LHC physics dataset for unsupervised New Physics detection at 40 MHz

TL;DR

This paper introduces a dataset of proton collision events from the LHC, designed to facilitate the development of unsupervised algorithms for detecting new physics phenomena in real-time data streams.

Contribution

The paper provides a realistic dataset emulating LHC data streams, enabling research on unsupervised New Physics detection tailored to real-time processing constraints.

Findings

Dataset captures typical LHC collision events with pre-filtering for electrons or muons.

Facilitates development of novel unsupervised algorithms for real-time event selection.

Aims to improve sensitivity to new physics phenomena in high data rate environments.

Abstract

In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data rate is reduced to a sustainable level by a real-time event filter processing system which decides whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events which emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, by publishing this dataset we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised New Physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.