The LOFAR Tied-Array All-Sky Survey (LOTAAS): Survey overview and initial pulsar discoveries

TL;DR

LOTAAS is a deep all-sky radio pulsar survey using LOFAR, leading to 73 new pulsar discoveries, including the slowest-spinning pulsar known, and providing insights into pulsar populations and survey biases.

Contribution

This paper presents the first results of LOTAAS, a novel deep all-sky pulsar survey with LOFAR, including new pulsar discoveries and methodological lessons for future radio telescopes.

Findings

Discovered 73 new pulsars, including the slowest-spinning known pulsar.

Detected 311 known pulsars with flux densities matching literature.

Found that LOTAAS discovers longer-period pulsars, indicating potential selection biases.

Abstract

We present an overview of the LOFAR Tied-Array All-Sky Survey (LOTAAS) for radio pulsars and fast transients. The survey uses the high-band antennas of the LOFAR Superterp, the dense inner part of the LOFAR core, to survey the northern sky (dec > 0 deg) at a central observing frequency of 135 MHz. A total of 219 tied-array beams (coherent summation of station signals, covering 12 square degrees), as well as three incoherent beams (covering 67 square degrees) are formed in each survey pointing. For each ofthe 222 beams, total intensity is recorded at 491.52 us time resolution. Each observation integrates for 1 hr and covers 2592 channels from 119 to 151 MHz. This instrumental setup allows LOTAAS to reach a detection threshold of 1 to 5 mJy for periodic emission. Thus far, the LOTAAS survey has resulted in the discovery of 73 radio pulsars. Among these are two mildly recycled binary millisecond pulsars (P = 13 and 33 ms), as well as the slowest-spinning radio pulsar currently known (P = 23.5 s). The survey has thus far detected 311 known pulsars, with spin periods ranging from 4 ms to 5.0 s and dispersion measures from 3.0 to 217 pc/cc. Known pulsars are detected at flux densities consistent with literature values. We find that the LOTAAS pulsar discoveries have, on average, longer spin periods than the known pulsar population. This may reflect different selection biases between LOTAAS and previous surveys, though it is also possible that slower-spinning pulsars preferentially have steeper radio spectra. LOTAAS is the deepest all-sky pulsar survey using a digital aperture array; we discuss some of the lessons learned that can inform the approach for similar surveys using future radio telescopes such as the Square Kilometre Array.

Figures

Tables

Table 1. Table 1: The dedispersion plan used by the LOTAAS survey, showing the ranges in dispersion measure between DM min and DM max , with a DM step-size of δ 𝛿 \delta DM, yielding N DM subscript 𝑁 DM N_{\mathrm{DM}} dispersion measure trials. The downsampling factor is denoted by d 𝑑 d , and indicates by what factor the temporal resolution is averaged to yield the sampling time t samp subscript 𝑡 samp t_{\mathrm{samp}} .

${\mathrm{DM}}_{\min }$	${\mathrm{DM}}_{\max }$	$\delta \mathrm{DM}$	$N_{\mathrm{DM}}$	$d$	$t_{\mathrm{samp}}$
(pc cm-3)	(pc cm-3)	(pc cm-3)			(ms)
0.00	40.48	0.01	4048	1	0.49152
40.48	141.68	0.05	2024	2	0.98304
141.68	546.48	0.10	4048	4	1.96605

Table 2. Table 2: LOTAAS pulsar discoveries and their properties; spin period P 𝑃 P , dispersion measure DM DM \mathrm{DM} , position in equatorial and Galactic coordinates, as well as the distance as predicted by the NE2001 model, and the pulse FWHM. Pulsar names are defined based on the TAB position of full LOFAR core follow-up observations. Here, we use the full core TAB FWHM as the positional uncertainty. If the pulsar is mentioned elsewhere with a different name, the alternate identification is provided in the comments. Uncertainties on measurements are shown in brackets corresponding to the least significant digit.

