17 research outputs found
GausSN: Bayesian Time-Delay Estimation for Strongly Lensed Supernovae
We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for
time-delay estimation with resolved systems of gravitationally lensed
supernovae (glSNe). GausSN models the underlying light curve non-parametrically
using a GP. Without assuming a template light curve for each SN type, GausSN
fits for the time delays of all images using data in any number of wavelength
filters simultaneously. We also introduce a novel time-varying magnification
model to capture the effects of microlensing alongside time-delay estimation.
In this analysis, we model the time-varying relative magnification as a sigmoid
function, as well as a constant for comparison to existing time-delay
estimation approaches. We demonstrate that GausSN provides robust time-delay
estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope
and the Vera C. Rubin Observatory's Legacy Survey of Space and Time
(Rubin-LSST). We find that up to 43.6% of time-delay estimates from Roman and
52.9% from Rubin-LSST have fractional errors of less than 5%. We then apply
GausSN to SN Refsdal and find the time delay for the fifth image is consistent
with the original analysis, regardless of microlensing treatment. Therefore,
GausSN maintains the level of precision and accuracy achieved by existing
time-delay extraction methods with fewer assumptions about the underlying shape
of the light curve than template-based approaches, while incorporating
microlensing into the statistical error budget rather than requiring
post-processing to account for its systematic uncertainty. GausSN is scalable
for time-delay cosmography analyses given current projections of glSNe
discovery rates from Rubin-LSST and Roman.Comment: 18 pages, 12 figures, submitted to MNRA
Recommended from our members
Evidence for Grain Growth in Molecular Clouds: A Bayesian Examination of the Extinction Law in Perseus
We investigate the shape of the extinction law in two square fields of the Perseus molecular cloud complex. We combine deep red-optical (r, i and z band) observations obtained using Megacam on the MMT with UKIRT (United Kingdom Infrared Telescope) Infrared Deep Sky Survey near-infrared (J, H and K band) data to measure the colours of background stars. We develop a new hierarchical Bayesian statistical model, including measurement error, intrinsic colour variation, spectral type and dust reddening, to simultaneously infer parameters for individual stars and characteristics of the population. We implement an efficient Markov chain Monte Carlo algorithm utilizing generalized Gibbs sampling to compute coherent probabilistic inferences. We find a strong correlation between the extinction and the slope of the extinction law (parametrized by . Because the majority of the extinction towards our stars comes from the Perseus molecular cloud, we interpret this correlation as evidence of grain growth at moderate optical depths. The extinction law changes from the ‘diffuse’ value of to the 'dense cloud' value of as the column density rises from to 10 mag. This relationship is similar for the two regions in our study, despite their different physical conditions, suggesting that dust grain growth is a fairly universal process.Astronom
The DEHVILS Survey Overview and Initial Data Release: High-Quality Near-Infrared Type Ia Supernova Light Curves at Low Redshift
While the sample of optical Type Ia Supernova (SN Ia) light curves (LCs)
usable for cosmological parameter measurements surpasses 2000, the sample of
published, cosmologically viable near-infrared (NIR) SN Ia LCs, which have been
shown to be good "standard candles," is still 200. Here, we present
high-quality NIR LCs for 83 SNe Ia ranging from as a part of
the Dark Energy, H, and peculiar Velocities using Infrared Light from
Supernovae (DEHVILS) survey. Observations are taken using UKIRT's WFCAM, where
the median depth of the images is 20.7, 20.1, and 19.3 mag (Vega) for , ,
and -bands, respectively. The median number of epochs per SN Ia is 18 for
all three bands () combined and 6 for each band individually. We fit 47 SN
Ia LCs that pass strict quality cuts using three LC models, SALT3, SNooPy, and
BayeSN and find scatter on the Hubble diagram to be comparable to or better
than scatter from optical-only fits in the literature. Fitting NIR-only LCs, we
obtain standard deviations ranging from 0.128-0.135 mag. Additionally, we
present a refined calibration method for transforming 2MASS magnitudes to WFCAM
magnitudes using HST CALSPEC stars that results in a 0.03 mag shift in the
WFCAM -band magnitudes.Comment: 24 pages, 9 figures. Accepted by MNRA
SN 2021hpr and its two siblings in the Cepheid calibrator galaxy NGC 3147: A hierarchical BayeSN analysis of a Type Ia supernova trio, and a Hubble constant constraint
