Year: 2022
Type: article
Abstract: Aims.We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method’s efficiency, but also to understand what merger properties the NN can learn and what their physical interpretation is. Methods.We created a class-balanced training dataset of 5860 galaxies split into mergers and non-mergers. The galaxy observations came from SDSS DR6 and were visually identified in Galaxy Zoo. The 2930 mergers were selected from known SDSS mergers and the respective non-mergers were the closest match in both redshift andrmagnitude. The NN architecture was built by testing a different number of layers with different sizes and variations of the dropout rate. We compared input spaces constructed using: the five SDSS filters:u,g,r,i, andz; combinations of bands, colours, and their errors; six magnitude types; and variations of input normalization. Results.We find that the fibre magnitude errors contribute the most to the training accuracy. Studying the parameters from which they are calculated, we show that the input space built from the sky error background in the five SDSS bands alone leads to 92.64 ± 0.15% training accuracy. We also find that the input normalization, that is to say, how the data are presented to the NN, has a significant effect on the training performance. Conclusions.We conclude that, from all the SDSS photometric information, the sky error background is the most sensitive to merging processes. This finding is supported by an analysis of its five-band feature space by means of data visualization. Moreover, studying the plane of thegandrsky error bands shows that a decision boundary line is enough to achieve an accuracy of 91.59%.
Source: Astronomy and Astrophysics
Authors: L. E. Suelves, W. J. Pearson, A. Pollo
Institution: National Centre for Nuclear Research
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,580
Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Princeton University, Ohio State University, University of Washington, Johns Hopkins University, University of Pittsburgh, Case Western Reserve University, Drexel University, Universität Basel, University of Portsmouth, New Mexico State University, Fundacja na rzecz Nauki Polskiej, Chinese Academy of Sciences, Max-Planck-Gesellschaft, Max-Planck-Institut für Astronomie, U.S. Naval Observatory, Fermilab, Los Alamos National Laboratory
OpenAlex ID: W4311243008