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Core-collapse, superluminous, and gamma-ray burst supernova host galaxy populations at low redshift: the importance of dwarf and starbursting galaxies

TL;DR: In this paper, an unbiased sample of 150 nearby ( = 0.014) core-collapse supernova (CCSN) host galaxies drawn from the All-Sky Automated Survey for Supernovae (ASAS-SN) for direct comparison to the nearest LGRB and SLSN hosts.
Abstract: We present a comprehensive study of an unbiased sample of 150 nearby ( = 0.014) core-collapse supernova (CCSN) host galaxies drawn from the All-Sky Automated Survey for Supernovae (ASAS-SN) for direct comparison to the nearest LGRB and SLSN hosts. We use public imaging surveys to gather multi-wavelength photometry for all CCSN host galaxies and fit their spectral energy distributions (SEDs) to derive stellar masses and integrated star formation rates. CCSNe populate galaxies across a wide range of stellar masses, from blue and compact dwarf galaxies to large spiral galaxies. We find 33(+4,-4) per cent of CCSNe are in dwarf galaxies (M 10^-8 yr^-1). We reanalyse low-redshift SLSN and LGRB hosts from the literature (out to $z<0.3$) in a homogeneous way and compare against the CCSN host sample. The relative SLSN to CCSN supernova rate is increased in low-mass galaxies and at high specific star-formation rates. These parameters are strongly covariant and we cannot break the degeneracy between them with our current sample, although there is some evidence that both factors may play a role. Larger unbiased samples of CCSNe from projects such as ZTF and LSST will be needed to determine whether host-galaxy mass (a proxy for metallicity) or specific star-formation rate (a proxy for star-formation intensity and potential IMF variation) is more fundamental in driving the preference for SLSNe and LGRBs in unusual galaxy environments.

Summary (8 min read)

Introduction

  • The authors reanalyse low-redshift SLSN and LGRB hosts from the literature (out to z < 0.3) in a homogeneous way and compare against the CCSN host sample.
  • More exotically, some models postulate that LGRBs and/or SLSNe may arise as the result of runaway collisions in young and dense star clusters (van den Heuvel & Portegies Zwart 2013).
  • Thus, it is still unclear to what extent the environmental properties of SLSNe and LGRBs (low-mass, lowmetallicity, and high sSFR) reflect their specific physical influences (progenitor and explosion mechanism).
  • Thirdly, the sample must be able to securely distinguish CCSNe from Ia SNe for all transients, ideally via spectroscopy.
  • The authors investigate star formation within the CCSN host galaxy sample and compare to a sample of SLSN and LGRBs.

2.1 Core collapse supernovae

  • The authors drew their CCSN sample from ASAS-SN, since it is shallow (mV ,limit ∼17 mag) but is all-sky, so the SNe it finds are bright and generally very nearby.
  • The authors also included any SNe that were not discovered by ASAS-SN, but were ‘recovered’ in their data and therefore do not have an ASAS-SN name designation.
  • There were some ambiguous classifications that the authors removed from the sample.
  • Also.
  • A mosaic showing their ASAS-SN CCSN host galaxy sample is provided in Figs 1 and 2.

2.2 Superluminous supernovae

  • The authors collated their initial SLSN sample based on archival SLSNe in the literature.
  • These SLSN candidates and their properties are summarized in Table 2.
  • Thumbnail images of each host are shown in the bottom row of Fig. 1; the physical scale is the same as for the CCSN hosts, with a yellow scale bar of 2 arcsec.
  • The authors restricted their analysis to SLSNe with a redshift of z < 0.3 for two main reasons.
  • First, including distant SLSNe could have caused incompleteness in the sample due to the increased difficulty 2We did not include SN1995av and SN1997cy since their classifications are unclear: SN1997cy could be a SN Ia or IIn and SN1995av may have been associated with a LGRB.the authors.

2.3 LGRBs

  • The authors LGRB sample consists of all z < 0.3 LGRBs discovered prior to the end of 2017 with an associated optical counterpart: a supernova, an optical afterglow, or both.
  • This sample was comprised of 17 LGRBs; 12 of which had confirmed SN associations and 5 without any reported SN (see Table 3).
  • These appear to have genuinely different progenitors (such as compact binary mergers) and/or explosion mechanisms from ordinary SN-associated long-duration GRBs, a possibility that makes scrutiny of their host properties particularly relevant.

3.1 CCSN host multiwavelength data

  • The galaxies in their CCSN sample are nearby (z < 0.08), so most were detectable in all-sky multiwavelength surveys.
  • Therefore, their primary image and source catalogues were public surveys.
  • The NSA is a unified catalogue of galaxies out to z ∼ 0.05, optimized for nearby extended objects since the flux measurements are derived from reprocessed SDSS images with a better background subtraction (Blanton et al. 2011).
  • Possible SLSNe-I from Quimby et al. (2018) are indicated by a∗; host analysis is done, but not included the SLSN statistical analysis due to uncertainty about the nature of the classification.
  • In the cases of galaxies with small angular size, the aperture was usually adequate, but in the case of high-mass, extended galaxies the aperture often missed a substantial fraction of the low surface-brightness flux in the outskirts of the galaxy, thus the authors redid the 2MASS photometry for these sources.

3.2 Procedure for CCSN hosts

  • The authors performed aperture photometry using the PYTHON programme PHOTUTILS.
  • 3We used an elliptical aperture and a curve-of-growth technique.the authors.
  • The authors derived the uncertainties on these photometric measurements by using the galaxy aperture to determine the brightness of the background sky.
  • For image calibration, the authors used catalogues of stars (PS1 Object Catalogue, 2MASS Point Source Catalogue, and the SDSS Imaging Catalogue) to calculate the zero-point for each image.

3.3 Galaxies requiring special attention

  • Some host galaxies in their sample required extra care when performing photometry and when fitting SED models.
  • These galaxies were either diffuse, low surface-brightness galaxies, galaxies which showed signs of interaction with nearby galaxies, galaxies contaminated with foreground stars (or other objects), or galaxies where 3https://github.com/astropy/photutils/tree/v0.3 4SN 2003ma pierces through the Large Magellanic Cloud.

3.3.1 Interacting galaxies

  • A significant number of host galaxies (in the both CCSN and extreme-SN samples) showed evidence of physical companions, some of which appeared to be in the process of interacting or merging.
  • This system would barely be detectable as two individual galaxies if it was discovered at a similar redshift (z∼ 0.2) to the SLSN or LGRB sample; therefore, the authors quoted two different measurements for photometry: one of the entire system and one of the single galaxy from which the SN originated.
  • The authors used the photometry of the system for the SED fit.
  • There was a small, red object to east of the host galaxy (see panel 5 in Fig. 1).
  • For this reason, the authors were careful not to include this object in the photometry aperture.

