create Quality Control plots is available to TraP commissioners here (new version in development): ! ! This script needs running on each new dataset to determine the optimum thresholds for the ﬂux ratio high bound, ellipticity and minimum A-Team separation https://github.com/transientskp/scripts/tree/master/TraP_QC_plots
newly detected sources are inserted into transient list However, the list is dominated by sources hovering at/near the source extraction detection threshold Will be resolved in Release 2 of the TraP Script providing temporary (approximate) ﬁx available here: https://github.com/transientskp/scripts/tree/master/TraP_sort_transients
create these plots is available to TraP commissioners here: ! ! Also, this script identiﬁes transient candidates for a given sigma threshold: https://github.com/transientskp/scripts/tree/master/TraP_source_overview https://github.com/transientskp/scripts/tree/master/TraP_anomaly_detection
Amsterdam email@example.com Simulated Datasets: Simulation technique enhanced and standardised Gives a set of known stable sources and known transients Further information available on LOFAR wiki: Take existing measurement set Delete contents of data columns Use BBS to insert sources using a sky model Insert Gaussian noise Calibrate and image using standard pipeline http://www.lofar.org/operations/doku.php?id=tkp:simulateddata
at quiescent ﬂux but are stable after they turn on. ! N.B. if maximum ﬂux is comparable to detection threshold, they are undistinguishable from faint stable sources (this will be rectiﬁed in release 2 of TraP) !
transient list dominated by fake “new” sources close to source detection threshold - to be resolved in TraP Release 2 (temporary ﬁx available) Choose quality control parameters and transient thresholds very carefully to avoid spurious transients Tools to help available on GitHub (frequently improved) Transients can be identiﬁed in several ways such as: Identify anomalous transient parameters: ideally using a sigma threshold (~2.7 sigma for the Radio Sky Monitor dataset) Classify using multiple features and logistic regression (Preliminary)