Speaker
Description
Discovering new particles from beyond the Standard Model remains one of the main goals of present-day particle physics. Traditional searches for new physics at the Large Hadron Collider rely on specific theoretical scenarios and simulation-based background estimates, limiting their reach and introducing modeling uncertainties. We present an anomaly detection method that uses normalizing flows to identify anomalous events without assuming a particular signal model, with the goal of estimating backgrounds directly from data rather than simulation. By training a flow on data and splitting the resulting latent representation into two independent parts, we construct two decorrelated anomaly scores that allow the background in the signal region to be estimated using the well-established ABCD method. We test this approach on a benchmark new physics scenario and show that it successfully decorrelates the anomaly scores and correctly estimates the S/√B in the signal region.