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The Large Synoptic Survey Telescope (LSST) is an ambitious astronomical survey with a similarly ambitious Data Management component. Data Management for LSST includes processing on both nightly and yearly cadences to generate transient…

Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, projected to…

Modern time-domain astronomical surveys produce high throughput data streams which require tools for processing and analysis. This will be critical for programs making full use of the alert stream from the Vera Rubin Observatory (VRO),…

The Vera C. Rubin Observatory is preparing for the execution of the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently in its final phase of construction in the Andes mountains in Chile…

The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification loop. Here we present the inner workings of a framework,…

Among solar system objects, comets coming from the Oort Cloud are an elusive population, intrinsically rare and difficult to detect. Nonetheless, as the more pristine objects we can observe, they encapsulate critical cues on the formation…

The Asteroid Terrestrial impact Last Alert System (ATLAS) system consists of two 0.5m Schmidt telescopes with cameras covering 29 square degrees at plate scale of 1.86 arcsec per pixel. Working in tandem, the telescopes routinely survey the…

We present the Keck Infrared Transient Survey (KITS), a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of…

A community meeting on the topic of "Radio Astronomy in the LSST Era" was hosted by the National Radio Astronomy Observatory in Charlottesville, VA (2013 May 6--8). The focus of the workshop was on time domain radio astronomy and sky…

Imminent impactors are natural bodies discovered in space before impacting the Earth. They provide a rare opportunity to characterize individual near-Earth objects (NEOs) in great detail as asteroids in space, meteors in Earth's atmosphere…

Spatial-temporal information has been proven to be of great significance for click-through rate prediction tasks in online Location-Based Services (LBS), especially in mainstream food ordering platforms such as DoorDash, Uber Eats, Meituan,…

Both the Rubin Observatory and the first telescopes of the CTAO will be collecting data by 2026, marking a new era in optical and gamma-ray astronomy. Compared to predecessors, their enhanced sensitivity will extend extragalactic…

We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of…

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of…

The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all the SNe discovered. Thus, photometric classification is…

Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of…

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the…

With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending…

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any…

Accurate classification of transients obtained from spectroscopic data are important to understand their nature and discover new classes of astronomical objects. For supernovae (SNe), SNID, NGSF (a Python version of SuperFit), and DASH are…