arXiv subjects

M. Smith

Publications and source records attributed to M. Smith.

At least 235 records · Page 13Linked to original sources

arXiv2021

Understanding the extreme luminosity of DES14X2fna

We present DES14X2fna, a high-luminosity, fast-declining type IIb supernova (SN IIb) at redshift $z=0.0453$, detected by the Dark Energy Survey (DES). DES14X2fna is an unusual member of its class, with a light curve showing a broad, luminous peak reaching $M_r\simeq-19.3$ mag 20 days after explosion. This object does not show a linear decline tail in the light curve until $\simeq$60 days after explosion, after which it declines very rapidly (4.38$\pm$0.10 mag 100 d$^{-1}$ in $r$-band). By fitting semi-analytic models to the photometry of DES14X2fna, we find that its light curve cannot be explained by a standard $^{56}$Ni decay model as this is unable to fit the peak and fast tail decline observed. Inclusion of either interaction with surrounding circumstellar material or a rapidly-rotating neutron star (magnetar) significantly increases the quality of the model fit. We also investigate the possibility for an object similar to DES14X2fna to act as a contaminant in photometric samples of SNe Ia for cosmology, finding that a similar simulated object is misclassified by a recurrent neural network (RNN)-based photometric classifier as a SN Ia in $\sim$1.1-2.4 per cent of cases in DES, depending on the probability threshold used for a positive classification.

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arXiv2021

Pushing automated morphological classifications to their limits with the Dark Energy Survey

We present morphological classifications of $\sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (\textit{n}), ellipticity ($ε$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.

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arXiv2021

Milky Way Satellite Census. III. Constraints on Dark Matter Properties from Observations of Milky Way Satellite Galaxies

We perform a comprehensive study of Milky Way (MW) satellite galaxies to constrain the fundamental properties of dark matter (DM). This analysis fully incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and marginalizes over uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk. Our results are consistent with the cold, collisionless DM paradigm and yield the strongest cosmological constraints to date on particle models of warm, interacting, and fuzzy dark matter. At $95\%$ confidence, we report limits on (i) the mass of thermal relic warm DM, $m_{\rm WDM} > 6.5\ \mathrm{keV}$ (free-streaming length, $λ_{\rm{fs}} \lesssim 10\,h^{-1}\ \mathrm{kpc}$), (ii) the velocity-independent DM-proton scattering cross section, $σ_{0} < 8.8\times 10^{-29}\ \mathrm{cm}^{2}$ for a $100\ \mathrm{MeV}$ DM particle mass (DM-proton coupling, $c_p \lesssim (0.3\ \mathrm{GeV})^{-2}$), and (iii) the mass of fuzzy DM, $m_ϕ> 2.9 \times 10^{-21}\ \mathrm{eV}$ (de Broglie wavelength, $λ_{\rm{dB}} \lesssim 0.5\ \mathrm{kpc}$). These constraints are complementary to other observational and laboratory constraints on DM properties.

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arXiv2021

The WaZP galaxy cluster sample of the Dark Energy Survey Year 1

We present a new (2+1)D galaxy cluster finder based on photometric redshifts called Wavelet Z Photometric (WaZP) applied to DES first year (Y1A1) data. The results are compared to clusters detected by the South Pole Telescope (SPT) survey and the redMaPPer cluster finder, the latter based on the same photometric data. WaZP searches for clusters in wavelet-based density maps of galaxies selected in photometric redshift space without any assumption on the cluster galaxy populations. The comparison to other cluster samples was performed with a matching algorithm based on angular proximity and redshift difference of the clusters. It led to the development of a new approach to match two optical cluster samples, following an iterative approach to minimize incorrect associations. The WaZP cluster finder applied to DES Y1A1 galaxy survey (1,511 deg2 up to i=23 mag) led to the detection of 60,547 galaxy clusters with redshifts 0.05 5. Considering the overlapping regions and redshift ranges between the DES Y1A1 and SPT cluster surveys, all SZ based SPT clusters are recovered by the WaZP sample.The comparison between WaZP and redMaPPer cluster samples showed an excellent overall agreement for clusters with richness Ngals (λ for redMaPPer) greater than 25 (20), with 95% recovery on both directions. Based on the cluster cross-match we explorethe relative fragmentation of the two cluster samples and investigate the possible signatures of unmatched clusters.

