Agentic Exclusion Limit Extraction

Description

Thousands of BSM searches at the LHC have produced exclusion limits archived across ATLAS/CMS papers and databases like SModelS. For a theorist proposing a new model, determining what parameter space is already ruled out requires extensive manual literature searches, cross-referencing final states, extracting digitized limit curves, and mapping them onto the model of interest — a tedious, error-prone process that represents a major bottleneck in phenomenological research. This project aims to automate and generalize that approach using agentic programming. This project builds an agentic system within the HEPTAPOD framework that automates this process. Given a BSM model specification (particle content, decay channels, production modes), the agent searches the literature and public databases, identifies relevant analyses, extracts applicable bounds, and compiles a structured summary with references and applicability caveats.

Duration

Total project length: 350 hours.

Task ideas

Survey existing limit databases and formats (HEPData, SModelS, ATLAS/CMS auxiliary material)
Implement HEPTAPOD tool modules for querying limit databases, parsing exclusion contours, and matching experimental final states to a user-specified model
Build agent workflows that autonomously retrieve and compile limits given a model specification
Add validation checks flagging when published analysis assumptions may not apply
Test on benchmark models (simplified SUSY, leptoquarks) where the correct relevant limits are known
Implement audit trails recording every search query, database hit, and reasoning step
Explore whether the agent can identify coverage gaps, parameter regions where no published search applies

Expected results:

Working agentic system integrated into HEPTAPOD for automated limit compilation
Validation against at least two well-characterized BSM scenarios
Transparent audit trail and decision log
Open-source code with documentation and examples

Difficulty level

Advanced

Requirements

Strong Python skills, experience with at least one ML/AI framework
Familiarity with LLM APIs and tool-use / function-calling paradigms
Interest in BSM phenomenology and limit-setting concepts
Ability to work independently with significant design freedom

Test

Please use this link to access the test for this project.

Mentors

Steve Mrenna (Fermilab)
Konstantin Matchev (University of Alabama)
Alex Roman (University of Alabama)
Tony Menzo (University of Alabama + Fermilab)
Ian Pang (Rutgers University)

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

HEPSIM