To improve Type Ia supernova (SN Ia) standardisability, the consistency of
distance estimates to siblings -- SNe in the same host galaxy -- should be
investigated. We present Young Supernova Experiment Pan-STARRS-1
photometry of SN 2021hpr, the third spectroscopically confirmed SN Ia in the
high-stellar-mass Cepheid-calibrator galaxy NGC 3147. We analyse NGC 3147's
trio of SN Ia siblings: SNe 1997bq, 2008fv and 2021hpr, using a new version of
the BayeSN model of SN Ia spectral-energy distributions, retrained
simultaneously using optical-NIR (0.35--1.8 m) data. The
distance estimates to each sibling are consistent, with a sample standard
deviation 0.01 mag, much smaller than the total intrinsic scatter in
the training sample: mag. Fitting normal SN Ia siblings
in three additional galaxies, we estimate a 90% probability that the
siblings' intrinsic scatter is smaller than . We build a new
hierarchical model that fits light curves of siblings in a single galaxy
simultaneously; this yields more precise estimates of the common distance and
the dust parameters. Fitting the trio for a common dust law shape yields
. Our work motivates future hierarchical modelling of more
siblings, to tightly constrain their intrinsic scatter, and better understand
SN-host correlations. Finally, we estimate the Hubble constant, using a Cepheid
distance to NGC 3147, the siblings trio, and 109 Hubble flow () SNe Ia; marginalising over the siblings' and population's
intrinsic scatters, and the peculiar velocity dispersion, yields
.Comment: Submitted to MNRAS; 30 pages, 22 figure
Recommended from our members
<scp>Bird-Snack</scp>: Bayesian Inference of dust law <i>RV</i> Distributions using SN Ia Apparent Colours at peaK
Abstract
To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, RV, must be accurately constrained. We thus develop a computationally-inexpensive pipeline, Bird-Snack, to rapidly infer dust population distributions from optical-near infrared SN colours at peak brightness, and determine which analysis choices significantly impact the population mean RV inference, . Our pipeline uses a 2D Gaussian process to measure peak BVriJH apparent magnitudes from SN light curves, and a hierarchical Bayesian model to simultaneously constrain population distributions of intrinsic and dust components. Fitting a low-to-moderate-reddening sample of 65 low-redshift SNe yields , with posterior upper bounds on the population dispersion, \sigma _{R_V}<0.92(1.96). This result is robust to various analysis choices, including: the model for intrinsic colour variations, fitting the shape hyperparameter of a gamma dust extinction distribution, and cutting the sample based on the availability of data near peak. However, these choices may be important if statistical uncertainties are reduced. With larger near-future optical and near-infrared SN samples, Bird-Snack can be used to better constrain dust distributions, and investigate potential correlations with host galaxy properties. Bird-Snack is publicly available; the modular infrastructure facilitates rapid exploration of custom analysis choices, and quick fits to simulated datasets, for better interpretation of real-data inferences.</jats:p
Recommended from our members
A BayeSN distance ladder: <i>H</i>0 from a consistent modelling of type ia supernovae from the optical to the near infrared
Abstract
The local distance ladder estimate of the Hubble constant (H0) is important in cosmology, given the recent tension with the early universe inference. We estimate H0 from the Type Ia supernova (SN Ia) distance ladder, inferring SN Ia distances with the hierarchical Bayesian SED model, BayeSN. This method has a notable advantage of being able to continuously model the optical and near-infrared (NIR) SN Ia light curves simultaneously. We use two independent distance indicators, Cepheids or the tip of the red giant branch (TRGB), to calibrate a Hubble-flow sample of 67 SNe Ia with optical and NIR data. We estimate H0 = 74.82 ± 0.97 (stat) ± 0.84 (sys) km s−1 Mpc−1 when using the calibration with Cepheid distances to 37 host galaxies of 41 SNe Ia, and 70.92 ± 1.14 (stat) ± 1.49 (sys) km s−1 Mpc−1 when using the calibration with TRGB distances to 15 host galaxies of 18 SNe Ia. For both methods, we find a low intrinsic scatter σint ≲ 0.1 mag. We test various selection criteria and do not find significant shifts in the estimate of H0. Simultaneous modelling of the optical and NIR yields up to ∼15% reduction in H0 uncertainty compared to the equivalent optical-only cases. With improvements expected in other rungs of the distance ladder, leveraging joint optical-NIR SN Ia data can be critical to reducing the H0 error budget.</jats:p
