Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Photometric identification of compact galaxies, stars, and quasars using multiple neural networks

Journal Article · · Monthly Notices of the Royal Astronomical Society
 [1];  [1];  [2];  [3];  [4];  [5];  [6]
  1. Indian Institutes of Science Education and Research (IISER), Bhopal (India)
  2. Indian Institute of Technology (IIT), Bombay (India)
  3. Pune Institute of Computer Technology (India)
  4. Millennium Institute of Astrophysics (MAS), Santiago (Chile)
  5. Indian Institute of Astrophysics, Koramangala, Bengaluru (India)
  6. Inter University Centre for Astronomy and Astrophysics (IUCAA), Pune (India)
+ Show Author Affiliations
We present MargNet, a deep learning-based classifier for identifying stars, quasars, and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey Data Release 16 catalogue. MargNet consists of a combination of convolutional neural network and artificial neural network architectures. Using a carefully curated data set consisting of 240 000 compact objects and an additional 150 000 faint objects, the machine learns classification directly from the data, minimizing the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming surveys, such as Dark Energy Survey and images from the Vera C. Rubin Observatory.
Research Organization:
US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC)
OSTI ID:
1900552
Alternate ID(s):
OSTI ID: 2425248
Journal Information:
Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 518; ISSN 0035-8711
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

References (54)

MargNet: Photometric identification of compact galaxies, stars and quasars dataset January 2022
A logical calculus of the ideas immanent in nervous activity journal December 1943
Machine and Deep Learning applied to galaxy morphology - A comparative study journal January 2020
Stacked Denoising Autoencoders Applied to Star/Galaxy Classification journal April 2017
Classification of star/galaxy/QSO and star spectral types from LAMOST data release 5 with machine learning approaches journal February 2021
Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data journal March 2018
Deep learning journal May 2015
Array programming with NumPy journal September 2020
SciPy 1.0: fundamental algorithms for scientific computing in Python journal February 2020
Stellar classification from single-band imaging using machine learning journal June 2016
Photometric redshift estimation via deep learning: Generalized and pre-classification-less, image based, fully probabilistic redshifts journal January 2018
Photometric redshifts from SDSS images using a convolutional neural network journal December 2018
Identifying galaxies, quasars, and stars with machine learning: A new catalogue of classifications for 111 million SDSS sources without spectra journal July 2020
A difference boosting neural network for automated star-galaxy classification journal April 2002
The Sloan Digital Sky Survey Photometric System journal April 1996
Systematic properties of compact groups of galaxies journal April 1982
The angular correlation function of galaxies as a function of magnitude journal October 1986
The Sloan Digital Sky Survey Photometric Camera journal December 1998
The Sloan Digital Sky Survey: Technical Summary journal September 2000
The 2.5 m Telescope of the Sloan Digital Sky Survey journal April 2006
Robust Machine Learning Applied to Astronomical Data Sets. I. Star‐Galaxy Classification of the Sloan Digital Sky Survey DR3 Using Decision Trees journal October 2006
Photometric Response Functions of the Sloan Digital sky Survey Imager journal March 2010
The Wide-Field Infrared Survey Explorer (Wise): Mission Description and Initial On-Orbit Performance journal November 2010
Decision tree Classifiers for Star/Galaxy Separation journal May 2011
The Multi-Object, Fiber-Fed Spectrographs for the Sloan Digital sky Survey and the Baryon Oscillation Spectroscopic Survey journal July 2013
Star-Galaxy Classification in Multi-Band Optical Imaging journal October 2012
Machine Learning for the Zwicky Transient Facility journal January 2019
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) journal August 2012
Identifying strong lenses with unsupervised machine learning using convolutional autoencoder journal April 2020
Stellar spectral interpolation using machine learning journal June 2020
On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2 journal July 2021
Fraction of broad absorption line quasars in different radio morphologies journal January 2022
Star/galaxy separation at faint magnitudes: application to a simulated Dark Energy Survey journal April 2015
Rotation-invariant convolutional neural networks for galaxy morphology prediction journal April 2015
Star–galaxy classification using deep convolutional neural networks journal October 2016
The PAU survey: star–galaxy classification with multi narrow-band data journal November 2018
Improving galaxy morphologies for SDSS with Deep Learning journal February 2018
Detection of bars in galaxies using a deep convolutional neural network journal March 2018
The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies, and redshifts with 12 optical filters journal August 2019
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning journal October 2019
Application of convolutional neural networks for stellar spectral classification journal November 2019
SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification journal December 2019
Subaru Prime Focus Camera — Suprime-Cam journal December 2002
AI-NET: Attention Inception Neural Networks for Hyperspectral Image Classification conference July 2018
Matplotlib: A 2D Graphics Environment journal January 2007
The NumPy Array: A Structure for Efficient Numerical Computation journal March 2011
A photometric catalogue of quasars and other point sources in the Sloan Digital Sky Survey: A photometric catalogue journal November 2011
The Dark Energy Survey journal June 2005
seaborn: statistical data visualization journal April 2021
Data Structures for Statistical Computing in Python conference January 2010
Photometric Supernova Classification with Machine Learning journal August 2016
LSST: From Science Drivers to Reference Design and Anticipated Data Products journal March 2019
The 16th Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra journal June 2020
MargNet: Photometric identification of compact galaxies, stars and quasars dataset January 2022

Similar Records

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties
Journal Article · Thu Feb 16 19:00:00 EST 2023 · Monthly Notices of the Royal Astronomical Society · OSTI ID:2425349

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Astronomy & Astrophysics
galaxies: general
methods: data analysis
quasars: general
software: data analysis
stars: general
techniques: photometric