The Transiting Exoplanet Survey Satellite (TESS) has now revealed over 3,000 planet candidates, known as Objects of Interest (TOIs). Nearby eclipsing binaries or NEBs—binary stars whose diluted eclipses are mistaken for planetary transits—are common among TOIs and account for ~50% of all false positives. One method of identifying NEBs involves reducing TESS full frame images (FFIs) by placing small 1-pixel apertures in the location of stars near the target star and searching for off-target signals. Using this approach to eliminate NEBs before they are followed-up reduces the observing time spent on false positives and thus increases the percentage of follow-up data that is published. In previous work, we implemented this method using AstroImageJ and demonstrated its effectiveness; despite the large 21” pixels of FFIs that typically blend multiple stars, we conclusively identified 10 NEBs from a sample of 34 TOIs.
Currently, this process is entirely manual, and analyzing a large number of TOIs (which will yield the most results) requires large amounts of repetitive, manual labor. Some processes that are identical between reductions of different TOIs include downloading data, performing aperture photometry, and phasing the resulting light curves. Our project aims to automate all of these repetitive steps, requiring only the interpretation of the results to be done manually. We have created a Python script that can download all required files for a list of TOIs, and are now developing an AstroImageJ macro that will perform photometry on the list of TOIs and store the light curves for later manual review. In the near future, we expect this tool to be able to consecutively reduce an arbitrary number of TOIs with minimal input required. It is estimated that the manual time spent on each TOI will be reduced by a factor of 6 after the completion of this tool. Additional functions, in particular those that will aid the interpretation, are planned to be later implemented to further increase efficiency.