accelerated Bayesian SED Modeling with
simulation-based inference
changhoon.hahn@princeton.edu
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upcoming spectroscopic missions to study galaxy evolution will face a number of major challenges
1
current spectral energy distribution (SED) models limit our ability to measure galaxy properties from spectra
2
galaxy formation models don't reproduce observations so they can't be used for spectral simulations
3
current SED modeling cannot be scaled-up to meet the demands of future spectroscopic missions
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3
with deep generative models we can address each of these major challenges
deep generative models (e.g. VAEs, GANs) provide powerful unsupervised methods for learning the data distribution
low-dimensional representation of spectra in latent variable-space
a neural network that maps latent variables to spectral-space
Variational Autoencoders, Generative Adversarial Networks
a neural network that maps spectra to compact latent variables
spectra from MOS missions
reconstructed spectra from the generative model
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by embedding a galaxy SED model in the architecture, we can build physics- and data-driven SED models
neural network emulator of a galaxy stellar population synthesis model (e.g. Alsing+2020)
latent variable space now includes physical galaxy properties (e.g. SFH, metallicity)
physical transformation (redshifting) included in the causal structure
decoder outputs high-resolution, rest-frame galaxy SED
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deep generative SED models can more accurately model observed spectra and account for systematics
our model can accurately model emission lines, which means we can extract galaxy property information from them
our model can accurately model emission lines, which means we can extract galaxy property information from them
our model provides a higher resolution SED that matches the observed spectra more accurately
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generative models can be combined with simulation-based inference* to accelerate Bayesian SED modeling
*a.k.a. "likelihood-free" inference
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we can reframe Bayesian inference into a density estimation problem with SBI
estimate the probability distribution using simulated samples
generative models can be combined with simulation-based inference* to accelerate Bayesian SED modeling
*a.k.a. "likelihood-free" inference
obligatory normalizing flow gif
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generative + SBI based SED modeling: ~3 hrs to train, full posterior 0.2 seconds/galaxy
11-dimensional SED model with non-parametric star formation and metallicity histories
scalable Bayesian analysis for future spectroscopic missions
rigorous Bayesian inference --- not blackbox ML
generative model + SBI based SED modeling applied to 144k galaxies in DESI survey validation --- PROVABGS-SV
the PRObabilistic Value-Added Bright Galaxy Survey-SV will be public available later this year!
the PRObabilistic Value-Added Bright Galaxy Survey-SV will be public available later this year!
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inferred stellar masses from full posteriors