While conventional Type Ia supernova (SN Ia) cosmology analyses rely primarily on rest-frame optical light curves to determine distances, SNe Ia are excellent standard candles in near-infrared (NIR) light, which is significantly less sensitive to dust extinction. A SN Ia spectral energy distribution (SED) model capable of fitting rest-frame NIR observations is necessary to fully leverage current and future SN Ia datasets from ground- and space-based telescopes including HST, LSST, JWST, and RST. We construct a hierarchical Bayesian model for SN Ia SEDs, continuous over time and wavelength, from the optical to NIR ($B$ through $H$, or $0.35 -1.8\, μ$m). We model the SED as a combination of physically-distinct host galaxy dust and intrinsic spectral components. The distribution of intrinsic SEDs over time and wavelength is modelled with probabilistic functional principal components and the covariance of residual functions. We train the model on a nearby sample of 79 SNe Ia with joint optical and NIR light curves by sampling the global posterior distribution over dust and intrinsic latent variables, SED components, and population hyperparameters. The photometric distances of SNe Ia with NIR data near maximum light obtain a total RMS error of 0.10 mag with our BayeSN model, compared to 0.14 mag with SNooPy and SALT2 for the same sample. Jointly fitting the optical and NIR data of the full sample for a global host dust law, we find $R_V = 2.9 \pm 0.2$, consistent with the Milky Way average.
We investigate the statistical dependence of the peak intrinsic colors of Type Ia supernovae (SN Ia) on their expansion velocities at maximum light, measured from the Si II 6355 spectral feature. We construct a new hierarchical Bayesian regression model, accounting for the random effects of intrinsic scatter, measurement error, and reddening by host galaxy dust, and implement a Gibbs sampler and deviance information criteria to estimate the correlation. The method is applied to the apparent colors from BVRI light curves and Si II velocity data for 79 nearby SNe Ia. The apparent color distribut…
We present a holistic Bayesian hierarchical model for reconstructing the continuous and dynamic evolution of relative sea-level (RSL) change with fully quantified uncertainty. The reconstruction is produced from biological (foraminifera) and geochemical (δ13C) sea-level indicators preserved in dated cores of salt-marsh sediment. Our model is comprised of three modules: (1) A Bayesian transfer function for the calibration of foraminifera into tidal elevation, which is flexible enough to formally accommodate additional proxies (in this case bulk-sediment δ13C values); (2) A chronology developed …
BayeSN $\times$ Dovekie: Joint Photometric Cross-calibration and SED Modelling of Type Ia Supernovae
Institute of Astronomy and Kavli Institute for Cosmology, University of Cambridge（剑桥大学天体物理研究所与卡弗里宇宙学研究所）；School of Physics and Astronomy, University of Southampton（南安普顿大学物理与天文学院）
The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate parameter. After training and testing the neural network, the SED sequence could give both the spectrum with wavelength range from 3000Å~to 8000Å~ and the light curve with phase from 20 days before to 50 days after the maximum luminosity for the supernovae with different colors and decline rates. Therefore, we call this the Artificial Neural Network Spectral Light…
Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model is constructed from SNe Ia spectral properties and spectrophotometric data from The Nearby Supernova Factory colla…
Recent developments in time domain astronomy, like the Zwicky Transient Facility, have made possible a daily scan of the entire visible sky, leading to the discovery of hundreds of new transients every night. Among them, 10 to 15 are supernovae (SNe), which have to be classified prior to cosmological use. The Spectral Energy Distribution machine (SEDm), a low resolution Integral Field Spectrograph, has been designed, built, and operated to spectroscopically classify targets detected by the ZTF main camera. The current Pysedm pipeline is limited by contamination when the transient is too close …
The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet those requirements. Standard template fitting delivers interpretable models and errors, but lacks flexibility to learn inaccuracies in the observed photometry or the spectral templates. Machine learning addresses those issues, but requires representative training data, and the resulting models and uncertainties cannot be interpreted in the context of a physical model or …
We present Keck high-quality rest-frame ultraviolet (UV) through optical spectra of 21 Type Ia supernovae (SNe Ia) in the redshift range 0.11 < z < 0.37 and a mean redshift of 0.22 that were discovered during the Sloan Digital Sky Survey-II (SDSS-II) SN Survey. Using the broad-band photometry of the SDSS survey, we are able to reconstruct the SN host-galaxy spectral energy distributions (SEDs), allowing for a correction for the host-galaxy contamination in the SN Ia spectra. Comparison of composite spectra constructed from a subsample of 17 high-quality spectra to those created from a low-reds…
