martin Camalich, Jonas Spinner +1
We present new supernova (SN 1987A) cooling bounds on sub-MeV fermionic dark matter with effective couplings to electrons. These bounds probe the parameter space relevant for direc…
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant
Jonas Spinner, Luigi Favaro, Peter Lippmann +4
Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choic…
Extrapolating Jet Radiation with Autoregressive Transformers
Anja Butter, François Charton, Javier Mariño Villadamigo +3
Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of partic…
A Lorentz-Equivariant Transformer for All of the LHC
Johann Brehmer, Víctor Bresó, Pim de Haan +4
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Colli…
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
Jonas Spinner, Victor Bresó, Pim de Haan +3
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorent…
Supernova Limits on Muonic Dark Forces
Claudio Andrea Manzari, Jorge Martin Camalich, Jonas Spinner +1
Proto-neutron stars formed during core-collapse supernovae are hot and dense environments that contain a sizable population of muons. If these interact with new long-lived particle…
Jet Diffusion versus JetGPT -- Modern Networks for the LHC
Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer +3
We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainti…
The Axion-Higgs Portal
Martin Bauer, Guillaume Rostagni, Jonas Spinner
The phenomenology of axions and axion-like particles strongly depends on their couplings to Standard Model particles. The focus of this paper is the phenomenology of the unique dim…