Abstract
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
).
Abstract
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.
We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) 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, amongst 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 {\em Gaia} DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with \(\geq6\) \(g\)-band or \(\geq6\) \(r\)-band detections in ZTF (868,371 sources as of 2020/06/09), 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 \href{http://alerce.online}{ALeRCE Explorer website}.
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 which 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 multi--band 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 \url{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 \(9.7\times10^7\) alerts, the stamp classification of \(1.9\times10^7\) objects, the light curve classification of \(8.5\times10^5\) objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi--stream ecosystem dominated by LSST.
Des anticorps anti-myelin oligodendrocyte glycoprotein (MOG-IgG) ont été récemment rapportées chez des patients atteints de neuromyélite optique de Devic (NMO) séronégatifs pour les anti-aquaporine 4 ...(AQP4-IgG).
Le but de notre étude fut d’évaluer la fréquence et l’intérêt clinique des MOG-IgG après un premier épisode de myélite aiguë transverse longitudinale étendue (MATLE) chez des patients testés AQP4-IgG séronégatifs.
Cinquante-six patients, issus de deux hôpitaux français et deux hôpitaux espagnols, furent inclus de manière rétrospective dans l’étude. Les données épidémiologiques, cliniques, biologiques et d’imagerie furent analysées. Tous les sérums des patients furent testés pour les AQP4-IgG et MOG-IgG par technique de « cell-based assay ».
Treize (23,2 %) patients étaient MOG-IgG positifs. Il n’y avait pas de différence en termes de sex-ratio entre les deux groupes. Les patients MOG-IgG positifs étaient plus jeunes au diagnostic (âge médian 32,5 vs 44,1ans ; p=0,0068), et avaient un plus faible handicap à long terme (score EDSS médian=2,0 vs 3,0 ; p=0,04) que les séronégatifs (durée moyenne de suivi de 3,52ans). Une plus grande proportion de patients MOG-IgG positifs présentaient une pleïocytose du LCR (92,3 % vs 45,2 % ; p=0,003).
Sur les 56 patients de la cohorte, 6 (10,7 %) évoluèrent vers une NMO certaine, 2 (3,6 %) vers une sclérose en plaques certaine, 8 (14,2 %) vers un tableau de myélites récidivantes et 40 (71,4 %) restèrent monophasiques. Les patients MOG-IgG positifs étaient plus à risque de développer un poussée de névrite optique et donc de convertir vers une NMO (hazard ratio HR 8,99, 95 % confidence interval CI 1,60–50,59 ; p=0,01).
Les anticorps MOG-IgG doivent être recherchés après un épisode de MATLE car ils sont associés à des caractéristiques épidémiologiques et cliniques spécifiques, distinctes des myélites AQP4-IgG et double séronégatives.
Ce travail est soutenu par l’ARSEP.
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