3.3.2 Unclear host galaxy

  • This SN was originally reported to TNS as being hosted by the elliptical galaxy NGC 2444, which is interacting with NGC 2445.
  • SN 2017ati was originally reported to TNS as a hostless supernova.
  • When the authors looked at a larger image of the field, the SN was located between two galaxies.
  • This placed the supernova ∼10 kpc (36 arcsec) away from the galaxy nucleus.
  • In their analysis the authors assigned the SN to the nearest galaxy since this would be how they would treat this SN if it were at a typical SLSN redshift.

3.3.3 Foreground star contamination

  • These hosts were large and extended objects low surface-brightness hosts.
  • This host galaxy has a small background galaxy and a few foreground stars covering the host.
  • The authors removed the flux from these stars in the images.
  • Therefore in each case, the aperture was chosen carefully so that the stellar flux was not included in the flux measurements.

3.3.4 Active galactic nuclei

  • The authors checked if any of the host galaxies in their sample had an observable AGN present.
  • While visual inspection of the host galaxy suggested that the AGN is unlikely to contribute significantly to the optical flux measured in SDSS/PS1, it could contribute more significantly to the IR flux, which could in turn affect the SED derived parameters including ages of the stellar populations, SFRs and also dust attenuation in the host galaxy.
  • Hence, for 14de the authors excluded NIR photometry for the SED fit.
  • The authors also checked the ALLWISE colours (W1–W2 and W3–W2) of the host galaxies as another diagnostic to test whether an AGN was present in the host galaxies (see fig. 12 of Wright et al. 2010).
  • The authors also obtained a spectrum of ASAS-SN 14ma in Taggart et al. (in preparation) from the WHT and they found a line ratio of log ([N II]6583 /H α) = −0.83, indicating that AGN contribution was minimal.

3.4 Literature photometry

  • Photometry of the SLSN and LGRB hosts was gathered primarily from the published literature.
  • For clarity, all sources are listed in Table 4.
  • Photometry is not corrected for Galactic foreground extinction.
  • All photometry is available online in a machine-readable form.
  • If the uncertainties were not given in the photometry from the literature, it was assumed that they were negligible and the authors therefore assign an uncertainty of 0.01 mag when performing the SED modelling.

3.5 New LGRB and SLSN host photometry

  • The authors supplemented the SLSN and LGRB photometry from the literature with new photometry from a variety of sources, detailed below.
  • Most of the LGRB hosts in their sample were observed using the Infrared Array Camera (IRAC; Fazio et al. 2004) on the Spitzer Space Telescope (Werner et al. 2004) as part of the extended Swift/Spitzer Host Galaxy Legacy Survey (SHOALS; Perley et al. 2016a).
  • Data from some archival programmes were also reanalysed using a consistent methodology.
  • The companion spiral is approximately 6 magnitudes brighter and offset by 6.5 arcsec; subtraction of its halo also left some residuals in the sky background.
  • As a result, in both these cases the uncertainty on the host flux is relatively large.

3.5.2 Keck / MOSFIRE

  • LGRB 130702A was observed in imaging mode using the MultiObject Spectrograph for Infrared Exploration (MOSFIRE; McLean et al. 2010, 2012) at Keck Observatory on the night of 2014 Jun 16 in the J and Ks filters.
  • The authors reduced these data using a custom pipeline.
  • The resolution of these images (and of archival optical data) are sufficient that there are no issues with background contamination from the nearby galaxies.
  • Aperture photometry was performed in a standard fashion using nearby 2MASS standards.

3.5.3 Palomar / WIRC

  • The authors reduced these data using their custom pipeline, which included cleaning of noise signatures associated with the replacement-detector.
  • Aperture photometry was performed in a standard fashion using nearby 2MASS standards.

3.5.4 Palomar / P60

  • LGRB 150818A was observed extensively with the CCD imager on the Palomar 60-inch robotic telescope (Cenko et al. 2006) as part of a campaign to follow-up the supernova associated with this event (Sanchez-Ramirez et al. in preparation).
  • A series of late-time reference images in griz filters were taken on 2016 February 14 for the purposes of galaxy subtraction against the earlier supernova imaging; the authors employed these here to measure the host flux in these bands.

3.6 CCSN distances

  • The authors did not have their own spectroscopy for each CCSN host galaxy.
  • The fractional distance errors from peculiar velocities could have has implications for the analysis of their hosts.
  • This model accounted for peculiar velocities due to the Virgo Cluster, the Great Attractor and the Shapley Supercluster and was typically a 6–8 per cent correction.
  • The authors estimated the uncertainty based on data from the Bright Transient Survey (Fremling et al. 2020).
  • Therefore the authors adopted this uncertainty estimate in the distance.

4.1 Spectral energy distribution fitting

  • To quantify the stellar parameters of the host galaxies, including stellar mass and SFR, the authors modelled the spectral energy distribution (SED) of each host galaxy using UV through NIR photometry.
  • If the reduced chi-squared 1 (before the Monte Carlo sampling) and the SED photometry was well-sampled in the UV, optical and IR, the authors applied the additional uncertainty to the photometry.
  • Distribution of the physical properties plotted against redshift for each host galaxy sample.
  • Each upper panel is a Gaussian kernel density estimation of each physical property.
  • Watson et al. (2011) studied the mid-infrared spectrum and did not find any evidence for PAH emission in the host of LGRB 031203.

4.2 Redshift evolution correction

  • The overall SFR density of the Universe, and of individual galaxies, rises rapidly with increasing redshift (e.g. Lilly et al. 1996), making it likely that the rare, luminous SNe that are typically found at higher redshifts than common, less luminous SNe will tend to be found in galaxies with higher SFRs simply on account of the effects of cosmic evolution.
  • To make a direct comparison between their samples and to avoid systematic errors introduced by cosmic evolution, the authors corrected for redshift evolution in SFR by empirically re-scaling all SFRs to z = 0.
  • SFRcorrected = SFRmeasured SFRMS(M,0) SFRMS(M,z) (1) We parametrized the main sequence as a power law, as in equation (2).the authors.
  • The SFR and sSFR parameters have not been corrected, unless specifically indicated in the text and figure caption.
  • The authors provide the derived physical parameters from SED fits without applying this SFR correction in Tables B1–B3.

4.3 Sequence-offset parameter

  • As an alternative to applying a redshift evolution correction to the SFR to deal with cosmic evolution, the authors defined a metric of star formation intensity, the ‘sequence-offset’ parameter ( S).
  • Photometry are not corrected for Galactic foreground extinction.
  • All photometry is available online in a machine-readable form.

5 R ESULTS

  • LGRBs and the ASAS-SN CCSN.the authors.
  • Basic statistical properties of each sample are summarized in Table 6.
  • Uncertainties (1σ ) are calculated using a simple bootstrap.

5.1 Basic properties of CCSN hosts and comparisons to nearby star-forming galaxies

  • A key goal of their study is to produce a uniform and unbiased (by galaxy mass) sample of CCSN hosts, providing a galaxy-luminosityindependent tracer of the sites of star formation in the local Universe.
  • Most LVL galaxies are observed to populate the main sequence of star-forming galaxies, where mass and SFR are strongly correlated in a fairly narrow band of sSFR between 10−9 and 10−10 yr−1.
  • Similarly, small but statistically significant differences are also seen in other parameters (SFR, sSFR, and sequence offset).
  • Using their sample, the authors measure the fraction of CCSNe in dwarf galaxies and the fraction in ‘starburst’ galaxies.
  • The authors use the Bayesian beta distribution quantile technique to derive the 1σ uncertainties, following methods outlined in Cameron (2011).

