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.

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A high percentage of patients with CF also present with IGT. This increases with age and is more common among patients homozygous for the deltaF508 mutation and is not related to clinical status. Alterations in the kinetics of insulin secretion play an important role in the appearance of IGT and CF. We suggest that the OGTT is a more sensitive method than IVGTT for identifying early alterations in CF-related diabetes mellitus.

Although the use of RGB photometry has exploded in the last decades due to the advent of high-quality and inexpensive digital cameras equipped with Bayer-like color filter systems, there is surprisingly no catalogue of bright stars that can be used for calibration purposes. Since due to their excessive brightness, accurate enough spectrophotometric measurements of bright stars typically cannot be performed with modern large telescopes, we have employed historical 13-color medium-narrow-band photometric data, gathered with quite reliable photomultipliers, to fit the spectrum of 1346 bright stars using stellar atmosphere models. This not only constitutes a useful compilation of bright spectrophotometric standards well spread in the celestial sphere, the UCM library of spectrophotometric spectra, but allows the generation of a catalogue of reference RGB magnitudes, with typical random uncertainties ∼0.01 mag. For that purpose, we have defined a new set of spectral sensitivity curves, computed as the median of 28 sets of empirical sensitivity curves from the literature, that can be used to establish a standard RGB photometric system. Conversions between RGB magnitudes computed with any of these sets of empirical RGB curves and those determined with the new standard photometric system are provided. Even though particular RGB measurements from single cameras are not expected to provide extremely accurate photometric data, the repeatability and multiplicity of observations will allow access to a large amount of exploitable data in many astronomical fields, such as the detailed monitoring of light pollution and its impact on the night sky brightness, or the study of meteors, solar system bodies, variable stars, and transient objects. In addition, the RGB magnitudes presented here make the sky an accessible and free laboratory for the calibration of the cameras themselves.