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We present a comprehensive, configurable open-source software framework for estimating the rate of electromagnetic detection of kilonovae (KNe) associated with gravitational wave detections of binary neutron star (BNS) mergers. We simulate the current LIGO-Virgo-KAGRA (LVK) observing run (O4) using current sensitivity and uptime values as well as using predicted sensitivites for the next observing run (O5). We find the number of discoverable kilonovae during LVK O4 to be |${ 1}_{- 1}^{+ 4}$| or |${ 2 }_{- 2 }^{+ 3 }$|⁠ , (at 90 per cent confidence) depending on the distribution of NS masses in coalescing binaries, with the number increasing by an order of magnitude during O5 to |${ 19 }_{- 11 }^{+ 24 }$|⁠. Regardless of mass model, we predict at most five detectable KNe (at 95 per cent confidence) in O4. We also produce optical and near-infrared light curves that correspond to the physical properties of each merging system. We have collated important information for allocating observing resources for search and follow-up observations, including distributions of peak magnitudes in several broad-bands and time-scales for which specific facilities can detect each KN. The framework is easily adaptable, and new simulations can quickly be produced in response to updated information such as refined merger rates and NS mass distributions. Finally, we compare our suite of simulations to the thus-far completed portion of O4 (as of 2023, October 14), finding a median number of discoverable KNe of 0 and a 95 percentile upper limit of 2, consistent with no detections so far in O4. [ABSTRACT FROM AUTHOR]

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 ± 0.97 (stat) |$\pm \, 0.84$| (sys) km  |${\rm s}^{-1}\, {\rm Mpc}^{-1}$|  when using the calibration with Cepheid distances to 37 host galaxies of 41 SNe Ia, and 70.92 ± 1.14 (stat) |$\pm \, 1.49$| (sys) km  |${\rm s}^{-1}\, {\rm Mpc}^{-1}$|  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 int ≲ 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 ∼15  per cent 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. [ABSTRACT FROM AUTHOR]

As we observe a rapidly growing number of astrophysical transients, we learn more about the diverse host galaxy environments in which they occur. Host galaxy information can be used to purify samples of cosmological Type Ia supernovae, uncover the progenitor systems of individual classes, and facilitate low-latency follow-up of rare and peculiar explosions. In this work, we develop a novel data-driven methodology to simulate the time-domain sky that includes detailed modelling of the probability density function for multiple transient classes conditioned on host galaxy magnitudes, colours, star formation rates, and masses. We have designed these simulations to optimize photometric classification and analysis in upcoming large synoptic surveys. We integrate host galaxy information into the snana simulation framework to construct the simulated catalogue of optical transients and correlated hosts (SCOTCH, a publicly available catalogue of 5-million idealized transient light curves in LSST passbands and their host galaxy properties over the redshift range 0 < z < 3. This catalogue includes supernovae, tidal disruption events, kilonovae, and active galactic nuclei. Each light curve consists of true top-of-the-galaxy magnitudes sampled with high (≲2 d) cadence. In conjunction with SCOTCH, we also release an associated set of tutorials and transient-specific libraries to enable simulations of arbitrary space- and ground-based surveys. Our methodology is being used to test critical science infrastructure in advance of surveys by the Vera C. Rubin Observatory and the Nancy G. Roman Space Telescope. [ABSTRACT FROM AUTHOR]

New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time, 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 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 prioritized followup of unusual transients from new large-scale surveys. [ABSTRACT FROM AUTHOR]

As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova (KN) from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with KNe, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo – the only EM counterpart of a binary neutron star merger discovered to date – and AT2019npv – a supernova that was initially suspected as a counterpart of the GW event, GW190814, but was later ruled out after further analysis. [ABSTRACT FROM AUTHOR]

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 Supernova Survey data set. We train a new version of BayeSN , continuous from 0.35 to 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 RV 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 ≲ 0.3). Splitting the population at the median host mass, we find RV = 2.84 ± 0.31 in low-mass hosts, and RV = 2.58 ± 0.23 in high-mass hosts, both consistent with the global value of RV = 2.61 ± 0.21 that we estimate for the full sample. For all choices of mass split we consider, RV is consistent across the step within ≲ 1.2σ. 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(RV) = 2.70 ± 0.25 with a 95 per cent upper bound on the population σ(RV) < 0.61. The RV population means are consistent within ≲ 1.2σ. We find that simultaneous fitting of host-mass-dependent dust properties within our hierarchical model does not account for the conventional mass step. [ABSTRACT FROM AUTHOR]

