Object-Brightness Analyzer for Rubin Observatory (OBARO)

We have developed and written a software called Object-Brightness Analyzer for Rubin Observatory(OBARO) in Python using the Gaussian Model Mixture(GMM) machine learning(ML) algorithm that can automatically detect and calculate the brightness of all astronomical objects from the PhoSim Rubin(LSST)-Survey-#1 simulation data sets and from Rubin Observatory's Data Preview (DP0.2) simulation data sets. The PhoSim Rubin(LSST)-Survey-#1 data sets were generated using Bellarmine University's Tier2 Grid Supercomputer that is linked to the Open Science Grid (OSG) cyberinfrastructure. The OBARO software uses statistical analysis and machine learning to plough through and scan all the astronomical objects and calculate the mean pixel value, mean pixel value error, surface brightness, surface brightness error, area, pixel count for both the object and its background as well as the flux and magnitude for the astronomical object. Once the OBARO software runs all the FITS files, it will produce an output file in XML format for analysis. The OBARO software can run on a PC/laptop. We will present the results of brightness studies of astronomical objects in some of the PhoSim Rubin (LSST) Simulated Survey #1 data sets using Phosim versions 5.1.7 and 5.3.23 and in some of the Data Preview 0.2 data sets.