Anomaly detection under coordinate transformations
نویسندگان
چکیده
There is a growing need for machine learning-based anomaly detection strategies to broaden the search Beyond-the-Standard-Model (BSM) physics at Large Hadron Collider (LHC) and elsewhere. The first step of any approach specify observables then use them decide on set anomalous events. One common choice select events that have low probability density. It well-known fact densities are not invariant under coordinate transformations, so sensitivity can depend initial coordinates. broader learning community has recently connected with our goal bring awareness this issue high energy literature detection. In addition analytical explanations, we provide numerical examples from simple random variables LHC Olympics Dataset show how using density as an score lead being classified or depending frame.
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ژورنال
عنوان ژورنال: Physical review
سال: 2023
ISSN: ['0556-2813', '1538-4497', '1089-490X']
DOI: https://doi.org/10.1103/physrevd.107.015009