Papers › A Search for "New Physics'' "Beyond the Standard Model'' in Open Data with Machine Learning
A Search for "New Physics'' "Beyond the Standard Model'' in Open Data with Machine Learning
Rikab Gambhir
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In this new era of large data, it is important to make sure we do not miss any signs of new physics. Using the publicly-available open data collected by the arXiv.org experiment in the \texttt{hep-ph} channel, corresponding to a raw total integrated ℒiterature of 65,276 papers, we perform a search for ``New Physics'' and related signals. In the worst-case, we are able to detect ``New Physics'' with ``the LHC'' at a significance level of at least 6.5σ. This ``New Physics'' signature is primarily ``Dark'' in nature, and is potentially axion(-like) dark matter. We also show the potential for further improvement in the future, and that ``New Physics'' can be found with ``a Future Collider'' at at least 8.9σ, as well as the potential to find ``New Physics'' without any collider at all. This search is performed using code that was 80% written by Machine Learning methods.
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