| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 06021 | |
| Number of page(s) | 6 | |
| Section | 6 - Physics Analysis | |
| DOI | https://doi.org/10.1051/epjconf/202024506021 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024506021
Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC
University of Adelaide, North Terrace, Adelaide, SA 5005
* e-mail: adam.leinweber@adelaide.edu.au
** e-mail: martin.white@adelaide.edu.au
Published online: 16 November 2020
Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.
© The Authors, published by EDP Sciences, 2020
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