We present Reverberation Mapping (RM) results for 17 high-redshift, high-luminosity quasars with good quality R-band and emission line light curves. We are able to measure statistically significant lags for Lyα (11 objects), SiIV (5 objects), CIV (11 objects), and CIII] (2 objects). Using our results and previous lag determinations taken from the literature, we present an updated CIV radiusluminosity relation and provide for the first time radius-luminosity relations for Lyα, SiIV and CIII]. While in all cases the slope of the correlations are statistically significant, the zero points are poorly constrained because of the lack of data at the low luminosity end. We find that the emissivity weighted distance from the central source of the Lyα, SiIV and CIII] line emitting regions are all similar, which corresponds to about half that of the Hβ region. We also find that 3/17 of our sources show an unexpected behavior in some emission lines, two in the Lyα light curve and one in the SiIV light curve, in that they do not seem to follow the variability of the UV continuum. Finally, we compute RM black hole masses for those quasars with highly significant lag measurements and compare them with CIV single-epoch (SE) mass determinations. We find that the RM-based black hole mass determinations seem smaller than those found using SE calibrations.

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.