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Register a new userPulsar Candidate Selection Using Ensemble Networks for FAST Drift-Scan Survey
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The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope (FAST) Survey (CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system (PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks (CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual--GPU and 24--core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.
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We developed a pulsar search pipeline based on PRESTO (PulsaR Exploration and Search Toolkit). This pipeline simply runs dedispersion, FFT (Fast Fourier Transformation), and acceleration search in process-level parallel to shorten the processing time. With two parallel strategies, the pipeline can highly shorten the processing time in both the normal searches or acceleration searches. This pipeline was first tested with PMPS (Parkes Multibeam Pulsar Survery) data and discovered two new faint pulsars. Then, it was successfully used in processing the FAST (Five-hundred-meter Aperture Spherical radio Telescope) drift scan data with tens of new pulsar discoveries up to now. The pipeline is only CPU-based and can be easily and quickly deployed in computing nodes for testing purposes or data processes.
We report the discovery of a highly dispersed fast radio burst, FRB~181123, from an analysis of $sim$1500~hr of drift-scan survey data taken using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The pulse has three distinct emission components, which vary with frequency across our 1.0--1.5~GHz observing band. We measure the peak flux density to be $>0.065$~Jy and the corresponding fluence $>0.2$~Jy~ms. Based on the observed dispersion measure of 1812~cm$^{-3}$~pc, we infer a redshift of $sim 1.9$. From this, we estimate the peak luminosity and isotropic energy to be $lesssim 2times10^{43}$~erg~s$^{-1}$ and $lesssim 2times10^{40}$~erg, respectively. With only one FRB from the survey detected so far, our constraints on the event rate are limited. We derive a 95% confidence lower limit for the event rate of 900 FRBs per day for FRBs with fluences $>0.025$~Jy~ms. We performed follow-up observations of the source with FAST for four hours and have not found a repeated burst. We discuss the implications of this discovery for our understanding of the physical mechanisms of FRBs.
We investigate the 1/f noise of the Five-hundred-meter Aperture Spherical Telescope (FAST) receiver system using drift-scan data from an intensity mapping pilot survey. All the 19 beams have 1/f fluctuations with similar structures. Both the temporal and the 2D power spectrum densities are estimated. The correlations directly seen in the time series data at low frequency $f$ are associated with the sky signal, perhaps due to a coupling between the foreground and the system response. We use Singular Value Decomposition (SVD) to subtract the foreground. By removing the strongest components, the measured 1/f noise power can be reduced significantly. With 20 modes subtraction, the knee frequency of the 1/f noise in a 10 MHz band is reduced to $1.8 times 10^{-3}Hz$, well below the thermal noise over 500-seconds time scale. The 2D power spectra show that the 1/f-type variations are restricted to a small region in the time-frequency space and the correlations in frequency can be suppressed with SVD modes subtraction. The residual 1/f noise after the SVD mode subtraction is uncorrelated in frequency, and a simple noise diode frequency-independent calibration of the receiver gain at 8s interval does not affect the results. The 1/f noise can be important for HI intensity mapping, we estimate that the 1/f noise has a knee frequency $(f_{k}) sim$ 6 $times$ 10$^{-4}$Hz, and time and frequency correlation spectral indices $(alpha) sim 0.65$, $(beta) sim 0.8$ after the SVD subtraction of 30 modes. This can bias the HI power spectrum measurement by 10 percent.
Machine learning methods are increasingly helping astronomers identify new radio pulsars. However, they require a large amount of labelled data, which is time consuming to produce and biased. Here we describe a Semi-Supervised Generative Adversarial Network (SGAN) which achieves better classification performance than the standard supervised algorithms using majority unlabelled datasets. We achieved an accuracy and mean F-Score of 94.9% trained on only 100 labelled candidates and 5000 unlabelled candidates compared to our standard supervised baseline which scored at 81.1% and 82.7% respectively. Our final model trained on a much larger labelled dataset achieved an accuracy and mean F-score value of 99.2% and a recall rate of 99.7%. This technique allows for high quality classification during the early stages of pulsar surveys on new instruments when limited labelled data is available. We open-source our work along with a new pulsar-candidate dataset produced from the High Time Resolution Universe - South Low Latitude Survey. This dataset has the largest number of pulsar detections of any public dataset and we hope it will be a valuable tool for benchmarking future machine learning models.
Recycled pulsars are old ($gtrsim10^{8}$ yr) neutron stars that are descendants from close, interacting stellar systems. In order to understand their evolution and population, we must find and study the largest number possible of recycled pulsars in a way that is as unbiased as possible. In this work, we present the discovery and timing solutions of five recycled pulsars in binary systems (PSRs J0509$+$0856, J0709$+$0458, J0732$+$2314, J0824$+$0028, J2204$+$2700) and one isolated millisecond pulsar (PSR J0154$+$1833). These were found in data from the Arecibo 327-MHz Drift-Scan Pulsar Survey (AO327). All these pulsars have a low dispersion measure (DM) ($lesssim 45 , rm{pc}, cm^{-3}$), and have a DM-determined distance of $lesssim$ 3 kpc. Their timing solutions, have data spans ranging from 1 to $sim$ 7 years, include precise estimates of their spin and astrometric parameters, and for the binaries, precise estimates of their Keplerian binary parameters. Their orbital periods range from about 4 to 815 days and the minimum companion masses (assuming a pulsar mass of 1.4 $rm{M_{odot}}$) range from $sim$ 0.06--1.11 $rm{M_{odot}}$. For two of the binaries we detect post-Keplerian parameters; in the case of PSR~J0709$+$0458 we measure the component masses but with a low precision, in the not too distant future the measurement of the rate of advance of periastron and the Shapiro delay will allow very precise mass measurements for this system. Like several other systems found in the AO327 data, PSRs J0509$+$0854, J0709$+$0458 and J0732$+$2314 are now part of the NANOGrav timing array for gravitational wave detection.
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