The PAU Survey: star-galaxy classification with multi narrow-band data

Abstract

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.

arXiv comment13 pages, 10 figures, the catalog with the ALHAMBRA classification is available at http://cosmohub.pic.es

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• The PAU Survey: Photometric redshifts using transfer learning from simulations
• Star-galaxy classification in the Dark Energy Survey Y1 dataset
• Data mining techniques on astronomical spectra data. II : Classification Analysis
• The PAU Survey: Measurement of Narrow-band galaxy properties with Approximate Bayesian Computation
• The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies
• Photometric identification of compact galaxies, stars and quasars using multiple neural networks
• The PAU Survey: Operation and orchestration of multi-band survey data
• An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks
• AutoSourceID-Classifier. Star-Galaxy Classification using a Convolutional Neural Network with Spatial Information
• The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties
• Enhanced Astronomical Source Classification with Integration of Attention Mechanisms and Vision Transformers
• The PAU Survey: Background light estimation with deep learning techniques
• The PAU Survey: Close galaxy pairs identification and analysis
• Morpho-Photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog
• The miniJPAS survey quasar selection I: Mock catalogues for classification