Automatic anomaly detection in high energy collider data

Michel Herquet; Simon de Visscher

REVIEW

Automatic anomaly detection in high energy collider data

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1104.2404 v1 pith:GLECGJWW submitted 2011-04-13 hep-ph hep-exphysics.data-an

Automatic anomaly detection in high energy collider data

Simon de Visscher , Michel Herquet This is my paper

classification hep-ph hep-exphysics.data-an

keywords detectionanomalyapproachautomaticcolliderdataenergyhigh

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

We address the problem of automatic anomaly detection in high energy collider data. Our approach is based on the random generation of analytic expressions for kinematical variables, which can then be evolved following a genetic programming procedure to enhance their discriminating power. We apply this approach to three concrete scenarios to demonstrate its possible usefulness, both as a detailed check of reference Monte-Carlo simulations and as a model independent tool for the detection of New Physics signatures.

Automatic anomaly detection in high energy collider data

discussion (0)