References#

1

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre GR Day, Clint Richardson, Charles K Fisher, and David J Schwab. A high-bias, low-variance introduction to machine learning for physicists. Physics reports, 810:1–124, 2019.

2

R Khalek and others. Science Requirements and Detector Concepts for the Electron-Ion Collider - EIC Yellow Report. 2021. URL: https://arxiv.org/abs/2103.05419, doi:10.48550/ARXIV.2103.05419.

3

Amber Boehnlein, Markus Diefenthaler, Nobuo Sato, Malachi Schram, Veronique Ziegler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P Kuchera, Dean Lee, and others. Colloquium: Machine learning in nuclear physics. Reviews of Modern Physics, 94(3):031003, 2022.

4

Matthew Feickert and Benjamin Nachman. A living review of machine learning for particle physics. arXiv preprint arXiv:2102.02770, 2021.

5

C. Fanelli. Machine learning in high energy physics — infn school. 2021. URL: https://github.com/cfteach/ml4hep.

6

Miguel Arratia, Daniel Britzger, Owen Long, and Benjamin Nachman. Reconstructing the kinematics of deep inelastic scattering with deep learning. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1025:166164, 2022.