Aims. We describe the observing strategy, data reduction tools, and early results of a supernova (SN) search project, named SUDARE, conducted with the ESO VST telescope, which is aimed at measuring the rate of the different types of SNe in the redshift range 0.2 < z < 0.8. Methods. The search was performed in two of the best studied extragalactic fields, CDFS and COSMOS, for which a wealth of ancillary data are available in the literature or in public archives. We developed a pipeline for the data reduction and rapid identification of transients. As a result of the frequent monitoring of the two selected fields, we obtained light curve and colour information for the transients sources that were used to select and classify SNe by means of an especially developed tool. To accurately characterise the surveyed stellar population, we exploit public data and our own observations to measure the galaxy photometric redshifts and rest frame colours. Results. We obtained a final sample of 117 SNe, most of which are SN Ia (57%) with the remaining ones being core collapse events, of which 44% are type II, 22% type IIn and 34% type Ib/c. To link the transients, we built a catalogue of ∼1.3 × 10 5 galaxies in the redshift range 0 < z ≤ 1, with a limiting magnitude K AB = 23.5 mag. We measured the SN rate per unit volume for SN Ia and core collapse SNe in different bins of redshifts. The values are consistent with other measurements from the literature. Conclusions. The dispersion of the rate measurements for SNe-Ia is comparable to the scatter of the theoretical tracks for single degenerate (SD) and double degenerate (DD) binary systems models, therefore it is not possible to disentangle among the two different progenitor scenarios. However, among the three tested models (SD and the two flavours of DD that either have a steep DDC or a wide DDW delay time distribution), the SD appears to give a better fit across the whole redshift range, whereas the DDC better matches the steep rise up to redshift ∼1.2. The DDW instead appears to be less favoured. Unlike recent claims, the core collapse SN rate is fully consistent with the prediction that is based on recent estimates of star formation history and standard progenitor mass range.

We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥6 g-band or ≥6 r-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (http://alerce.online).

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve-based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see https://alerce.science). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 10 8 alerts, the stamp classification of 3.4 × 10 7 objects, the light-curve classification of 1.1 × 10 6 objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.

Context. One of the most peculiar characteristics of active galactic nuclei (AGNs) is their variability over all wavelengths. This property has been used in the past to select AGN samples and is foreseen to be one of the detection techniques applied in future multi-epoch surveys, complementing photometric and spectroscopic methods. Aims. In this paper, we aim to construct and characterise an AGN sample using a multi-epoch dataset in the r band from the SUDARE-VOICE survey. Methods. Our work makes use of the VST monitoring programme of an area surrounding the Chandra Deep Field South to select variable sources. We use data spanning a six-month period over an area of 2 square degrees, to identify AGN based on their photometric variability.Results. The selected sample includes 175 AGN candidates with magnitude r < 23 mag. We distinguish different classes of variable sources through their lightcurves, as well as X-ray, spectroscopic, SED, optical, and IR information overlapping with our survey. Conclusions. We find that 12% of the sample (21/175) is represented by supernovae (SN). Of the remaining sources, 4% (6/154) are stars, while 66% (102/154) are likely AGNs based on the available diagnostics. We estimate an upper limit to the contamination of the variability selected AGN sample 34%, but we point out that restricting the analysis to the sources with available multi-wavelength ancillary information, the purity of our sample is close to 80% (102 AGN out of 128 non-SN sources with multi-wavelength diagnostics). Our work thus confirms the efficiency of the variability selection method, in agreement with our previous work on the COSMOS field. In addition we show that the variability approach is roughly consistent with the infrared selection.