We describe an ongoing search for pulsars and dispersed pulses of radio emission, such as those from rotating radio transients (RRATs) and fast radio bursts (FRBs), at 350 MHz using the Green Bank Telescope. With the Green Bank Ultimate Pulsar Processing Instrument, we record 100 MHz of bandwidth divided into 4,096 channels every 81.92 µs. This survey will cover the entire sky visible to the Green Bank Telescope (δ > −40 • , or 82% of the sky) and outside of the Galactic Plane will be sensitive enough to detect slow pulsars and low dispersion measure (<30 pc cm −3 ) millisecond pulsars (MSPs) with a 0.08 duty cycle down to 1.1 mJy. For pulsars with a spectral index of −1.6, we will be 2.5 times more sensitive than previous and ongoing surveys over much of our survey region. Here we describe the survey, the data analysis pipeline, initial discovery parameters for 62 pulsars, and timing solutions for 5 new pulsars. PSR J0214+5222 is an MSP in a long-period (512 days) orbit and has an optical counterpart identified in archival data. PSR J0636+5129 is an MSP in a very shortperiod (96 minutes) orbit with a very low mass companion (8 M J ). PSR J0645+5158 is an isolated MSP with a timing residual RMS of 500 ns and has been added to pulsar timing array experiments. PSR J1434+7257 is an isolated, intermediate-period pulsar that has been partially recycled. PSR J1816+4510 is an eclipsing MSP in a short-period orbit (8.7 hours) and may have recently completed its spin-up phase.

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.

We provide timing solutions for 45 radio pulsars discovered by the Robert C. Byrd Green Bank Telescope. These pulsars were found in the Green Bank North Celestial Cap pulsar survey, an all-GBT-sky survey being carried out at a frequency of 350 MHz. We include pulsar timing data from the Green Bank Telescope and Low Frequency Array. Our sample includes five fully recycled millisecond pulsars (MSPs, three of which are in a binary system), a new relativistic double neutron star system, an intermediate-mass binary pulsar, a mode-changing pulsar, a 138 ms pulsar with a very low magnetic field, and several nulling pulsars. We have measured two post-Keplerian parameters and thus the masses of both objects in the double neutron star system. We also report a tentative companion mass measurement via Shapiro delay in a binary MSP. Two of the MSPs can be timed with high precision and have been included in pulsar timing arrays being used to search for low-frequency gravitational waves, while a third MSP is a member of the black widow class of binaries. Proper motion is measurable in five pulsars, and we provide an estimate of their space velocity. We report on an optical counterpart to a new black widow system and provide constraints on the optical counterparts to other binary MSPs. We also present a preliminary analysis of nulling pulsars in our sample. These results demonstrate the scientific return of long timing campaigns on pulsars of all types.

We present high-quality ULTRACAM photometry of the eclipsing detached double white dwarf binary NLTT 11748. This system consists of a carbon/oxygen white dwarf and an extremely low mass (<0.2 M ) helium-core white dwarf in a 5.6 hr orbit. To date, such extremely low-mass white dwarfs, which can have thin, stably burning outer layers, have been modeled via poorly constrained atmosphere and cooling calculations where uncertainties in the detailed structure can strongly influence the eventual fates of these systems when mass transfer begins. With precise (individual precision ≈1%), high-cadence (≈2 s), multicolor photometry of multiple primary and secondary eclipses spanning >1.5 yr, we constrain the masses and radii of both objects in the NLTT 11748 system to a statistical uncertainty of a few percent. However, we find that overall uncertainty in the thickness of the envelope of the secondary carbon/oxygen white dwarf leads to a larger (≈13%) systematic uncertainty in the primary He WD's mass. Over the full range of possible envelope thicknesses, we find that our primary mass (0.136-0.162 M ) and surface gravity (log(g) = 6.32-6.38; radii are 0.0423-0.0433 R ) constraints do not agree with previous spectroscopic determinations. We use precise eclipse timing to detect the Rømer delay at 7σ significance, providing an additional weak constraint on the masses and limiting the eccentricity to e cos ω = (−4 ± 5) × 10 −5 . Finally, we use multicolor data to constrain the secondary's effective temperature (7600±120 K) and cooling age (1.6-1.7 Gyr).

Modern radio pulsar surveys produce a large volume of prospective candidates, the majority of which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determine if they are real pulsars. This process can be labor intensive. In this paper, we introduce an algorithm called PEACE (Pulsar Evaluation Algorithm for Candidate Extraction) which improves the efficiency of identifying pulsar signals. The algorithm ranks the candidates based on a score function. Unlike popular machinelearning based algorithms, no prior training data sets are required. This algorithm has been applied to data from several large-scale radio pulsar surveys. Using the humanbased ranking results generated by students in the Arecibo Remote Command Center programme, the statistical performance of PEACE was evaluated. It was found that PEACE ranked 68% of the student-identified pulsars within the top 0.17% of sorted candidates, 95% within the top 0.34%, and 100% within the top 3.7%. This clearly demonstrates that PEACE significantly increases the pulsar identification rate by a factor of about 50 to 1000. To date, PEACE has been directly responsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars that may be useful for pulsar timing based gravitational-wave detection projects. c 2010 RAS