Thomas Matheson, Carl Stubens, Nicholas Wolf, Chien-Hsiu Lee, Gautham Narayan, Abhijit Saha, Adam Scott, Monika Soraisam, Adam S. Bolton, Benjamin Hauger, David… 展开作者
Thomas Matheson, Carl Stubens, Nicholas Wolf, Chien-Hsiu Lee, Gautham Narayan, Abhijit Saha, Adam Scott, Monika Soraisam, Adam S. Bolton, Benjamin Hauger, David R. Silva, John Kececioglu, Carlos Scheidegger, Richard Snodgrass, Patrick D. Aleo, Eric Evans-Jacquez, Navdeep Singh, Zhe Wang, Shuo Yang, Zhenge Zhao
We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.
The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint project of the National Optical Astronomy Observatory and the Department of Computer Science at the University of Arizona. The goal is to build the software infrastructure necessary to process and filter alerts produced by time-domain surveys, with the ultimate source of such alerts being the Large Synoptic Survey Telescope (LSST). The ANTARES broker will add value to alerts by annotating them with information from external sources such as previous surveys from across the electromagnetic spectrum. In addition…
The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint effort of NOAO and the Department of Computer Science at the University of Arizona to build prototype software to process alerts from time-domain surveys, especially LSST, to identify those alerts that must be followed up immediately. Value is added by annotating incoming alerts with existing information from previous surveys and compilations across the electromagnetic spectrum and from the history of past alerts. Comparison against a knowledge repository of properties and features of known or predicted kinds…
Alertissimo -- a tool for orchestration of LSST broker streams
Astronomical Observatory（天文台）；University of Belgrade, Institute of Physics Belgrade（贝尔格莱德大学物理研究所）；University of Belgrade, Faculty of Organizational Sciences（贝尔格莱德大学组织科学学院）
The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using…
The Zwicky Transient Facility (ZTF), a state-of-the-art optical robotic sky survey, registers on the order of a million transient events - such as supernova explosions, changes in brightness of variable sources, or moving object detections - every clear night, and generates associated real-time alerts. We present Alert-Classifying Artificial Intelligence (ACAI), an open-source deep-learning framework for the phenomenological classification of ZTF alerts. ACAI uses a set of five binary classifiers to characterize objects which, in combination with the auxiliary/contextual event information avai…
We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference images. This is the first time that sequences of images are used directly for the classification of variable objects in astronomy. The second contribution of this work is the image simulation process. We generate synthetic image sequences that take into account the instrumental and observing conditions, obtaining a realistic, set of movies for each astronomi…
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…
Efficient searches for electromagnetic counterparts to gravitational wave, high-energy neutrino, and gamma-ray burst events demand rapid processing of image arithmetic and geometry set operations in a database to cross-match galaxy catalogs, observation footprints, and all-sky images. Here we introduce HEALPix Alchemy, an open-source, pure Python implementation of a set of methods that enables rapid all-sky geometry calculations. HEALPix Alchemy is built upon HEALPix, a spatial indexing strategy that is widely used in astronomical databases as well as the native format of LIGO-Virgo-KAGRA grav…
We present the Active Galactic Nuclei (AGN) classifier as currently implemented within the Fink broker. Features were built upon summary statistics of available photometric points, as well as color estimation enabled by symbolic regression. The learning stage includes an active learning loop, used to build an optimized training sample from labels reported in astronomical catalogs. Using this method to classify real alerts from the Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall. We also describe the modifications necessary to enable processing data…
Millennium Institute of Astrophysics (MAS)（千年天体物理研究所）；Universidad de Chile（智利大学）；Universidad Adolfo Ibañez（阿道夫·伊巴涅斯大学）；Data Observatory（数据观测台）
We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the \textit{science, reference} and \textit{difference} images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids and bogus classes, with high accuracy ($\si…
University of Cambridge（剑桥大学）；Space Telescope Science Institute（太空望远镜科学研究所）；Kavli Institute for Cosmology（卡夫利宇宙学研究所）；Stockholm University（斯德哥尔摩大学）；University of Toronto（多伦多大学）
We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present the first method specifically designed to provide early classifications of astronomical time-series data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from…
GOATS: The next generation software infrastructure for time-domain astronomy at Gemini/NOIRLab. Application to alerts from Vera C. Rubin Observatory's Legacy Survey of Space and Time