5.2 Basic properties of exotic SN hosts

  • In Figs 4(b)–(d), the authors also plot the mass and SFRs of the ‘exotic’ SN samples in comparison to local galaxies.
  • These populations are clearly quite different from ordinary CCSNe.
  • SN-less LGRBs have sSFR of −9.6(0.4), which is more consistent with the CCSN population.
  • This effect can be seen more clearly in Fig. 5, which shows specific star formation versus stellar mass.
  • Perley et al. (2016c) and Schulze et al. (2018) also noted that many SLSN-I host galaxies in PTF and SUSHIES samples are undergoing intense star formation.

5.3 Relative rates of SN sub-types

  • While the authors can qualitatively observe that the distributions of certain samples in Figs 3–5 seem similar or dissimilar, this is not a statistical statement.
  • The authors employ several different methods to quantify the significance and model the nature of these apparent differences below.

5.3.1 Cumulative distribution tests

  • In Fig. 6, the authors show the cumulative distributions of mass, SFR, sSFR, and sequence offset for each of their galaxy samples.
  • The step sizes of local galaxies in LVL are weighted by star formation to create a population consistent with one that traces star formation.
  • The CCSNe and LVL samples have remarkably similar sSFR and S distributions, while the rarer SN sub-types seem to show different distributions in most properties.
  • These differences can be tested formally using Anderson–Darling tests.
  • The authors compute the Anderson–Darling (AD) statistic, and associated p-value, for each pair of samples and for each parameter of interest: stellar mass, SFR, sSFR, and the sequence offset parameter ( S).

5.3.2 Relative rate formalism for univariate comparisons

  • While the Anderson–Darling tests above confirm that differences exist between some distributions, they do not tell us anything about the degree or quantitative nature of the differences between any two distributions.
  • The authors empirically re-scale all SFRs to z = 0 for all host galaxy samples (CCSNe, SLSNe-I, SLSNe-II and LGRBs) using the procedure in 4.2.
  • Note that because windows within 1 dex overlap, values of R within 1 dex of each other are not fully independent.
  • To calculate the confidence intervals on the relative rate the authors draw a new CCSN sample and a new SLSN sample from the original samples (with replacement) for 1000 bootstrap iterations.

5.3.3 Relative rate formalism for bivariate comparisons

  • Their relative-rate formalism above can be extended to ascertain whether a difference in distributions associated with a control parameter (e.g. stellar mass) can completely explain an observed difference in distributions for another parameter (e.g. SFR).
  • To test this, the authors reweight the comparison sample (sample ‘B’).
  • The weights for each galaxy in the comparison sample are interpolated from the relative rate for the control parameter.
  • The authors use the same confidence intervals derived from the univariate bootstrap procedure and rescale them using the same factor to the weighted relative rate.

5.4 SLSNe-I versus CCSNe

  • The relative rate, R, of SLSNe-I versus CCSNe is plotted in the lefthand panels of Fig. 7 as purple dashed lines with the 2σ confidence intervals in a lighter colour against sSFR, sequence offset, redshift corrected sSFR scaled to z ∼ 0 and stellar mass.
  • The rate is also enhanced for galaxies with a sequence offset parameter S > 5, which corresponds to galaxies with SFR > 5 times that predicted of galaxies on the main sequence with the same stellar mass and redshift.
  • The right-hand panels of Fig. 7 show the original relative rates as a purple dashed line.
  • This may hint that the rate of SLSNeI production is increased as a result of high sSFR and low stellar mass.
  • A larger sample size should help to solidify this claim.

5.5 LGRBs versus CCSNe

  • Using the same method as described above, the authors also calculate the relative rate R of LGRBs versus CCSNe in Fig.
  • Given the rather limited low-z LGRB sample, the results are generally less constraining than for SLSNe, and the authors cannot conclusively (for any 1- dex bin) state that R = 1 for LGRBs versus SNe, given this analysis.
  • Formally, the relative rate of LGRBs is enhanced in galaxies with sSFRs exceeding 10−9 yr−1 (after correcting for redshift evolution) by a factor of ∼3; it is enhanced in galaxies with sequence offsets >2 by a factor of approximately 2, and it is enhanced in low-mass dwarfs <108 M by a factor of approximately 2.5.
  • As with SLSNe, these effects are degenerate and given the small sample sizes, the authors cannot yet determine which parameter (if any) is the primary cause of the differences.

5.6 SLSNe-I versus LGRBs

  • The authors can also compare the LGRB and SLSN-I host populations directly against each other.
  • In their work, the authors find that SLSNe-I and LGRBs are statistically consistent with being drawn from the same galaxy populations in terms of all measured parameters (see Table 7), similar to the findings of Japelj et al. (2018).
  • The authors note that due to their selection of nearby events, their sample size for LGRBs is smaller than in these studies.
  • In terms of sSFR, the authors do not find any statistical differences (pAD = 0.11).
  • Both LGRBs and SLSNe-I have a higher median logarithmic sSFR than CCSNe –9.6(0.1).

5.7 SN-less LGRBs versus LGRB-SNe

  • To address whether the sub-population of ‘SN-less’ LGRBs may represent a distinct class from the remainder of LGRBs, the authors compare the host properties of the five events above to the remainder of the sample (Table 6).
  • Only a few per cent (2+2−1) of CCSN hosts are undergoing starbursts with rapid star formation sSFR > 108 yr−1, all of which are dwarf galaxies with stellar masses <109 M . (iv) LGRB SN and SLSN-I host populations exhibit similar host galaxy properties.
  • The authors also acknowledge useful feedback from R. Lunnan, M. Modjaz, S. Schulze, S. Vergani, J. Japelj, A. Gal-Yam, and useful conversations with A. Wetzel and D. Bersier.
  • Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration.
  • The authors recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community.

