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Register a new userTowards a Real-time Transient Classification Engine
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J. S. Bloom
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Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation wide-field synoptic surveys are poised to revolutionize our understanding of just about anything that goes bump in the night (which is just about everything at some level). Still, even the most ambitious surveys will require targeted spectroscopic follow-up to fill in the physical details of newly discovered transients. We are now building a new system intended to ingest and classify transient phenomena in near real-time from high-throughput imaging data streams. Described herein, the Transient Classification Project at Berkeley will be making use of classification techniques operating on ``features extracted from time series and contextual (static) information. We also highlight the need for a community adoption of a standard representation of astronomical time series data (i.e., ``VOTimeseries).
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We present the design and first results of a real-time search for transients within the 650 sq. deg. area around the Magellanic Clouds, conducted as part of the OGLE-IV project and aimed at detecting supernovae, novae and other events. The average sampling of about 4 days from September to May, yielded a detection of 238 transients in 2012/2013 and 2013/2014 seasons. The superb photometric and astrometric quality of the OGLE data allows for numerous applications of the discovered transients. We use this sample to prepare and train a Machine Learning-based automated classifier for early light curves, which distinguishes major classes of transients with more than 80% of correct answers. Spectroscopically classified 49 supernovae Type Ia are used to construct a Hubble Diagram with statistical scatter of about 0.3 mag and fill the least populated region of the redshifts range in the Union sample. We investigate the influence of host galaxy environments on supernovae statistics and find the mean host extinction of A_I=0.19+-0.10 mag and A_V=0.39+-0.21 mag based on a subsample of supernovae Type Ia. We show that the positional accuracy of the survey is of the order of 0.5 pixels (0.13 arcsec) and that the OGLE-IV Transient Detection System is capable of detecting transients within the nuclei of galaxies. We present a few interesting cases of nuclear transients of unknown type. All data on the OGLE transients are made publicly available to the astronomical community via the OGLE website.
Modern multiple object tracking (MOT) systems usually follow the emph{tracking-by-detection} paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simply a sum of the two steps without investigating potential structures that can be shared between them. Existing research efforts on real-time MOT usually focus on the association step, so they are essentially real-time association methods but not real-time MOT system. In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model. Specifically, we incorporate the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings. We further propose a simple and fast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports the first (near) real-time MOT system, with a running speed of 22 to 40 FPS depending on the input resolution. Meanwhile, its tracking accuracy is comparable to the state-of-the-art trackers embodying separate detection and embedding (SDE) learning ($64.4%$ MOTA vs $66.1%$ MOTA on MOT-16 challenge). Code and models are available at url{https://github.com/Zhongdao/Towards-Realtime-MOT}.
We report on the results from the first six months of the Catalina Real-time Transient Survey (CRTS). In order to search for optical transients with timescales of minutes to years, the CRTS analyses data from the Catalina Sky Survey which repeatedly covers twenty six thousand of square degrees on the sky. The CRTS provides a public stream of transients that are bright enough to be followed up using small telescopes. Since the beginning of the survey, all CRTS transients have been made available to astronomers around the world in real-time using HTML tables, RSS feeds and VOEvents. As part of our public outreach program the detections are now also available in KML through Google Sky. The initial discoveries include over 350 unique optical transients rising more than two magnitudes from past measurements. Sixty two of these are classified as supernovae, based on light curves, prior deep imaging and spectroscopic data. Seventy seven are due to cataclysmic variables (only 13 previously known), while an additional 100 transients were too infrequently sampled to distinguish between faint CVs and SNe. The remaining optical transients include AGN, Blazars, high proper motions stars, highly variable stars (such as UV Ceti stars) and transients of an unknown nature. Our results suggest that there is a large population of SNe missed by many current supernova surveys because of selection biases. These objects appear to be associated with faint host galaxies. We also discuss the unexpected discovery of white dwarf binary systems through dramatic eclipses.
We present 855 cataclysmic variable candidates detected by the Catalina Real-time Transient Survey (CRTS) of which at least 137 have been spectroscopically confirmed and 705 are new discoveries. The sources were identified from the analysis of five years of data, and come from an area covering three quarters of the sky. We study the amplitude distribution of the dwarf novae CVs discovered by CRTS during outburst, and find that in quiescence they are typically two magnitudes fainter compared to the spectroscopic CV sample identified by SDSS. However, almost all CRTS CVs in the SDSS footprint have ugriz photometry. We analyse the spatial distribution of the CVs and find evidence that many of the systems lie at scale heights beyond those expected for a Galactic thin disc population. We compare the outburst rates of newly discovered CRTS CVs with the previously known CV population, and find no evidence for a difference between them. However, we find that significant evidence for a systematic difference in orbital period distribution. We discuss the CVs found below the orbital period minimum and argue that many more are yet to be identified among the full CRTS CV sample. We cross-match the CVs with archival X-ray catalogs and find that most of the systems are dwarf novae rather than magnetic CVs.
Efficient automated detection of flux-transient, reoccurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTFs alerts and light curves (Kowalski), and to label the data (Zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units (TPUs) that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.
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