PSR	$P$	$\mathrm{DM}$	${\alpha }_{\mathrm{J2000}}$	${\delta }_{\mathrm{J2000}}$	$l$	$b$	$d_{\mathrm{NE2001}}$	$w_{50}$	Comments
	(s)	(pc cm-3)			(°)	(°)	(kpc)	(ms)
J0039$+$35	$0.5367$	$53.04\left(2\right)$	${00}^{h}39\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+35°45\prime \left(3\prime \right)$	$120.11$	$-27.04$	$4.16$	$11$
J0059$+$69	$1.1459$	$63.53\left(5\right)$	${00}^{h}59\stackrel{m}{.}5\left(0\stackrel{m}{.}6\right)$	$+69°55\prime \left(3\prime \right)$	$123.63$	$7.07$	$2.36$	$23$
J0100$+$80	$1.4936$	$55.76\left(13\right)$	${01}^{h}00\stackrel{m}{.}3\left(1\stackrel{m}{.}2\right)$	$+80°22\prime \left(3\prime \right)$	$123.32$	$17.52$	$2.62$	$60$
J0107$+$13	$1.1974$	$22.02\left(8\right)$	${01}^{h}07\stackrel{m}{.}6\left(0\stackrel{m}{.}2\right)$	$+13°25\prime \left(3\prime \right)$	$128.95$	$-49.25$	$1.01$	$36$
J0115$+$63	$0.5215$	$65.04\left(7\right)$	${01}^{h}15\stackrel{m}{.}6\left(0\stackrel{m}{.}4\right)$	$+63°24\prime \left(3\prime \right)$	$125.63$	$0.67$	$2.23$	$31$
J0121$+$14	$1.3890$	$17.77\left(9\right)$	${01}^{h}22\stackrel{m}{.}0\left(0\stackrel{m}{.}2\right)$	$+14°16\prime \left(3\prime \right)$	$134.01$	$-47.94$	$0.79$	$42$	P
J0139$+$33	$1.2479$	$21.21\left(6\right)$	${01}^{h}40\stackrel{m}{.}0\left(0\stackrel{m}{.}2\right)$	$+33°37\prime \left(3\prime \right)$	$134.38$	$-28.17$	$0.98$	$25$	RRAT, L-sps, P
J0210$+$58	$1.7663$	$76.70\left(12\right)$	${02}^{h}11\stackrel{m}{.}0\left(0\stackrel{m}{.}4\right)$	$+58°44\prime \left(3\prime \right)$	$133.11$	$-2.55$	$2.51$	$53$
J0250$+$58	$23.5355$	$45.8\left(1.1\right)$	${02}^{h}50\stackrel{m}{.}3\left(0\stackrel{m}{.}4\right)$	$+58°54\prime \left(3\prime \right)$	$137.77$	$-0.50$	$1.67$	$471$	J0250$+$5854
J0302$+$22	$1.2072$	$19.09\left(11\right)$	${03}^{h}02\stackrel{m}{.}5\left(0\stackrel{m}{.}2\right)$	$+22°50\prime \left(3\prime \right)$	$158.44$	$-30.84$	$0.75$	$48$	J0301$+$20, L-sps, P
J0305$+$11	$0.8621$	$27.97\left(6\right)$	${03}^{h}05\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+11°23\prime \left(3\prime \right)$	$167.46$	$-39.66$	$1.10$	$26$
J0317$+$13	$1.9743$	$12.90\left(4\right)$	${03}^{h}17\stackrel{m}{.}9\left(0\stackrel{m}{.}2\right)$	$+13°29\prime \left(3\prime \right)$	$168.75$	$-36.03$	$0.49$	$20$	L-sps, P
J0349$+$23	$2.4208$	$63.15\left(11\right)$	${03}^{h}49\stackrel{m}{.}9\left(0\stackrel{m}{.}2\right)$	$+23°41\prime \left(3\prime \right)$	$167.42$	$-23.38$	$3.30$	$48$
J0421$+$32	$0.9001$	$77.0\left(0.5\right)$	${04}^{h}21\stackrel{m}{.}4\left(0\stackrel{m}{.}2\right)$	$+32°54\prime \left(3\prime \right)$	$165.83$	$-11.96$	$2.62$	$207$	P
J0454$+$45	$1.3892$	$20.82\left(6\right)$	${04}^{h}54\stackrel{m}{.}9\left(0\stackrel{m}{.}3\right)$	$+45°28\prime \left(3\prime \right)$	$160.71$	$1.22$	$0.78$	$28$	L-sps
J0518$+$51	$0.9125$	$39.15\left(6\right)$	${05}^{h}18\stackrel{m}{.}3\left(0\stackrel{m}{.}3\right)$	$+51°25\prime \left(3\prime \right)$	$158.26$	$7.88$	$1.35$	$27$
J0742$+$43	$0.6062$	$36.23\left(4\right)$	${07}^{h}42\stackrel{m}{.}6\left(0\stackrel{m}{.}3\right)$	$+43°33\prime \left(3\prime \right)$	$175.54$	$27.19$	$1.34$	$18$
J0811$+$37	$1.2483$	$16.95\left(11\right)$	${08}^{h}11\stackrel{m}{.}2\left(0\stackrel{m}{.}3\right)$	$+37°28\prime \left(3\prime \right)$	$183.67$	$31.22$	$0.59$	$50$	P
J0813$+$22	$0.5314$	$52.29\left(5\right)$	${08}^{h}13\stackrel{m}{.}9\left(0\stackrel{m}{.}2\right)$	$+22°01\prime \left(3\prime \right)$	$200.89$	$27.48$	$2.59$	$21$
J0827$+$53	$0.0135$	$23.103\left(1\right)$	${08}^{h}27\stackrel{m}{.}8\left(0\stackrel{m}{.}3\right)$	$+53°00\prime \left(3\prime \right)$	$165.46$	$35.64$	$0.90$	$1$	Binary MSP
J0857$+$33	$0.2430$	$24.025\left(16\right)$	${08}^{h}57\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+33°48\prime \left(3\prime \right)$	$190.15$	$39.72$	$0.89$	$7$
J0928$+$30	$2.0915$	$21.95\left(9\right)$	${09}^{h}29\stackrel{m}{.}0\left(0\stackrel{m}{.}2\right)$	$+30°38\prime \left(3\prime \right)$	$195.82$	$45.91$	$0.84$	$42$	P
J0935$+$33	$0.9615$	$18.35\left(6\right)$	${09}^{h}35\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+33°11\prime \left(3\prime \right)$	$192.36$	$47.52$	$0.68$	$29$	P
J1017$+$30	$0.4528$	$27.16\left(6\right)$	${10}^{h}17\stackrel{m}{.}6\left(0\stackrel{m}{.}2\right)$	$+30°10\prime \left(3\prime \right)$	$198.48$	$56.27$	$1.23$	$27$
J1226$+$00	$2.2851$	$18.50\left(10\right)$	${12}^{h}26\stackrel{m}{.}2\left(0\stackrel{m}{.}2\right)$	$+00°03\prime \left(3\prime \right)$	$289.27$	$62.26$	$0.87$	$46$
J1235$-$02	$3.5976$	$18.8\left(0.2\right)$	${12}^{h}35\stackrel{m}{.}9\left(0\stackrel{m}{.}2\right)$	$-02°05\prime \left(3\prime \right)$	$295.03$	$60.54$	$0.88$	$108$
J1303$+$38	$0.3963$	$19.000\left(9\right)$	${13}^{h}03\stackrel{m}{.}3\left(0\stackrel{m}{.}3\right)$	$+38°13\prime \left(3\prime \right)$	$111.08$	$78.62$	$1.76$	$4$
J1334$+$10	$0.9111$	$24.00\left(4\right)$	${13}^{h}34\stackrel{m}{.}5\left(0\stackrel{m}{.}2\right)$	$+10°05\prime \left(3\prime \right)$	$335.77$	$70.16$	$2.53$	$18$
