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Context.Accreting ultracompact white dwarf binaries contain a white dwarf that is accreting from a degenerate object. They have orbital periods shorter than 65 min. Aims.We report the discovery and the orbital period of four new eclipsing accreting ultracompact white dwarf binaries found using the Zwicky Transient Facility (ZTF) and discuss their photometric properties. Methods.We searched through a list of 4171 dwarf novae compiled using the ZTF and used the box least square method to search for periodic signals in the data. Results.We found four eclipsing accreting ultracompact binaries with orbital periods between 25.9 and 56 min. One had previously been published as an AM Canum Venaticorum (AM CVn), and the other three systems are new discoveries. The two shorter-period systems are likely also AM CVn systems, while the longest-period system, with a period of 56 min, showed multiple super-outbursts over two years, which is more consistent with it being a helium CV. 

ABSTRACT Long-period comets are planetesimal remnants constraining the environment and volatiles of the protoplanetary disc. We report the discovery of hyperbolic long-period comet C/2022 E3 Zwicky Transient Facility (ZTF), which has a perihelion ∼1.11 au, an eccentricity ≳1 and an inclination ∼109°, from images taken with the Palomar 48-inch telescope during morning twilight on 2022 March 2. Additionally, we report the characterization of C/2022 E3 (ZTF) from observations taken with the Palomar 200-inch, the Palomar 60-inch, and the NASA Infrared Telescope Facility in early 2023 February to 2023 March when the comet passed within ∼0.28 au of the Earth and reached a visible magnitude of ∼5. We measure g–r = 0.70 ± 0.01, r–i = 0.20 ± 0.01, i–z = 0.06 ± 0.01, z–J = 0.90 ± 0.01, J–H = 0.38 ± 0.01, and H–K = 0.15 ± 0.01 colours for the comet from observations. We measure the A(0°)fρ (0.8 μm) in a 6500 km radius from the nucleus of 1483 ± 40 cm, and CN, C3, and C2 production of 5.43 ± 0.11 × 1025, 2.01 ± 0.04 × 1024, and 3.08 ± 0.5 × 1025 mol s−1, similar to other long-period comets. We additionally observe the appearance of jet-like structures at a scale of ∼4000 km in wide-field g-band images, which may be caused by the presence of CN gas in the near-nucleus coma. 

Context. With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good performance they usually require the presence of a large set of observations. However, well-designed, targeted models can reach their classification goals with fewer computing resources. Aims. The aim of this study is to assist in the observational astronomy task of deciding whether a newly detected transient warrants follow-up observations. Methods. This paper presents SNGuess, a model designed to find young extragalactic nearby transients with high purity. SNGuess works with a set of features that can be efficiently calculated from astronomical alert data. Some of these features are static and associated with the alert metadata, while others must be calculated from the photometric observations contained in the alert. Most of the features are simple enough to be obtained or to be calculated already at the early stages in the lifetime of a transient after its detection. We calculate these features for a set of labeled public alert data obtained over a time span of 15 months from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of an ensemble of decision trees, which are trained via gradient boosting. Results. Approximately 88% of the candidates suggested by SNGuess from a set of alerts from ZTF spanning from April 2020 to August 2021 were found to be true relevant supernovae (SNe). For alerts with bright detections, this number ranges between 92% and 98%. Since April 2020, transients identified by SNGuess as potential young SNe in the ZTF alert stream are being published to the Transient Name Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any transient observed by ZTF can be accessed via a web service https://ampel.zeuthen.desy.de/api/live/docs . The source code of SNGuess is publicly available https://github.com/nmiranda/SNGuess . Conclusions. SNGuess is a lightweight, portable, and easily re-trainable model that can effectively suggest transients for follow-up. These properties make it a useful tool for optimizing follow-up observation strategies and for assisting humans in the process of selecting candidate transients. 

