332 citations · 2.1k across the 97 of their papers we have counts for
29 papers · 1 filter
Machine Learning in the Search for New Fundamental Physics
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz +2
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new…
Impact of jet-production data on the next-to-next-to-leading-order determination of HERAPDF2.0 parton distributions
H1, ZEUS Collaborations, : +234
The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA da…
SymmetryGAN: Symmetry Discovery with Deep Learning
Krish Desai, Benjamin Nachman, Jesse Thaler
What are the symmetries of a dataset? Whereas the symmetries of an individual data element can be characterized by its invariance under various transformations, the symmetries of a…
Online-compatible Unsupervised Non-resonant Anomaly Detection
Vinicius Mikuni, Benjamin Nachman, David Shih
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on…
Computationally Efficient Zero Noise Extrapolation for Quantum Gate Error Mitigation
Vincent R. Pascuzzi, Andre He, Christian W. Bauer +2
Zero noise extrapolation (ZNE) is a widely used technique for gate error mitigation on near term quantum computers because it can be implemented in software and does not require kn…
Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
Miguel Arratia, Daniel Britzger, Owen Long +1
We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits th…