J1344$+$66	$1.3941$	$30.02\left(16\right)$	${13}^{h}43\stackrel{m}{.}9\left(0\stackrel{m}{.}5\right)$	$+66°33\prime \left(3\prime \right)$	$114.90$	$49.75$	$1.83$	$70$	J1340$+$65, L-sps
J1404$+$11	$2.6505$	$18.48\left(12\right)$	${14}^{h}04\stackrel{m}{.}6\left(0\stackrel{m}{.}2\right)$	$+11°57\prime \left(3\prime \right)$	$355.02$	$67.10$	$1.41$	$53$	J1404$+$1159, L-sps, P
J1426$+$52	$0.9958$	$25.37\left(2\right)$	${14}^{h}27\stackrel{m}{.}0\left(0\stackrel{m}{.}3\right)$	$+52°10\prime \left(3\prime \right)$	$93.89$	$59.23$	$1.41$	$10$
J1529$+$40	$0.4764$	$6.61\left(16\right)$	${15}^{h}29\stackrel{m}{.}2\left(0\stackrel{m}{.}3\right)$	$+40°49\prime \left(3\prime \right)$	$66.21$	$54.90$	$0.68$	$71$	P
J1623$+$58	$0.6518$	$26.40\left(3\right)$	${16}^{h}23\stackrel{m}{.}8\left(0\stackrel{m}{.}4\right)$	$+58°49\prime \left(3\prime \right)$	$89.19$	$41.82$	$1.48$	$13$
J1635$+$23	$1.2087$	$37.60\left(5\right)$	${16}^{h}35\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+23°31\prime \left(3\prime \right)$	$41.98$	$39.74$	$4.84$	$24$	P
J1638$+$40	$0.7677$	$33.35\left(4\right)$	${16}^{h}38\stackrel{m}{.}8\left(0\stackrel{m}{.}3\right)$	$+40°05\prime \left(3\prime \right)$	$63.77$	$41.76$	$2.41$	$15$
J1643$+$13	$1.0991$	$35.97\left(7\right)$	${16}^{h}43\stackrel{m}{.}8\left(0\stackrel{m}{.}2\right)$	$+13°25\prime \left(3\prime \right)$	$31.01$	$34.29$	$2.06$	$33$	P
J1655$+$62	$0.7762$	$35.41\left(5\right)$	${16}^{h}55\stackrel{m}{.}9\left(0\stackrel{m}{.}4\right)$	$+62°02\prime \left(3\prime \right)$	$91.92$	$37.18$	$2.35$	$23$
J1657$+$33	$1.5702$	$24.04\left(7\right)$	${16}^{h}57\stackrel{m}{.}7\left(0\stackrel{m}{.}2\right)$	$+33°03\prime \left(3\prime \right)$	$55.32$	$37.14$	$1.40$	$31$	P
J1658$+$36	$0.0330$	$3.050\left(2\right)$	${16}^{h}58\stackrel{m}{.}4\left(0\stackrel{m}{.}2\right)$	$+36°30\prime \left(3\prime \right)$	$59.62$	$37.59$	$0.49$	$1$	J1658$+$3630, binary MSP
J1707$+$35	$0.1598$	$19.240\left(7\right)$	${17}^{h}07\stackrel{m}{.}0\left(0\stackrel{m}{.}2\right)$	$+35°56\prime \left(3\prime \right)$	$59.26$	$35.78$	$1.17$	$3$
J1713$+$78	$0.4325$	$36.96\left(3\right)$	${17}^{h}13\stackrel{m}{.}5\left(1\stackrel{m}{.}0\right)$	$+78°09\prime \left(3\prime \right)$	$110.29$	$31.47$	$2.12$	$13$
J1715$+$46	$0.5481$	$19.82\left(5\right)$	${17}^{h}15\stackrel{m}{.}8\left(0\stackrel{m}{.}3\right)$	$+46°03\prime \left(3\prime \right)$	$71.86$	$35.36$	$1.17$	$22$
J1722$+$35	$0.8216$	$23.83\left(6\right)$	${17}^{h}22\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+35°18\prime \left(3\prime \right)$	$59.24$	$32.65$	$1.41$	$25$	P
J1735$+$63	$0.5107$	$41.81\left(3\right)$	${17}^{h}35\stackrel{m}{.}1\left(0\stackrel{m}{.}4\right)$	$+63°19\prime \left(3\prime \right)$	$92.71$	$32.55$	$3.18$	$15$
J1740$+$27	$1.0582$	$35.46\left(5\right)$	${17}^{h}40\stackrel{m}{.}5\left(0\stackrel{m}{.}2\right)$	$+27°13\prime \left(3\prime \right)$	$51.46$	$26.72$	$1.99$	$21$	P
J1741$+$38	$0.8289$	$47.26\left(6\right)$	${17}^{h}41\stackrel{m}{.}2\left(0\stackrel{m}{.}3\right)$	$+38°54\prime \left(3\prime \right)$	$64.28$	$29.70$	$3.74$	$25$
J1745$+$12	$1.0599$	$66.32\left(17\right)$	${17}^{h}45\stackrel{m}{.}7\left(0\stackrel{m}{.}2\right)$	$+12°51\prime \left(3\prime \right)$	$37.36$	$20.30$	$2.98$	$74$
J1745$+$42	$0.3051$	$38.00\left(3\right)$	${17}^{h}45\stackrel{m}{.}8\left(0\stackrel{m}{.}3\right)$	$+42°53\prime \left(3\prime \right)$	$68.97$	$29.63$	$2.36$	$12$
J1749$+$59	$0.4360$	$45.09\left(6\right)$	${17}^{h}49\stackrel{m}{.}6\left(0\stackrel{m}{.}4\right)$	$+59°51\prime \left(3\prime \right)$	$88.59$	$30.90$	$4.05$	$26$
J1809$+$17	$2.0667$	$47.32\left(9\right)$	${18}^{h}09\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+17°04\prime \left(3\prime \right)$	$43.84$	$16.87$	$2.27$	$41$
J1810$+$07	$0.3077$	$79.41\left(9\right)$	${18}^{h}10\stackrel{m}{.}7\left(0\stackrel{m}{.}2\right)$	$+07°03\prime \left(3\prime \right)$	$34.65$	$12.25$	$3.00$	$40$
J1814$+$22	$0.2537$	$62.313\left(11\right)$	${18}^{h}14\stackrel{m}{.}6\left(0\stackrel{m}{.}2\right)$	$+22°23\prime \left(3\prime \right)$	$49.50$	$17.77$	$3.27$	$5$
J1848$+$15	$2.2338$	$77.42\left(10\right)$	${18}^{h}48\stackrel{m}{.}9\left(0\stackrel{m}{.}2\right)$	$+15°17\prime \left(3\prime \right)$	$46.33$	$7.45$	$3.29$	$45$	J1849$+$15, L-sps, P
J1849$+$25	$0.5193$	$74.970\left(12\right)$	${18}^{h}49\stackrel{m}{.}8\left(0\stackrel{m}{.}2\right)$	$+25°58\prime \left(3\prime \right)$	$56.22$	$11.86$	$3.88$	$5$
J1910$+$56	$0.3419$	$20.75\left(12\right)$	${19}^{h}10\stackrel{m}{.}7\left(0\stackrel{m}{.}4\right)$	$+56°55\prime \left(3\prime \right)$	$87.58$	$19.95$	$1.52$	$51$
J1916$+$32	$1.1374$	$83.99\left(5\right)$	${19}^{h}16\stackrel{m}{.}1\left(0\stackrel{m}{.}2\right)$	$+32°24\prime \left(3\prime \right)$	$64.63$	$9.43$	$4.46$	$23$
J1933$+$53	$2.0526$	$33.52\left(9\right)$	${19}^{h}33\stackrel{m}{.}0\left(0\stackrel{m}{.}3\right)$	$+53°32\prime \left(3\prime \right)$	$85.54$	$15.75$	$2.18$	$41$
J1953$+$30	$1.2712$	$43.61\left(11\right)$	${19}^{h}53\stackrel{m}{.}8\left(0\stackrel{m}{.}2\right)$	$+30°13\prime \left(3\prime \right)$	$66.58$	$1.32$	$3.12$	$51$

