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Register a new userAutomated Transient Detection with Shapelet Analysis in Image-subtracted Data
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We present a method for characterizing image-subtracted objects based on shapelet analysis to identify transient events in ground-based time-domain surveys. We decompose the image-subtracted objects onto a set of discrete Zernike polynomials and use their resulting coefficients to compare them to other point-like objects. We derive a norm in this Zernike space that we use to score transients for their point-like nature and show that it is a powerful comparator for distinguishing image artifacts, or residuals, from true astrophysical transients. Our method allows for a fast and automated way of scanning overcrowded, wide-field telescope images with minimal human interaction and we reduce the large set of unresolved artifacts left unidentified in subtracted observational images. We evaluate the performance of our method using archival intermediate Palomar Transient Factory and Dark Energy Camera survey images. However, our technique allows flexible implementation for a variety of different instruments and data sets. This technique shows a reduction in image subtraction artifacts by 99.95% for surveys extending up to hundreds of square degrees and has strong potential for automated transient identification in electromagnetic follow-up programs triggered by the Laser Interferometer Gravitational Wave Observatory-Virgo Scientific Collaboration.
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We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each technique is assessed based on the scoring metrics of Precision, Recall, and their harmonic mean F1, measured on the DIA results as standalone techniques, and also in the results after the application of ML algorithms, on transient source injections over simulated and real data. This simulations cover a wide range of instrumental configurations, as well as a variety of scenarios of observation conditions, by exploring a multi dimensional set of relevant parameters, allowing us to extract general conclusions related to the identification of transient astrophysical events. The newest subtraction techniques, and particularly the methodology published in Zackay et al. (2016) are implemented in an Open Source Python package, named properimage, suitable for many other astronomical image analyses. This together with the ML libraries we describe, provides an effective transient detection software pipeline. Here we study the effects of the different ML techniques, and the relative feature importances for classification of transient candidates, and propose an optimal combined strategy. This constitutes the basic elements of pipelines that could be applied in searches of electromagnetic counterparts to GW sources.
The VST Telescope Control Software logs continuously detailed information about the telescope and instrument operations. Commands, telemetries, errors, weather conditions and anything may be relevant for the instrument maintenance and the identification of problem sources is regularly saved. All information are recorded in textual form. These log files are often examined individually by the observatory personnel for specific issues and for tackling problems raised during the night. Thus, only a minimal part of the information is normally used for daily maintenance. Nevertheless, the analysis of the archived information collected over a long time span can be exploited to reveal useful trends and statistics about the telescope, which would otherwise be overlooked. Given the large size of the archive, a manual inspection and handling of the logs is cumbersome. An automated tool with an adequate user interface has been developed to scrape specific entries within the log files, process the data and display it in a comprehensible way. This pipeline has been used to scan the information collected over 5 years of telescope activity.
We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (Sep. 2013 through Feb. 2014) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1 percent of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithms performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at http://portal.nersc.gov/project/dessn/autoscan.
The Palomar Transient Factory (PTF) is a multi-epochal robotic survey of the northern sky that acquires data for the scientific study of transient and variable astrophysical phenomena. The camera and telescope provide for wide-field imaging in optical bands. In the five years of operation since first light on December 13, 2008, images taken with Mould-R and SDSS-g camera filters have been routinely acquired on a nightly basis (weather permitting), and two different H-alpha filters were installed in May 2011 (656 nm and 663 nm). The PTF image-processing and data-archival program at the Infrared Processing and Analysis Center (IPAC) is tailored to receive and reduce the data, and, from it, generate and preserve astrometrically and photometrically calibrated images, extracted source catalogs, and coadded reference images. Relational databases have been deployed to track these products in operations and the data archive. The fully automated system has benefited by lessons learned from past IPAC projects and comprises advantageous features that are potentially incorporable into other ground-based observatories. Both off-the-shelf and in-house software have been utilized for economy and rapid development. The PTF data archive is curated by the NASA/IPAC Infrared Science Archive (IRSA). A state-of-the-art custom web interface has been deployed for downloading the raw images, processed images, and source catalogs from IRSA. Access to PTF data products is currently limited to an initial public data release (M81, M44, M42, SDSS Stripe 82, and the Kepler Survey Field). It is the intent of the PTF collaboration to release the full PTF data archive when sufficient funding becomes available.
Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were applied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.
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