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 …

The many ways in which machine and deep learning are transforming the analysis and
simulation of data in particle physics are reviewed. The main methods based on boosted …

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many
death cases and affected all sectors of human life. With gradual progression of time, COVID …

Despite the success of neural networks at solving concrete physics problems, their use as a
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …

We leverage recent breakthroughs in neural density estimation to propose a new
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …

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 …

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 …

We introduce a potentially powerful new method of searching for new physics at the LHC,
using autoencoders and unsupervised deep learning. The key idea of the autoencoder is …

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 …

Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it
is critical to broaden the search program. A variety of model-independent searches have …