42 citations · 131 across the 15 of their papers we have counts for
16 papers
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…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
A universal vision transformer for fast calorimeter simulations
Luigi Favaro, Andrea Giammanco, Claudius Krause
The high-dimensional complex nature of detectors makes fast calorimeter simulations a prime application for modern generative machine learning. Vision transformers (ViTs) can emula…
Towards Precise Simulations and Inference for the Neutron EDM
Skyler Degenkolb, Luigi Favaro, Peter Fierlinger +3
Precision measurements of neutron properties, like its permanent electric dipole moment, rely on understanding complex experimental setups in detail. We show how the properties of…
Fast, accurate, and precise detector simulation with vision transformers
Luigi Favaro, Andrea Giammanco, Claudius Krause
The speed and fidelity of detector simulations in particle physics pose compelling questions about LHC analysis and future colliders. The sparse high-dimensional data, combined wit…
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…
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…
How to Unfold Top Decays
Luigi Favaro, Roman Kogler, Alexander Paasch +3
Using unfolded top-quark decay data we can measure the top quark mass, as well as search for unexpected kinematic effects. We present a new generative unfolding method for the two…
Accurate Surrogate Amplitudes with Calibrated Uncertainties
Henning Bahl, Nina Elmer, Luigi Favaro +3
Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activa…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
CaloDREAM -- Detector Response Emulation via Attentive flow Matching
Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer +1
Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. W…
Normalizing Flows for High-Dimensional Detector Simulations
Florian Ernst, Luigi Favaro, Claudius Krause +2
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimete…
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…
How to Understand Limitations of Generative Networks
Ranit Das, Luigi Favaro, Theo Heimel +3
Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustr…
Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden +2
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR tech…
A Normalized Autoencoder for LHC Triggers
Barry M. Dillon, Luigi Favaro, Tilman Plehn +2
Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distr…