A Smart and Colorful Cadence for Wide-Fast Deep Survey : To ensure the efficient detection of TDEs in the LSST WFD survey, the cadence should be able to measure the u-r color evolution and catch the pre-peak light curve. This paper proposed a smart cadence that allows for efficient photometric transient classification.
TDEs with LSST: This paper suggest that a WFD survey with 2 visits in different filters every night or at least every second night will increase the detection of TDEs.
TDEsDb.py: Compare the performance of six opsim databases for detection of TDEs. Both skymap and the light curve can be obtained.
Figure 1: A detected light curve from opsim database. It meets the minimum requirements: one detection before peak, three filters near peak, and two filters post peak within two weeks.
Figure 2: Results from baseline operation within first two years. Pontus_2573 has the best performance.
I write a metric class TDEsMetricTest to evaluate the detection of TDEs using simulated light curve. It can be used to put requirements on the number of observations/filters at prePeak/nearPeak/postPeak. I use it to compare six opsim databases and find that pontus_2573.db has the best performance. This is because it operates with mixed-filter pairs therefore has large probability to meet the filter requirements.