arXiv subjects

Kaisey S. Mandel

Publications and source records attributed to Kaisey S. Mandel.

At least 37 records · Page 2Linked to original sources

arXiv2024

Using Rest-Frame Optical and NIR Data from the RAISIN Survey to Explore the Redshift Evolution of Dust Laws in SN Ia Host Galaxies

We use rest-frame optical and near-infrared (NIR) observations of 42 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project at low-$z$ and 37 from the RAISIN Survey at high-$z$ to investigate correlations between SN Ia host galaxy dust, host mass, and redshift. This is the first time the SN Ia host galaxy dust extinction law at high-$z$ has been estimated using combined optical and rest-frame NIR data ($YJ$-band). We use the BayeSN hierarchical model to leverage the data's wide rest-frame wavelength range (extending to $\sim$1.0-1.2 microns for the RAISIN sample at $0.2\lesssim z\lesssim0.6$). By contrasting the RAISIN and CSP data, we constrain the population distributions of the host dust $R_V$ parameter for both redshift ranges. We place a limit on the difference in population mean $R_V$ between RAISIN and CSP of $-1.16<Δμ(R_V)<1.38$ with 95% posterior probability. For RAISIN we estimate $μ(R_V)=2.58\pm0.57$, and constrain the population standard deviation to $σ(R_V)<0.90~[2.42]$ at the 68 [95]% level. Given that we are only able to constrain the size of the low- to high-$z$ shift in $μ(R_V)$ to $\lesssim1.4$ - which could still propagate to a substantial bias in the equation of state parameter $w$ - these and other recent results motivate continued effort to obtain rest-frame NIR data at low and high redshifts (e.g. using the Roman Space Telescope).

astro-ph.CO↗

arXiv2023

Flight of the Bumblebee: the Early Excess Flux of Type Ia Supernova 2023bee revealed by $TESS$, $Swift$ and Young Supernova Experiment Observations

We present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee in NGC~2708 ($D = 32 \pm 3$ Mpc), finding excess flux in the first days after explosion relative to the expected power-law rise from an expanding fireball. This deviation from typical behavior for SNe Ia is particularly obvious in our 10-minute cadence $TESS$ light curve and $Swift$ UV data. Compared to a few other normal SNe Ia with detected early excess flux, the excess flux in SN 2023bee is redder in the UV and less luminous. We present optical spectra of SN 2023bee, including two spectra during the period where the flux excess is dominant. At this time, the spectra are similar to those of other SNe Ia but with weaker Si II, C II and Ca II absorption lines, perhaps because the excess flux creates a stronger continuum. We compare the data to several theoretical models that have been proposed to explain the early flux excess in SNe Ia. Interaction with either a nearby companion star or close-in circumstellar material is expected to produce a faster evolution than seen in the data. Radioactive material in the outer layers of the ejecta, either from a double detonation explosion or simply an explosion with a $^{56}$Ni clump near the surface, can not fully reproduce the evolution either, likely due to the sensitivity of early UV observable to the treatment of the outer part of ejecta in simulation. We conclude that no current model can adequately explain the full set of observations. We find that a relatively large fraction of nearby, bright SNe Ia with high-cadence observations have some amount of excess flux within a few days of explosion. Considering potential asymmetric emission, the physical cause of this excess flux may be ubiquitous in normal SNe Ia.

astro-ph.HE↗

arXiv2023

Bird-Snack: Bayesian Inference of dust law $R_V$ Distributions using SN Ia Apparent Colours at peaK

To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, $R_V$, 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 $R_V$ inference, $μ_{R_V}$. 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 $μ_{R_V}=2.61^{+0.38}_{-0.35}$, with $68\%(95\%)$ posterior upper bounds on the population dispersion, $σ_{R_V}<0.92(1.96)$. 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.

astro-ph.GA↗

arXiv2023

Relative intrinsic scatter in hierarchical Type Ia supernova siblings analyses: Application to SNe 2021hpr, 1997bq & 2008fv in NGC 3147

