J/MNRAS/455/370 Predicted LIR for SDSS galaxies (Ellison+, 2016)
The infrared luminosities of ∼332000 SDSS galaxies predicted from artificial
neural networks and the Herschel Stripe 82 survey.
Ellison S.L., Teimoorinia H., Rosario D.J., Trevor Mendel J.
<Mon. Not. R. Astron. Soc., 455, 370-385 (2016)>
=2016MNRAS.455..370E 2016MNRAS.455..370E (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, IR ; Redshifts
Keywords: methods: data analysis - methods: numerical - galaxies: active -
galaxies: fundamental parameters - galaxies: statistics -
infrared: galaxies
Abstract:
The total infrared (IR) luminosity (LIR) can be used as a robust
measure of a galaxy's star formation rate (SFR), even in the presence
of an active galactic nucleus (AGN), or when optical emission lines
are weak. Unfortunately, existing all sky far-IR surveys, such as the
Infrared Astronomical Satellite (IRAS) and AKARI, are relatively
shallow and are biased towards the highest SFR galaxies and lowest
redshifts. More sensitive surveys with the Herschel Space Observatory
are limited to much smaller areas. In order to construct a large
sample of LIR measurements for galaxies in the nearby Universe, we
employ artificial neural networks (ANNs), using 1136 galaxies in the
Herschel Stripe 82 sample as the training set. The networks are
validated using two independent data sets (IRAS and AKARI) and
demonstrated to predict the LIR with a scatter σ∼0.23dex, and
with no systematic offset. Importantly, the ANN performs well for both
star-forming galaxies and those with an AGN. A public catalogue is
presented with our LIR predictions which can be used to determine
SFRs for 331926 galaxies in the Sloan Digital Sky Survey (SDSS),
including ∼129000 SFRs for AGN-dominated galaxies for which SDSS SFRs
have large uncertainties.
Description:
In this work we will make use of data from three separate spacecraft
that collected data in the FIR: the Infrared Astronomical Satellite
(IRAS; Neugebauer et al., 1984ApJ...278L...1N 1984ApJ...278L...1N), AKARI (Murakami et
al., 2007PASJ...59S.369M 2007PASJ...59S.369M) and Herschel (Pilbratt et al.,
2010A&A...518L...1P 2010A&A...518L...1P).
Based on a sample of 1136 galaxies identified in a cross-match between
the SDSS and Herschel Stripe 82 Survey, we have trained an ANN to
predict IR luminosities based on 23 input parameters measured from
SDSS imaging and spectroscopy.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table2.dat 57 331926 Catalogue of artificial neural networks (ANN)
predicted LIR for SDSS galaxies
--------------------------------------------------------------------------------
Byte-by-byte Description of file: table2.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 18 I18 --- SDSS SDSS objID
20- 28 F9.5 deg RAdeg Right ascension (J2000)
30- 38 F9.5 deg DEdeg Declination (J2000)
40- 46 F7.5 --- z Redshift
48- 52 F5.2 [10-7W] logLIR ANN-predicted LIR luminosity
54- 57 F4.2 [10-7W] s_logLIR Scatter in ANN-predicted LIR
--------------------------------------------------------------------------------
History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 28-Jul-2016