ABSTRACT:
This talk will briefly define and motivate anomaly detection at the Large Hadron Collider and will then give an overview of various method classifications based on the underlying physics goals and assumptions (and how these translate to statistical concepts). No one method will be able to cover all possibilities and it is essential to have a spectrum of techniques to achieve broad...
ABSTRACT:
I will review goodness of fit testing and two sample testing in the
context of trying to test for a new signal. My goal is to point to
results in the statistics literature that might be unfamiliar in the
physics community. Topics will include: optimal tests,
classifier-based tests, reproducing kernel Hilbert space tests, level set tests, bump tests and robustness.
ABSTRACT:
In recent years, there have been many proposed methodologies for machine learning anomaly detection at the LHC, such as those reported in the LHC Olympics and Dark Machines community reports. The first search using machine-learning anomaly detection was performed by ATLAS in the dijet final state, a fully data-driven analysis that uses the Classification Without Labels method and...
ABSTRACT:
We are at the beginning of a new era of data-driven, model-agnostic new physics searches at colliders that combine recent breakthroughs in anomaly detection and machine learning. This contribution will report on the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D...
ABSTRACT:
Data-driven methods are becoming increasingly popular and could give us new insights when searching for signals from new physics. On the other hand, theoretical models and supervised learning approaches should not be neglected.
In this talk we present and compare different ways of defining "signal regions" at the Large Hadron Collider that are of interest for a "goodness-of-fit"...
