In a groundbreaking development for the field of astrophysics, researchers have introduced Synference, a novel Python framework designed for galaxy Spectral Energy Distribution (SED) fitting using simulation-based inference (SBI). This innovative tool leverages the Synthesizer package for flexible forward-modelling of galaxy SEDs and integrates the LtU-ILI package to ensure best practices in model training and validation. The research team, led by Thomas Harvey and including notable contributors such as Christopher C. Lovell and Sophie Newman, has demonstrated Synference’s capabilities by training a neural posterior estimator on a million simulated galaxies. This model is based on a flexible 8-parameter physical model and is used to infer galaxy properties from 14-band Hubble Space Telescope (HST) and James Webb Space Telescope (JWST) photometry.
The validation of Synference has shown remarkable results, with excellent parameter recovery and accurate posterior calibration against nested sampling results. For instance, the framework achieved an R-squared value greater than 0.99 for stellar mass (M⋆), indicating a high degree of accuracy in its predictions. The researchers applied their trained model to 3,088 spectroscopically-confirmed galaxies in the JADES GOODS-South field, showcasing the framework’s ability to perform amortized inference exceptionally quickly. The entire sample was processed in approximately three minutes on a single CPU, translating to a processing rate of 18 galaxies per second. This represents a staggering 1700-fold speedup over traditional nested sampling or Markov Chain Monte Carlo (MCMC) techniques.
One of the standout features of Synference is its ability to simultaneously infer photometric redshifts and physical parameters. This capability is crucial for rapid Bayesian model comparison, as demonstrated by the researchers. They highlighted systematic stellar mass differences between two commonly used stellar population synthesis models, showcasing Synference’s utility in identifying and comparing model discrepancies. The framework’s speed and accuracy make it a powerful, scalable tool poised to maximize the scientific return of next-generation galaxy surveys.
The implications of Synference extend beyond mere speed and accuracy. By providing a flexible and efficient means of conducting simulation-based inference, Synference opens new avenues for exploring the complexities of galaxy formation and evolution. Its ability to handle large datasets quickly and accurately makes it an invaluable tool for astronomers and astrophysicists. As next-generation telescopes like JWST continue to gather vast amounts of data, tools like Synference will be essential in extracting meaningful insights and advancing our understanding of the universe.