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 the contributions towards the SN Ia intrinsic scatter (post-standardisation 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 towards 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 $σ_{Rel}\approxσ_0$ is not ruled out. We thus combine the distances by marginalizing over $σ_{Rel}$ with an informative prior: $σ_{Rel}\sim U(0,σ_0)$. 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, and dust parameters for siblings-host correlation studies. Incorporating NGC 3147's Cepheid-distance yields $H_0=78.4\pm 6.5\,$km/s/Mpc. Our work motivates analyses of homogeneous siblings samples, to constrain $σ_{Rel}$, and its SN-model dependence.

astro-ph.CO↗

arXiv2023

A BayeSN Distance Ladder: $H_0$ from a consistent modelling of Type Ia supernovae from the optical to the near infrared

The local distance ladder estimate of the Hubble constant ($H_0$) is important in cosmology, given the recent tension with the early universe inference. We estimate $H_0$ 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 $H_0 = 74.82 \pm 0.97$ (stat) $\pm\, 0.84$ (sys) km\,s$^{-1}$\,Mpc$^{-1}$ when using the calibration with Cepheid distances to 37 host galaxies of 41 SNe~Ia, and $70.92 \pm 1.14$ (stat) $\pm\,1.49$ (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 $σ_{\rm int} \lesssim 0.1$ mag. We test various selection criteria and do not find significant shifts in the estimate of $H_0$. Simultaneous modelling of the optical and NIR yields up to $\sim$15\% reduction in $H_0$ 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 $H_0$ error budget.

astro-ph.CO↗

arXiv2023

SN2023ixf 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 $R$-band to Spitzer 4.5 $μ$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 $R_{\odot}$ circumstellar shell of dusty material with a mass $\sim$5$\times10^{-5} M_{\odot}$ surrounding a $\log(L/L_{\odot})=4.74\pm0.07$ and $T_{\rm eff}=3920\substack{+200\\-160}$ K RSG. This luminosity is consistent with RSG models of initial mass 11 $M_{\odot}$, depending on assumptions of rotation and overshooting. In addition, the counterpart was significantly variable in pre-explosion Spitzer 3.6 $μ$m and 4.5 $μ$m imaging, exhibiting $\sim$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).

astro-ph.SR↗

arXiv2023

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 $\lesssim$ 200. Here, we present high-quality NIR LCs for 83 SNe Ia ranging from $0.002 < z < 0.09$ as a part of the Dark Energy, H$_0$, 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 $Y$, $J$, and $H$-bands, respectively. The median number of epochs per SN Ia is 18 for all three bands ($YJH$) 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 $Y$-band magnitudes.

astro-ph.CO↗

arXiv2023

Evidence for a Massive Andromeda Galaxy Using Satellite Galaxy Proper Motions

We present new mass estimates for Andromeda (M31) using the orbital angular momenta of four satellite galaxies (M33, NGC 185, NGC 147, IC 10) derived from existing proper motions, distances, and line-of-sight velocities. We infer two masses for M31: $M_{\rm vir}= 2.85^{+1.47}_{-0.77}\times10^{12}\, M_{\odot}$ using satellite galaxy phase space information derived with HST-based M31 proper motions and $M_{\rm vir}=3.02^{+1.30}_{-0.69}\times10^{12}\, M_{\odot}$ using phase space information derived with the weighted average of HST+Gaia-based M31 proper motions. The precision of our new M31 mass estimates (23-50%) improves by a factor of two compared to previous mass estimates using a similar methodology with just one satellite galaxy and places our results amongst the highest precision M31 estimates in recent literature. Furthermore, our results are consistent with recently revised estimates for the total mass of the Local Group (LG), with the stellar mass--halo mass relation, and with observed kinematic data for both M31 and its entire population of satellites. An M31 mass $> 2.5 \times 10^{12}\, M_{\odot}$ could have major implications for our understanding of LG dynamics, M31's merger and accretion history, and our understanding of LG galaxies in a cosmological context.