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arXiv2021

A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the $griz$ bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for $10,699$ test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code BAGPIPES, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under $6$ min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed GALPRO, a highly intuitive and efficient Python package to rapidly generate multivariate PDFs on-the-fly. GALPRO is documented and available for researchers to use in their cosmology and galaxy evolution studies.

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arXiv2021

No Evidence for Orbital Clustering in the Extreme Trans-Neptunian Objects

The apparent clustering in longitude of perihelion $\varpi$ and ascending node $Ω$ of extreme trans-Neptunian objects (ETNOs) has been attributed to the gravitational effects of an unseen 5-10 Earth-mass planet in the outer solar system. To investigate how selection bias may contribute to this clustering, we consider 14 ETNOs discovered by the Dark Energy Survey, the Outer Solar System Origins Survey, and the survey of Sheppard and Trujillo. Using each survey's published pointing history, depth, and TNO tracking selections, we calculate the joint probability that these objects are consistent with an underlying parent population with uniform distributions in $\varpi$ and $Ω$. We find that the mean scaled longitude of perihelion and orbital poles of the detected ETNOs are consistent with a uniform population at a level between $17\%$ and $94\%$, and thus conclude that this sample provides no evidence for angular clustering.

astro-ph.EP↗

arXiv2021

Dark Energy Survey Year 3 Results: Optimizing the Lens Sample in Combined Galaxy Clustering and Galaxy-Galaxy Lensing Analysis

We investigate potential gains in cosmological constraints from the combination of galaxy clustering and galaxy-galaxy lensing by optimizing the lens galaxy sample selection using information from Dark Energy Survey (DES) Year 3 data and assuming the DES Year 1 Metacalibration sample for the sources. We explore easily reproducible selections based on magnitude cuts in $i$-band as a function of (photometric) redshift, $z_{\rm phot}$, and benchmark the potential gains against those using the well established redMaGiC sample. We focus on the balance between density and photometric redshift accuracy, while marginalizing over a realistic set of cosmological and systematic parameters. Our optimal selection, the MagLim sample, satisfies $i < 4 \, z_{\rm phot} + 18$ and has $\sim 30\%$ wider redshift distributions but $\sim 3.5$ times more galaxies than redMaGiC. Assuming a wCDM model and equivalent scale cuts to mitigate nonlinear effects, this leads to $40\%$ increase in the figure of merit for the pair combinations of $Ω_m$, $w$, and $σ_8$, and gains of $16\%$ in $σ_8$, $10\%$ in $Ω_m$, and $12\%$ in $w$. Similarly, in LCDM we find an improvement of $19\%$ and $27\%$ on $σ_8$ and $Ω_m$, respectively. We also explore flux-limited samples with a flat magnitude cut finding that the optimal selection, $i < 22.2$, has $\sim 7$ times more galaxies and $\sim 20\%$ wider redshift distributions compared to MagLim, but slightly worse constraints. We show that our results are robust with respect to the assumed galaxy bias and photometric redshift uncertainties with only moderate further gains from increased number of tomographic bins or the inclusion of bin cross-correlations, except in the case of the flux-limited sample, for which these gains are more significant.

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arXiv2021

DES Y1 results: Splitting growth and geometry to test $Λ$CDM

We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters -- focusing on $Ω_m$ -- are split into versions associated with structure growth (e.g. $Ω_m^{\rm grow}$) and expansion history (e.g. $Ω_m^{\rm geo}$). Once the parameters have been specified for the $Λ$CDM cosmological model, which includes general relativity as a theory of gravity, it uniquely predicts the evolution of both geometry (distances) and the growth of structure over cosmic time. Any inconsistency between measurements of geometry and growth could therefore indicate a breakdown of that model. Our growth-geometry split approach therefore serves as both a (largely) model-independent test for beyond-$Λ$CDM physics, and as a means to characterize how DES observables provide cosmological information. We analyze the same multi-probe DES data as arXiv:1811.02375 : DES Year 1 (Y1) galaxy clustering and weak lensing, which are sensitive to both growth and geometry, as well as Y1 BAO and Y3 supernovae, which probe geometry. We additionally include external geometric information from BOSS DR12 BAO and a compressed Planck 2015 likelihood, and external growth information from BOSS DR12 RSD. We find no significant disagreement with $Ω_m^{\rm grow}=Ω_m^{\rm geo}$. When DES and external data are analyzed separately, degeneracies with neutrino mass and intrinsic alignments limit our ability to measure $Ω_m^{\rm grow}$, but combining DES with external data allows us to constrain both growth and geometric quantities. We also consider a parameterization where we split both $Ω_m$ and $w$, but find that even our most constraining data combination is unable to separately constrain $Ω_m^{\rm grow}$ and $w^{\rm grow}$. Relative to $Λ$CDM, splitting growth and geometry weakens bounds on $σ_8$ but does not alter constraints on $h$.