Table 3. Table A.1: List of known pulsars detected by LOTAAS. The table shows the detected spin period ( P 𝑃 P ) and DM DM \mathrm{DM} of the pulsars, the offset between the known position of the pulsars and the centre position of the TAB ( θ TAB subscript 𝜃 TAB \theta_{\mathrm{TAB}} ) and SAP ( θ SAP subscript 𝜃 SAP \theta_{\mathrm{SAP}} ) where the pulsar is detected, the signal-to-noise σ 𝜎 \sigma and estimated flux density of the pulsars at 135 MHz, before ( S 135 uncor superscript subscript 𝑆 135 uncor S_{135}^{\mathrm{uncor}} ) and after ( cor 135 superscript subscript absent 135 cor {}_{135}^{\mathrm{cor}} ) applying the correction factor ( S 135 × superscript subscript 𝑆 135 S_{135}^{\times} ) due to the offset in position. Flux densities at 400 MHz ( S 400 subscript 𝑆 400 S_{400} ) and 150 MHz ( S 150 subscript 𝑆 150 S_{150} ), and spectral indices ( α 𝛼 \alpha ) as listed in PSRCAT v1.59 ( Manchester et al. 2005 ) are also provided, and used to estimate the expected flux density at 135 MHz ( S 135 exp superscript subscript 𝑆 135 exp S_{135}^{\mathrm{exp}} ).