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Taggart, K and Perley, DA
Core-collapse, superluminous, and gamma-ray burst supernova host galaxy
populations at low redshift: the importance of dwarf and starbursting galaxies
http://researchonline.ljmu.ac.uk/id/eprint/14945/
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Citation (please note it is advisable to refer to the publisher’s version if you
intend to cite from this work)
Taggart, K and Perley, DA (2021) Core-collapse, superluminous, and
gamma-ray burst supernova host galaxy populations at low redshift: the
importance of dwarf and starbursting galaxies. Monthly Notices of the
Royal Astronomical Society, 503 (3). pp. 3931-3952. ISSN 0035-8711
LJMU Research Online

MNRAS 503, 3931–3952 (2021) doi:10.1093/mnras/stab174
Advance Access publication 2021 March 02
Core-collapse, superluminous, and gamma-ray burst supernova host
galaxy populations at low redshift: the importance of dwarf and
starbursting galaxies
K. Taggart
1,2
andD.A.Perley
1
1
Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF, UK
2
Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA
Accepted 2021 January 18. Received 2021 January 18; in original form 2020 July 4
ABSTRACT
We present a comprehensive study of an unbiased sample of 150 nearby (median redshift, z = 0.014) core-collapse supernova
(CCSN) host galaxies drawn from the All-Sky Automated Survey for Supernovae (ASAS-SN) for direct comparison to the
nearest long-duration gamma-ray burst (LGRB) and superluminous supernova (SLSN) hosts. We use public imaging surveys to
gather multiwavelength photometry for all CCSN host galaxies and fit their spectral energy distributions (SEDs) to derive stellar
masses and integrated star formation rates (SFRs). CCSNe populate galaxies across a wide range of stellar masses, from blue
and compact dwarf galaxies to large spiral galaxies. We find 33
+4
4
per cent of CCSNe are in dwarf galaxies (M
< 10
9
M
)
and 2
+2
1
per cent are in dwarf starburst galaxies [specific star formation rate (sSFR) > 10
8
yr
1
]. We reanalyse low-redshift
SLSN and LGRB hosts from the literature (out to z<0.3) in a homogeneous way and compare against the CCSN host sample.
The relative SLSN to CCSN supernova rate is increased in low-mass galaxies and at high sSFRs. These parameters are strongly
covariant and we cannot break the degeneracy between them with our current sample, although there is some evidence that both
factors may play a role. Larger unbiased samples of CCSNe from projects such as ZTF and LSST will be needed to determine
whether host-galaxy mass (a proxy for metallicity) or sSFR (a proxy for star formation intensity and potential IMF variation) is
more fundamental in driving the preference for SLSNe and LGRBs in unusual galaxy environments.
Key words: transients: supernovae transients: gamma-ray bursts galaxies: dwarf galaxies: photometry galaxies: star
formation.
1 INTRODUCTION
Massive stars (>8M
) evolve rapidly, and after a short life (up to a
fewFor image calibration, we used catalogues of stars (PS1 tens of
million years), they die in violent core-collapse supernova (CCSN)
explosions. CCSNe have a profound influence on their environment:
they produce heavy elements and deposit large amounts of energy
into their environments, driving feedback and chemical evolution
in galaxies (e.g. Chevalier 1977). In addition, because of the short
progenitor lifetime, the volumetric CCSN rate is a direct tracer of
star formation. Thus, CCSNe can be used to quantify the contribution
to cosmic star formation from distinct galaxy sub-classes and to
pinpoint rare individual star-forming galaxies, especially at low
stellar mass, where galaxy catalogues are incomplete (e.g. Sedgwick
et al. 2019).
Candidate CCSN progenitors are diverse, as are the explosion
properties they produce. Observations of CCSN explosions and their
progenitors provide a means to test theories of stellar evolution and
the explosion channels of very massive stars. However, despite the
importance of CCSNe to many areas of astrophysics, mapping a star’s
evolution (accounting for complicating factors such as metallicity,
binarity, and rotation) from its beginning to end is acomplex problem.
E-mail: k.taggart@ucsc.edu
Observationally, CCSNe are classified into types I and II based on
the presence (II) or absence (I) of hydrogen emission lines in their
spectra at maximum light (Filippenko 1997). Some CCSN progen-
itors lose part/all of their hydrogen stellar envelope prior to their
explosion due to stellar winds (Maeder & Meynet 2000) or binary
mass transfer (Podsiadlowski, Joss & Hsu 1992) and are observed
as a helium-rich (Ib and IIb) or helium-poor (Ic) stripped-envelope
SNe (Smartt 2009). In recent years, due to a new generation of
all-sky surveys and ever-increasing observational capabilities, many
new types of stellar explosion have emerged beyond this classical
picture. One example is the class of superluminous supernovae
(SLSNe) which are also classified into types I and II, but whose
extreme luminosities exceed ordinary CCSNe by a factor of 10–
100 (Quimby et al. 2011; Gal-Yam 2012; see Moriya, Sorokina &
Chevalier 2018; Gal-Yam 2019 for more recent reviews) and likely
require an additional power source.
SLSNe-II are most likely powered by SN interaction with a
dense circumstellar shell of hydrogen created by an ultra-massive
progenitor star before the explosion (Chevalier & Irwin 2011;
Ginzburg & Balberg 2012;Moriyaetal.2013) or episodic mass-
loss in a pulsational pair-instability explosion (PPISNe; Woosley,
Blinnikov & Heger 2007; Chatzopoulos & Wheeler 2012). However,
the mechanism that powers SLSN-I is still puzzling. In theory, an
extremely massive stellar core (Moriya et al. 2010; Young et al.
2010) could produce enough
56
Ni to power an SLSN via radioactive
C
2021 The Author(s)
Published by Oxford University Press on behalf of Royal Astronomical Society
Downloaded from https://academic.oup.com/mnras/article/503/3/3931/6156623 by guest on 05 May 2021

