  In the realm of dijet searches in high-energy physics, a significant
challenge has emerged: with experiments producing more and more data, the
traditional methods of using analytic functions to describe dijet mass spectra
start to fail. To address this, we suggest the application of an anomaly
detection approach to eliminate less interesting background events based on
event final states. This method not only bypasses the limitations of
conventional background models but also significantly enhances our ability to
detect potential signals of new physics. Through simulations that mimic the
conditions of the upcoming High-Luminosity Large Hadron Collider, we
demonstrate the strength and efficiency of this approach in dealing with large
data volumes. The integration of unsupervised machine learning into our
experimental framework paves the way for a promising avenue to unveil hidden
physics discoveries within the overwhelming influx of data.
