Generator Based Inference (GBI)
This work addresses the need for more efficient and interpretable anomaly detection in high-energy physics, though it is incremental as it builds on existing simulation-based inference methods.
The paper tackles the problem of statistical inference in physics by proposing a general framework called Generator Based Inference (GBI) that integrates machine learning with data generators, focusing on resonant anomaly detection. It shows that using data-derived generators for parameter estimation improves interpretability and achieves a new state-of-the-art sensitivity on the LHCO benchmark dataset.
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of the underlying theory. Modern machine learning has supercharged this workflow to enable high-dimensional and unbinned analyses to utilize much more information than ever before. We propose a general framework for describing the integration of machine learning with generators called Generator Based Inference (GBI). A well-studied special case of this setup is Simulation Based Inference (SBI) where the generator is a physics-based simulator. In this work, we examine other methods within the GBI toolkit that use data-driven methods to build the generator. In particular, we focus on resonant anomaly detection, where the generator describing the background is learned from sidebands. We show how to perform machine learning-based parameter estimation in this context with data-derived generators. This transforms the statistical outputs of anomaly detection to be directly interpretable and the performance on the LHCO community benchmark dataset establishes a new state-of-the-art for anomaly detection sensitivity.