Machine learning has played an important role in the analysis of high-energy physics data
for decades. The emergence of deep learning in 2012 allowed for machine learning tools …

The last two decades have witnessed the discovery of a myriad of new and unexpected
hadrons. The future holds more surprises for us, thanks to new-generation experiments …

We present a technique for translating a black-box machine-learned classifier operating on
a high-dimensional input space into a small set of human-interpretable observables that can …

We investigate whether state-of-the-art classification features commonly used to distinguish
electrons from jet backgrounds in collider experiments are overlooking valuable information …

The production of heavy neutral mass resonances,\(\text {Z}^{\prime}\), has been widely
studied theoretically and experimentally. Although the nature, mass, couplings, and …

Jet identification is one of the fields in high energy physics that machine learning has begun
to make an impact. More often than not, convolutional neural networks are used to classify …

We propose to replace the exact amplitudes used in Monte Carlo event generators for
trained machine learning regressors, with the aim of speeding up the evaluation of slow …

We propose a method to organize experimental data from particle collision experiments in a
general format which can enable a simple visualization and effective classification of …

Future electron-positron colliders will play a leading role in the precision measurement of
Higgs boson couplings which is one of the central interests in particle physics. Aiming at …

In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM)
inputs are studied using several Monte Carlo event samples for various pp collision …