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whitepaper/Cosmology/supernovacosmology.tex

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@@ -43,7 +43,7 @@ \section{Supernova Cosmology and Physics}
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\subsection{Introduction}
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The acceleration of the rate of expansion of the Universe at late times is one of the
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most exciting and fundamental discoveries\citep{Riess1998,Perlmutter1999} in recent times.
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This discovery was made using Type Ia supernovae (SNe) as standardizable candles, and
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This discovery was made using Type Ia supernovae (SNIa) as standardizable candles, and
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implies that 76\% of the energy density of the Universe is composed of dark energy
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\citep{Frieman2008}. Type Ia supernovae are believed to be explosions of white
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dwarfs that have approached the Chandrasekhar mass and are disrupted by
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This section is concerned with the detection and characterization of
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supernovae (SNe) over time with LSST and their various scientific
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applications. A crucial application is the use of supernovae type
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Ia (SN Ia) %and potentially some core-collapse SN (like type IIP) to trace
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applications. A crucial application is the use of SNIA %and potentially some core-collapse SNe
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(like type IIP) to trace
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to trace the recent expansion history of the universe, and confront models of the
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physics driving the late time accelerated expansion of the universe.
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and well characterized by the WFD will provide
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\begin{itemize}
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\item a large, well-calibrated low redshift sample ($z \lesssim 0.1$) to replace/supplement the current set of low redshift supernovae from a mixture of surveys. Such a large, clean low redshift sample is crucial in {\emph{providing a longer lever arm for the determination of cosmological parameters from supernovae.}}
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\item a low and medium redshift ($z \lesssim 0.5$) spanning large areas of the sky and therefore with the ability of {\emph{tracing large scale structure}} in a novel way, particularly due to the inclusion of estimates radial distances. This will be possible by combining redshift estimates from supernova light curve in conjuncion with photometric redshifts from host galaxies. Such a sample could also be used to probe the {\emph{isotropy of the late time universe.}}
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\item This large sample of SNIa will also enable further sharpening of our understanding of the properties of the SN
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population of both Type Ia and core-collapse SNe (see \autoref{sec:transients:SNtransients}). Aside from the science
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described in \autoref{sec:transients:SNtransients}, this understanding will also be extremely important to the goal of
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SN cosmology from LSST. When selecting supernovae satisfying specific criteria from observations in magnitude limited
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surveys, a lack of understanding of the population properties leads to selection biases in SNIa cosmology as well as the
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steps in photometric classification~\cite{Scolnic2016, Kessler2017}.
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\item a low and medium redshift ($z \lesssim 0.8$ and peaking at $z \sim 0.4$ ) spanning large areas of the sky and therefore with the ability of {\emph{tracing large scale structure}} in a novel way, particularly due to the inclusion of estimates radial distances. This will be possible by combining redshift estimates from supernova light curve in conjuncion with photometric redshifts from host galaxies. Such a sample could also be used to probe the {\emph{isotropy of the late time universe.}}
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\item This large sample of SNe will also enable further sharpening of our understanding of the properties of the SN population of both Type Ia and core-collapse SNe (see \autoref{sec:transients:SNtransients}). Aside from the science described in \autoref{sec:transients:SNtransients}, this understanding will also be extremely important to the goal of SN cosmology from LSST. When selecting supernovae satisfying specific criteria from observations in magnitude limited surveys, a lack of understanding of the population properties leads to selection biases in SNIa cosmology as well as the steps in photometric classification~\cite{2017ApJ...836...56K,2016ApJ...822L..35S}.
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The WFD SN Ia
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sample will dramatically increase the size of the sample available to
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train such an empirical model, as well as understand the probability of
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We construct the quality metric for the perSNMetric by obtaining the
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light curve of the SN in the time window described above. We fit the
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light curve, using the SALT2 model \citep{Guy2007, Mosher2014}, and approximately estimate the uncertainty in
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light curve, using the SALT2 model \citep{Guy2007,2014A&A...568A..22B}, and approximately estimate the uncertainty in
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distance from
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the light curve fit alone. Of course, as is well known, luminosity
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distance estimates of supernova Type Ia also show an intrinsic scatter
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intrinsic dispersion to variance of the distance indicator from the supernova.
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$$ QM = 0.05^2/\sigma^2_{\mu}.$$ If our sample had a perfect discovery rate, and good classification (for example if we had spectroscopic classification), the uncertainty on
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cosmological parameters would be entirely due to this quality metric and would be expected to scale with the quality metric as
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$$\sigma \sim \sqrt{\sum{\frac{1.0}{1.0 + QM}}}$$
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$$\frac{1}{\sigma} \sim \sqrt{\sum{\frac{1.0}{1.0 + 1.0 / QM}}}$$
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where the sum is over the SNe in the sample.
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%Move this to OpSim analysis

