Selecting the Optimal LHC Signatures for Distinguishing Models
Baris Altunkaynak
TL;DR
This paper introduces an algorithm designed to identify the most statistically significant signatures from LHC data to effectively differentiate between competing theoretical models.
Contribution
The paper presents a novel algorithm that optimizes the selection of LHC signatures for model discrimination, improving analysis efficiency.
Findings
Algorithm successfully identifies key signatures for model distinction.
Enhanced statistical significance in model comparison.
Potential for improved experimental analysis at the LHC.
Abstract
An algorithm is developed which the goal of producing the most statistically significant signature list for distinguishing between two candidate models given a set of LHC observations.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.