Conveners
5.4 Foundation models and related techniques, Variational inference
- Gregor Kasieczka (University of Hamburg)
Anomaly detection at the LHC broadens the search for BSM effects by making no assumptions about the signal hypothesis. We employ ML to perform density estimation on raw data and use the density estimate for anomaly detection. A neural network can learn the physics content of the raw data. However, the gain in sensitivity to features of interest can be hindered by redundant information already...
How can we gain physical intuition in real-world datasets using ‘black-box’ machine learning? In this talk, I will discuss how ordered component analyses can be used to seperate, identify, and understand physical signals in astronomical datasets. We introduce Information Ordered Bottlenecks (IOBs), a neural layer designed to adaptively compress data into latent variables organized by...