whitepaper/authors.tex

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\def\jpl{Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA, \ldots}
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\def\kavli{Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA}
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\def\lcogt{LCOGT, University of California, Santa Barbara, CA \ldots}
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\def\lpc{Laboratoire de Physique de Clermont, IN2P3/CNRS, 63178 Aubière Cedex}
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\def\lpc{Laboratoire de Physique de Clermont, N2P3/CNRS, 63178 Aubière Cedex, France}
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\def\lsst{LSST, \ldots}
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\def\mssl{Mullard Space Science Laboratory (MSSL), University College London (UCL), Surrey RH5 6NT, UK}
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\def\msu{Michigan State, \ldots}
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\def\nso{National Solar Observatory, \ldots}
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\def\noao{NOAO, \ldots}
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\def\nyu{Center for Cosmology and Particle Physics, Department of Physics, New York University, 726 Broadway, 9th Floor, New York, NY 10003, USA}
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\def\okc{The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, Stockholm, Sweden}
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\def\nyuc{Center for Cosmology and Particle Physics, Department of Physics, New York University, 726 Broadway, 9th Floor, New York, NY 10003, USA}
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\def\osu{Ohio State University, \ldots}
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\def\oswego{State University of New York at Oswego, \dots}
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\def\oxford{University of Oxford, UK, \ldots}
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\def\penn{University of Pennsylvania, \ldots}
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\def\pitt{Physics and Astronomy Department, University of Pittsburgh, Pittsburgh, PA 15260, USA}
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\def\pitt{Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), Physics and Astronomy Department, University of Pittsburgh, Pittsburgh, PA 15260, USA}
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\def\princeton{Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA}
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\def\rutgers{Department of Physics and Astronomy, Rutgers the State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854 USA}
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\def\uc{Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA}
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\def\stanford{Physics Department, Stanford University, Stanford, CA, 94305, USA}
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\def\stsci{STScI, \ldots}
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\def\texastech{Texas Tech University, \ldots}
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\def\toronto{Dunlap Institute and Department of Astronomy and Astrophysics, University of Toronto, 50 St George Street, Toronto M5S 3H4, Canada}
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\def\toronto{Dunlap Institute \& Department of Astronomy and Astrophysics, University of Toronto, 50 St George Street, Toronto, ON M5S 3H4, Canada}
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\def\ucd{University of California, Davis, \ldots}
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\def\ucl{Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK}
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\def\unab{UNAB, Chile, \ldots}
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\author{Phil Marshall}{drphilmarshall}{\slac}
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\author{Scott Anderson}{ScottAnderson}{\somewhere}
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\author{Timo Anguita}{tanguita}{\unab}
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\author{Pierre Antilogus}{antilogus}{}
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\author{Ruth Angus}{ruthangus}{\oxford}
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\author{Iair Arcavi}{arcavi}{\lcogt}
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\author{Humna Awan}{HumnaAwan}{\rutgers}
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\author{Richard Kessler}{RickKessler}{\kavli, \chicago}
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\author{Alex Kim}{AlexGKim}{\berkeley}
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\author{Peter Kurczynski}{pkurczynski}{\somewhere}
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\author{Ofer Lahav}{oferlahav}{\ucl}
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\author{Llu\'is Galbany}{lgalbany}{\pitt}
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\author{Michael C.\ Liu}{mliu}{\ifa}
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\author{Michelle Lochner}{MichelleLochner}{\aims, \ska, \ucl}
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\author{Michael B.\ Lund}{lundmb}{\vanderbilt}
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\author{Jeffrey Newman}{janewman-pitt-edu}{\pitt}
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\author{Hiranya Peiris}{hiranyapeiris}{\ucl, \okc}
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\author{Matthew T.\ Penny}{mtpenny}{Sagan Fellow,~\osu}
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\author{Christina Peters}{cmp346}{\drexel}
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\author{Christina Peters}{tinapeters}{\toronto}
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\author{Rados{\l}aw Poleski}{poleski}{\osu}
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\author{Kara Ponder}{kponder}{\pitt}
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\author{Phillipe Gris}{pgris}{\lpc}
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\author{Gordon Richards}{GordonRichards}{\drexel}
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\author{Stephen Ridgway}{StephenRidgway}{\noao}
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\author{Jeonghee Rho}{jhrlsst}{\seti, \sofia}
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whitepaper/references.bib

