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% Use vector fonts, so it zooms properly in on-screen viewing software
% Don't change these lines unless you know what you are doing
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%%%%% AUTHORS - PLACE YOUR OWN PACKAGES HERE %%%%%
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%%%%% AUTHORS - PLACE YOUR OWN COMMANDS HERE %%%%%
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\title[Machine learning methods for radio source cross-identification]{Radio Galaxy Zoo: Machine learning methods for radio source host galaxy cross-identification}

\author[Alger et al.]{M.~J.~Alger$^{1}$, J.~K.~Banfield$^{1, 2}$, C.~S.~Ong$^{4, 5}$, O.~I.~Wong$^{2, 6}$, L.~Rudnick$^{7}$, R.~P.~Norris$^{3, 8}$, others
\\
% List of institutions
$^{1}$Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia\\
$^{2}$ARC Centre of Excellence for All-Sky Astrophysics (CAASTRO)\\
$^{3}$Western Sydney University, Locked Bag 1797, Penrith South, NSW 1797, Australia\\
$^{4}$Data61, CSIRO, Canberra ACT 2601, Australia\\
$^{5}$Research School of Computer Science, The Australian National University, Canberra, ACT 2601, Australia\\
$^{6}$International Centre for Radio Astronomy Research-M468, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia\\
$^{7}$Minnesota Institute for Astrophysics, University of Minnesota, 116 Church St. SE, Minneapolis, MN 55455\\
$^{8}$CSIRO Astronomy \& Space Science, PO Box 76, Epping, NSW 1710, Australia
}

% These dates will be filled out by the publisher
\date{Accepted XXX. Received XXX}

% Enter the current year, for the copyright statements etc.
\pubyear{2017}

% Don't change these lines
\begin{document}
\label{firstpage}
\pagerange{\pageref{firstpage}--\pageref{lastpage}}
\maketitle

% Abstract of the paper
\begin{abstract}
  We present a machine learning approach for the task of determining the
  infrared host galaxies of radio emissions detected in wide-area radio
  surveys. We propose a method for reducing the cross identification task
  to a binary classification task, which is well understood and well
  supported by machine learning software.
  We test our methods on the radio sources detected in the $1.4$~GHz
  Australian Telescope Large Area Survey (ATLAS) observations of the
  \emph{Chandra} Deep Field - South (CDFS) and the ESO Large Area ISO Survey -
  South 1 (ELAIS-S1) fields, and cross identify them with
  \emph{Spitzer} Wide-area Infrared Extragalactic survey (SWIRE).
  We compared two categories of labels:
  expert cross-identifications of CDFS from the initial ATLAS data release and
  crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo (RGZ).
  Our empirical results show that the cross-identification accuracy of
  the predictor trained on RGZ labels is comparable to the predictor trained
  on expert labels, demonstrating the value of crowdsourced labels.
  In addition to cross validation performance on CDFS, we demonstrate that
  our method also generalizes to ELAIS-S1.<commit_msg>RGZ -> Radio Galaxy Zoo<commit_after>\PassOptionsToPackage{pdfpagelabels=false}{hyperref}
\documentclass[fleqn,usenatbib,usedcolumn]{mnras}
%==============================================================================%
\usepackage[british]{babel}             % British English hyphenation
\usepackage{txfonts}                  % Good fonts
% Use vector fonts, so it zooms properly in on-screen viewing software
% Don't change these lines unless you know what you are doing
%\usepackage[T1]{fontenc}
%\usepackage{ae,aecompl}
%%%%% AUTHORS - PLACE YOUR OWN PACKAGES HERE %%%%%
\usepackage{graphicx}	% Including figure files
\usepackage{hyperref} % hyperlinks
\usepackage{natbib}
\usepackage{aastexmacros}
\usepackage{tikz}
\usepackage[caption=false]{subfig}
\usepackage[mediumspace,mediumqspace,Grey,squaren]{SIunits}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%% AUTHORS - PLACE YOUR OWN COMMANDS HERE %%%%%
\usetikzlibrary{shapes,arrows,calc,positioning}

\renewcommand{\vec}[1]{\mathbf{#1}}
% \newcommand{\text}{\mathrm}
\newcommand{\jansky}{\text{Jy}}
\newcommand{\cheng}[1]{ {\color{teal}[{\bf Cheng TODO:~{#1}}]} }
\newcommand{\matthew}[2]{ {\color{white!20!violet}[{\bf TODO(#1):~{#2}}]} }
\newcommand{\todo}[1]{ {\color{red}[{\bf TODO:~{#1}}]} }

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%% TITLE PAGE %%%%%%%%%%%%%%%%%%%

\title[Machine learning methods for radio source cross-identification]{Radio Galaxy Zoo: Machine learning methods for radio source host galaxy cross-identification}

\author[Alger et al.]{M.~J.~Alger$^{1}$, J.~K.~Banfield$^{1, 2}$, C.~S.~Ong$^{4, 5}$, O.~I.~Wong$^{2, 6}$, L.~Rudnick$^{7}$, R.~P.~Norris$^{3, 8}$, others
\\
% List of institutions
$^{1}$Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia\\
$^{2}$ARC Centre of Excellence for All-Sky Astrophysics (CAASTRO)\\
$^{3}$Western Sydney University, Locked Bag 1797, Penrith South, NSW 1797, Australia\\
$^{4}$Data61, CSIRO, Canberra ACT 2601, Australia\\
$^{5}$Research School of Computer Science, The Australian National University, Canberra, ACT 2601, Australia\\
$^{6}$International Centre for Radio Astronomy Research-M468, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia\\
$^{7}$Minnesota Institute for Astrophysics, University of Minnesota, 116 Church St. SE, Minneapolis, MN 55455\\
$^{8}$CSIRO Astronomy \& Space Science, PO Box 76, Epping, NSW 1710, Australia
}

% These dates will be filled out by the publisher
\date{Accepted XXX. Received XXX}

% Enter the current year, for the copyright statements etc.
\pubyear{2017}

% Don't change these lines
\begin{document}
\label{firstpage}
\pagerange{\pageref{firstpage}--\pageref{lastpage}}
\maketitle

% Abstract of the paper
\begin{abstract}
  We present a machine learning approach for the task of determining the
  infrared host galaxies of radio emissions detected in wide-area radio
  surveys. We propose a method for reducing the cross identification task
  to a binary classification task, which is well understood and well
  supported by machine learning software.
  We test our methods on the radio sources detected in the $1.4$~GHz
  Australian Telescope Large Area Survey (ATLAS) observations of the
  \emph{Chandra} Deep Field - South (CDFS) and the ESO Large Area ISO Survey -
  South 1 (ELAIS-S1) fields, and cross identify them with
  \emph{Spitzer} Wide-area Infrared Extragalactic survey (SWIRE).
  We compared two categories of labels:
  expert cross-identifications of CDFS from the initial ATLAS data release and
  crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo.
  Our empirical results show that the cross-identification accuracy of
  the predictor trained on Radio Galaxy Zoo labels is comparable to the predictor trained
  on expert labels, demonstrating the value of crowdsourced labels.
  In addition to cross validation performance on CDFS, we demonstrate that
  our method also generalizes to ELAIS-S1.
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