3932 K. Taggart and D. A. Perley
decay, but mass-loss during a star’s lifetime makes it difficult to
retain such a massive core. Several other theoretical mechanisms
have been proposed to explain SLSN-I, including interaction with
non-hydrogen circumstellar-material (Chatzopoulos & Wheeler
2012; Sorokina et al. 2016; Vreeswijk et al. 2017), a Pair-Instability
SN (PISN; Barkat, Rakavy & Sack 1967; Rakavy & Shaviv 1967)
from a very massive and metal-poor star (0.2 Z
; Yusof et al.
2013) or an engine-driven scenario (similar to that invoked for
long-duration gamma-ray bursts) which would provide a long-lived
energy source behind the SN ejecta (e.g. Kasen & Bildsten 2010;
Metzger et al. 2015).
Long-duration gamma-ray bursts (LGRBs) are brief, but ex-
tremely luminous flashes of high-energy radiation associated with
the formation of a relativistic jet from a ‘central engine’ (a fast-
spinning neutron star or black hole) at the centre of a collapsing
and rapidly rotating massive stellar core. While most LGRBs occur
at high redshifts, events that occur sufficiently nearby are typically
observed in association with CCSNe (Galama et al. 1998;Hjorthetal.
2003; Woosley & Bloom 2006); these SNe are universally luminous,
helium-poor stripped-envelope SNe with broad spectral features (Ic-
BL) indicating large ejecta velocities (Cano et al. 2017b).
However, despite this association, the nature of LGRB progenitors
is uncertain, including whether the progenitor is a single star (Yoon,
Langer & Norman 2006) or a binary system (Cantiello et al. 2007)
and it is not yet firmly established whether all LGRBs occur in
association with SN Ic-BL, and vice versa. Two LGRBs from
2006 have no reported SN association to deep limits (Della Valle
et al. 2006; Fynbo et al. 2006; Gal-Yam et al. 2006;Gehrels
et al. 2006), although it has been suggested that some SN-less
LGRBs are not associated with the death of massive stars, but
may be compact binary mergers with unusually long duration
(e.g. Ofek et al. 2007; Kann et al. 2011). In addition, most
known SN Ic-BL are found in optical surveys with no observed
association with a LGRB. Some of these may represent LGRBs
observed off-axis, but they could also represent events in which
the jet fails to break out of the star or is not produced to begin
with.
The physical powering mechanisms and progenitors of SLSNe
and LGRBs are still under debate. However, it is highly unlikely that
pre-explosion imaging will ever uncover the progenitor properties
of SLSN or LGRBs due to a combination of their low volumetric
rate (1 in 1000 CCSNe; Quimby et al. 2013; Prajs et al. 2017)
and their high-redshift nature: the closest SLSN discovered to date
is at a distance of 110 Mpc (SN 2018bsz; Anderson et al. 2018)
and the closest LGRB-SN is at 40 Mpc (SN 1998bw; Galama et al.
1998). This motivates the use of indirect methods to probe SLSN and
LGRB progenitor properties and to constrain their poorly understood
explosion mechanisms. One method is to analyse the properties of
the galaxies they inhabit, to search for trends in morphology, colour,
chemical composition, and star formation, which can be tied to the SN
progenitor models themselves. For example, a PISN likely requires
a low-metallicity, star-forming environment to produce a star with
sufficient initial mass and to avoid losing its mass in line-driven
winds. Single-star progenitor mechanisms for central-engine models
of LGRBs also likely require a low metallicity, since line-driven
winds would otherwise quickly sap the progenitor of its rotational
energy. More exotically, some models postulate that LGRBs and/or
SLSNe may arise as the result of runaway collisions in young and
dense star clusters (van den Heuvel & Portegies Zwart 2013). In
this scenario, one may expect to find SLSNe more frequently in
galaxies undergoing an exceptionally high rate of star formation,
even after accounting for the fact that any CCSN is proportionally
more likely to occur in a galaxy with a high star formation rate
(SFR).
There is ample evidence that LGRB and SLSN-I host galaxies
differ from the bulk of the star-forming galaxy population. For
example, both LGRBs and SLSNe-I seem to occur preferentially in
faint, low-mass galaxies with irregular structure (Fruchter et al. 2006;
Neill et al. 2011; Lunnan et al. 2014; Angus et al. 2016
). Japelj et al.
(2016b) found the B-band luminosity, stellar mass, SFR and sSFR
of SLSNe-I and LGRBs are statistically similar between a redshift
range of 0.3 <z<0.7; and Lunnan et al. (2014) found that SLSN-I
host galaxies at 0.1 <z<1.6 (discovered in the PS medium deep
survey) are statistically indistinguishable from LGRB host galaxies.
There is also good evidence in particular that metallicity affects
SLSN and LGRB production: high-metallicity environments rarely
produce LGRBs (Kr
¨
uhler et al. 2015; Vergani et al. 2015;Japeljetal.
2016a;Perleyetal.2016b;Palmerioetal.2019)orSLSNe(Perley
et al. 2016a;Chenetal.2017a; Schulze et al. 2018).
1
However, population studies with larger sample sizes show that
there may also be some subtle differences between the SLSNe and
LGRBs populations themselves. For example, the median half-light
radius of LGRB host galaxies is 1700 pc (Lyman et al. 2017),
and for SLSNe it is 900 pc (Lunnan et al. 2015). In addition,
Lunnan et al. (2014) bolstered the PS medium deep survey SLSNe-I
with SLSNe-I from the literature (typically at lower redshift) and
found that SLSNe-I were statistically distinct from LGRBs, with a
fainter B-band luminosity and lower stellar mass. Leloudas et al.
(2015) suggested that on average, SLSNe-I explode in lower mass
and higher sSFR than the hosts of LGRBs (0.1 <z<1.6). These
findings were further supported by Schulze et al. (2018), who used
the largest sample size of LGRBs and SLSNe (in comparison to
previous studies) and found that the B-band luminosity, stellar mass,
and sSFR of SLSNe-I and LGRBs are statistically distinct over a
redshift range of 0.3 <z<1.
In contrast, CCSNe have typically been found in massive spiral
galaxies. In part, this was a reflection of the fact that CCSN samples
(prior to untargeted all-sky surveys) were found via targeted surveys
of pre-selected nearby galaxies. Therefore, CCSNe were always
found in massive, nearby galaxies (most of which were massive
spirals), but about half of high-redshift (0.28 <z<1.2) CCSNe
found blindly in deep surveys (covering small) fields of view also
explode in spiral galaxies (Svensson et al. 2010), in contrast to only
10 per cent of LGRB hosts.
Graur et al. (2017a, b) found that the relative rate of Ib/c
stripped-envelope SNe versus non-stripped CCSNe declines in low-
mass (<10
10
M
) galaxies; they are underrepresented by a factor
of 3. In addition, Graur et al. (2017a, b) also note that there
appears to be a strong metallicity bias, with the relative rate of
Ib/c to II SNe increasing with metallicity. However, this is not
interpreted as evidence for the single-star scenario: the single-star
stellar evolution models underpredict the observed absolute numbers
of SE–SN; therefore, the binary scenario could be important and
there could be multiple channels at play. In addition, the binary
scenario can also show a strong metallicity dependence, although
binary star channels are much more uncertain than the single-star
channel.
1
However, this is not the entire picture since over the past few years, as the
statistical sizes of nearby SLSN and LGRBs have increased, there have been
a handful of cases of large spiral galaxies with high-metallicities hosting
SLSN-I (MLS121104, PTF10uhf, SN 2017egm, Lunnan et al. 2014; Perley
et al. 2016c; Dong et al. 2017) and nearby LGRBs (e.g. Izzo et al. 2019).
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CCSN host galaxies from ASAS-SN 3933
Nevertheless, there is some disagreement in the literature; Arcavi
et al. (2010) found that while the relative proportion of Ic SNe versus
non-stripped CCSNe decreased in low-mass galaxies, the relative
rates of all other stripped-envelope SNe (Ic-BL, Ib, IIb) versus non-
stripped CCSNe increased in low-mass, low-metallicity galaxies,
which may be a result of a reduced metallicity-driven mass-loss