PSR	$P$	$\mathrm{DM}$	${\theta }_{\mathrm{TAB}}$	${\theta }_{\mathrm{SAP}}$	$\sigma$	$S_{135}^{\mathrm{uncor}}$	$S_{135}^{\times }$	$S_{135}^{\mathrm{cor}}$	$S_{400}$	$S_{150}$	$\alpha$	$S_{135}^{\exp }$	Notes
	(s)	(pc cm-3)	(°)	(°)		(mJy)		(mJy)	(mJy)	(mJy)		(mJy)
B0011$+$47	1.2407	30.3	0.1	0.7	63.6	22.2	1.4	31.1	14.0	35.0	$-$1.00	38.9
B0031$-$07	0.9430	10.8	6.7	5.0	30.3	54.2	$\dots$	$\dots$	52.0	640.0	$-$1.60	757.5
B0037$+$56	1.1182	92.6	0.0	1.2	33.6	15.3	1.3	19.4	7.5	33.0	$-$1.70	39.5
B0045$+$33	1.2171	39.9	0.1	0.2	107.6	20.4	1.2	24.9	2.3	13.0	$-$2.40	16.7
B0052$+$51	2.1152	44.1	0.0	2.1	16.3	3.7	2.1	7.9	3.4	9.4	$-$1.00	10.4
B0053$+$47	0.4720	18.1	0.1	0.7	59.9	20.5	1.1	23.3	3.0	7.5	$-$1.20	8.5
B0059$+$65	1.6792	65.8	0.1	1.0	11.9	5.4	1.4	7.4	8.5	$\dots$	$-$1.60	48.3
B0105$+$65	1.2837	30.5	0.0	1.4	89.4	28.9	1.4	39.2	10.0	41.0	$-$1.40	47.5
B0105$+$68	1.0711	61.1	0.0	2.5	7.0	2.7	3.0	8.3	3.7	8.9	$-$1.50	10.4
B0114$+$58	0.1014	49.4	0.0	1.2	46.8	43.9	1.2	54.3	7.6	71.3	$-$2.45	92.3
B0136$+$57	0.2725	73.8	0.1	1.0	130.9	121.2	1.6	197.6	28.0	154.5	$-$1.57	182.3
B0138$+$59	1.2230	34.9	0.0	1.1	289.5	84.6	1.2	102.6	49.0	138.2	$-$1.53	162.4
B0144$+$59	0.1963	40.1	0.0	1.4	43.3	19.3	1.4	26.3	6.6	33.1	$-$1.23	37.7	3
B0153$+$39	1.8116	59.6	0.0	1.7	10.6	2.2	1.6	3.5	4.0	8.5	$-$1.40	9.9
B0154$+$61	2.3519	30.2	0.0	1.2	10.5	3.9	1.2	4.8	6.5	$\dots$	$-$1.20	23.9	3
B0226$+$70	1.4668	46.7	0.1	0.6	45.1	10.5	1.2	12.3	2.4	5.3	$-$1.50	6.2
B0301$+$19	1.3876	15.6	0.9	1.5	35.1	12.1	$\dots$	$\dots$	27.0	42.0	$-$1.40	48.7
B0320$+$39	3.0321	26.2	0.0	2.3	269.1	43.8	2.5	109.6	34.0	92.0	$-$2.80	123.6
B0329$+$54	0.7145	26.8	2.2	1.2	434.2	168.8	$\dots$	$\dots$	1500.0	$\dots$	$-$1.60	8528.1
B0331$+$45	0.2692	47.1	0.1	0.1	50.6	11.7	1.1	13.4	6.0	34.0	$-$1.90	41.5
B0339$+$53	1.9345	67.5	2.1	1.1	15.2	4.3	$\dots$	$\dots$	3.7	$\dots$	$-$1.90	29.1
B0355$+$54	0.1564	57.1	0.0	2.3	68.0	35.3	2.5	88.4	46.0	$\dots$	$-$0.90	122.3
B0402$+$61	0.5946	65.3	0.0	1.6	69.6	36.3	1.5	55.2	$\dots$	$\dots$	$-$1.20	$\dots$	6
B0410$+$69	0.3907	27.5	0.0	1.4	55.4	16.3	1.3	21.7	6.4	28.0	$-$1.80	33.8
B0450$+$55	0.3407	14.6	0.0	2.3	73.4	28.5	2.3	65.9	59.0	91.0	$-$1.20	103.3
B0523$+$11	0.3544	79.5	0.0	1.7	43.5	37.2	1.6	59.9	19.5	34.0	$-$1.90	41.5
B0525$+$21	3.7456	50.9	0.0	1.4	234.7	21.8	1.4	29.5	57.0	230.0	$-$1.50	269.4
B0531$+$21	0.0337	56.8	3.0	2.5	9.6	432.5	$\dots$	$\dots$	550.0	7500.0	$-$3.10	10397.0
B0540$+$23	0.2460	77.7	0.0	1.5	25.1	15.8	1.4	22.4	29.0	36.0	$-$0.70	38.8
B0609$+$37	0.2980	27.1	0.0	1.0	30.8	9.1	1.2	10.6	16.0	21.0	$-$1.50	24.6
B0611$+$22	0.3350	96.9	0.1	0.5	67.8	55.1	1.2	66.3	29.0	$\dots$	$-$1.77	198.3
B0621$-$04	1.0391	70.8	0.0	2.0	17.1	8.1	1.9	15.8	4.9	$\dots$	$-$1.00	14.5	3
B0626$+$24	0.4766	84.2	0.2	0.6	97.3	32.8	$\dots$	$\dots$	31.0	73.0	$-$1.50	85.5
B0643$+$80	1.2144	33.3	0.0	0.9	65.6	11.6	1.1	13.1	6.5	19.0	$-$1.50	22.3
B0655$+$64	0.1957	8.8	0.2	0.7	376.1	80.6	1.9	157.1	5.0	51.0	$-$2.20	64.3
B0656$+$14	0.3849	14.1	0.0	1.1	16.0	7.2	1.2	8.6	6.5	11.0	$-$1.10	12.4
B0751$+$32	1.4423	40.0	0.2	0.7	44.8	10.5	2.2	22.9	8.0	17.0	$-$1.50	19.9
B0809$+$74	1.2922	5.8	4.1	2.0	390.5	101.8	$\dots$	$\dots$	79.0	300.0	$-$1.70	358.8
B0820$+$02	0.8649	23.8	0.0	2.0	110.6	41.0	1.9	77.9	30.0	$\dots$	$-$2.30	364.8
B0823$+$26	0.5307	19.5	0.0	2.0	126.3	306.1	2.0	605.6	73.0	520.0	$-$1.60	615.5
B0834$+$06	1.2738	12.9	2.0	2.0	1266.0	286.2	$\dots$	$\dots$	89.0	$\dots$	$-$2.70	1671.3
B0841$+$80	1.6022	34.9	0.1	0.4	10.8	3.0	1.5	4.6	1.5	5.9	$-$1.60	7.0
B0917$+$63	1.5680	13.1	0.0	1.8	29.2	6.2	1.7	10.8	5.0	22.0	$-$1.50	25.8
B0919$+$06	0.4306	27.3	2.6	1.1	271.6	254.2	$\dots$	$\dots$	52.0	$\dots$	$-$1.80	367.4
B0940$+$16	1.0874	20.2	0.0	1.2	27.7	15.5	1.3	19.6	7.0	26.0	$-$1.00	28.9
B0943$+$10	1.0977	15.3	0.2	0.8	597.8	156.8	2.1	330.2	4.0	440.0	$\dots$	509.9
B0950$+$08	0.2531	3.0	0.2	0.4	278.5	1098.6	2.2	2368.0	400.0	2600.0	$-$1.30	2981.7
B1112$+$50	1.6564	9.2	0.0	1.1	286.6	22.5	1.2	27.4	12.0	50.0	$-$1.80	60.4
B1133$+$16	1.1879	4.8	0.0	2.7	879.9	217.8	3.5	756.3	257.0	940.0	$-$1.50	1100.9
B1237$+$25	1.3824	9.3	0.0	1.9	301.9	67.4	1.7	117.5	110.0	170.0	$-$1.80	205.5
B1257$+$12	0.0062	10.2	0.2	0.7	161.6	121.6	1.8	218.4	20.0	80.9	$-$1.80	97.8
B1322$+$83	0.6700	13.3	0.0	1.3	73.7	19.2	1.3	25.0	11.0	28.0	$-$1.70	33.5
B1508$+$55	0.7397	19.6	3.3	4.0	95.9	13.8	$\dots$	$\dots$	114.0	770.0	$-$2.30	981.1
B1530$+$27	1.1248	14.7	0.0	1.6	126.8	38.4	1.5	57.3	13.0	33.0	$-$2.00	40.7
B1534$+$12	0.0379	11.6	0.0	0.5	24.1	14.9	1.1	15.7	36.0	$\dots$	$-$1.90	283.5	3
B1540$-$06	0.7091	18.4	6.1	3.8	35.7	14.6	$\dots$	$\dots$	40.0	$\dots$	$-$2.50	604.5
B1541$+$09	0.7484	35.0	0.0	2.2	286.6	260.7	2.2	572.2	78.0	770.0	$-$2.10	960.7
B1604$-$00	0.4218	10.7	0.2	0.7	278.8	138.0	2.4	329.9	54.0	$\dots$	$-$1.90	425.3
B1612$+$07	1.2068	21.4	0.0	1.9	83.3	29.3	1.8	52.3	9.6	$\dots$	$-$2.70	180.3
B1633$+$24	0.4905	24.3	0.0	1.5	104.7	45.6	1.4	63.8	9.1	79.0	$-$2.10	98.6
B1642$-$03	0.3877	35.7	0.0	1.2	933.0	658.8	1.3	830.5	393.0	$\dots$	$-$2.30	4779.3

