Galaxy Spectra Networks (GaSNet). III. Generative pre-trained network for spectrum reconstruction, redshift estimate and anomaly detection

Abstract

Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that takes advantage of generative neural networks to perform these three tasks at once with very high efficiency. We use two different generative networks, an autoencoder-like network and U-Net, to reconstruct the rest-frame spectrum (after redshifting). The autoencoder-like network operates similarly to the classical PCA, learning templates (eigenspectra) from the training set and returning modeling parameters. The U-Net, in contrast, functions as an end-to-end model and shows an advantage in noise reduction. By reconstructing spectra, we can achieve classification, redshift estimation, and anomaly detection in the same framework. Each rest-frame reconstructed spectrum is extended to the UV and a small part of the infrared (covering the blueshift of stars). Owing to the high computational efficiency of deep learning, we scan the chi-squared value for the entire type and redshift space and find the best-fitting point. Our results show that generative networks can achieve accuracy comparable to the classical PCA methods in spectral modeling with higher efficiency, especially achieving an average of $>98\%$ classification across all classes ($>99.9\%$ for star), and $>99\%$ (stars), $>98\%$ (galaxies) and $>93\%$ (quasars) redshift accuracy under cosmology research requirements. By comparing different peaks of chi-squared curves, we define the ``robustness'' in the scanned space, offering a method to identify potential ``anomalous'' spectra. Our approach provides an accurate and high-efficiency spectrum modeling tool for handling the vast data volumes from future spectroscopic sky surveys.

17 pages and 16 figures. Submitted to MNRAS

References in corpus (21)

• The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release
• Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument
• Full spectrum fitting with photometry in pPXF: stellar population versus dynamical masses, non-parametric star formation history and metallicity for 3200 LEGA-C galaxies at redshift z~0.8
• The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument
• Star-galaxy Classification Using Deep Convolutional Neural Networks
• Machine Learning in Astronomy: a practical overview
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees
• Learning to Denoise Astronomical Images with U-nets
• Redshift Measurement and Spectral Classification for eBOSS Galaxies with the Redmonster Software
• Autoencoding Galaxy Spectra I: Architecture
• Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection
• Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
• Deep Learning-based galaxy image deconvolution
• Deep Learning in Searching the Spectroscopic Redshift of Quasars
• Deriving the star formation histories of galaxies from spectra with simulation-based inference
• De-noising of galaxy optical spectra with autoencoders
• Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions
• Inferring redshift and galaxy properties via a multi-task neural net with probabilistic outputs: An application to simulated MOONS spectra
• Galaxy Spectra neural Networks (GaSNets). I. Searching for strong lens candidates in eBOSS spectra using Deep Learning
• Beyond Gaia DR3: Tracing the [$\alpha$/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks

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