Refining occultation detection parameters through simulations

Occultations occur when an outer solar system object passes in front of a distant background star, temporarily blocking its light. Observation of these events provide information on the occulting objects, including size and shape. To improve the detection rate of these events, the proposed Argus Array of 900 telescopes would be capable of monitoring the entire visible sky in short intervals. To prepare for data analysis, I developed a Python-based occultation simulator to assess the sensitivity of the Argus prototype's telescopes. This simulator inserted occultations into previously recorded light curves, featuring a detection algorithm that efficiently identified occultation events while minimizing false positives from noise and atmospheric effects. The algorithm self-adjusts until it reaches an optimal threshold in standard deviations from the mean apparent magnitude. Our findings suggest that dimmer stars require a more substantial flux decrease for occultation detection than brighter stars. These parameters, as determined by the simulation, can be applied to datasets for natural stellar occultation searches, ultimately expanding our knowledge of the objects in the outer solar system.