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Journal Articles The Astrophysical Journal Letters Year : 2019

Bringing Manifold Learning and Dimensionality Reduction to SED Fitters

Shoubaneh Hemmati
  • Function : Author
Peter Capak
Milad Pourrahmani
  • Function : Author
Hooshang Nayyeri
Daniel Stern
  • Function : Author
Bahram Mobasher
  • Function : Author
Behnam Darvish
Iary Davidzon
Daniel Masters
Abtin Shahidi
  • Function : Author


We show that unsupervised machine learning techniques are a valuable tool for both visualizing and computationally accelerating the estimation of galaxy physical properties from photometric data. As a proof of concept, we use self-organizing maps (SOMs) to visualize a spectral energy distribution (SED) model library in the observed photometry space. The resulting visual maps allow for a better understanding of how the observed data maps to physical properties and allows for better optimization of the model libraries for a given set of observational data. Next, the SOMs are used to estimate the physical parameters of 14,000 z ∼ 1 galaxies in the COSMOS field and are found to be in agreement with those measured with SED fitting. However, the SOM method is able to estimate the full probability distribution functions for each galaxy up to ∼106 times faster than direct model fitting. We conclude by discussing how this acceleration, as well as learning how the galaxy data manifold maps to physical parameter space and visualizing this mapping in lower dimensions, helps overcome other challenges in galaxy formation and evolution.

Dates and versions

insu-03666735 , version 1 (12-05-2022)



Shoubaneh Hemmati, Peter Capak, Milad Pourrahmani, Hooshang Nayyeri, Daniel Stern, et al.. Bringing Manifold Learning and Dimensionality Reduction to SED Fitters. The Astrophysical Journal Letters, 2019, 881, ⟨10.3847/2041-8213/ab3418⟩. ⟨insu-03666735⟩
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