Table 4. Table A.1: Continued.

PSR	$P$	$\mathrm{DM}$	${\theta }_{\mathrm{TAB}}$	${\theta }_{\mathrm{SAP}}$	$\sigma$	$S_{135}^{\mathrm{uncor}}$	$S_{135}^{\times }$	$S_{135}^{\mathrm{cor}}$	$S_{400}$	$S_{150}$	$\alpha$	$S_{135}^{\exp }$	Notes
	(s)	(pc cm-3)	(°)	(°)		(mJy)		(mJy)	(mJy)	(mJy)		(mJy)
B1718$-$02	0.4777	66.9	0.2	0.2	22.9	23.7	2.5	60.3	22.0	$\dots$	$-$2.20	240.0	3
B1726$-$00	0.3860	41.0	0.0	2.0	41.7	23.9	2.0	47.2	11.0	$\dots$	$-$2.30	133.8	3
B1737$+$13	0.8031	48.6	0.0	2.9	58.6	21.6	4.4	94.4	24.0	81.0	$-$1.40	93.9
B1753$+$52	0.7971	35.1	0.0	1.0	8.1	1.9	1.2	2.3	4.9	2.4	$-$1.10	2.7
B1758$-$03	0.9215	120.5	2.5	1.5	10.8	17.9	$\dots$	$\dots$	17.0	$\dots$	$-$2.60	286.4	3
B1802$+$03	0.2187	81.0	0.0	1.1	7.9	15.3	1.2	18.7	5.0	$\dots$	$\dots$	22.9	3
B1811$+$40	0.9311	41.6	0.1	0.2	162.3	31.7	1.2	37.7	8.0	50.0	$-$1.20	56.7
B1818$-$04	0.5981	84.4	0.0	2.2	43.2	153.5	2.2	335.2	157.0	830.0	$-$2.10	1035.5
B1821$+$05	0.7529	66.8	0.0	2.7	59.4	35.3	3.5	124.1	18.0	$\dots$	$-$1.70	114.1
B1822$+$00	0.7790	56.6	0.1	0.4	16.7	18.8	1.2	21.7	8.0	$\dots$	$-$2.30	97.3	3
B1831$-$00	0.5210	89.0	0.2	0.2	17.6	33.6	2.9	97.3	5.1	$\dots$	$\dots$	23.3	3
B1831$-$04	0.2901	79.4	0.0	1.9	55.6	350.4	1.8	621.6	77.0	$\dots$	$-$1.30	316.0
B1839$+$09	0.3813	49.2	0.0	2.2	37.2	31.2	2.2	69.0	20.0	130.0	$-$2.00	160.5
B1839$+$56	1.6529	26.8	0.0	1.6	203.3	37.5	1.5	55.0	21.0	76.0	$-$1.40	88.1
B1842$+$14	0.3755	41.5	0.0	0.4	345.9	207.4	1.1	222.1	20.0	110.0	$-$1.95	135.1
B1848$+$12	1.2053	70.6	0.0	1.6	20.6	14.4	1.5	21.4	8.0	37.0	$-$1.80	44.7
B1848$+$13	0.3456	60.2	0.1	0.7	19.3	13.0	1.4	17.9	6.0	5.7	$-$1.50	6.7
B1859$+$01	0.2882	105.4	0.2	0.2	46.2	152.9	1.9	283.0	13.7	$\dots$	$-$2.90	319.7	3
B1905$+$39	1.2358	30.9	0.1	0.5	119.0	43.1	1.2	53.8	23.0	46.0	$-$2.00	56.8
B1907$+$00	1.0170	112.8	0.0	1.1	26.4	30.2	1.2	36.3	12.0	$\dots$	$-$1.80	84.8
B1907$+$02	0.9898	171.7	0.0	1.7	19.0	44.5	1.6	70.2	21.0	$\dots$	$-$2.36	272.6
B1907$+$03	2.3303	82.7	0.1	0.4	18.5	34.7	1.5	50.7	21.0	$\dots$	$-$0.70	44.9	3
B1907$+$10	0.2836	149.9	0.0	1.0	40.8	186.9	1.2	217.2	50.0	$\dots$	$-$2.50	755.6
B1907$-$03	0.5046	206.0	0.0	2.3	4.6	15.5	2.4	36.6	27.0	$\dots$	$-$2.60	454.8	3
B1911$-$04	0.8259	89.4	0.0	1.7	100.0	139.0	1.6	222.5	118.0	$\dots$	$-$2.60	1987.8
B1913$+$167	1.6162	62.6	0.1	0.4	13.4	6.4	1.1	7.0	4.5	$\dots$	$-$1.50	23.0	3
B1914$+$09	0.2703	61.0	0.1	0.4	36.5	79.6	1.4	110.3	20.0	$\dots$	$-$2.30	243.2
B1915$+$13	0.1946	94.7	0.0	1.4	71.1	145.9	1.4	198.7	43.0	$\dots$	$-$1.80	303.8
B1915$+$22	0.4259	134.7	0.2	0.7	13.0	9.6	2.4	23.2	3.0	12.0	$-$2.22	15.2
B1917$+$00	1.2723	90.2	0.0	2.5	31.0	32.8	2.8	92.0	16.0	$\dots$	$-$2.30	194.6
B1918$+$19	0.8210	153.6	0.2	0.9	28.7	29.2	$\dots$	$\dots$	34.0	$\dots$	$-$2.40	460.9	3
B1918$+$26	0.7855	27.7	0.0	1.9	36.9	11.3	1.7	19.8	6.0	21.0	$-$1.30	24.1
B1919$+$14	0.6182	91.7	0.0	1.3	17.5	19.3	1.3	25.6	3.2	$\dots$	$-$1.00	9.5	3
B1919$+$21	1.3373	12.4	1.6	1.8	283.4	82.2	$\dots$	$\dots$	57.0	1300.0	$-$1.90	1588.1
B1920$+$21	1.0779	217.0	0.1	0.2	64.8	60.1	1.1	68.4	30.0	$\dots$	$-$2.40	406.7
B1923$+$04	1.0741	102.1	0.0	1.4	20.3	18.2	1.4	24.6	22.0	$\dots$	$-$2.70	413.1
B1926$+$18	1.2205	111.9	0.0	1.6	11.0	7.5	1.5	11.3	1.7	$\dots$	$\dots$	7.8	3
B1929$+$10	0.2265	3.2	2.8	3.4	60.2	65.3	$\dots$	$\dots$	303.0	540.0	$-$1.70	645.9
B1931$+$24	0.8137	105.9	0.0	2.7	10.4	8.5	3.5	29.7	7.5	$\dots$	$\dots$	34.3	3
B1933$+$16	0.3587	158.6	0.0	1.0	118.8	126.9	1.2	148.3	242.0	$\dots$	$-$1.40	1107.2	3
B1935$+$25	0.2010	53.3	0.0	1.4	11.9	12.8	1.3	17.2	6.6	$\dots$	$-$0.70	14.1	3
B1942$-$00	1.0456	59.7	0.0	2.8	18.6	10.2	3.8	38.6	6.0	$\dots$	$-$1.80	42.4	3
B1944$+$17	0.4406	16.1	0.0	1.5	35.7	30.9	1.4	44.6	40.0	41.0	$-$0.90	45.1
B1946$+$35	0.7173	128.9	0.3	0.9	7.0	10.7	$\dots$	$\dots$	145.0	170.0	$-$2.20	214.3
B1949$+$14	0.2750	31.5	0.0	1.2	31.9	12.3	1.2	15.2	6.0	6.4	$-$1.00	7.1
B1951$+$32	0.0395	45.2	0.0	1.7	40.2	50.6	1.6	78.8	7.0	$\dots$	$-$1.60	39.8	3
B1952$+$29	0.4267	7.9	0.0	2.1	43.5	27.5	2.1	56.9	6.6	$\dots$	0.00	6.6	3
B1953$+$50	0.5189	32.0	0.0	1.6	129.1	48.9	1.5	74.4	26.0	48.0	$-$1.30	55.0
B2016$+$28	0.5580	14.2	0.0	2.7	153.9	180.0	3.4	618.8	314.0	810.0	$-$2.30	1032.1
B2020$+$28	0.3434	24.6	0.0	1.7	283.3	138.2	1.6	217.7	71.0	150.0	$-$0.40	156.5
B2021$+$51	0.5292	22.6	0.0	0.5	75.1	44.5	1.1	48.6	77.0	59.0	$-$0.70	63.5
B2022$+$50	0.3726	33.0	0.2	1.1	17.5	15.4	2.2	33.6	6.5	36.0	$-$1.10	40.4
B2027$+$37	1.2168	191.0	0.0	1.5	4.6	66.3	1.5	96.7	18.0	$\dots$	$-$2.60	303.2	3
B2028$+$22	0.6305	71.8	0.2	0.9	8.4	4.2	2.3	9.7	5.0	7.4	$-$1.20	8.4
B2034$+$19	2.0744	36.9	0.0	1.2	22.6	4.9	1.3	6.3	2.0	20.0	$-$2.50	26.0
B2043$-$04	1.5469	35.9	0.0	1.8	66.5	24.9	1.7	41.6	20.0	$\dots$	$-$1.70	126.8
B2044$+$15	1.1383	39.8	0.3	0.7	52.0	20.3	$\dots$	$\dots$	11.5	33.0	$-$1.60	39.1
B2045$+$56	0.4767	101.8	0.0	1.3	39.4	14.5	1.3	18.5	4.6	25.0	$-$1.80	30.2
B2053$+$21	0.8152	36.4	0.0	1.2	96.5	22.9	1.3	28.7	9.0	29.0	$-$1.10	32.6
B2053$+$36	0.2215	97.5	0.1	0.8	10.4	18.9	1.6	30.1	28.0	29.0	$-$1.90	35.4
B2106$+$44	0.4149	140.1	0.0	0.8	9.0	24.9	1.1	28.1	26.0	$\dots$	$-$1.30	106.7
B2110$+$27	1.2029	25.2	0.0	1.8	217.9	41.5	1.7	70.7	18.0	100.0	$-$2.30	127.4
B2111$+$46	1.0147	141.6	0.2	0.9	68.6	91.7	2.7	244.5	230.0	$\dots$	$-$2.00	2019.2	3
B2113$+$14	0.4402	56.2	0.1	0.9	31.7	14.4	1.4	19.5	9.0	9.4	$-$1.70	11.2
B2122$+$13	0.6941	30.2	0.1	0.8	19.1	7.0	1.6	11.1	4.0	4.0	$-$1.50	4.7