The Milky Way hosts on average a few supernova explosions per century, yet in the past millennium only five supernovae have been identified confidently in the historical record. This deficit of naked-eye supernovae is at least partly due to dust extinction in the Galactic plane. We explore this effect quantitatively, developing a formalism for the supernova probability distribution in space and on the sky, accounting for dust and for the observer's flux limit. We then construct a fiducial axisymmetric model for the spatial supernova and dust densities, featuring an exponential dependence on galactocentric radius and height, with core-collapse events in a thin disc and Type Ia events including a thick disc component. When no flux limit is applied, our model predicts that on the sky, supernovae are intrinsically concentrated in the Galactic plane, with Type Ia events extending to higher latitudes. We then apply a flux limit and include dust effects, to predict the sky distribution of historical supernovae. We use well-observed supernovae as light-curve templates, and introduce naked-eye discovery criteria. The resulting sky distributions are strikingly inconsistent with the locations of confident historical supernovae, none of which lie near our model's central peaks. Indeed, SN 1054 lies off the plane almost exactly in the anticentre, and SN 1181 is in the second Galactic quadrant. We discuss possible explanations for these discrepancies. We calculate the percentage of all supernovae bright enough for historical discovery: |$\simeq 13{{\ \rm per\ cent}}$| of core-collapse and |$\simeq 33{{\ \rm per\ cent}}$| of Type Ia events. Using these and the confident historical supernovae, we estimate the intrinsic Galactic supernova rates, finding general agreement with other methods. Finally, we urge searches for supernovae in historical records from civilizations in the Southern hemisphere. [ABSTRACT FROM AUTHOR]

Type Iax supernovae (SNe Iax) are the most common class of peculiar SNe. While they are thought to be thermonuclear white-dwarf (WD) SNe, SNe Iax are observationally similar to, but distinct from SNe Ia. Unlike SNe Ia, where roughly 30 per cent occur in early-type galaxies, only one SN Iax has been discovered in an early-type galaxy, suggesting a relatively short delay time and a distinct progenitor system. Furthermore, one SN Iax progenitor system has been detected in pre-explosion images with its properties consistent with either of two models: a short-lived (<100 Myr) progenitor system consisting of a WD primary and a He-star companion, or a singular Wolf–Rayet progenitor star. Using deep Hubble Space Telescope images of nine nearby SN Iax host galaxies, we measure the properties of stars within 200 pc of the SN position. The ages of local stars, some of which formed with the SN progenitor system, can constrain the time between star formation and SN, known as the delay time. We compare the local stellar properties to synthetic photometry of single-stellar populations, fitting to a range of possible delay times for each SN. With this sample, we uniquely constrain the delay-time distribution for SNe Iax, with a median and 1σ confidence interval delay time of |$63_{- 15}^{+ 58} \times 10^{6}$| yr. The measured delay-time distribution provides an excellent constraint on the progenitor system for the class, indicating a preference for a WD progenitor system over a Wolf–Rayet progenitor star. [ABSTRACT FROM AUTHOR]

In this paper we advance the simple analytic photometric redshift estimator for Type Ia supernovae (SNe Ia) proposed by Wang, and use it to study simulated SN Ia data. We find that better than 0.5 per cent accuracy in zphot{with } is possible for SNe Ia with well-sampled light curves in three observed passbands ( riz) with a signal-to-noise ratio of 25 at peak brightness, if the extinction by dust is negligible. The corresponding bias in zphot (the mean of zphot− zspec) is 5.4 × 10−4. If dust extinction is taken into consideration in the riz observer-frame light curves, the accuracy in zphot deteriorates to 4.4 per cent, with a bias in zphot of 8.0 × 10−3. Adding the g-band light curve improves the accuracy in zphot to 2.5 per cent, and reduces the bias in zphot to − 1.5 × 10−3. Our results have significant implications for the design of future photometric surveys of SNe Ia from both ground and space telescopes. Accurate and precise photometric redshifts boost the cosmological utility of such surveys. [ABSTRACT FROM AUTHOR]