A Search for "New Physics'' "Beyond the Standard Model'' in Open Data with Machine Learning

Abstract: 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 $\mathcal{L}$iterature of 65,276 papers, we perform a search for New Physics'' and related signals. In the worst-case, we are able to detectNew Physics'' with the LHC'' at a significance level of at least $6.5\sigma$. ThisNew 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 thatNew Physics'' can be found with a Future Collider'' at at least $8.9\sigma$, as well as the potential to findNew Physics'' without any collider at all. This search is performed using code that was $80\%$ written by Machine Learning methods.