The purpose of this RAMP was to detect anomalies in the LHC Atlas detector. The RAMP took place in may 2016 at the Auditorium Pierre Lehmann (LAL).
Anomaly detection, where we seek to identify events or datasets that deviate from those normally encountered, is a common task in experimental particle physics. For example, two runs recorded on the same day with identical accelerator and detector conditions and the same trigger menu should not be distinguishable statistically. If they are, some unexpected systematic effect must be present which acts to skew each event or a subset of the events, leading to a collective anomaly. There are many ways in which such problems can arise: for instance, the data acquisition or reconstruction software might be misconfigured, or some subcomponent of the detector might be malfunctioning. Conversely, an otherwise normal dataset may contain individual events which are somehow unusual. These point anomalies may arise due to a problem with the detector, data acquisition, trigger or reconstruction that only occur in very rare circumstances. For both cases it would be highly desirable to devise a mechanism that could automatically scan all new datasets, detect any anomalous features, and alert a human being to enable detailed investigation.
The prediction task
The nature of the challenge was to devise a classifier that can distinguish the anomalous cases from the bulk of the data in a test dataset, having first trained the classifier on a test dataset. Whilst the anomalous events are labelled in the training set, no distinction is made between the different types of distortion. The challenge in this RAMP was to Separate a skewed data point from a original data point.
