An Efficient Approach to Obtaining Large Numbers of Distant Supernova Host Galaxy Redshifts

Lidman, C.; Ruhlmann-Kleider, V.; Sullivan, M.; Myzska, J.; Dobbie, P. D.; Glazebrook, Karl; Mould, Jeremy; Astier, P.; Balland, C.; Betoule, M.; Carlberg, R. G.; Conley, A.; Fouchez, D.; Guy, J.; Hardin, D.; Hook, I. M.; Howell, D. A.; Pain, R.; Palanque-Delabrouille, N.; Perrett, K.; Pritchet, C. J.; Regnault, Nicolas; Rich, J.

doi:10.1017/pasa.2012.001

Cited by 12 publications

(7 citation statements)

References 39 publications

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“…We assign redshift information as follows. 1694 have spectroscopic redshifts, either from spectra of the transients (Balland et al 2009;Bronder et al 2008;Ellis et al 2008;Howell et al 2005) or of the host galaxy from redshift catalogues in the SNLS search fields (e.g., Lilly et al 2007;Le Fèvre et al 2013;Lidman et al 2013). Where a spectroscopic redshift is not available, we use photometric redshift estimates from Ilbert et al (2006), which provides photometric redshift information for galaxies in the SNLS fields at i M < 25.…”

Section: Identifying Slsne In Snlsmentioning

confidence: 99%

“…A by-product of this search was a deep, homogeneous catalogue of optical transients down to a limiting magnitude of i ∼ 24 (Perrett et al 2010), with more than 500 optical transient spectra, including two confirmed SLSNe-I (Howell et al 2013). This, combined with a significant amount of ancillary redshift information in the search fields (e.g., Ilbert et al 2006;Lilly et al 2007;Le Fèvre et al 2013;Lidman et al 2013), makes it a perfect controlled dataset for the study of supernova rates (Neill et al 2006;Bazin et al 2009;Perrett et al 2012), including SLSNe. This paper is presented as follows.…”

Section: Introductionmentioning

confidence: 99%

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The volumetric rate of superluminous supernovae atz∼ 1

Prajs

Sullivan

Smith

et al. 2016

Mon. Not. R. Astron. Soc.

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We present a measurement of the volumetric rate of superluminous supernovae (SLSNe) at z∼1.0, measured using archival data from the first four years of the Canada-FranceHawaii Telescope Supernova Legacy Survey (SNLS). We develop a method for the photometric classification of SLSNe to construct our sample. Our sample includes two previously spectroscopically-identified objects, and a further new candidate selected using our classification technique. We use the point-source recovery efficiencies from Perrett et al. (2010) and a Monte Carlo approach to calculate the rate based on our SLSN sample. We find that the three identified SLSNe from SNLS give a rate of 91 +76 −36 SNe Yr −1 Gpc −3 at a volume-weighted redshift of z = 1.13. This is equivalent to 2.2 +1.8 −0.9 × 10 −4 of the volumetric core collapse supernova rate at the same redshift. When combined with other rate measurements from the literature, we show that the rate of SLSNe increases with redshift in a manner consistent with that of the cosmic star formation history. We also estimate the rate of ultra-long gamma ray bursts (ULGRBs) based on the events discovered by the Swift satellite, and show that it is comparable to the rate of SLSNe, providing further evidence of a possible connection between these two classes of events. We also examine the host galaxies of the SLSNe discovered in SNLS, and find them to be consistent with the stellar-mass distribution of other published samples of SLSNe.

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Section: Identifying Slsne In Snlsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The volumetric rate of superluminous supernovae atz∼ 1

Prajs

Sullivan

Smith

et al. 2016

Mon. Not. R. Astron. Soc.

