EPJ Special Topics (EPJ ST)

Open Access

Editorial

Modern machine learning and particle physics: an in-depth review p. 2421

Biplob Bhattacherjee and Swagata Mukherjee

Published online: 16 October 2024

DOI: 10.1140/epjs/s11734-024-01364-3

Regular Article

Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks p. 2425

Arghya Choudhury, Arpita Mondal and Subhadeep Sarkar

Published online: 26 September 2024

DOI: 10.1140/epjs/s11734-024-01308-x

Open Access

Review

Unveiling the secrets of new physics through top quark tagging p. 2465

Rameswar Sahu, Saiyad Ashanujjaman and Kirtiman Ghosh

Published online: 24 September 2024

DOI: 10.1140/epjs/s11734-024-01257-5

Open Access

Review

Top-philic machine learning p. 2497

Rahool Kumar Barman and Sumit Biswas

Published online: 25 July 2024

DOI: 10.1140/epjs/s11734-024-01237-9

Review

Interplay of traditional methods and machine learning algorithms for tagging boosted objects p. 2531

Camellia Bose, Amit Chakraborty, Shreecheta Chowdhury and Saunak Dutta

Published online: 31 July 2024

DOI: 10.1140/epjs/s11734-024-01256-6

Review

Unsupervised and lightly supervised learning in particle physics p. 2559

Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, Cyrin Neeraj and Monalisa Patra

Published online: 08 July 2024

DOI: 10.1140/epjs/s11734-024-01235-x

Review

Probing intractable beyond-standard-model parameter spaces armed with machine learning p. 2597

Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra and Satyajit Roy

Published online: 15 July 2024

DOI: 10.1140/epjs/s11734-024-01236-w

Open Access

Review

Foundations of automatic feature extraction at LHC–point clouds and graphs p. 2619

Akanksha Bhardwaj, Partha Konar and Vishal Ngairangbam

Published online: 11 September 2024

DOI: 10.1140/epjs/s11734-024-01306-z

Open Access

Review

How deep learning is complementing deep thinking in ATLAS p. 2641

Deepak Kar

Published online: 05 July 2024

DOI: 10.1140/epjs/s11734-024-01238-8

Review

Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC p. 2657

Spandan Mondal and Luca Mastrolorenzo

Published online: 19 July 2024

DOI: 10.1140/epjs/s11734-024-01234-y

Review

Machine learning in experimental neutrino physics p. 2687

N. Poonthottathil

Published online: 19 August 2024

DOI: 10.1140/epjs/s11734-024-01280-6

EPJ

The European Physical Journal Special Topics

Volume 233 / No 15-16 (November 2024)

Modern Machine Learning and Particle Physics: An In-Depth Review

Modern Machine Learning and Particle Physics: An In-Depth Review

Modern machine learning and particle physics: an in-depth review p. 2421

Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks p. 2425

Unveiling the secrets of new physics through top quark tagging p. 2465

Top-philic machine learning p. 2497

Interplay of traditional methods and machine learning algorithms for tagging boosted objects p. 2531

Unsupervised and lightly supervised learning in particle physics p. 2559

Probing intractable beyond-standard-model parameter spaces armed with machine learning p. 2597

Foundations of automatic feature extraction at LHC–point clouds and graphs p. 2619

How deep learning is complementing deep thinking in ATLAS p. 2641

Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC p. 2657

Machine learning in experimental neutrino physics p. 2687

Conference announcements