causing some massive stars that would have exploded as a Ic SN
in a metal-rich galaxy to retain some H and He and explode as a
Ib/IIb event instead. There are also differences in the environments
of stripped-envelope CCSNe themselves. Ordinary Ic CCSNe are
found in more metal-rich galaxies with lower sSFRs than their
more energetic Ic-BL cousins (with and without LGRB associations)
that may suggest that Ic-BL harbour LGRB jets from a compact
central engine, which in turn requires a low-metallicity environment,
whereas ordinary Ic SNe may not require such an environment (Japelj
et al. 2018; Modjaz et al. 2020).
Additionally, there are also some indications that metallicity alone
may not fully explain the unusual properties of the host galaxies of
SLSNe and LGRBs. In particular, many SLSN-I hosts show very
high specific star formation rates (sSFR = SFR/M
)aswellas
low metallicities, evidenced by their very high equivalent widths
(Leloudas et al. 2015): as many as 50 per cent of SLSNe-I are
found in extreme emission-line galaxies (EELGs; Leloudas et al.
2015). While sometimes attributed to a very young progenitor that
simply explodes earlier than other types of SNe (Leloudas et al. 2015;
Th
¨
one et al. 2015), it could also point towards an intrinsic preference
in starbursting galaxies that favours the production of SLSNe, such as
a top-heavy IMF (e.g. Dabringhausen, Kroupa & Baumgardt 2009)
or the collisional model of van den Heuvel & Portegies Zwart (2013).
A complicating factor is that all key galaxy observational parame-
ters we may want to use to diagnose the nature of the progenitor (e.g.
stellar mass, metallicity and sSFR) correlate across the star-forming
galaxy population (e.g. Tremonti et al. 2004; Salim et al. 2007). For
example, a low-mass and low-metallicity galaxy tends to have a star
formation history with short bursts of concentrated star formation and
therefore is more likely to be observed as a starburst than a high-mass
and high-metallicity galaxy. Thus, it is still unclear to what extent
the environmental properties of SLSNe and LGRBs (low-mass, low-
metallicity, and high sSFR) reflect their specific physical influences
(progenitor and explosion mechanism).
In order to disentangle the role of metallicity and SFR and to
determine if both properties are equally important in governing
SLSN and LGRB production, we need an unbiased and representative
sample of star-forming galaxies to provide testable predictions for
where we might expect SLSNe and LGRBs to occur under various
hypotheses about their formation preferences. Ideally, the sample
of star-forming galaxies should be selected in the same manner
as an SLSN or an LGRB via the explosion of a massive star
as detected in a time-domain imaging survey to minimize the
large methodological differences between selecting via SNe versus
selecting via galaxy counts in flux-limited surveys. In other words,
we require a high-quality sample of ‘ordinary’ CCSNe.
This sample must have several properties. First, it must enclose
a sufficiently large volume to be representative of the average dis-
tribution of galaxies, since large-scale structure can potentially bias
the galaxy population seen within smaller volumes. Secondly, the
SNe must be discovered in an unbiased way (not via galaxy-targeted
surveys). Thirdly, the sample must be able to securely distinguish
CCSNe from Ia SNe for all transients, ideally via spectroscopy.
Finally, it must have multiwavelength galaxy data from UV to NIR
in order to derive physical parameters for the hosts. Few existing
SN samples have these properties, and until recently, none of these
samples havebeen at low redshift where detailed host studies are most
practical. Examples of other large, untargeted SN samples include
SDSS (Frieman et al. 2008;Sakoetal.2008) and SNLS (Bazin et al.
2009) but these surveys are not spectroscopically complete, and this
leads to ambiguities in the classifications.
In this paper, we address this need by compiling a large, unbiased,
representative sample of CCSN host galaxies (which we assume
sample the explosions of ‘typical’ massive stars, unlike SLSNe and
LGRBs). We provide photometry of this sample with integrated UV-
through-NIR SEDs and stellar masses and SFRs derived from these
measurements. We investigate star formation within the CCSN host
galaxy sample and compare to a sample of SLSN and LGRBs.
The paper is organized as follows. Section 2 describes how the
transient host galaxies are selected to form our CCSN, SLSN, and
LGRB samples. In Section 3, we describe our photometry method
and show all other archival photometry that has been used in this
paper. In Section 4, we present the methodology used to measure the
SFRs and stellar masses of each host galaxy based on UV through
NIR colours. In Section 5, we show our results, and in Section 6,
we summarize our findings and present our conclusions. Throughout
this paper, we adopt CDM cosmology, with
0
= 0.27,
= 0.73,
and H
0
= 70 km s
1
Mpc
1
(Komatsu et al. 2011).
2 HOST GALAXY SAMPLES
2.1 Core collapse supernovae
A variety of galaxy-untargeted SN catalogues exist, including the
Dark Energy Survey (Flaugher 2005), Catalina Real-Time Survey
(Drake et al. 2009), the Palomar Transient Factory (Law et al.
2009), SuperNova Legacy Survey (Bazin et al. 2009), Pan-STARRS
(Kaiser et al. 2002), La Silla Quest (Hadjiyska et al. 2012), the
Gaia transient survey (Hodgkin et al. 2013), SkyMapper (Keller
et al. 2007), SDSS Supernova Survey (Frieman et al. 2008), and
the All-Sky Automated Survey for Supernovae (ASAS-SN; Shappee
et al. 2014). We drew our CCSN sample from ASAS-SN, since it
is shallow (m
V,limit
17 mag) but is all-sky, so the SNe it finds
are bright and generally very nearby. This means that excellent
photometric galaxy information exists in public catalogues and that
almost all SNe are bright enough (even with small telescopes) for
the global SN community to follow-up, spectroscopically classify
and derive a redshift estimate. Therefore, the ASAS-SN sample is
spectroscopically complete for SNe with peak V-band light-curve
magnitudes m
V
< 15.8 and is roughly 50 per cent complete at m
V
=
17 (Holoien et al. 2017a). This was important since we required an
unambiguous sample of CCSN selected host galaxies and a reliable
SN redshift estimate for our host analysis.
We compiled all spectroscopically confirmed CCSNe discovered
by ASAS-SN (2013–2014, 2015, 2016, 2017; Holoien et al. 2017a,
b, c, 2019), and adopted any SN classifications and redshift estimates
that were updated since the initial classification was made. We also
included any SNe that were not discovered by ASAS-SN, but were
‘recovered’ in their data and therefore do not have an ASAS-SN
name designation. We refer to these SNe in the paper text using the
designated IAU name, or the discovery group name (6 SNe) when
there is no IAU name to our knowledge. For the sake of brevity, we
shortened any possible supernova (PSN) object names to the first
eight digits.
There were some ambiguous classifications that we removed from
the sample. We removed two claimed SLSNe: ASAS-SN 15lh was
classified as an SLSN-I (Dong et al. 2016), but was omitted since
it was unclear whether this event was an SLSN or a tidal disruption
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3934 K. Taggart and D. A. Perley
event (Leloudas et al. 2016; Margutti et al. 2017) and ASAS-SN 17jz
was re-classified as an SLSN-II, but its classification is ambiguous; it
could be a very luminous SN-II (Xhakaj et al. 2017) or alternatively
itcouldbeanAGN(Arcavietal.2017). In addition, we removed
SN 2015bh since the classification was ambiguous. Despite having
a data set spanning a 21-yr time period, it was unclear whether SN
2015bh was the terminal explosion of the star resulting in a CCSN
or if it was a precursor LBV hyper-eruption (Elias-Rosa et al. 2016;
Th
¨
one et al. 2017).
We limited our sample to a declination greater than 30
because
uniform, public, deep optical survey data is not available across
the entire Southern hemisphere. Two supernovae (SN 2016afa and
2017ivu) have the same host (NGC 5962) and this galaxy is included
twice in the host galaxy analysis. We also imposed a galactic latitude
