Filtering and Reliability Scoring#
To reduce the proportion of spurious alerts sent to users, several layers of filtering are applied. These will continue to be tuned over time as more on-sky data is obtained.
Filtering#
Artifacts are filtered at several stages.
The difference image detection stage rejects sources with pixels off image; edge; interpolated bad, edge, no data, saturated, and saturated template center pixels; as well as those occurring in modeled diffraction spikes.
Later stages filter sources with cosmic rays in their center pixels as well as those with high template variance and those with negative direct image fluxes indicative of background subtraction problems.
Artificial Satellites#
The aim is to remove artifical satellites from the alert stream.
The predicted positions of known satellites are used to filter DiaSources prior to alerts.
Trailed sources with motions faster than 10 deg/day are also filtered.
“Glint trails” due to rotating space junks are identified by linear fits to the detected DiaSources.
Glints are not filtered, but are flagged with a glint_trail boolean.
Reliability#
A Convolutional Neural Network (CNN) was trained with a mixture of simulated data and human-labeled transients, variables, and moving objects.
It uses cutout science, template, and difference images to estimate whether a DiaSource is likely astrophysical.
The resulting reliability score ranges from 0-1, with larger scores corresponding to more likely real objects.
To minimize the number of false positives, DiaSources with reliability scores less than 0.5 are filtered out.
Users may select alerts with higher reliability scores to maximize purity.
Important
Continued training and refinement of the reliability model is underway, and the version of the model is reported in the alert packets.
A technote describing the training and performance of the reliability models is available at (DMTN-337). Interested users are encouraged to consult it to understand the impact of reliability selections on their objects of interest.