DSPS: Differentiable Stellar Population Synthesis

Abstract

Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for stellar population synthesis. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, and ii) support for the diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modeling the colors of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speedup over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy--halo connection that fully forward model galaxy spectra and photometry.

13 pages, 2 appendices. Version accepted for publication in MNRAS

References in corpus (30)

• Modules for Experiments in Stellar Astrophysics (MESA)
• 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
• K-corrections and filter transformations in the ultraviolet, optical, and near infrared
• UV Star Formation Rates in the Local Universe
• Machine learning accelerated computational fluid dynamics
• An ultra-deep near-infrared spectrum of a compact quiescent galaxy at z=2.2
• Binary Population and Spectral Synthesis Version 2.1: construction, observational verification and new results
• Stellar Population Inference with Prospector
• The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
• exoplanet: Gradient-based probabilistic inference for exoplanet data & other astronomical time series
• How Well Can We Measure the Stellar Mass of a Galaxy: The Impact of the Assumed Star Formation History Model in SED Fitting
• Non-parametric Star Formation History Reconstruction with Gaussian Processes I: Counting Major Episodes of Star Formation
• Cosmic Calibration
• The cosmology dependence of galaxy clustering and lensing from a hybrid $N$-body-perturbation theory model
• Constraints on Physical Properties of z~6 Galaxies Using Cosmological Hydrodynamic Simulations
• On the importance of using appropriate spectral models to derive physical properties of galaxies at 0.7<z<2.8
• Accurate emulator for the redshift-space power spectrum of dark matter halos and its application to galaxy power spectrum
• Matter Power Spectrum Emulator for f(R) Modified Gravity Cosmologies
• FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver
• Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
• Deep Realistic Extragalactic Model (DREaM) Galaxy Catalogs: Predictions for a Roman Ultra-Deep Field
• Forward modeling of galaxy populations for cosmological redshift distribution inference
• A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos
• Diffstar: A Fully Parametric Physical Model for Galaxy Assembly History
• Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models
• The DESI PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) Mock Challenge
• Surrogate modelling the Baryonic Universe II: on forward modelling the colours of individual and populations of galaxies
• DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation
• Differentiable Predictions for Large Scale Structure with SHAMNet
• An Interpretable Machine Learning Framework for Modeling High-Resolution Spectroscopic Data

Cited by in corpus (1)

• Analytic auto-differentiable $\Lambda$CDM cosmography