starslikely

starslikely is a computational tool designed for analyzing astronomical survey data. Its primary function is to identify and characterize stellar populations within large datasets, often derived from photometric surveys. The tool employs probabilistic methods to estimate the likelihood of an astronomical object being a star, distinguishing it from other celestial objects such as galaxies or quasars.

The methodology behind starslikely involves comparing observed photometric properties of celestial objects with predicted spectral energy distributions of various stellar types. By considering factors like color indices, magnitudes, and potentially spectroscopic information if available, starslikely assigns a probability score to each object, indicating its likelihood of being a star. This approach allows for efficient filtering and classification of vast amounts of astronomical data.

Researchers utilize starslikely to compile catalogs of stars, study stellar evolution, and investigate the structure of the Milky Way and other galaxies. Its ability to handle large-scale datasets makes it an invaluable asset in modern astrophysics, where observational surveys are continuously generating unprecedented volumes of information. The tool is often integrated into larger data processing pipelines for astronomical surveys, contributing to the extraction of scientifically relevant objects from raw observational data.