Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
Quantum computing for high-energy physics: State of the art and challenges
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …
natural sciences and beyond, with the potential for achieving a so-called quantum …
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
G Kasieczka, B Nachman, D Shih… - Reports on progress …, 2021 - iopscience.iop.org
A new paradigm for data-driven, model-agnostic new physics searches at colliders is
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …
emerging, and aims to leverage recent breakthroughs in anomaly detection and machine …
Anomaly detection with density estimation
B Nachman, D Shih - Physical Review D, 2020 - APS
We leverage recent breakthroughs in neural density estimation to propose a new
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …
Jet substructure at the Large Hadron Collider: a review of recent advances in theory and machine learning
AJ Larkoski, I Moult, B Nachman - Physics Reports, 2020 - Elsevier
Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC),
where it has provided numerous innovative new ways to search for new physics and to …
where it has provided numerous innovative new ways to search for new physics and to …
Energy flow networks: deep sets for particle jets
A bstract A key question for machine learning approaches in particle physics is how to best
represent and learn from collider events. As an event is intrinsically a variable-length …
represent and learn from collider events. As an event is intrinsically a variable-length …
Classifying anomalies through outer density estimation
A Hallin, J Isaacson, G Kasieczka, C Krause… - Physical Review D, 2022 - APS
We propose a new model-agnostic search strategy for physics beyond the standard model
(BSM) at the LHC, based on a novel application of neural density estimation to anomaly …
(BSM) at the LHC, based on a novel application of neural density estimation to anomaly …
Autoencoders for unsupervised anomaly detection in high energy physics
T Finke, M Krämer, A Morandini, A Mück… - Journal of High Energy …, 2021 - Springer
A bstract Autoencoders are widely used in machine learning applications, in particular for
anomaly detection. Hence, they have been introduced in high energy physics as a …
anomaly detection. Hence, they have been introduced in high energy physics as a …
Variational autoencoders for new physics mining at the large hadron collider
A bstract Using variational autoencoders trained on known physics processes, we develop a
one-sided threshold test to isolate previously unseen processes as outlier events. Since the …
one-sided threshold test to isolate previously unseen processes as outlier events. Since the …
The dark machines anomaly score challenge: benchmark data and model independent event classification for the large hadron collider
T Aarrestad, M van Beekveld, M Bona, A Boveia… - SciPost Physics, 2022 - scipost.org
We describe the outcome of a data challenge conducted as part of the Dark Machines
Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged …
Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged …