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SN 1987A was an unusual hydrogen-rich core-collapse supernova originating from a blue supergiant star. Similar blue supergiant explosions remain a small family of events, and are broadly characterized by their long rises to peak. The Zwicky Transient Facility Census of the Local Universe (CLU) experiment aims to construct a spectroscopically complete sample of transients occurring in galaxies from the CLU galaxy catalog. We identify 13 long-rising (>40 days) Type II supernovae from the volume-limited CLU experiment during a 3.5 yr period from 2018 June to 2021 December, approximately doubling the previously known number of these events. We present photometric and spectroscopic data of these 13 events, finding peakr-band absolute magnitudes ranging from −15.6 to −17.5 mag and the tentative detection of Baiilines in nine events. Using our CLU sample of events, we derive a long-rising Type II supernova rate of${1.37}_{-0.30}^{+0.26}\times {10}^{-6}$Mpc⁻³yr⁻¹, ≈1.4% of the total core-collapse supernova rate. This is the first volumetric rate of these events estimated from a large, systematic, volume-limited experiment.

 

Abstract: Detecting gravitationally lensed supernovae is among the biggest challenges in astronomy. It involves a combination of two very rare phenomena: catching the transient signal of a stellar explosion in a distant galaxy and observing it through a nearly perfectly aligned foreground galaxy that deflects light towards the observer. Here we describe how high-cadence optical observations with the Zwicky Transient Facility, with its unparalleled large field of view, led to the detection of a multiply imaged type Ia supernova, SN Zwicky, also known as SN 2022qmx. Magnified nearly 25-fold, the system was found thanks to the standard candle nature of type Ia supernovae. High-spatial-resolution imaging with the Keck telescope resolved four images of the supernova with very small angular separation, corresponding to an Einstein radius of only θ E  = 0.167″ and almost identical arrival times. The small θ E and faintness of the lensing galaxy are very unusual, highlighting the importance of supernovae to fully characterize the properties of galaxy-scale gravitational lenses, including the impact of galaxy substructures. 

Abstract The current Cepheid-calibrated distance ladder measurement of H 0 is reported to be in tension with the values inferred from the cosmic microwave background (CMB), assuming standard cosmology. However, some tip of the red giant branch (TRGB) estimates report H 0 in better agreement with the CMB. Hence, it is critical to reduce systematic uncertainties in local measurements to understand the Hubble tension. In this paper, we propose a uniform distance ladder between the second and third rungs, combining Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility (ZTF) with a TRGB calibration of their absolute luminosity. A large, volume-limited sample of both calibrator and Hubble flow SNe Ia from the same survey minimizes two of the largest sources of systematics: host-galaxy bias and nonuniform photometric calibration. We present results from a pilot study using the existing TRGB distance to the host galaxy of ZTF SN Ia SN 2021rhu (aka ZTF21abiuvdk) in NGC7814. Combining the ZTF calibrator with a volume-limited sample from the first data release of ZTF Hubble flow SNe Ia, we infer H 0 = 76.94 ± 6.4 km s −1 Mpc −1 , an 8.3% measurement. The error budget is dominated by the single object calibrating the SN Ia luminosity in this pilot study. However, the ZTF sample includes already five other SNe Ia within ∼20 Mpc for which TRGB distances can be obtained with the Hubble Space Telescope. Finally, we present the prospects of building this distance ladder out to 80 Mpc with James Webb Space Telescope observations of more than 100 ZTF SNe Ia. 

Among the supernovae (SNe) that show strong interaction with a circumstellar medium (CSM), there is a rare subclass of Type Ia supernovae, SNe Ia-CSM, which show strong narrow hydrogen emission lines much like SNe IIn but on top of a diluted Type Ia spectrum. The only previous systematic study of this class identified 16 SNe Ia-CSM, eight historic and eight from the Palomar Transient Factory (PTF). Now using the successor survey to PTF, the Zwicky Transient Facility (ZTF), we have classified 12 additional SNe Ia-CSM through the systematic Bright Transient Survey (BTS). Consistent with previous studies, we find these SNe to have slowly evolving optical light curves with peak absolute magnitudes between −19.1 and −21, spectra having weak Hβand large Balmer decrements of ∼7. Out of the 10 SNe from our sample observed by NEOWISE, nine have 3σdetections, with some SNe showing a reduction in the red wing of Hα, indicative of newly formed dust. We do not find our SN Ia-CSM sample to have a significantly different distribution of equivalent widths of Heiλ5876 than SNe IIn as observed in Silverman et al. The hosts tend to be late-type galaxies with recent star formation. We derive a rate estimate of${29}_{-21}^{+27}$Gpc⁻³yr⁻¹for SNe Ia-CSM, which is ∼0.02%–0.2% of the SN Ia rate. We also identify six ambiguous SNe IIn/Ia-CSM in the BTS sample and including them gives an upper limit rate of 0.07%–0.8%. This work nearly doubles the sample of well-studied Ia-CSM objects in Silverman et al., increasing the total number to 28.

 

The Earth close approach of near-Earth asteroid 2005 LW3 on 2022 November 23 represented a good opportunity for a second observing campaign to test the timing accuracy of astrometric observation. With 82 participating stations, the International Asteroid Warning Network collected 1046 observations of 2005 LW3 around the time of the close approach. Compared to the previous timing campaign targeting 2019 XS, some individual observers were able to significantly improve the accuracy of their reported observation times. In particular, U.S. surveys achieved good timing performance. However, no broad, systematic improvement was achieved compared to the previous campaign, with an overall negative bias persisting among the different observers. The calibration of observing times and the mitigation of timing errors should be important future considerations for observers and orbit computers, respectively.

 

SkyPortalis an open-source software package designed to discover interesting transients efficiently, manage follow-up, perform characterization, and visualize the results. By enabling fast access to archival and catalog data, crossmatching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, aSkyPortal-based platform has been operating at scale for >2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. WhileSkyPortalemphasizes rich user experiences across common front-end workflows, recognizing that scientific inquiry is increasingly performed programmatically,SkyPortalalso surfaces an extensive and well-documented application programming interface system. From back-end and front-end software to data science analysis tools and visualization frameworks, theSkyPortaldesign emphasizes the reuse and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance,SkyPortalnow leverages ChatGPT large language models to generate and surface source-level human-readable summaries automatically. With the imminent restart of the next generation of gravitational-wave detectors,SkyPortalnow also includes dedicated multimessenger features addressing the requirements of rapid multimessenger follow-up: multitelescope management, team/group organizing interfaces, and crossmatching of multimessenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for newcomers to the field. This paper focuses on the detailed implementations, capabilities, and early science results that establishSkyPortalas a community software package ready to take on the data science challenges and opportunities presented by this next chapter in the multimessenger era.

 

We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R ∼100) data. The goal of SNIascore is fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network (RNN) architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a <0.6% FPR while classifying up to 90% of the low-resolution SN Ia spectra obtained by the BTS. SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of <0.005 in the range from z=0.01 to z=0.12). For the magnitude-limited ZTF BTS survey (≈70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by ≈60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real-time to the public immediately following a finished observation during the night.