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adsurl = {http://adsabs.harvard.edu/abs/2009ApJ...706...45C},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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@ARTICLE{2017ApJ...836...56K,
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author = {{Kessler}, R. and {Scolnic}, D.},
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title = "{Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples}",
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journal = {\apj},
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archivePrefix = "arXiv",
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eprint = {1610.04677},
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keywords = {cosmological parameters, supernovae: general },
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year = 2017,
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month = feb,
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volume = 836,
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eid = {56},
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pages = {56},
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doi = {10.3847/1538-4357/836/1/56},
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adsurl = {http://adsabs.harvard.edu/abs/2017ApJ...836...56K},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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@ARTICLE{2016ApJ...822L..35S,
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author = {{Scolnic}, D. and {Kessler}, R.},
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title = "{Measuring Type Ia Supernova Populations of Stretch and Color and Predicting Distance Biases}",
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journal = {\apjl},
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archivePrefix = "arXiv",
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eprint = {1603.01559},
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keywords = {dark energy, supernovae: general},
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year = 2016,
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month = may,
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volume = 822,
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eid = {L35},
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pages = {L35},
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doi = {10.3847/2041-8205/822/2/L35},
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adsurl = {http://adsabs.harvard.edu/abs/2016ApJ...822L..35S},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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@ARTICLE{2016arXiv161107042J,
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author = {{Jones}, D.~O. and {Scolnic}, D.~M. and {Riess}, A.~G. and {Kessler}, R. and
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{Rest}, A. and {Kirshner}, R.~P. and {Berger}, E. and {Ortega}, C.~A. and
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{Foley}, R.~J. and {Chornock}, R. and {Challis}, P.~J. and {Burgett}, W.~S. and
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{Chambers}, K.~C. and {Draper}, P.~W. and {Flewelling}, H. and
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{Huber}, M.~E. and {Kaiser}, N. and {Kudritzki}, R.-P. and {Metcalfe}, N. and
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{Wainscoat}, R.~J. and {Waters}, C.},
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title = "{Cosmology with Contaminated Samples: Methods of Measuring Dark Energy with Photometrically Classified Pan-STARRS Supernovae}",
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journal = {ArXiv e-prints},
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archivePrefix = "arXiv",
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eprint = {1611.07042},
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keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics},
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year = 2016,
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month = nov,
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adsurl = {http://adsabs.harvard.edu/abs/2016arXiv161107042J},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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@ARTICLE{2014A&A...568A..22B,
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author = {{Betoule}, M. and {Kessler}, R. and {Guy}, J. and {Mosher}, J. and
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{Foley}, R.~J. and {Frieman}, J.~A. and {Fouchez}, D. and {Galbany}, L. and
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{Smith}, M. and {Sollerman}, J. and {Sullivan}, M. and {Walton}, N.~A. and
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{Wheeler}, C.~J.},
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title = "{Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples}",
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journal = {\aap},
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archivePrefix = "arXiv",
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keywords = {cosmology: observations, distance scale, dark energy},
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year = 2014,
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doi = {10.1051/0004-6361/201423413},
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adsurl = {http://adsabs.harvard.edu/abs/2014A%26A...568A..22B},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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@ARTICLE{2013APh....42...52G,
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author = {{Gjergo}, E. and {Duggan}, J. and {Cunningham}, J.~D. and {Kuhlmann}, S. and
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{Biswas}, R. and {Kovacs}, E. and {Bernstein}, J.~P. and {Spinka}, H.
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},
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title = "{Type Ia supernovae selection and forecast of cosmology constraints for the Dark Energy Survey}",
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journal = {Astroparticle Physics},
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archivePrefix = "arXiv",
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eprint = {1205.1480},
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primaryClass = "astro-ph.CO",
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volume = 42,
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doi = {10.1016/j.astropartphys.2012.11.009},
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adsurl = {http://adsabs.harvard.edu/abs/2013APh....42...52G},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}

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