The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

The minimum, median, and maximum best total improvements for each technique applied on each of the signals in the Hackathon (top) and Secret (middle) dataset.

Abstract

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10\,\mathrm{fb}^{−1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at phenoMLdata.org. Code to reproduce the analysis is provided at DarkMachines-UnsupervisedChallenge.

Caterina Doglioni
Caterina Doglioni
Professor of Particle Physics

Caterina is an ATLAS physicist with experience in searches for new physics, focusing on dark matter. She leads projects on non-standard data-taking and analysis workflows as well as machine learning, and is a contributor of the Dark Machines project.

Baptiste Ravina
Baptiste Ravina
Saltire Fellow (Royal Society of Edinburgh)

I’m an experimental particle physicist looking for new physics at the ATLAS experiment with machine learning.