Speaker
Description
Learned anomaly triggers such as CMS AXOL1TL and CICADA are calibrated against a fixed background model, but there is no standard way to check in real time whether that calibration still holds as detector conditions change. I present two components that address this from different angles.
The first is a sequential change-point detector that monitors trigger calibration health online. Instead of watching the output threshold alone, it tracks the anomaly score jointly with its main driving covariates (pileup, object multiplicity) through a scalar residual calibrated against expected luminosity trends. I implemented CUSUM and Bayesian Online Change-Point Detection (BOCPD) for this and compared them against standard drift detectors (ADWIN, Page-Hinkley, KSWIN).
The second is an adaptive conformal inference (ACI) layer that recalibrates decision thresholds on the fly using miscoverage feedback from Zero-Bias control data and delayed offline validation. This matters because standard conformal prediction assumes exchangeable data, which does not hold at the LHC due to beam current decay and pileup drift. Since triggers process millions of events per second, single-event error control is not sufficient on its own, so I added online false discovery rate control (LORD, SAFFRON) evaluated over windowed batches.
I evaluated both components on public CMS and LHC Open Data, using simulated gradual drift (pileup evolving across a fill) and abrupt shifts (subdetector conditions changing, e.g. masked readout channels). For the change-point detector, I report Average Run Length, detection latency, and false-alarm rate; for the conformal layer, I report empirical coverage, online FDR, and detection efficiency against a fixed-threshold baseline. I also examine robustness when the assumed drift model is misspecified.
Together, these provide a model-agnostic way to detect when a trigger's background assumptions break down, and to correct for it without retraining.