Finding exoplanets is like searching for fireflies next to stadium floodlights - except the fireflies are billions of miles away and the floodlights are stars. Every exoplanet survey faces the same fundamental question: given our instruments’ limitations and the properties of target stars, what are our actual chances of detecting planets around each system? This is where statistical modeling meets observational astronomy.
Exo-DMC transforms this challenge into quantifiable probabilities using Monte Carlo simulations. The tool ingests stellar catalogs and instrument detection limits, then runs thousands of synthetic planet populations through realistic detection scenarios. The result? Detailed probability maps showing exactly where surveys are most likely to succeed. Built on the robust foundation of numpy, scipy, and astropy, it handles everything from direct imaging surveys to transit photometry campaigns, accounting for orbital dynamics, stellar brightness, and instrumental noise.
The tool has already proven its worth in cutting-edge research, from JWST’s Early Release Science program to the SPHERE infrared survey for exoplanets (SHINE). Major observatories and survey teams rely on Exo-DMC to optimize their observation strategies, ensuring precious telescope time is spent where discovery potential is highest. As next-generation instruments come online, this Monte Carlo approach becomes increasingly vital for maximizing scientific return in humanity’s quest to map the exoplanet census.
⭐ Stars: 5
💻 Language: Python
🔗 Repository: mbonav/Exo_DMC