cut (|b|> 15
) in order to eliminate the galaxies where stellar crowd-
ing significantly affects the photometry and thus remove SN 2015an,
2015W, 2016bpq, 2016G, 2017eaw, 2017gpn, ASAS-SN 17ny, 17kr,
and PSNJ1828 from the sample. In addition, we imposed a minimum
distance cut out to 10 Mpc, meaning that one supernova (AT 2014ge)
was removed from our sample. Primarily, we made this cut since
performing consistent photometry for very extended galaxies within
this volume using the same methods used for more distant galaxies
is difficult. Also, making this cut avoided sample overlap with the
comparison sample used in this analysis [the Local Volume Legacy
(LVL) survey] which is volume-complete to within 11 Mpc.
Our sample is comprised of 150 SNe discovered from 2013 to the
end of 2017. The redshift distribution covers the range of 0.00198–
0.08, with a median value of 0.014. Table1 details the division of
transient types within our samples and a table with details of these
host galaxies can be found in Appendix B1. A mosaic showing our
ASAS-SN CCSN host galaxy sample is provided in Figs 1 and 2.
We used methods detailed by Lupton et al. (2004) to convert PS1 gri
images into a colour composite image. Each cutout has a constant
physical size scale in the rest frame of the SN host of 21 kpc on each
side and a scale bar showing an angular size of 10 arcsec is shown
on each cutout.
2.2 Superluminous supernovae
We collated our initial SLSN sample based on archival SLSNe in
the literature. We included SLSN hosts from Neill et al. (2011),
2
SUSHIES (Schulze et al. 2018), and PTF (Perley et al. 2016c). In
addition, we included five candidates identified by Quimby et al.
(2018) following their reanalysis of archival PTF spectra: two likely
SLSNe-I (PTF12gty and PTF12hni) and three possible SLSNe-I
(PTF09q, PTF10gvb, and PTF11mnb) at slightly lower luminosities
(M > 21 mag) than the PTF sample of SLSN host galaxies by
Perley et al. (2016c). These SLSN candidates and their properties are
summarized in Table 2. Rest frame g-band magnitudes for PTF12gty
and PTF12hni were taken from De Cia et al. (2018) and PTF09q,
PTF10gvb, and PTF11mnb were taken from Quimby et al. (2018).
Thumbnail images of each host are shown in the bottom row of Fig. 1;
the physical scale is the same as for the CCSN hosts, with a yellow
scale bar of 2 arcsec.
We restricted our analysis to SLSNe with a redshift of z<0.3
for two main reasons. First, including distant SLSNe could have
caused incompleteness in the sample due to the increased difficulty
2
We did not include SN1995av and SN1997cy since their classifications are
unclear: SN1997cy could be a SN Ia or IIn and SN1995av may have been
associated with a LGRB.
in spectroscopically confirming members of this class without a
bright associated host galaxy. Secondly, we wanted to reduce cosmic
evolution effects when comparing to the z 0.014 CCSN sample.
After making this cut and excluding PTF09q, PTF10gvb, and
PTF11mnb, our final statistical sample consisted of 29 SLSNe-I
and 21 SLSNe-II in total.
2.3 LGRBs
Our LGRB sample consists of all z<0.3 LGRBs discovered prior to
the end of 2017 with an associated optical counterpart: a supernova,
an optical afterglow, or both. The requirement for an optical afterglow
was imposed to better match the optical selection of SNe used for
comparison and to ensure a high degree of confidence in the host-
galaxy association: while many additional low-z LGRBs have been
reported based on X-ray associations alone, it is difficult to rule out
the possibility that these are higher-z events seen in coincidence with
a foreground galaxy. This sample was comprised of 17 LGRBs; 12
of which had confirmed SN associations and 5 without any reported
SN (see Table 3).
Of the five LGRBs without reported SNe, two were highly
publicized events from 2006 (LGRBs 060505 and 060614) for which
an SN was ruled out to deep limits (Fynbo et al. 2006; Gal-Yam et al.
2006; Della Valle et al. 2006; Gehrels et al. 2006). These appear
to have genuinely different progenitors (such as compact binary
mergers) and/or explosion mechanisms from ordinary SN-associated
long-duration GRBs, a possibility that makes scrutiny of their
host properties particularly relevant. The remaining events, LGRBs
050826, 080517, and 111225A, have relatively poor constraints on
the extinction column towards the LGRB and/or on the presence of
an SN peaking 1–3 weeks after the event (e.g. Stanway et al. 2015).
3 PHOTOMETRY
3.1 CCSN host multiwavelength data
The galaxies in our CCSN sample are nearby (z<0.08), so most
were detectable in all-sky multiwavelength surveys. Therefore, our
primary image and source catalogues were public surveys. We used
images from the Galaxy Evolution Explorer (GALEX; Martin et al.
2005), the Panoramic Survey Telescope and Rapid Response System
(PS1; Kaiser et al. 2010), the Sloan Digital Sky Survey (SDSS; York
et al. 2000),andtheTwoMicronAll-SkySurvey(2MASS;Huchra
et al. 2012).
Our aim was to derive consistent mass and star formation estimates
for our host galaxy sample, thus we matched aperture sizes across
the optical and NIR wavelengths. This was particularly important
for nearby and massive galaxies, since the aperture size can signif-
icantly increase or decrease the flux measurements. In addition, the
automated pipeline of GALEX, 2MASS, and WISE often incorrectly
deblends galaxies with a large angular diameter on the sky and does
not capture the lowsurface-brightness parts of the galaxy. If available,
we used SDSS ugriz and GALEX FUV and NUV photometry from the
NASA Sloan Atlas (NSA; Blanton et al. 2011). The NSA is a unified
catalogue of galaxies out to z 0.05, optimized for nearby extended
objects since the flux measurements are derived from reprocessed
SDSS images with a better background subtraction (Blanton et al.
2011). We used the elliptical petrosian aperture photometry, with an
elliptical aperture radius defined by the shape of the light profile of
the galaxy as in Blanton et al. (2011) and Yasuda et al. (2001). The
NSA flux measurements were available for about half of the Northern
hemisphere sample. Otherwise, we performed the photometry using
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Journal ArticleDOI
TL;DR: In this article, the spectral evolution of stellar populations at ages between 100,000 yr and 20 Gyr at a resolution of 3 A across the whole wavelength range from 3200 to 9500 A for a wide range of metallicities.
Abstract: We present a new model for computing the spectral evolution of stellar populations at ages between 100,000 yr and 20 Gyr at a resolution of 3 A across the whole wavelength range from 3200 to 9500 A for a wide range of metallicities. These predictions are based on a newly available library of observed stellar spectra. We also compute the spectral evolution across a larger wavelength range, from 91 A to 160 micron, at lower resolution. The model incorporates recent progress in stellar evolution theory and an observationally motivated prescription for thermally-pulsing stars on the asymptotic giant branch. The latter is supported by observations of surface brightness fluctuations in nearby stellar populations. We show that this model reproduces well the observed optical and near-infrared colour-magnitude diagrams of Galactic star clusters of various ages and metallicities. Stochastic fluctuations in the numbers of stars in different evolutionary phases can account for the full range of observed integrated colours of star clusters in the Magellanic Clouds. The model reproduces in detail typical galaxy spectra from the Early Data Release (EDR) of the Sloan Digital Sky Survey (SDSS). We exemplify how this type of spectral fit can constrain physical parameters such as the star formation history, metallicity and dust content of galaxies. Our model is the first to enable accurate studies of absorption-line strengths in galaxies containing stars over the full range of ages. Using the highest-quality spectra of the SDSS EDR, we show that this model can reproduce simultaneously the observed strengths of those Lick indices that do not depend strongly on element abundance ratios [abridged].

