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Register a new userRobust Assessment of Clustering Methods for Fast Radio Transient Candidates
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Fast radio transient search algorithms identify signals of interest by iterating and applying a threshold on a set of matched filters. These filters are defined by properties of the transient such as time and dispersion. A real transient can trigger hundreds of search trials, each of which has to be post-processed for visualization and classification tasks. In this paper, we have explored a range of unsupervised clustering algorithms to cluster these redundant candidate detections. We demonstrate this for Realfast, the commensal fast transient search system at the Very Large Array. We use four features for clustering: sky position (l, m), time and dispersion measure (DM). We develop a custom performance metric that makes sure that the candidates are clustered into a small number of pure clusters, i.e, clusters with either astrophysical or noise candidates. We then use this performance metric to compare eight different clustering algorithms. We show that using sky location along with DM/time improves clustering performance by $sim$10% as compared to the traditional DM/time-based clustering. Therefore, positional information should be used during clustering if it can be made available. We conduct several tests to compare the performance and generalisability of clustering algorithms to other transient datasets and propose a strategy that can be used to choose an algorithm. Our performance metric and clustering strategy can be easily extended to different single-pulse search pipelines and other astronomy and non-astronomy-based applications.
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Pioneering efforts aiming at the development of multi-messenger gravitational wave and electromagnetic astronomy have been made. An electromagnetic observation follow-up program of candidate gravitational wave events has been performed (Dec 17 2009 to Jan 8 2010 and Sep 4 to Oct 20 2010) during the recent runs of the LIGO and Virgo gravitational wave detectors. It involved ground-based and space electromagnetic facilities observing the sky at optical, X-ray and radio wavelengths. The joint gravitational wave and electromagnetic observation study requires the development of specific image analysis procedures able to discriminate the possible electromagnetic counterpart of gravitational wave triggers from contaminant/background events. The paper presents an overview of the electromagnetic follow-up program and the image analysis procedures.
We report on the results of a search for radio transients between 115 and 190,MHz with the LOw-Frequency ARray (LOFAR). Four fields have been monitored with cadences between 15 minutes and several months. A total of 151 images were obtained, giving a total survey area of 2275 deg$^2$. We analysed our data using standard LOFAR tools and searched for radio transients using the LOFAR Transients Pipeline (TraP). No credible radio transient candidate has been detected; however, we are able to set upper limits on the surface density of radio transient sources at low radio frequencies. We also show that low-frequency radio surveys are more sensitive to steep-spectrum coherent transient sources than GHz radio surveys. We used two new statistical methods to determine the upper limits on the transient surface density. One is free of assumptions on the flux distribution of the sources, while the other assumes a power-law distribution in flux and sets more stringent constraints on the transient surface density. Both of these methods provide better constraints than the approach used in previous works. The best value for the upper limit we can set for the transient surface density, using the method assuming a power-law flux distribution, is 1.3$cdot$10$^{-3}$ deg$^{-2}$ for transients brighter than 0.3 Jy with a time-scale of 15 min, at a frequency of 150 MHz. We also calculated for the first time upper limits for the transient surface density for transients of different time-scales. We find that the results can differ by orders of magnitude from previously reported, simplified estimates.
Tunka-Rex is detector for radio emission produced by cosmic-ray air-showers located in Siberia, triggered by Tunka-133, a co-located air-Cherenkov detector during night, and by a scintillator array Tunka-Grande during day. Tunka-Rex demonstrates that the radio technique can provide a cost-effective extension of existing air-shower arrays. Operating in the frequency range of 30-80 MHz, Tunka-Rex is limited by the galactic background, and suffers from the local radio interferences. We investigate the possibilities of the improving of measured data using different approaches, particularly, the multivariate background suppression is considered, as well as improved likelihood fit of the lateral distribution of amplitudes.
We present the first southern-hemisphere all-sky imager and radio-transient monitoring system implemented on two prototype stations of the low-frequency component of the Square Kilometre Array. Since its deployment the system has been used for real-time monitoring of the recorded commissioning data. Additionally, a transient searching algorithm has been executed on the resulting all-sky images. It uses a difference imaging technique, and has enabled identification of a wide variety of transient classes, ranging from human-made radio-frequency interference to genuine astrophysical events. Observations at the frequency 159.4 MHz and higher in a single coarse channel (0.926 MHz) were made with 2s time resolution, and multiple nights were analysed. Despite having modest sensitivity (~few Jy/beam), using a single coarse channel and 2-s imaging, the system detected bright transients from PSR B0950+08, proving that it can be used to detect bright transients of an astrophysical origin. The unusual, extreme activity of the pulsar PSR B0950+08 (up to ~155 Jy/beam) was initially detected in a blind search in the 2020-04-10/11 data and later assigned to this specific pulsar. The limitations of our data, however, prevent use from making firm conclusions of the effect being due to a combination of refractive and diffractive scintillation or intrinsic emission mechanisms. The system can routinely collect data over many days without interruptions; the large amount of recorded data at 159.4 and 229.7 MHz allowed us to determine a preliminary transient surface density upper limit of $1.32 times 10^{-9} text{deg}^{-2}$ for a timescale and limiting flux density of 2s and 42 Jy, respectively. We plan to extend the observing bandwidth to tens of MHz and improve time resolution to tens of milliseconds in order to increase the sensitivity and enable detections of Fast Radio Bursts below 300 MHz.
The observation of the transient sky through a multitude of astrophysical messengers hasled to several scientific breakthroughs these last two decades thanks to the fast evolution ofthe observational techniques and strategies employed by the astronomers. Now, it requiresto be able to coordinate multi-wavelength and multi-messenger follow-up campaign withinstruments both in space and on ground jointly capable of scanning a large fraction of thesky with a high imaging cadency and duty cycle. In the optical domain, the key challengeof the wide field of view telescopes covering tens to hundreds of square degrees is to dealwith the detection, the identification and the classification of hundreds to thousands of opticaltransient (OT) candidates every night in a reasonable amount of time. In the last decade, newautomated tools based on machine learning approaches have been developed to perform thosetasks with a low computing time and a high classification efficiency. In this paper, we presentan efficient classification method using Convolutional Neural Networks (CNN) to discard anybogus falsely detected in astrophysical images in the optical domain. We designed this toolto improve the performances of the OT detection pipeline of the Ground Wide field AngleCameras (GWAC) telescopes, a network of robotic telescopes aiming at monitoring the opticaltransient sky down to R=16 with a 15 seconds imaging cadency. We applied our trainedCNN classifier on a sample of 1472 GWAC OT candidates detected by the real-time detectionpipeline. It yields a good classification performance with 94% of well classified event and afalse positive rate of 4%.
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