Table 5. Table A.1: Continued.

PSR	$P$	$\mathrm{DM}$	${\theta }_{\mathrm{TAB}}$	${\theta }_{\mathrm{SAP}}$	$\sigma$	$S_{135}^{\mathrm{uncor}}$	$S_{135}^{\times }$	$S_{135}^{\mathrm{cor}}$	$S_{400}$	$S_{150}$	$\alpha$	$S_{135}^{\exp }$	Notes
	(s)	(pc cm-3)	(°)	(°)		(mJy)		(mJy)	(mJy)	(mJy)		(mJy)
B2148$+$52	0.3322	149.1	0.0	1.3	12.4	8.3	1.3	10.7	15.6	$\dots$	$-$1.30	64.0	3
B2148$+$63	0.3801	129.7	0.0	1.7	33.9	30.9	1.6	48.5	32.0	64.0	$-$1.70	76.6
B2154$+$40	1.5253	71.1	0.2	0.5	117.6	48.1	2.2	105.4	105.0	190.0	$-$1.50	222.5
B2210$+$29	1.0046	74.5	0.0	1.4	37.4	10.3	1.4	14.3	6.3	17.0	$-$1.40	19.7
B2217$+$47	0.5385	43.5	0.0	2.4	198.7	285.2	2.7	781.2	111.0	820.0	$-$1.98	1010.2
B2224$+$65	0.6825	36.4	0.0	1.7	155.6	81.9	1.5	126.3	22.0	160.0	$-$1.66	190.6
B2227$+$61	0.4431	124.6	0.0	1.1	44.8	48.2	1.2	58.9	17.0	90.0	$-$1.80	108.8
B2241$+$69	1.6645	41.0	0.0	1.3	39.4	9.7	1.3	12.4	2.4	26.0	$-$2.00	32.1
B2255$+$58	0.3683	151.1	0.1	0.2	30.6	140.6	1.1	149.0	34.0	$\dots$	$-$0.80	81.1
B2303$+$30	1.5759	49.6	0.0	0.5	162.3	32.5	1.1	34.9	24.0	70.0	$-$2.30	89.2
B2303$+$46	1.0665	62.0	0.0	1.3	23.5	8.3	1.3	10.7	1.9	16.0	$-$1.20	18.2
B2306$+$55	0.4751	46.6	0.1	0.4	78.5	89.9	1.5	133.4	19.0	99.0	$-$1.80	119.7
B2310$+$42	0.3494	17.3	0.0	2.8	126.3	33.5	3.9	129.5	89.0	130.0	$-$1.50	152.3
B2315$+$21	1.4447	20.9	0.0	2.3	71.3	19.0	2.3	44.3	15.0	73.0	$-$2.20	92.0
B2319$+$60	2.2565	94.3	0.1	0.8	21.1	8.0	1.6	12.6	36.0	$\dots$	$-$1.00	106.7	3
B2323$+$63	1.4363	197.3	0.0	1.0	5.6	4.9	1.2	5.8	8.0	$\dots$	$-$0.80	19.1	3
B2324$+$60	0.2337	122.3	0.0	1.6	17.7	14.7	1.5	22.0	17.0	$\dots$	$-$1.10	56.1	3
B2334$+$61	0.4954	58.4	0.0	1.2	45.3	23.0	1.2	28.7	10.0	$\dots$	$-$1.70	63.4
B2351$+$61	0.9448	94.9	0.0	1.3	18.6	7.9	1.3	10.5	17.0	$\dots$	$-$1.10	56.1	3
J0030$+$0451	0.0049	4.3	0.3	1.1	60.2	253.8	$\dots$	$\dots$	7.9	44.9	$-$1.93	55.0
J0033$+$57	0.3145	75.6	0.0	0.5	51.6	28.6	1.0	29.7	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0033$+$61	0.9120	37.5	0.0	0.4	21.0	6.7	1.0	6.9	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0051$+$0423	0.3547	13.9	0.0	1.4	115.8	55.6	1.3	73.9	$\dots$	$\dots$	$\dots$	$\dots$	6
J0104$+$64	1.3862	42.1	0.0	1.0	12.4	4.2	1.2	4.9	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J0137$+$1654	0.4148	26.1	0.1	0.7	33.4	11.8	1.3	15.3	1.4	6.2	$-$1.40	7.2
J0139$+$5621	1.7753	101.9	0.1	1.0	7.7	2.4	1.5	3.6	0.7	$\dots$	$-$2.60	11.8	3
J0158$+$21	0.5053	19.8	0.0	0.6	8.0	3.1	1.1	3.4	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 6
J0201$+$7002	1.3492	21.1	0.0	0.9	10.8	3.7	1.1	4.2	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J0212$+$5222	0.3764	38.2	0.2	0.8	14.6	5.2	2.2	11.5	4.1	12.0	$-$1.20	13.6
J0214$+$5222	0.0246	22.0	0.1	0.8	39.4	15.0	1.7	25.0	0.9	21.0	$\dots$	24.3	1
J0220$+$36	1.0298	45.3	0.0	0.2	22.5	14.5	1.0	14.6	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0242$+$62	0.5917	3.8	0.1	0.5	60.2	30.9	1.6	48.7	$\dots$	$\dots$	$\dots$	$\dots$	3
J0329$+$1654	0.8933	40.9	0.0	1.8	9.1	3.0	1.7	5.2	0.6	2.3	$-$1.30	2.6
J0338$+$66	1.7619	66.6	0.0	0.6	35.2	8.8	1.1	9.4	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0341$+$5711	1.8875	101.0	0.0	0.2	31.6	6.6	1.0	6.7	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0348$+$0432	0.0391	40.5	0.1	0.7	10.3	7.6	1.4	10.8	$\dots$	$\dots$	$\dots$	$\dots$	3
J0355$+$28	0.3649	48.7	0.0	0.6	10.4	4.2	1.1	4.5	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J0358$+$42	0.2265	46.3	0.0	0.3	61.6	21.6	1.0	21.9	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0358$+$66	0.0915	62.3	0.0	1.0	10.8	5.1	1.2	5.9	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0407$+$1607	0.0257	35.6	0.0	1.6	63.5	47.2	1.5	71.9	10.2	56.5	$\dots$	65.5
J0408$+$551	1.8376	56.3	0.0	0.9	22.6	5.8	1.1	6.6	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0413$+$58	0.6865	57.1	0.0	0.8	18.1	7.1	1.1	7.9	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0417$+$35	0.6544	48.5	0.0	1.4	11.4	3.7	1.3	5.0	$\dots$	$\dots$	$-$2.40	$\dots$	5, 6
J0426$+$4933	0.9225	84.4	0.1	0.1	10.6	3.6	1.4	5.1	$\dots$	$\dots$	$\dots$	$\dots$	3
J0435$+$27	0.3263	53.2	0.1	1.0	13.3	3.1	1.5	4.6	2.0	7.0	$-$2.30	8.9
J0457$+$23	0.5049	58.8	0.1	0.8	14.4	7.5	1.2	9.2	$\dots$	$\dots$	$\dots$	$\dots$	2, 3
J0459$-$0210	1.1331	21.0	0.0	1.9	88.4	35.0	1.7	60.9	11.0	$\dots$	$\dots$	50.3	3
J0517$+$22	0.2224	18.7	0.2	0.8	58.2	45.8	2.3	105.6	7.0	$\dots$	$\dots$	32.0	3
J0519$+$54	0.3402	42.3	0.0	0.7	27.3	10.3	1.1	11.2	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0533$+$0402	0.9630	83.3	0.2	0.5	3.2	4.0	2.9	11.5	$\dots$	$\dots$	$\dots$	$\dots$	3
J0538$+$2817	0.1432	39.9	0.2	0.7	20.5	18.2	1.8	33.3	8.2	$\dots$	$\dots$	37.5	3
J0540$+$3207	0.5243	62.1	0.1	1.6	45.5	16.4	1.7	28.1	$\dots$	$\dots$	$\dots$	$\dots$	3
J0546$+$2441	2.8439	72.9	0.2	0.2	24.0	2.8	2.0	5.7	2.6	$\dots$	$\dots$	12.1	3
J0555$+$3948	1.1469	36.4	0.0	0.2	19.7	4.0	1.0	4.0	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J0608$+$00	1.0762	48.5	0.0	0.7	17.3	10.0	1.1	10.8	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 6
J0608$+$16	0.9458	86.1	0.0	0.9	77.1	27.8	1.1	31.4	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J0609$+$2130	0.0557	38.8	0.1	0.6	20.4	10.2	1.5	15.0	0.8	$\dots$	$\dots$	3.7	3
J0610$+$37	0.4439	39.3	0.0	1.0	50.8	14.8	1.2	17.4	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0611$+$30	1.4121	45.2	0.0	0.7	81.2	25.1	1.1	27.2	$\dots$	$\dots$	$-$3.20	$\dots$	6
J0613$+$3731	0.6192	19.0	0.2	0.9	46.5	11.5	2.7	30.8	1.6	$\dots$	$-$1.80	11.3	3
J0621$+$0336	0.2700	72.5	0.1	0.6	24.4	12.6	1.6	19.8	$\dots$	$\dots$	$\dots$	$\dots$	3
J0621$+$1002	0.0289	36.5	0.0	1.8	6.5	18.7	1.7	31.6	$\dots$	20.0	$-$1.90	24.4
J0627$+$0706	0.2379	138.2	0.0	1.6	15.8	25.8	1.5	38.0	6.0	$\dots$	$-$1.60	34.1	3
J0645$+$5158	0.0089	18.3	0.2	1.1	9.5	9.4	4.0	37.7	2.4	4.7	$\dots$	5.4	1
J0645$+$80	0.6579	49.5	0.0	0.7	12.6	4.2	1.1	4.5	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6

