Bringing Manifold Learning and Dimensionality Reduction to SED Fitters
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Bringing Manifold Learning and Dimensionality Reduction to SED Fitters. / Hemmati, Shoubaneh; Capak, Peter; Pourrahmani, Milad; Nayyeri, Hooshang; Stern, Daniel; Mobasher, Bahram; Darvish, Behnam; Davidzon, Iary; Ilbert, Olivier; Masters, Daniel; Shahidi, Abtin.
In: Astrophysics Journal Letters, Vol. 881, No. 1, L14, 01.08.2019.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Bringing Manifold Learning and Dimensionality Reduction to SED Fitters
AU - Hemmati, Shoubaneh
AU - Capak, Peter
AU - Pourrahmani, Milad
AU - Nayyeri, Hooshang
AU - Stern, Daniel
AU - Mobasher, Bahram
AU - Darvish, Behnam
AU - Davidzon, Iary
AU - Ilbert, Olivier
AU - Masters, Daniel
AU - Shahidi, Abtin
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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.
AB - 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.
KW - galaxies: fundamental parameters
KW - galaxies: statistics
U2 - 10.3847/2041-8213/ab3418
DO - 10.3847/2041-8213/ab3418
M3 - Journal article
VL - 881
JO - The Astrophysical Journal Letters
JF - The Astrophysical Journal Letters
SN - 2041-8205
IS - 1
M1 - L14
ER -
ID: 236165052