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arXiv2021

The Effect of Environment on Type Ia Supernovae in the Dark Energy Survey Three-Year Cosmological Sample

Analyses of type Ia supernovae (SNe Ia) have found puzzling correlations between their standardised luminosities and host galaxy properties: SNe Ia in high-mass, passive hosts appear brighter than those in lower-mass, star-forming hosts. We examine the host galaxies of SNe Ia in the Dark Energy Survey three-year spectroscopically-confirmed cosmological sample, obtaining photometry in a series of "local" apertures centred on the SN, and for the global host galaxy. We study the differences in these host galaxy properties, such as stellar mass and rest-frame $U-R$ colours, and their correlations with SN Ia parameters including Hubble residuals. We find all Hubble residual steps to be $>3σ$ in significance, both for splitting at the traditional environmental property sample median and for the step of maximum significance. For stellar mass, we find a maximal local step of $0.098\pm0.018$ mag; $\sim 0.03$ mag greater than the largest global stellar mass step in our sample ($0.070 \pm 0.017$ mag). When splitting at the sample median, differences between local and global $U-R$ steps are small, both $\sim 0.08$ mag, but are more significant than the global stellar mass step ($0.057\pm0.017$ mag). We split the data into sub-samples based on SN Ia light curve parameters: stretch ($x_1$) and colour ($c$), finding that redder objects ($c > 0$) have larger Hubble residual steps, for both stellar mass and $U-R$, for both local and global measurements, of $\sim0.14$ mag. Additionally, the bluer (star-forming) local environments host a more homogeneous SN Ia sample, with local $U-R$ r.m.s. scatter as low as $0.084 \pm 0.017$ mag for blue ($c < 0$) SNe Ia in locally blue $U-R$ environments.

astro-ph.CO↗

arXiv2021

Is diffuse intracluster light a good tracer of the galaxy cluster matter distribution?

We explore the relation between diffuse intracluster light (central galaxy included) and the galaxy cluster (baryonic and dark) matter distribution using a sample of 528 clusters at $0.2\leq z \leq 0.35$ found in the Dark Energy Survey (DES) Year 1 data. The surface brightness of the diffuse light shows an increasing dependence on cluster total mass at larger radius, and appears to be self-similar with a universal radial dependence after scaling by cluster radius. We also compare the diffuse light radial profiles to the cluster (baryonic and dark) matter distribution measured through weak lensing and find them to be comparable. The IllustrisTNG galaxy formation simulation, TNG300, offers further insight into the connection between diffuse stellar mass and cluster matter distributions -- the simulation radial profile of the diffuse stellar component does not have a similar slope with the total cluster matter content, although that of the cluster satellite galaxies does. Regardless of the radial trends, the amount of diffuse stellar mass has a low-scatter scaling relation with cluster's total mass in the simulation, out-performing the total stellar mass of cluster satellite galaxies. We conclude that there is no consistent evidence yet on whether or not diffuse light is a faithful radial tracer of the cluster matter distribution. Nevertheless, both observational and simulation results reveal that diffuse light is an excellent indicator of the cluster's total mass.

astro-ph.GA↗

arXiv2020

A statistical standard siren measurement of the Hubble constant from the LIGO/Virgo gravitational wave compact object merger GW190814 and Dark Energy Survey galaxies

We present a measurement of the Hubble constant $H_0$ using the gravitational wave (GW) event GW190814, which resulted from the coalescence of a 23 $M_\odot$ black hole with a 2.6 $M_\odot$ compact object, as a standard siren. No compelling electromagnetic counterpart has been identified for this event, thus our analysis accounts for thousands of potential host galaxies within a statistical framework. The redshift information is obtained from the photometric redshift (photo-$z$) catalog from the Dark Energy Survey. The luminosity distance is provided by the LIGO/Virgo gravitational wave sky map. Since this GW event has the second-smallest localization volume after GW170817, GW190814 is likely to provide the best constraint on cosmology from a single standard siren without identifying an electromagnetic counterpart. Our analysis uses photo-$z$ probability distribution functions and corrects for photo-$z$ biases. We also reanalyze the binary-black hole GW170814 within this updated framework. We explore how our findings impact the $H_0$ constraints from GW170817, the only GW merger associated with a unique host galaxy. From a combination of GW190814, GW170814 and GW170817, our analysis yields $H_0 = 72.0^{+ 12}_{- 8.2 }~{\rm km~s^{-1}~Mpc^{-1}}$ (68\% Highest Density Interval, HDI) for a prior in $H_0$ uniform between $[20,140]~{\rm km~s^{-1}~Mpc^{-1}}$. The addition of GW190814 and GW170814 to GW170817 improves the 68\% HDI from GW170817 alone by $\sim 18\%$, showing how well-localized mergers without counterparts can provide a significant contribution to standard siren measurements, provided that a complete galaxy catalog is available at the location of the event.

