Spatio-Spectroscopic Representation Learning using Unsupervised Convolutional Long-Short Term Memory Networks
Authors: Kameswara Bharadwaj Mantha, Lucy Fortson, Ramanakumar Sankar, Claudia Scarlata, Chris Lintott, Sandor Kruk, Mike Walmsley, Hugh Dickinson, +3 more
Organizations: Department of Physics and Astronomy, University of Minnesota Twin Cities, Minneapolis, USA · Company Name, Location, Country · European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n 28692 Villanueva de la Cañada, Madrid, Spain · Jodrell Bank Centre for Astrophyics, Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom · School of Physical Sciences, The Open University, Milton Keynes, MK7 6AA, UK · Departments of Physics and Astronomy, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA · Physics Department, Lancaster University, Lancaster, LA1 4YB, UK
Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a new unsupervised deep learning framework using Convolutional Long-Short Term Memory Network Autoencoders to encode generalized feature representations across both spatial and spectroscopic dimensions spanning 19 optical emission lines (3800A <λ< 8000A) among a sample of ∼9000 galaxies from the MaNGA IFS survey. As a demonstrative exercise, we assess our model on a sample of 290 Active Galactic Nuclei (AGN) and highlight scientifically interesting characteristics of some highly anomalous AGN.
Figures & tables
Figure 1: A conceptual overview of our 2DConvLSTM-vAE framework. For the 2DConvLSTM-AE architecture, the mean and variance layers are omitted.
Figure 2: Visualization of latent space embedding for our ∼9000 galaxies extracted from the 2DConvLSTM-AE (left) and 2DConvLSTM-vAE (right) using a UMAP along with the inset plot showing the histogram of anomaly scores.
Figure 3: Example highly anomalous AGN galaxies (top panel) and example showcase of latent space nearest neighbour search for two anomalous AGN as query galaxies (bottom panel).
Department of Physics & Astronomy, University of Missouri Kansas City, Kansas City, MO, 64110, USA. · Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA. · Department of Physics, University of California, Santa Cruz, CA 95064, USA. +1
IRISA, UMR CNRS 6074, Université Bretagne Sud, Vannes, France · Royal Belgian Institute for Space Aeronomy, Brussels, Belgium · UiT - The Arctic University of Norway, Tromsø, Norway