astro-ph.GA↗

arXiv2022

Real-Time Detection of Anomalies in Large-Scale Transient Surveys

New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe millions of transient alerts each night, making standard approaches of visually identifying new and interesting transients infeasible. We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first modelling approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We demonstrate our methods' ability to provide anomaly scores as a function of time on light curves from the Zwicky Transient Facility. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model. The parametric model is able to identify anomalies with respect to common supernova classes with high precision and recall scores, achieving area under the precision-recall curves (AUCPR) above 0.79 for most rare classes such as kilonovae, tidal disruption events, intermediate luminosity transients, and pair-instability supernovae. Our ability to identify anomalies improves over the lifetime of the light curves. Our framework, used in conjunction with transient classifiers, will enable fast and prioritised followup of unusual transients from new large-scale surveys.

astro-ph.IM↗

arXiv2022

Constraining the SN Ia Host Galaxy Dust Law Distribution and Mass Step: Hierarchical BayeSN Analysis of Optical and Near-Infrared Light Curves

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 point estimates is computed, overestimates the $R_V$ population variance $σ_R^2$. This bias is exacerbated when neglecting residual intrinsic colour variation beyond that due to light curve shape. Instead, Bayesian shrinkage estimates of $σ_R$ are more accurate, with fully hierarchical analysis of the light curves being ideal. For the 75 SNe with low-to-moderate reddening (peak apparent $B-V\leq0.3$), we estimate an $R_V$ distribution with population mean $μ_R=2.59\pm0.14$, and standard deviation $σ_R=0.62\pm0.16$. Splitting this subsample at the median host galaxy mass ($10^{10.57}~\mathrm{M}_\odot$) yields consistent estimated $R_V$ distributions between low- and high-mass galaxies, with $μ_R=2.79\pm0.18$, $σ_R=0.42\pm0.24$, and $μ_R=2.35\pm0.27$, $σ_R=0.74\pm0.36$, respectively. When estimating distances from the full optical-NIR light curves while marginalising over various forms of the dust $R_V$ distribution, a mass step of $\gtrsim0.06$ mag persists in the Hubble residuals at the median host mass.

astro-ph.CO↗

arXiv2021

Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data

Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. Some of these transients are the explosive deaths of stars known as supernovae while others are rare, exotic, or entirely new kinds of exciting stellar explosions. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients, making standard approaches of visually identifying new and interesting transients infeasible. To meet this demand, we present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model.

cs.LG↗

arXiv2021

An Early-Time Optical and Ultraviolet Excess in the type-Ic SN 2020oi

We present photometric and spectroscopic observations of Supernova 2020oi (SN 2020oi), a nearby ($\sim$17 Mpc) type-Ic supernova (SN Ic) within the grand-design spiral M100. We undertake a comprehensive analysis to characterize the evolution of SN 2020oi and constrain its progenitor system. We detect flux in excess of the fireball rise model $δt \approx 2.5$ days from the date of explosion in multi-band optical and UV photometry from the Las Cumbres Observatory and the Neil Gehrels Swift Observatory, respectively. The derived SN bolometric luminosity is consistent with an explosion with $M_{\rm ej} = 0.81 \pm 0.03 M_{\odot}$, $E_{k}= 0.79 \pm 0.09 \times 10^{51} \rm{erg} \rm{s}^{-1}$, and $M_{\rm Ni56} = 0.08 \pm 0.02 M_{\odot}$. Inspection of the event's decline reveals the highest $Δm_{15,\rm{bol}}$ reported for a stripped-envelope event to date. Modeling of optical spectra near event peak indicates a partially mixed ejecta comparable in composition to the ejecta observed in SN 1994I, while the earliest spectrum shows signatures of a possible interaction with material of a distinct composition surrounding the SN progenitor. Further, Hubble Space Telescope (HST) pre-explosion imaging reveals a stellar cluster coincident with the event. From the cluster photometry, we derive the mass and age of the SN progenitor using stellar evolution models implemented in the BPASS library. Our results indicate that SN 2020oi occurred in a binary system from a progenitor of mass $M_{\rm ZAMS} \approx 9.5 \pm 1.0 M_{\odot}$, corresponding to an age of $27 \pm 7$ Myr. SN 2020oi is the dimmest SN Ic event to date for which an early-time flux excess has been observed, and the first in which an early excess is unlikely to be associated with shock-cooling.

astro-ph.HE↗

arXiv2021

Testing the Consistency of Dust Laws in SN Ia Host Galaxies: A BayeSN Examination of Foundation DR1