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arXiv2020

Shadows in the Dark: Low-Surface-Brightness Galaxies Discovered in the Dark Energy Survey

We present a catalog of 23,790 extended low-surface-brightness galaxies (LSBGs) identified in $\sim 5000 °^2$ from the first three years of imaging data from the Dark Energy Survey (DES). Based on a single-component Sérsic model fit, we define extended LSBGs as galaxies with $g$-band effective radii $R_{eff}(g) > 2.5''$ and mean surface brightness $\barμ_{eff}(g) > 24.2 \,mag ȧrcsec^{-2}$. We find that the distribution of LSBGs is strongly bimodal in $(g-r)$ vs.\ $(g-i$) color space. We divide our sample into red ($g-i \geq 0.60$) and blue ($g-i<0.60$) galaxies and study the properties of the two populations. Redder LSBGs are more clustered than their blue counterparts and are correlated with the distribution of nearby ($z < 0.10$) bright galaxies. Red LSBGs constitute $\sim 33\%$ of our LSBG sample, and $\sim 30\%$ of these are located within 1 deg of low-redshift galaxy groups and clusters (compared to $\sim 8\%$ of the blue LSBGs). For nine of the most prominent galaxy groups and clusters, we calculate the physical properties of associated LSBGs assuming a redshift derived from the host system. In these systems, we identify 41 objects that can be classified as ultra-diffuse galaxies, defined as LSBGs with projected physical effective radii $R_{eff} > 1.5 \,kpc$ and central surface brighthness $μ_0(g) > 24.0\, mag \,arcsec^{-2}$. The wide-area sample of LSBGs in DES can be used to test the role of environment on models of LSBG formation and evolution.

astro-ph.GA↗

arXiv2020

Dark Energy Survey Year 3 Results: Clustering Redshifts -- Calibration of the Weak Lensing Source Redshift Distributions with redMaGiC and BOSS/eBOSS

We present the calibration of the Dark Energy Survey Year 3 (DES Y3) weak lensing source galaxy redshift distributions $n(z)$ from clustering measurements. In particular, we cross-correlate the weak lensing (WL) source galaxies sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) and a spectroscopic sample from BOSS/eBOSS to estimate the redshift distribution of the DES sources sample. Two distinct methods for using the clustering statistics are described. The first uses the clustering information independently to estimate the mean redshift of the source galaxies within a redshift window, as done in the DES Y1 analysis. The second method establishes a likelihood of the clustering data as a function of $n(z)$, which can be incorporated into schemes for generating samples of $n(z)$ subject to combined clustering and photometric constraints. Both methods incorporate marginalisation over various astrophysical systematics, including magnification and redshift-dependent galaxy-matter bias. We characterise the uncertainties of the methods in simulations; the first method recovers the mean $z$ of tomographic bins to RMS (precision) of $\sim 0.014$. Use of the second method is shown to vastly improve the accuracy of the shape of $n(z)$ derived from photometric data. The two methods are then applied to the DES Y3 data.

astro-ph.CO↗

arXiv2020

The Dark Energy Survey Supernova Program: Modelling selection efficiency and observed core collapse supernova contamination

The analysis of current and future cosmological surveys of type Ia supernovae (SNe Ia) at high-redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-year photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 5.8 to 9.3 per cent, with an average of 7.0 per cent and r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty.

