Accelerating the BSM interpretation of LHC data with machine learning
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14714
- Type
- D - Journal article
- DOI
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10.1016/j.dark.2019.100293
- Title of journal
- PHYSICS OF THE DARK UNIVERSE
- Article number
- ARTN 100293
- First page
- -
- Volume
- 24
- Issue
- -
- ISSN
- 2212-6864
- Open access status
- Technical exception
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. This paper develops a cheap-to-evaluate probabilistic surrogate model (milliseconds, not hours) to emulate those expensive event generators and detector simulators. Our approach is the first of its kind and makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC, thereby providing a framework for accelerating theoretical research in high-energy physics.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -