Speaker
Description
Weakly-supervised anomaly detection methods offer a powerful approach for discovering new physics by comparing data to a background-only reference. However, the sensitivity of existing strategies can be significantly limited by rare signals or high-dimensional, noisy feature spaces. We present Prior-Assisted Weak Supervision (PAWS), a novel machine-learning technique that significantly boosts search sensitivity by incorporating physical priors from a class of signal models into the weakly-supervised framework. PAWS pre-trains parameterized neural network classifiers on simulated signal events and then fine-tunes these models to distinguish data from a background reference, with the signal parameters themselves being learnable during this second stage. This approach allows PAWS to achieve the sensitivity of a fully-supervised search for signals within the pre-specified class, without needing to know the exact signal parameters in advance. Applied to the LHC Olympics anomaly detection benchmark, PAWS extends the discovery reach by a factor of 10 in cross section over previous methods. Crucially, PAWS demonstrates remarkable robustness to irrelevant noise features, unlike traditional methods whose performance degrades substantially. This work highlights the power of integrating domain knowledge into machine learning models for high energy physics, offering a promising path towards more sensitive and robust anomaly detection in jet-based searches and beyond.
