SciPost: Topic details

Machine learning (ML)

Publications

Learning lattice quantum field theories with equivariant continuous flows
by Mathis Gerdes, Pim de Haan, Corrado Rainone, Roberto Bondesan, Miranda C. N. Cheng,
SciPost Phys. 15, 238 (2023)
Exploring unsupervised top tagging using Bayesian inference
by Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman, Santiago A. Tanco, Tatiana Tarutina,
SciPost Phys. Core 6, 046 (2023)
Loop amplitudes from precision networks
by Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn,
SciPost Phys. Core 6, 034 (2023)
Machine learning and LHC event generation
by Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan,
SciPost Phys. 14, 079 (2023)
Real-time discrimination of photon pairs using machine learning at the LHC
by Sean Benson, Adrián Casais Vidal, Xabier Cid Vidal, Albert Puig Navarro,
SciPost Phys. 7, 062 (2019)
Investigating ultrafast quantum magnetism with machine learning
by Giammarco Fabiani, Johan H. Mentink,
SciPost Phys. 7, 004 (2019)
Quark-gluon tagging: Machine learning vs detector
by Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson,
SciPost Phys. 6, 069 (2019)
Deep-learned Top Tagging with a Lorentz Layer
by Anja Butter, Gregor Kasieczka, Tilman Plehn, Michael Russell,
SciPost Phys. 5, 028 (2018)

Submissions (unpublished only)

Transformer Neural Networks in the Measurement of $t\bar{t}H$ Production in the $H\,{\to}\,b\bar{b}$ Decay Channel with ATLAS
by Chris Scheulen on behalf of the ATLAS Collaboration
(submitted 2024-12-12 to SciPost Physics Proceedings)
Scaling Laws in Jet Classification
by Joshua Batson, Yonatan Kahn
(submitted 2024-12-04 to SciPost Physics)
Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
by Andrew J. Larkoski
(submitted 2024-12-03 to SciPost Physics)
Learning the Simplicity of Scattering Amplitudes
by Clifford Cheung, Aurélien Dersy, Matthew D. Schwartz
(submitted 2024-11-20 to SciPost Physics)
Systematic Interpretability and the Likelihood for Boosted Top Quark Identification
by Andrew J. Larkoski
(submitted 2024-11-12 to SciPost Physics)
Neutrino Classification Through Deep Learning
by María Fernanda Romo-Fuentes, Luis Eduardo Falcón-Morales
(submitted 2024-10-31 to SciPost Physics Proceedings)
MACK: Mismodeling Addressed with Contrastive Knowledge
by Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris
(submitted 2024-10-23 to SciPost Physics)
Predicting Nuclear Binding Energy Using Generalized Additive Model
by Kristiyan Laoli
(submitted 2024-10-14 to SciPost Physics)
Identifying Quantum Phase Transitions with Minimal Prior Knowledge by Unsupervised Learning
by Mohamad Ali Marashli, Ho Lai Henry Lam, Hamam Mokayed, Fredrik Sandin, Marcus Liwicki, Ho-Kin Tang, Wing Chi Yu
(submitted 2024-09-06 to SciPost Physics)

Top experts

Machine learning (ML)