Learning invariant representations with applications to high-energy physics

Abstract

In searches for new physics in high-energy physics, experimental analyses are primarily concerned with physical processes which are rare or hitherto unobserved. To claim a statistically significant discovery or exclusion of new physics when studying such decays, it is necessary to maintain an appropriate signal to noise ratio. This makes systems capable of efficient discrimination of signal from datasets overwhelmingly dominated by background events an important component of modern experimental analyses. However, na\"ive application of these methods is liable to raise poorly understood systematic effects which may ultimately degrade the significance of the final measurement. To understand th..