55413960
SEARCHING FOR PULSARS USING IMAGE PATTERN RECOGNITION
In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.
118616891
X-RAY OBSERVATIONS OF HIGH-B RADIO PULSARS
The study of high-magnetic-field pulsars is important for examining the relationships between radio pulsars, magnetars, and X-ray-isolated neutron stars (XINSs). Here, we report on X-ray observations of three such high-magnetic-field radio pulsars. We first present the results of a deep XMM-Newton observation of PSR J1734–3333, taken to follow up on its initial detection in 2009. The pulsar's spectrum is well fit by a blackbody with a temperature of 300 ± 60 eV, with bolometric luminosity for a distance of 6.1 kpc. We detect no X-ray pulsations from the source, setting a 1σ upper limit on the pulsed fraction of 60% in the 0.5-3 keV band. We compare PSR J1734–3333 to other rotation-powered pulsars of similar age and find that it is significantly hotter, supporting the hypothesis that the magnetic field affects the observed thermal properties of pulsars. We also report on XMM-Newton and Chandra observations of PSRs B1845–19 and J1001–5939. We do not detect either pulsar, setting 3σ upper limits on their blackbody temperatures of 48 and 56 eV, respectively. Despite the similarities in rotational properties, these sources are significantly cooler than all but one of the XINSs, which we attribute to the two groups having been born with different magnetic fields and hence evolving differently.
121492263
Antishadowing properties in the small-x region
Abstract The properties of the modified Gribov-Levin-Ryskin evolution equation which incorporates both shadowing and antishadowing are analyzed. We find that some important conclusions of the GLR equation should be rewritten.