We present a novel class of models for Type Ia supernova time-evolving spectral energy distributions (SED) and absolute magnitudes: they are each modeled as stochastic functions described by Gaussian processes. The values of the SED and absolute magnitudes are defined through well-defined regression prescriptions, so that data directly inform the models. As a proof of concept, we implement a model for synthetic photometry built from the spectrophotometric time series from the Nearby Supernova Factory. Absolute magnitudes at peak $B$ brightness are calibrated to 0.13 mag in the $g$-band and to …
SNe driven winds are widely thought to be very influential in the high-redshift Universe, shaping the properties of the circum-galactic medium, enriching the IGM with metals and driving the evolution of low-mass galaxies. However, it is not yet fully understood how SNe driven winds interact with their surroundings in a cosmological context, nor is it clear whether they are able to significantly impact the evolution of low-mass galaxies from which they originate by altering the amount of cold material these accrete from the cosmic web. We implement a standard Taylor-Sedov type solution, widely …
Characterising the Standardisation Properties of Type Ia Supernovae in the z band with Hierarchical Bayesian Modelling
University of Cambridge（剑桥大学）；University of Birmingham（伯明翰大学）；University of Hawai’i（夏威夷大学）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Stockholm University（斯德哥尔摩大学）
Supernovae Unite: Host-Galaxy Mass Measurements of Type Ia Supernovae and Their Impact on Cosmology
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；University of Queensland（昆士兰大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）；Institute of Space Sciences (ICE-CSIC)（空间科学研究所（ICE-CSIC））；Institut d’Estudis Espacials de Catalunya (IEEC)（加泰罗尼亚空间研究学院（IEEC））；University of Pennsylvania（宾夕法尼亚大学）；University of Southampton（南安普顿大学）；Trinity College Dublin, The University of Dublin（都柏林三一学院，都柏林大学）；Universidad Arturo Prat（阿图罗·普拉特大学）；Duke University（杜克大学）；University of Oxford（牛津大学）
We have constructed a comprehensive statistical model for Type Ia supernova (SN Ia) light curves spanning optical through near infrared (NIR) data. A hierarchical framework coherently models multiple random and uncertain effects, including intrinsic supernova light curve covariances, dust extinction and reddening, and distances. An improved BayeSN MCMC code computes probabilistic inferences for the hierarchical model by sampling the global probability density of parameters describing individual supernovae and the population. We have applied this hierarchical model to optical and NIR data of 12…
An improved standardization framework for Type II-P supernovae using a hierarchical Bayesian approach
Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Laboratoire de Physique Nucléaire et des Hautes Energies, CNRS/IN2P3, Sorbonne Université, Université de Paris（核物理与高能物理实验室，法国国家科学研究中心/法国国家原子能和替代能源委员会，索邦大学，巴黎大学）；Yale University（耶鲁大学）；European Space Astronomy Center (ESAC), European Space Agency（欧洲空间天文中心，欧洲航天局）；Univ Lyon, Université Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon（里昂大学，克莱贝尔·贝尔纳里昂第一大学，法国国家科学研究中心/法国国家原子能和替代能源委员会，里昂粒子物理研究所）；European Southern Observatory（欧洲南方天文台）
The ejected mass distribution of type Ia supernovae directly probes progenitor evolutionary history and explosion mechanisms, with implications for their use as cosmological probes. Although the Chandrasekhar mass is a natural mass scale for the explosion of white dwarfs as type Ia supernovae, models allowing type Ia supernovae to explode at other masses have attracted much recent attention. Using an empirical relation between the ejected mass and the light curve width, we derive ejected masses $M_\mathrm{ej}$ and $^{56}$Ni masses $M_\mathrm{Ni}$ for a sample of 337 type Ia supernovae with red…
We re-examine the contentious question of constraints on anisotropic expansion from Type Ia supernovae (SNIa) in the light of a novel determination of peculiar velocities, which are crucial to test isotropy with supernovae out to distances $\lesssim 200/h$ Mpc. We re-analyze the Joint Light-Curve Analysis (JLA) Supernovae (SNe) data, improving on previous treatments of peculiar velocity corrections and their uncertainties (both statistical and systematic) by adopting state-of-the-art flow models constrained independently via the 2M$++$ galaxy redshift compilation. We also introduce a novel pro…
Accurate standardisation of Type Ia supernovae (SNIa) is instrumental to the usage of SNIa as distance indicators. We analyse a homogeneous sample of 22 low-z SNIa, observed by the Carnegie Supernova Project (CSP) in the optical and near infra-red (NIR). We study the time of the second peak in the NIR band due to re-brightening, t2, as an alternative standardisation parameter of SNIa peak brightness. We use BAHAMAS, a Bayesian hierarchical model for SNIa cosmology, to determine the residual scatter in the Hubble diagram. We find that in the absence of a colour correction, t2 is a better standa…
