Speaker
Description
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detection (AD) at collider experiments. The Higgs And X Anomaly Detection (HAXAD) strategy offers a principled approach to searching for anomalies occurring in association with a Higgs boson. In the kinematic region of the Higgs peak, HAXAD employs machine-learning-based feature embedding, data- and simulation-driven background estimation, and weakly supervised classification. This contribution features the latest developments, including two new embedding strategies and a completely updated inference framework that extracts both model-independent and model-dependent limits.
We evaluate HAXAD on an extensive simulated dataset of diphoton final states, with a large benchmark suite of signal models. HAXAD achieves strong signal sensitivity and, when benchmarked against a representative multi-category cut-based search, matches or exceeds the best individual cut-based limits for a wide variety of signal models. Together, these results establish HAXAD as a viable and compelling AD-based search strategy with novel discovery potential at colliders.