PopSED: Population-Level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation

Abstract

We present PopSED, a framework for the population-level inference of galaxy properties from photometric data. Unlike the traditional approach of first analyzing individual galaxies and then combining the results to determine the physical properties of the entire galaxy population, we directly make the population distribution the inference objective. We train normalizing flows to approximate the population distribution by minimizing the Wasserstein distance between the synthetic photometry of the galaxy population and the observed data. We validate our method using mock observations and apply it to galaxies from the GAMA survey. PopSED reliably recovers the redshift and stellar mass distribution of $10^5$ galaxies using broadband photometry within $<1$ GPU hr, being $10^{5-6}$ times faster than the traditional spectral energy distribution modeling method. From the population posterior, we also recover the star-forming main sequence for GAMA galaxies at $z<0.1$. With the unprecedented number of galaxies in upcoming surveys, our method offers an efficient tool for studying galaxy evolution and deriving redshift distributions for cosmological analyses.

13 pages, 4 figures. AJ accepted

References in corpus (17)

• Array Programming with NumPy
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• The 2.5 m Telescope of the Sloan Digital Sky Survey
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• The propagation of uncertainties in stellar population synthesis modeling I: The relevance of uncertain aspects of stellar evolution and the IMF to the derived physical properties of galaxies
• Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory
• A Highly Consistent Framework for the Evolution of the Star-Forming "Main Sequence" from z~0-6
• Introducing the Illustris Project: the evolution of galaxy populations across cosmic time
• An Objective Definition for the Main Sequence of Star-Forming Galaxies
• The Mass-Metallicity and the Fundamental Metallicity Relation revisited on a fully Te-based abundance scale for galaxies
• Galaxy And Mass Assembly (GAMA): the G02 field, Herschel-ATLAS target selection and Data Release 3
• Fast likelihood-free cosmology with neural density estimators and active learning
• Galaxy And Mass Assembly (GAMA): The galaxy stellar mass function to $z=0.1$ from the r-band selected equatorial regions
• Photometric Redshifts for Next-Generation Surveys
• The Euclid mission design
• The Star Forming Main Sequence of Dwarf Low Surface Brightness Galaxies
• DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation

Cited by in corpus (12)

• Quantifying the Effects of Known Unknowns on Inferred High-redshift Galaxy Properties: Burstiness, the IMF, and Nebular Physics
• pop-cosmos: A comprehensive picture of the galaxy population from COSMOS data
• pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
• Simulation-based inference of galaxy properties from JWST pixels
• Modelling the selection of galaxy groups with end to end simulations
• pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
• Data-Space Validation of High-Dimensional Models by Comparing Sample Quantiles
• galsbi: A Python package for the GalSBI galaxy population model
• Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer
• GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference
• GalSBI-SPS: a stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications
• Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation