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.
Presto-Color: A Photometric Survey Cadence for Explosive Physics and Fast Transients: This paper proposed a cadence to identify fast transients:
- Observations in two filters within 0.5hr;
- A same filter revisit separated by hours.
TDEsMonteMetric.py: Source code of TDEsMonteMetric, which evaluate the detection of TDEs using Monte Carlo method.
TDEsMonteMetric.ipynb: Introduction to running TDEsMonteMetric and analyzing the output result.
TDEsMonteDbCompare.ipynb: Compare the performance of six opsim databases for detection of TDEs using TDEsMonteMetric.
Figure 1: Results from baseline operation using TDEsAsciiMetric within first two years. Pontus_2573 has the best performance.
Figure 2: Results from baseline operation using TDEsMonteMetric within first two years. Same as above, pontus_2573 has the best performance.
Figure 3: An detected light curve from pontus_2573. Pontus_2573 has many detections with paired filters, thus it is the best one to observe transients.
I wrote two metric class TDEsAsciiMetric and TDEsMonteMetric to evaluate the detection of TDEs from simulated light curve. Both metric can be used to put requirements on the number of observations/filters at prePeak/nearPeak/postPeak. TDEsAsciiMetric injects continuous saw-tooth shaped transients into sky, while TDEsMonteMetric using a Monte Carlo approach, i.e, injects light curve at times randomly selected from a Poisson distribution. I use them to compare six opsim databases and find that pontus_2573.db has the best performance from both metrics. Also Monte Carlo approach is more precise and similar to real scenario.