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arXiv2020

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered "Planet 9", may be present in the outer Solar System. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a dataset consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimised, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) $= 0.996 \pm 0.001$. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

astro-ph.EP↗

arXiv2020

Increasing the census of L and T dwarfs in wide binary and multiple systems using Dark Energy Survey DR1 and Gaia DR2 data

We present the discovery of 255 binary and six multiple system candidates with wide > 5" separation composed by ultracool dwarfs companions to stars, plus nine double ultracool dwarf systems. These systems were selected based on common distance criteria. About 90% of the total sample has proper motions available and 73% of the systems also satisfy a common proper motion criterion. The sample of ultracool candidates was taken from the Dark Energy Survey (DES) and the candidate stellar primaries are from Gaia DR2 and DES data. We compute chance alignment probabilities in order to assess the physical nature of each pair. We find that 174 candidate pairs with Gaia DR2 primaries and 81 pairs with a DES star as a primary have chance alignment probabilities < 5%. Only nine candidate systems composed of two ultracool dwarfs were identified. The sample of candidate multiple systems is made up of five triple systems and one quadruple system. The majority of the ultracool dwarfs found in binaries and multiples are of early L type and the typical wide binary fraction over the L spectral types is 2-4%. Our sample of candidate wide binaries with ultracool dwarfs as secondaries constitutes a substantial increase over the known number of such systems, which are very useful to constrain the formation and evolution of ultracool dwarfs.

astro-ph.SR↗

arXiv2020

Dark Energy Survey Year 1 Results: the lensing imprint of cosmic voids on the Cosmic Microwave Background

Cosmic voids gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint on degree scales. We use the simulated CMB lensing convergence map from the MICE N-body simulation to calibrate our detection strategy for a given void definition and galaxy tracer density. We then identify cosmic voids in DES Year 1 data and stack the Planck 2015 lensing convergence map on their locations, probing the consistency of simulated and observed void lensing signals. When fixing the shape of the stacked convergence profile to that calibrated from simulations, we find imprints at the $3σ$ significance level for various analysis choices. The best measurement strategies based on the MICE calibration process yield $S/N \sim 4$ for DES Y1, and the best-fit amplitude recovered from the data is consistent with expectations from MICE ($A \sim 1$). Given these results as well as the agreement between them and N-body simulations, we conclude that the previously reported excess integrated Sachs-Wolfe (ISW) signal associated with cosmic voids in DES Y1 has no counterpart in the Planck CMB lensing map.

astro-ph.CO↗

arXiv2020

From core collapse to superluminous: The rates of massive stellar explosions from the Palomar Transient Factory

We present measurements of the local core collapse supernova (SN) rate using SN discoveries from the Palomar Transient Factory (PTF). We use a Monte Carlo simulation of hundreds of millions of SN light curve realizations coupled with the detailed PTF survey detection efficiencies to forward-model the SN rates in PTF. Using a sample of 86 core collapse SNe, including 26 stripped-envelope SNe (SESNe), we show that the overall core collapse SN volumetric rate is $r^\mathrm{CC}_v=9.10_{-1.27}^{+1.56}\times10^{-5}\,\text{SNe yr}^{-1}\,\text{Mpc}^{-3}\, h_{70}^{3}$ at $ \langle z \rangle = 0.028$, and the SESN volumetric rate is $r^\mathrm{SE}_v=2.41_{-0.64}^{+0.81}\times10^{-5}\, \text{SNe yr}^{-1}\,\text{Mpc}^{-3}\, h_{70}^{3}$. We further measure a volumetric rate for hydrogen-free superluminous SNe (SLSNe-I) using 8 events at $z{\le}0.2$ of $r^\mathrm{SLSN-I}_v=35_{-13}^{+25}\, \text{SNe yr}^{-1}\text{Gpc}^{-3}\, h_{70}^{3}$, which represents the most precise SLSN-I rate measurement to date. Using a simple cosmic star-formation history to adjust these volumetric rate measurements to the same redshift, we measure a local ratio of SLSN-I to SESN of $\sim1/810^{+1500}_{-94}$, and of SLSN-I to all CCSN types of $\sim 1/3500^{+2800}_{-720}$. However, using host galaxy stellar mass as a proxy for metallicity, we also show that this ratio is strongly metallicity dependent: in low-mass ($\mathrm{log} M_{*} < 9.5 \mathrm{M}_\odot$) galaxies, which are the only environments that host SLSN-I in our sample, we measure a SLSN-I to SESN fraction of $1/300^{+380}_{-170}$ and $1/1700^{+1800}_{-720}$ for all CCSN. We further investigate the SN rates a function of host galaxy stellar mass and show that the specific rates of all core collapse SNe decrease with increasing stellar mass.

astro-ph.HE↗