"Studies of fundamental matter properties and ground-level physics, like those conducted at the Large Hadron Collider, generate vast amounts of data, much of which is background noise. To measure rare physics events, it's essential to filter out this background noise and isolate signal events. Background suppression has always been a key aspect of high-energy physics data analysis. Historically, simple cuts on track properties were used, but nowadays, machine learning techniques are widely employed.
I'd like to present an overview of the signal event separation for high energy physics. I'm currently doing analysis of the LHCb collaboration data as part of my master's thesis. We'll focus on how we select training data for boosted decision trees and the validation and cross-checking procedures for our model on example of my master thesis work."
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