Scientific discovery in the age of artificial intelligence
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …
and accelerate research, helping scientists to generate hypotheses, design experiments …
On scientific understanding with artificial intelligence
An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …
products of every possible chemical reaction or the function of every protein would …
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 …
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 …
Graph neural networks at the Large Hadron Collider
G DeZoort, PW Battaglia, C Biscarat… - Nature Reviews …, 2023 - nature.com
From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
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 …
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
E Govorkova, E Puljak, T Aarrestad, T James… - Nature Machine …, 2022 - nature.com
To study the physics of fundamental particles and their interactions, the Large Hadron
Collider was constructed at CERN, where protons collide to create new particles measured …
Collider was constructed at CERN, where protons collide to create new particles measured …
Anomaly detection in high-energy physics using a quantum autoencoder
The lack of evidence for new interactions and particles at the Large Hadron Collider (LHC)
has motivated the high-energy physics community to explore model-agnostic data-analysis …
has motivated the high-energy physics community to explore model-agnostic data-analysis …
Mining for gluon saturation at colliders
A Morreale, F Salazar - Universe, 2021 - mdpi.com
Quantum chromodynamics (QCD) is the theory of strong interactions of quarks and gluons
collectively called partons, the basic constituents of all nuclear matter. Its non-abelian …
collectively called partons, the basic constituents of all nuclear matter. Its non-abelian …