J/A+A/643/A177 Candidate Cluster Members with Deep learning (Angora+, 2020)
The search for galaxy cluster members with deep learning of panchromatic HST
imaging and extensive spectroscopy.
Angora G., Rosati P., Brescia M., Mercurio M., Grillo C., Caminha G.,
Meneghetti M., Nonino M., Vanzella E., Bergamini P., Biviano A., Lombardi M.
<Astron. Astrophys. 643, A177 (2020)>
=2020A&A...643A.177A 2020A&A...643A.177A (SIMBAD/NED BibCode)
ADC_Keywords: Clusters, galaxy ; Positional data
Keywords: Galaxy: general - galaxies: photometry -
galaxies: distances and redshifts - techniques: image processing -
methods: data analysis
Abstract:
The next generation of extensive and data-intensive surveys are bound
to produce a vast amount of data, which can be efficiently dealt with
using machine-learning and deep-learning methods to explore possible
correlations within the multi-dimensional parameter space.
We explore the classification capabilities of convolution neural
networks (CNNs) to identify galaxy cluster members (CLMs) by using
Hubble Space Telescope (HST) images of fifteen galaxy clusters at
redshift 0.19≲z≲0.60, observed as part of the CLASH and Hubble
Frontier Field programmes.
We used extensive spectroscopic information, based on the CLASH-VLT
VIMOS programme combined with MUSE observations, to define the
knowledge base. We performed various tests to quantify how well CNNs
can identify cluster members on ht basis of imaging information only.
Furthermore, we investigated the CNN capability to predict source
memberships outside the training coverage, in particular, by
identifying CLMs at the faint end of the magnitude distributions.
We find that the CNNs achieve a purity-completeness rate ≳90%,
demonstrating stable behaviour across the luminosity and colour of
cluster galaxies, along with a remarkable generalisation capability
with respect to cluster redshifts. We concluded that if extensive
spectroscopic information is available as a training base, the
proposed approach is a valid alternative to catalogue-based methods
because it has the advantage of avoiding photometric measurements,
which are particularly challenging and time-consuming in crowded
cluster cores. As a byproduct, we identified 372 photometric cluster
members, with mag(F814)<25, to complete the sample of 812
spectroscopic members in four galaxy clusters RX J2248-4431,
MACS J0416-2403, MACS J1206-0847 and MACS J1149+2223.
When this technique is applied to the data that are expected to become
available from forthcoming surveys, it will be an efficient tool for a
variety of studies requiring CLM selection, such as galaxy number
densities, luminosity functions, and lensing mass reconstruction.
Description:
The catalogue consists of 1184 cluster members, 812 of them are
spectroscopically classified, while the remaining sample (372) are
candidate members identified by a Convolutional Neural Network, with a
limiting magnitude of 25 AB in F814W filter, and membership
probability. Members have been identified in the four galaxy clusters
RX J2248.7-4431 (AS 1063), MACS J0416.1-2403, MACS J1206.2-0847, MACS
J1149.5+2223.
Objects:
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RA (2000) DE Designation(s)
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22 48 54.3 -44 31 07 RX J2248.7-4431 = ACO S1063
04 16 08.38 -24 04 20.8 MCS J0416.1-2403 = MCS J0416.1-2403
12 06 12.2 -08 48 02 MCS J1206.2-0847 = MCS J1206.2-0847
11 49 35.8 +22 23 55 MCS J1149.5+2223 = MCS J1149.5+2223
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File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
clms.dat 64 1184 Candidate CLMs identified by CNN
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Byte-by-byte Description of file: clms.dat
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Bytes Format Units Label Explanations
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1- 18 F18.14 deg RAdeg Right Ascension (J2000)
20- 38 F19.15 deg DEdeg Declination (J2000)
40- 58 F19.16 --- prob Membership probability
(-1: spectroscopic source)
60- 64 A5 --- FoV Corresponding cluster Field of View (1)
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Note (1): Fields of View as follows:
m0416 = MACS J0416.1-2403
m1149 = MACS J1149.5+2223
m1206 = MACS J1206.2-0847
r2248 = RX J2248.7-4431 (AS 1063)
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Acknowledgements:
Giuseppe Angora, gius.angora(at)gmail.com
(End) Giuseppe Angora [Univ. Ferrara], Patricia Vannier [CDS] 25-Sep-2020