Autoencoding Galaxy Spectra I: Architecture

Abstract

We introduce the neural network architecture SPENDER as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose spectral range and resolution exceeds that of the observing instrument. The decoder is followed by explicit redshift, resampling, and convolution transformations to match the observations. The architecture takes galaxy spectra at arbitrary redshifts and is robust to glitches like residuals of the skyline subtraction, so that spectra from a large survey can be ingested directly without additional preprocessing. We demonstrate the performance of SPENDER by training on the entire spectroscopic galaxy sample of SDSS-II; show its ability to create highly accurate reconstructions with substantially reduced noise; perform deconvolution and oversampling for a super-resolution model that resolves the [OII] doublet; introduce a novel method to interpret attention weights as proxies for important spectral features; and infer the main degrees of freedom represented in the latent space. We conclude with a discussion of future improvements and applications.

arXiv comment17 pages, 5 figures, submitted to AJ. Code available at https://github.com/pmelchior/spender

References in corpus (28)

• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Stellar Masses and Star Formation Histories for 10^5 Galaxies from the Sloan Digital Sky Survey
• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
• EAZY: A Fast, Public Photometric Redshift Code
• The Sixteenth Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra
• Improving the full spectrum fitting method: accurate convolution with Gauss-Hermite functions
• Spectral Classification and Redshift Measurement for the SDSS-III Baryon Oscillation Spectroscopic Survey
• Understanding Galaxy Evolution through Emission Lines
• Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe
• Optical Star-Formation Rate Indicators
• An Atlas of Galaxy Spectral Energy Distributions from the Ultraviolet to the Mid-Infrared
• Distributions of Galaxy Spectral Types in the Sloan Digital Sky Survey
• The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale Structure Catalogs for Cosmological Analysis
• Dimensionality Reduction of SDSS Spectra with Variational Autoencoders
• Astronomaly: Personalised Active Anomaly Detection in Astronomical Data
• SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry
• A Reexamination of Electron Density Diagnostics for Ionized Gaseous Nebulae
• Deep Generative Models for Galaxy Image Simulations
• ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys
• The stellar evolution of Luminous Red Galaxies, and its dependence on colour, redshift, luminosity and modelling
• Spectroscopic failures in photometric redshift calibration: cosmological biases and survey requirements
• Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection
• Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
• Unsupervised classification of SDSS galaxy spectra
• Forward Modeling of Spectroscopic Galaxy Surveys: Application to SDSS
• Classifying galaxy spectra at 0.5<z<1 with self-organizing maps

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