Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations

Understand

We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations.

• We train Convolutional Neural Networks to learn the relationship between synthetic galaxy spectra and high resolution SFHs from the EAGLE and Illustris models.
• To evaluate our SFH reconstruction we use Symmetric Mean Absolute Percentage Error (SMAPE), which acts as a true percentage error in the low-error regime.
• On dust-attenuated spectra we achieve high test accuracy (median SMAPE $= 10.5\%$).