Recommended from our members
SN 2023ixf in Messier 101: A Variable Red Supergiant as the Progenitor Candidate to a Type II Supernova
We present preexplosion optical and infrared (IR) imaging at the site of the type II supernova (SN II) 2023ixf in Messier 101 at 6.9 Mpc. We astrometrically registered a ground-based image of SN 2023ixf to archival Hubble Space Telescope (HST), Spitzer Space Telescope (Spitzer), and ground-based near-IR images. A single point source is detected at a position consistent with the SN at wavelengths ranging from HST R band to Spitzer 4.5 μm. Fitting with blackbody and red supergiant (RSG) spectral energy distributions (SEDs), we find that the source is anomalously cool with a significant mid-IR excess. We interpret this SED as reprocessed emission in a 8600 R⊙ circumstellar shell of dusty material with a mass ∼5 × 10−5M⊙ surrounding a and K RSG. This luminosity is consistent with RSG models of initial mass 11 M⊙, depending on assumptions of rotation and overshooting. In addition, the counterpart was significantly variable in preexplosion Spitzer 3.6 and 4.5 μm imaging, exhibiting ∼70% variability in both bands correlated across 9 yr and 29 epochs of imaging. The variations appear to have a timescale of 2.8 yr, which is consistent with κ-mechanism pulsations observed in RSGs, albeit with a much larger amplitude than RSGs such as α Orionis (Betelgeuse)
SN 2023ixf in Messier 101: a variable red supergiant as the progenitor candidate to a type II supernova
We present pre-explosion optical and infrared (IR) imaging at the site of the
type II supernova (SN II) 2023ixf in Messier 101 at 6.9 Mpc. We astrometrically
registered a ground-based image of SN 2023ixf to archival Hubble Space
Telescope (HST), Spitzer Space Telescope (Spitzer), and ground-based near-IR
images. A single point source is detected at a position consistent with the SN
at wavelengths ranging from HST -band to Spitzer 4.5 m. Fitting to
blackbody and red supergiant (RSG) spectral-energy distributions (SEDs), we
find that the source is anomalously cool with a significant mid-IR excess. We
interpret this SED as reprocessed emission in a 8600 circumstellar
shell of dusty material with a mass 5
surrounding a and K RSG. This luminosity is consistent with RSG
models of initial mass 11 , depending on assumptions of rotation and
overshooting. In addition, the counterpart was significantly variable in
pre-explosion Spitzer 3.6 m and 4.5 m imaging, exhibiting 70%
variability in both bands correlated across 9 yr and 29 epochs of imaging. The
variations appear to have a timescale of 2.8 yr, which is consistent with
-mechanism pulsations observed in RSGs, albeit with a much larger
amplitude than RSGs such as Orionis (Betelgeuse).Comment: 14 pages, 5 figures, submitted to ApJL, comments welcom
Recommended from our members
Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses: Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147
Abstract
We present Young Supernova Experiment grizy photometry of SN 2021hpr, the third Type Ia supernova sibling to explode in the Cepheid calibrator galaxy, NGC 3147. Siblings are useful for improving SN-host distance estimates and investigating their contributions toward the SN Ia intrinsic scatter (post-standardization residual scatter in distance estimates). We thus develop a principled Bayesian framework for analyzing SN Ia siblings. At its core is the cosmology-independent relative intrinsic scatter parameter, σ
Rel: the dispersion of siblings distance estimates relative to one another within a galaxy. It quantifies the contribution toward the total intrinsic scatter, σ
0, from within-galaxy variations about the siblings’ common properties. It also affects the combined distance uncertainty. We present analytic formulae for computing a σ
Rel posterior from individual siblings distances (estimated using any SN model). Applying a newly trained BayeSN model, we fit the light curves of each sibling in NGC 3147 individually, to yield consistent distance estimates. However, the wide σ
Rel posterior means σ
Rel ≈ σ
0 is not ruled out. We thus combine the distances by marginalizing over σ
Rel with an informative prior: σ
Rel ∼ U(0, σ
0). Simultaneously fitting the trio’s light curves improves constraints on distance and each sibling’s individual dust parameters, compared to individual fits. Higher correlation also tightens dust parameter constraints. Therefore, σ
Rel marginalization yields robust estimates of siblings distances for cosmology, as well as dust parameters for sibling–host correlation studies. Incorporating NGC 3147's Cepheid distance yields H
0 = 78.4 ± 6.5 km s−1 Mpc−1. Our work motivates analyses of homogeneous siblings samples, to constrain σ
Rel and its SN-model dependence.</jats:p