The dominant uncertainty in the current measurement of the Hubble constant ($H_0$) with strong gravitational lensing time delays is attributed to uncertainties in the mass profiles of the main deflector galaxies. Strongly lensed supernovae (glSNe) can provide, in addition to measurable time delays, lensing magnification constraints when knowledge about the unlensed apparent brightness of the explosion is imposed. We present a hierarchical Bayesian framework to combine a dataset of SNe that are not strongly lensed and a dataset of strongly lensed SNe with measured time delays. We jointly constr…
We analyze a set of 89 Type Ia supernovae (SN Ia) that have both optical and near-infrared (NIR) photometry to derive distances and construct low redshift ($z < 0.04$) Hubble diagrams. We construct mean light curve (LC) templates using a hierarchical Bayesian model. We explore both Gaussian process (GP) and template methods for fitting the LCs and estimating distances, while including peculiar velocity and photometric uncertainties. For the 56 SN Ia with both optical and NIR observations near maximum light, the GP method yields a NIR-only Hubble-diagram with a RMS of $0.117 \pm 0.014$ mag when…
Having two "sibling" Type Ia supernovae (SNe Ia) in the same galaxy offers additional advantages in reducing a variety of systematic errors involved in estimating the Hubble constant, $H_{0}$. NGC 4414 is a nearby galaxy included in the Hubble Space Telescope Key Project to measure its distance using Cepheid variables. It hosts two sibling SNe Ia: SN 2021J and SN 1974G. This provides the opportunity to improve the precision of the previous estimate of $H_{0}$, which was based solely on SN 1974G. Here we present new optical $BVRI$ photometry obtained at the Observatorio de Sierra Nevada and com…
Studying galaxies at different cosmic epochs entails several observational effects that need to be taken into account to compare populations across a large time span in a consistent manner. We use a sample of 166 nearby galaxies that hosted type Ia supernovae (SNe Ia) and have been observed with the integral field spectrograph MUSE through the AMUSING survey. Here, we present a study of the systematic errors and bias in the host stellar mass with increasing redshifts that are generally overlooked in SNe Ia cosmological analyses. We simulate observations at different redshifts (0.1<z<2.0) using…
A hierarchical Bayesian method is applied to the analysis of Type-Ia supernovae (SNIa) observations to constrain the properties of the dark matter haloes of galaxies along the SNIa lines-of-sight via their gravitational lensing effect. The full joint posterior distribution of the dark matter halo parameters is explored using the nested sampling algorithm {\sc MultiNest}, which also efficiently calculates the Bayesian evidence, thereby facilitating robust model comparison. We first demonstrate the capabilities of the method by applying it to realistic simulated SNIa data, based on the real 3-ye…
Type Ia supernovae (SNe Ia) are standardizable candles, but for over a decade, there has been a debate on how to properly account for their correlations with host galaxy properties. Using the Bayesian hierarchical model UNITY, we simultaneously fit for the SN Ia light curve and host galaxy standardization parameters on a set of 103 Sloan Digital Sky Survey II SNe Ia. We investigate the influences of host stellar mass, along with both localized ($r<3$ kpc) and host-integrated average stellar ages, derived from stellar population synthesis modeling. We find that the standardization for the light…
The distribution of cold gas in dark matter haloes is driven by key processes in galaxy formation: gas cooling, galaxy mergers, star formation and reheating of gas by supernovae. We compare the predictions of four different galaxy formation models for the spatial distribution of cold gas. We find that satellite galaxies make little contribution to the abundance or clustering strength of cold gas selected samples, and are far less important than they are in optically selected samples. The halo occupation distribution function of present-day central galaxies with cold gas mass > 10^9 h^-1 Msun i…
We use the BayeSN hierarchical probabilistic SED model to analyse the optical-NIR ($BVriYJH$) light curves of 86 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project to investigate the SN Ia host galaxy dust law distribution and correlations between SN Ia Hubble residuals and host mass. Our Bayesian analysis simultaneously constrains the mass step and dust $R_V$ population distribution by leveraging optical-NIR colour information. We demonstrate how a simplistic analysis where individual $R_V$ values are first estimated for each SN separately, and then the sample variance of these p…
CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry
Theoretical and Scientific Data Science, International School for Advanced Studies (SISSA), Trieste, Italy（1 理论与科学数据科学，国际先进研究学院（SISSA），意大利特里este）；Institute of Cosmos Sciences (ICC), University of Barcelona, Barcelona, Spain（2 大学巴塞罗那宇宙科学研究所（ICC），西班牙巴塞罗那）；Department of Physics, Imperial College London, London, UK（3 物理系，伦敦帝国理工学院，英国伦敦）；Institute for Fundamental Physics of the Universe (IFPU), Trieste, Italy（4 宇宙基本物理研究所（IFPU），意大利特里este）；Italian Research Center on High-Performance Computing, Big Data and Quantum Computing (ICSC), Casalecchio di Reno, Italy（5 意大利高性能计算、大数据和量子计算研究中心（ICSC），意大利Casalecchio di Reno）；Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain（6 哥伦布研究与高级研究机构（ICREA），西班牙巴塞罗那）
