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).
This paper focuses on optimal sensor deployment for indoor localization with a multi-objective evolutionary algorithm. Our goal is to obtain an algorithm to deploy sensors taking the number of sensors, accuracy and coverage into account. Contrary to most works in the literature, we consider the presence of obstacles in the region of interest (ROI) that can cause occlusions between the target and some sensors. In addition, we aim to obtain all of the Pareto optimal solutions regarding the number of sensors, coverage and accuracy. To deal with a variable number of sensors, we add speciation and structural mutations to the well-known non-dominated sorting genetic algorithm (NSGA-II). Speciation allows one to keep the evolution of sensor sets under control and to apply genetic operators to them so that they compete with other sets of the same size. We show some case studies of the sensor placement of an infrared range-difference indoor positioning system with a fairly complex model of the error of the measurements. The results obtained by our algorithm are compared to sensor placement patterns obtained with random deployment to highlight the relevance of using such a deployment algorithm.
Since 2006, the EURONEAR project has been contributing to the research of near Earth asteroids (NEAs) within an European network. One of the main aims is the amelioration of the orbits of NEAs, and starting in February 2014 we focus on the recovery of one-opposition NEAs using the Isaac Newton Telescope (INT) in La Palma in override mode. Part of this NEA recovery project, since June 2014 EURONEAR serendipitously started to discover and secure the first NEAs from La Palma and using the INT, thanks to the team-work including amateurs and students who promptly reduce the data, report discoveries and secure new objects recovered with the INT and few other telescopes from the EURONEAR network. Five NEAs were discovered with the INT, including 2014 LU14, 2014 NL52 (one very fast rotator), 2014 OL339 (the fourth known Earth quasi-satellite), 2014 SG143 (a quite large NEA) and 2014 VP. Another very fast moving NEA was discovered but was unfortunately lost due to lack of follow-up time. Additionally, another 14 NEA candidates were identified based on two models, all being rapidly followed-up using the INT and another 11 telescopes within the EURONEAR network. They include one object discovered by Pan-STARRS, two Mars crossers, two Hungarias, one Jupiter trojan, and other few inner MBAs. Using the INT and Sierra Nevada 1.5 m for photometry, then the Gran Telescopio de Canarias (GTC) for spectroscopy, we derived the very rapid rotation of 2014 NL52, then its albedo, magnitude, size, and its spectral class. Based on the total sky coverage in dark conditions, we evaluate the actual survey discovery rate using 2-m class telescopes. One NEA is possible to be discovered randomly within minimum 2.8 square degrees and maximum 5.5 square degrees. These findings update our past statistics, being based on double sky coverage and taking into account the recent increase in discovery.
We present time-series observations, spectra and archival outburst data of a newly-discovered variable star in Hercules, Var Her 04. Its orbital period, mass ratio, and outburst amplitude resemble those of the UGWZ-type subclass of UGSU dwarf novae. However, its supercycle and outburst light curve defy classification as a clear UGWZ. Var Her 04 is most similar to the small group of possible hydrogen-burning ``period bouncers'', dwarf novae that have passed beyond the period minimum and returned.Comment: 20 pages, 7 figures. Data in paper available at http://www.aavso.org/data/download/ Accepted for publication in PASP Dec 200
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