Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters

TL;DR

This paper describes a collaborative, week-long machine learning challenge where graduate students developed clustering algorithms for the hadronic calorimeter in a future electron-ion collider, fostering education and innovative solutions.

Contribution

It introduces a crowd-sourced, educational approach to solving complex physics problems using machine learning in a collaborative setting.

Findings

Multiple clustering approaches developed and compared.

Performance summaries of different algorithms.

Recommendations for future crowd-sourced scientific challenges.

Abstract

In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the development of future facilities where the simulations can be publicly distributed, such as the Electron-Ion Collider (EIC). In this paper, we discuss a so-called ``Power Week" where graduate students were able to learn about machine learning while also contributing to an unsolved problem at a future facility. Here, the problem of interest was the clustering of the forward hadronic calorimeter in the foreseen electron-proton/ion collider experiment (ePIC) detector at the EIC. The different possible approaches, developed over the course of a single week, and their performance are detailed and summarised. Feedback on the format of the week and recommendations for future similar programs are provided in the hopes to inspire future learning opportunities for students that also serve as a crowd-sourced approaches to unsolved problems.