We apply BayeSN, our new hierarchical Bayesian model for the SEDs of Type Ia supernovae (SNe Ia), to analyse the $griz$ light curves of 157 nearby SNe Ia ($0.015<z<0.08$) from the public Foundation DR1 dataset. We train a new version of BayeSN, continuous from 0.35--0.95 $μ$m, which we use to model the properties of SNe Ia in the rest-frame $z$-band, study the properties of dust in their host galaxies, and construct a Hubble diagram of SN Ia distances determined from full $griz$ light curves. Our $griz$ Hubble diagram has a low total RMS of 0.13 mag using BayeSN, compared to 0.16 mag using SALT2. Additionally, we test the consistency of the dust law $R_V$ between low- and high-mass host galaxies by using our model to fit the full time- and wavelength-dependent SEDs of SNe Ia up to moderate reddening (peak apparent $B-V \lesssim 0.3$). Splitting the population at the median host mass, we find $R_V=2.84\pm0.31$ in low-mass hosts, and $R_V=2.58\pm0.23$ in high-mass hosts, both consistent with the global value of $R_V=2.61\pm0.21$ that we estimate for the full sample. For all choices of mass split we consider, $R_V$ is consistent across the step within $\lesssim1.2σ$. Modelling population distributions of dust laws in low- and high-mass hosts, we find that both subsamples are highly consistent with the full sample's population mean $μ(R_V) = 2.70\pm0.25$ with a 95% upper bound on the population $σ(R_V) < 0.61$. The $R_V$ population means are consistent within $\lesssim1.2σ$. We find that simultaneous fitting of host-mass-dependent dust properties within our hierarchical model does not account for the conventional mass step.

astro-ph.GA↗

arXiv2020

A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical to Near-Infrared

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.

astro-ph.CO↗

arXiv2019

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

astro-ph.IM↗

arXiv2019

RAPID: Early Classification of Explosive Transients using Deep Learning

We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present the first method specifically designed to provide early classifications of astronomical time-series data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well-suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of observed transients from the ZTF data stream. We have made RAPID available as an open-source software package (https://astrorapid.readthedocs.io) for machine learning-based alert-brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds.

astro-ph.IM↗

arXiv2019

WFIRST: Enhancing Transient Science and Multi-Messenger Astronomy

Astrophysical transients have been observed for millennia and have shaped our most basic assumptions about the Universe. In the last century, systematic searches have grown from detecting handfuls of transients per year to over 7000 in 2018 alone. As these searches have matured, we have discovered both large samples of "normal" classes and new, rare classes. Recently, a transient was the first object observed in both gravitational waves and light. Ground-based observatories, including LSST, will discover thousands of transients in the optical, but these facilities will not provide the high-fidelity near-infrared (NIR) photometry and high-resolution imaging of a space-based observatory. WFIRST can fill this gap. With its survey designed to measure the expansion history of the Universe with Type Ia supernovae, WFIRST will also discover and monitor thousands of other transients in the NIR, revealing the physics for these high-energy events. Small-scale GO programs, either as a supplement to the planned survey or as specific target-of-opportunity observations, would significantly expand the scope of transient science that can be studied with WFIRST.

astro-ph.HE↗

arXiv2019

Type Ia Supernovae are Excellent Standard Candles in the Near-Infrared

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 referenced to the NIR maxima. For each NIR band, a comparable GP method RMS is obtained when referencing to NIR-max or B-max. Using NIR LC templates referenced to B-max yields a larger RMS value of $0.138 \pm 0.014$ mag. Fitting the corresponding optical data using standard LC fitters that use LC shape and color corrections yields larger RMS values of $0.179 \pm 0.018$ mag with SALT2 and $0.174 \pm 0.021$ mag with SNooPy. Applying our GP method to subsets of SN Ia NIR LCs at NIR maximum light, even without corrections for LC shape, color, or host-galaxy dust reddening, provides smaller RMS in the inferred distances, at the $\sim 2.3 - 4.1σ$ level, than standard optical methods that do correct for those effects. Our ongoing RAISIN program on the Hubble Space Telescope will exploit this promising infrared approach to limit systematic errors when measuring the expansion history of the universe to constrain dark energy.

astro-ph.CO↗