Context. Optical polarimeters are typically calibrated using measurements of stars with known and stable polarization parameters. However, there is a lack of such stars available across the sky. Many of the currently available standards are not suitable for medium and large telescopes due to their high brightness. Moreover, as we find, some of the polarimetric standards used are in fact variable or have polarization parameters that differ from their cataloged values. Aims. Our goal is to establish a sample of stable standards suitable for calibrating linear optical polarimeters with an accuracy down to 10 −3 in fractional polarization. Methods. For 4 yr, we have been running a monitoring campaign of a sample of standard candidates comprised of 107 stars distributed across the northern sky. We analyzed the variability of the linear polarization of these stars, taking into account the non-Gaussian nature of fractional polarization measurements. For a subsample of nine stars, we also performed multiband polarization measurements. Results. We created a new catalog of 65 stars (see Table 2) that are stable, have small uncertainties of measured polarimetric parameters, and can be used as calibrators of polarimeters at medium and large telescopes. 

Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set. 

ABSTRACT We present a sample of 14 hydrogen-rich superluminous supernovae (SLSNe II) from the Zwicky Transient Facility (ZTF) between 2018 and 2020. We include all classified SLSNe with peaks Mg < −20 mag with observed broad but not narrow Balmer emission, corresponding to roughly 20 per cent of all hydrogen-rich SLSNe in ZTF phase I. We examine the light curves and spectra of SLSNe II and attempt to constrain their power source using light-curve models. The brightest events are photometrically and spectroscopically similar to the prototypical SN 2008es, while others are found spectroscopically more reminiscent of non-superluminous SNe II, especially SNe II-L. 56Ni decay as the primary power source is ruled out. Light-curve models generally cannot distinguish between circumstellar interaction (CSI) and a magnetar central engine, but an excess of ultraviolet (UV) emission signifying CSI is seen in most of the SNe with UV data, at a wide range of photometric properties. Simultaneously, the broad H α profiles of the brightest SLSNe II can be explained through electron scattering in a symmetric circumstellar medium (CSM). In other SLSNe II without narrow lines, the CSM may be confined and wholly overrun by the ejecta. CSI, possibly involving mass lost in recent eruptions, is implied to be the dominant power source in most SLSNe II, and the diversity in properties is likely the result of different mass loss histories. Based on their radiated energy, an additional power source may be required for the brightest SLSNe II, however – possibly a central engine combined with CSI. 

ABSTRACT Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98 per cent true positive and 97 per cent true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP. 

Context. We present observations of ZTF20aatqesi (SN 2020faa). This Type II supernova (SN) displays a luminous light curve (LC) that started to rebrighten from an initial decline. We investigate this in relation to the famous SN iPTF14hls, which received a great deal of attention and multiple interpretations in the literature, but whose nature and source of energy still remain unknown. Aims. We demonstrate the great similarity between SN 2020faa and iPTF14hls during the first 6 months, and use this comparison to forecast the evolution of SN 2020faa and to reflect on the less well observed early evolution of iPTF14hls. Methods. We present and analyse our observational data, consisting mainly of optical LCs from the Zwicky Transient Facility in the gri bands and of a sequence of optical spectra. We construct colour curves and a bolometric lc, and we compare ejecta-velocity and black-body radius evolutions for the two supernovae (SNe) and for more typical Type II SNe. Results. The LCs show a great similarity with those of iPTF14hls over the first 6 months in luminosity, timescale, and colour. In addition, the spectral evolution of SN 2020faa is that of a Type II SN, although it probes earlier epochs than those available for iPTF14hls. Conclusions. The similar LC behaviour is suggestive of SN 2020faa being a new iPTF14hls. We present these observations now to advocate follow-up observations, since most of the more striking evolution of SN iPTF14hls came later, with LC undulations and a spectacular longevity. On the other hand, for SN 2020faa we have better constraints on the explosion epoch than we had for iPTF14hls, and we have been able to spectroscopically monitor it from earlier phases than was done for the more famous sibling.