10,384 citations

Journal ArticleDOI
TL;DR: The Sloan Digital Sky Survey (SDSS) as mentioned in this paper provides the data to support detailed investigations of the distribution of luminous and non-luminous matter in the Universe: a photometrically and astrometrically calibrated digital imaging survey of pi steradians above about Galactic latitude 30 degrees in five broad optical bands.
Abstract: The Sloan Digital Sky Survey (SDSS) will provide the data to support detailed investigations of the distribution of luminous and non- luminous matter in the Universe: a photometrically and astrometrically calibrated digital imaging survey of pi steradians above about Galactic latitude 30 degrees in five broad optical bands to a depth of g' about 23 magnitudes, and a spectroscopic survey of the approximately one million brightest galaxies and 10^5 brightest quasars found in the photometric object catalog produced by the imaging survey. This paper summarizes the observational parameters and data products of the SDSS, and serves as an introduction to extensive technical on-line documentation.

10,039 citations

Journal ArticleDOI
Donald G. York1, Jennifer Adelman2, John E. Anderson2, Scott F. Anderson3  +148 moreInstitutions (29)
TL;DR: The Sloan Digital Sky Survey (SDSS) as discussed by the authors provides the data to support detailed investigations of the distribution of luminous and non-luminous matter in the universe: a photometrically and astrometrically calibrated digital imaging survey of π sr above about Galactic latitude 30° in five broad optical bands to a depth of g' ~ 23 mag.
Abstract: The Sloan Digital Sky Survey (SDSS) will provide the data to support detailed investigations of the distribution of luminous and nonluminous matter in the universe: a photometrically and astrometrically calibrated digital imaging survey of π sr above about Galactic latitude 30° in five broad optical bands to a depth of g' ~ 23 mag, and a spectroscopic survey of the approximately 106 brightest galaxies and 105 brightest quasars found in the photometric object catalog produced by the imaging survey. This paper summarizes the observational parameters and data products of the SDSS and serves as an introduction to extensive technical on-line documentation.

9,835 citations

Journal ArticleDOI
01 Dec 2010
TL;DR: The Wide-field Infrared Survey Explorer (WISE) is mapping the whole sky following its launch on 14 December 2009 and completed its first full coverage of the sky on July 17 as discussed by the authors.
Abstract: The all sky surveys done by the Palomar Observatory Schmidt, the European Southern Observatory Schmidt, and the United Kingdom Schmidt, the InfraRed Astronomical Satellite and the 2 Micron All Sky Survey have proven to be extremely useful tools for astronomy with value that lasts for decades. The Wide-field Infrared Survey Explorer is mapping the whole sky following its launch on 14 December 2009. WISE began surveying the sky on 14 Jan 2010 and completed its first full coverage of the sky on July 17. The survey will continue to cover the sky a second time until the cryogen is exhausted (anticipated in November 2010). WISE is achieving 5 sigma point source sensitivities better than 0.08, 0.11, 1 and 6 mJy in unconfused regions on the ecliptic in bands centered at wavelengths of 3.4, 4.6, 12 and 22 micrometers. Sensitivity improves toward the ecliptic poles due to denser coverage and lower zodiacal background. The angular resolution is 6.1", 6.4", 6.5" and 12.0" at 3.4, 4.6, 12 and 22 micrometers, and the astrometric precision for high SNR sources is better than 0.15".

7,182 citations

Frequently Asked Questions (12)
Q1. What are the contributions in "Core-collapse, superluminous, and gamma-ray burst supernova host galaxy populations at low redshift: the importance of dwarf and starbursting galaxies" ?

The authors present a comprehensive study of an unbiased sample of 150 nearby ( median redshift, z = 0. 014 ) core-collapse supernova ( CCSN ) host galaxies drawn from the All-Sky Automated Survey for Supernovae ( ASAS-SN ) for direct comparison to the nearest long-duration gamma-ray burst ( LGRB ) and superluminous supernova ( SLSN ) hosts. These parameters are strongly covariant and the authors can not break the degeneracy between them with their current sample, although there is some evidence that both factors may play a role. Larger unbiased samples of CCSNe from projects such as ZTF and LSST will be needed to determine whether host-galaxy mass ( a proxy for metallicity ) or sSFR ( a proxy for star formation intensity and potential IMF variation ) is more fundamental in driving the preference for SLSNe and LGRBs in unusual galaxy environments. 

While their primary motivation for this exercise will be to compare this sample to ‘exotic’ supernova types (SLSNe and LGRBs) in order to constrain their progenitors, their CCSN sample is also useful for studying the nature of star formation at low-redshift: few galaxy surveys are complete beyond the dwarfgalaxy 109 M limit, with those that are typically confined to small volumes. 

The authors sampled from the distribution 1000 times and then ran the SED fit on each set of ‘noisy’ photometry and used the 16-to-84th percentile of each parameter as an estimate of its uncertainty. 

In addition, the authors imposed a minimum distance cut out to 10 Mpc, meaning that one supernova (AT 2014ge) was removed from their sample. 

The contribution of emission lines to the modelled spectra was based on the Kennicutt (1998) relations between SFR and UV luminosity. 

The contribution of Hα and [O II] lines to the photometry was included for galaxies with dust free colour bluer than (NUV–r)ABS ≤ 4 and the intensity of the emission lines was scaled according to the intrinsic UV luminosity of the galaxy. 

Dust attenuation in the galaxy was applied to the SED models using the Calzetti et al. (2000) extinction law for starburst galaxies. 

The authors used the programme GALFIT (Peng et al. 2002) to model and subtract any contaminating objects from the image and then used the procedure outlined in Section 3.2 to perform aperture photometry on the galaxy. 

Since the authors did not have spectra for every galaxy in their sample, the authors also inspected the images of each host (see Fig. 1) to check for a clear nuclear point source. 

The authors limited their sample to a declination greater than −30◦ because uniform, public, deep optical survey data is not available across the entire Southern hemisphere. 

If the reduced chi-squared 1 (before the Monte Carlo sampling) and the SED photometry was well-sampled in the UV, optical and IR, the authors applied the additional uncertainty to the photometry. 

The authors use the same confidence intervals derived from the univariate bootstrap procedure and rescale them using the same factor to the weighted relative rate.