Speaker
Description
The absence of BSM physics discoveries at the LHC suggests new physics could lie outside current trigger schemes. By applying unsupervised ML–based anomaly detection, we gain a model-agnostic way of spotting anomalous signatures that deviate from the current trigger’s expectations. Here we introduce a Run-3 trigger chain that embeds fast anomaly detection algorithms in both hardware and software levels. We will describe its design, integration, commissioning strategy with emphasis on rate stability and robustness, and show first validation results from its data stream. This marks ATLAS’s inaugural anomaly-detection trigger, laying the groundwork for future ML-driven triggers and novel sensitivity to a broad spectrum of new-physics signatures in Run-3 and beyond.