Bringing Manifold Learning and Dimensionality Reduction to SED Fitters
Research output: Contribution to journal › Journal article › Research › peer-review
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.
Original language | English |
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Article number | L14 |
Journal | Astrophysics Journal Letters |
Volume | 881 |
Issue number | 1 |
Number of pages | 6 |
ISSN | 2041-8205 |
DOIs | |
Publication status | Published - 1 Aug 2019 |
- galaxies: fundamental parameters, galaxies: statistics
Research areas
Links
- https://arxiv.org/pdf/1905.10379
Submitted manuscript
ID: 236165052