General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACT Recent work has exploited pulsar survey data to identify temporally isolated, millisecond-duration radio bursts with large dispersion measures (DMs). These bursts have been interpreted as arising from a population of extragalactic sources, in which case they would provide unprecedented opportunities for probing the intergalactic medium; they may also be linked to new source classes. Until now, however, all so-called fast radio bursts (FRBs) have been detected with the Parkes radio telescope and its 13-beam receiver, casting some concern about the astrophysical nature of these signals. Here we present FRB 121102, the first FRB discovery from a geographic location other than Parkes. FRB 121102 was found in the Galactic anti-center region in the 1.4 GHz Pulsar Arecibo L-band Feed Array (ALFA) survey with the Arecibo Observatory with a DM = 557.4 ± 2.0 pc cm −3 , pulse width of 3.0 ± 0.5 ms, and no evidence of interstellar scattering. The observed delay of the signal arrival time with frequency agrees precisely with the expectation of dispersion through an ionized medium. Despite its low Galactic latitude (b = −0.• 2), the burst has three times the maximum Galactic DM expected along this particular line of sight, suggesting an extragalactic origin. A peculiar aspect of the signal is an inverted spectrum; we interpret this as a consequence of being detected in a sidelobe of the ALFA receiver. FRB 121102's brightness, duration, and the inferred event rate are all consistent with the properties of the previously detected Parkes bursts.

The on-going Arecibo Pulsar-ALFA (PALFA) survey began in 2004 and is searching for radio pulsars in the Galactic plane at 1.4 GHz. Here we present a comprehensive description of one of its main data reduction pipelines that is based on the PRESTO software and includes new interference-excision algorithms and candidate selection heuristics. This pipeline has been used to discover 40 pulsars, bringing the survey's discovery total to 144 pulsars. Of the new discoveries, eight are millisecond pulsars (MSPs; P 10 < ms) and one is a Fast Radio Burst (FRB). This pipeline has also re-detected 188 previously known pulsars, 60 of them previously discovered by the other PALFA pipelines. We present a novel method for determining the survey sensitivity that accurately takes into account the effects of interference and red noise: we inject synthetic pulsar signals with various parameters into real survey observations and then attempt to recover them with our pipeline. We find that the PALFA survey achieves the sensitivity to MSPs predicted by theoretical models but suffers a degradation for P 100  ms that gradually becomes up to ∼10 times worse for P 4 s > at DM 150 < pc cm −3 . We estimate 33 ± 3% of the slower pulsars are missed, largely due to red noise. A population synthesis analysis using the sensitivity limits we measured suggests the PALFA survey should have found 224 ± 16 un-recycled pulsars in the data set analyzed, in agreement with the 241 actually detected. The reduced sensitivity could have implications on estimates of the number of longperiod pulsars in the Galaxy.

In the modern era of big data, many fields of astronomy are generating huge volumes, the analysis of which can sometimes be the limiting factor in research. Fortunately, powerful data-mining techniques have been developed by computer scientists, ready to be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys -3using image pattern recognition with deep neural nets-the PICS (Pulsar Imagebased 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 which searched 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 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 in recognizing 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, 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 90008 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.