Self Cite

View full text Add to dashboard Cite

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“…Purely photometrically selected SN Ia samples were first used for rate measurements [4], for cross-checking the Malmquist bias correction of a spectroscopic sample [5], or to prove that spectroscopic observations of their hosts can result in large SN Ia samples with a good coverage of spectroscopic host redshifts, see e.g. [6]. The latter has since then become common practice in current SN Ia projects ( [7] for DES or [8] for Pan-STARRS) while photometric classification has become more complex, see e.g.…”

Section: Jcap10(2022)065 1 Introductionmentioning

confidence: 99%

Type Ia supernova Hubble diagrams with host galaxy photometric redshifts

Ruhlmann-Kleider

Lidman

Möller

2022

J. Cosmol. Astropart. Phys.

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Systematic uncertainties associated to type Ia supernova (SN Ia) Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts are investigated. The host redshift uncertainties and the contamination by core-collapse SNe are both addressed. As a test case, we use the 3-year photometric SN Ia sample of the SuperNova Legacy Survey (SNLS), consisting of 437 objects between 0.1 and 1.05 in redshift with 4.7% contamination. We combine this sample with non-SNLS objects of the spectroscopic sample from the joint analysis (JLA) of the SDSS-II and SNLS collaborations, consisting of 501 objects mostly below 0.4 in redshift. We study two options for the origin of the redshifts of the photometric sample, either provided entirely from the host photometric redshift catalogue used in the selection or a mixed origin where around 75% of the sample can be assigned spectroscopic redshifts from dedicated measurements or external catalogues. Using light curve simulations subject to the same photometric selection as data, we study the impact of photometric redshift uncertainties and contamination on flat ΛCDM fits to Hubble diagrams from such combined samples. Our primary finding is that photometric redshifts and contamination lead to biased cosmological parameters. The magnitude of the bias is found to be similar for both redshift options. This bias can be largely accounted for if photometric redshift uncertainties and contamination are taken into account when computing the SN magnitude bias correction due to selection effects. To reduce the residual cosmological bias, we explore two methods to propagate redshift uncertainties into the cosmological likelihood computation, either by refitting photometric redshifts along with cosmology or by sampling the redshift resolution function. Redshift refitting fails at correcting the cosmological bias whatever the redshift origin, while sampling slightly reduces it in both cases. Finally, for actual data, we find compatible results with those from the JLA diagram for mixed photometric and spectroscopic redshifts, while the full photometric option is biased upwards, but consistent with JLA when all statistical and systematic uncertainties are included.

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“…We are therefore entering into an era where most SNe Ia will be classified from photometry and most redshifts will be obtained from host galaxies. The efficiency of such a strategy was demonstrated in Lidman et al (2013), where host galaxy redshifts of photometrically classified SNe Ia from the SNLS were obtained. An example of a cosmological analysis using photometrically classified SNe Ia from the SDSS-II supernova survey was presented in Campbell et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Cosmological Inference from Host-Selected Type Ia Supernova Samples

Uddin

Mould

Lidman

et al. 2017

Publ. Astron. Soc. Aust.

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We compare two Type Ia supernova (SN Ia) samples that are drawn from a spectroscopically confirmed SN Ia sample: a host-selected sample in which SNe Ia are restricted to those that have a spectroscopic redshift from the host; and a broader, more traditional sample in which the redshift could come from either the SN or the host. The host-selected sample is representative of SN samples that will use the redshift of the host to infer the SN redshift, long after the SN has faded from view. We find that SNe Ia that are selected on the availability of a redshift from the host differ from SNe Ia that are from the broader sample. The former tend to be redder, have narrower light curves, live in more massive hosts, and tend to be at lower redshifts. We find that constraints on the equation of state of dark energy, $w$, and the matter density, $\Omega_M$, remain consistent between these two types of samples. Our results are important for ongoing and future supernova surveys, which unlike previous supernova surveys, will have limited real-time follow-up to spectroscopically classify the SNe they discover. Most of the redshifts in these surveys will come from the hosts.Comment: Accepted for publication in PAS

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An Efficient Approach to Obtaining Large Numbers of Distant Supernova Host Galaxy Redshifts

Cited by 12 publications

References 39 publications

The volumetric rate of superluminous supernovae atz∼ 1

The volumetric rate of superluminous supernovae atz∼ 1

Type Ia supernova Hubble diagrams with host galaxy photometric redshifts

Cosmological Inference from Host-Selected Type Ia Supernova Samples

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