Table 6. Table A.1: Continued.

PSR	$P$	$\mathrm{DM}$	${\theta }_{\mathrm{TAB}}$	${\theta }_{\mathrm{SAP}}$	$\sigma$	$S_{135}^{\mathrm{uncor}}$	$S_{135}^{\times }$	$S_{135}^{\mathrm{cor}}$	$S_{400}$	$S_{150}$	$\alpha$	$S_{135}^{\exp }$	Notes
	(s)	(pc cm-3)	(°)	(°)		(mJy)		(mJy)	(mJy)	(mJy)		(mJy)
J0750$+$57	1.1749	26.8	0.0	0.2	18.2	5.3	1.0	5.4	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0815$+$4611	0.4342	11.3	0.0	0.2	35.5	9.6	1.0	9.7	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J0943$+$22	0.5330	27.2	0.1	1.1	31.6	4.2	1.3	5.4	5.5	6.3	$-$0.80	6.9	5
J0943$+$41	2.2294	21.4	0.0	2.0	10.1	1.1	1.9	2.1	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J0947$+$27	0.8510	28.9	0.0	1.2	29.2	5.3	1.3	6.6	1.0	10.0	$-$2.20	12.6
J1000$+$08	0.4404	21.8	0.0	0.6	14.6	5.1	1.1	5.4	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J1012$+$5307	0.0053	9.0	0.0	2.3	50.7	19.9	2.4	47.6	30.0	34.5	$-$1.70	41.3
J1022$+$1001	0.0165	10.3	0.0	2.4	137.4	48.2	2.6	125.8	20.0	40.7	$-$1.40	47.2
J1101$+$65	3.6312	19.2	0.0	0.2	15.1	1.3	1.0	1.3	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1110$+$58	0.7933	26.4	0.0	0.7	13.4	2.5	1.1	2.7	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1125$+$7819	0.0042	11.2	0.3	1.9	117.0	75.0	$\dots$	$\dots$	17.1	$\dots$	$\dots$	78.2	3
J1134$+$24	1.0021	23.7	0.0	1.0	21.1	3.7	1.2	4.3	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J1238$+$2152	1.1186	18.0	0.0	1.2	131.2	21.3	1.3	27.1	2.0	27.0	$-$0.80	29.4	3
J1239$+$32	0.0047	16.9	0.0	0.6	63.7	23.2	1.1	24.7	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J1242$+$39	1.3103	26.5	0.0	0.7	32.5	5.4	1.1	5.9	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J1246$+$22	0.4739	17.8	0.1	0.1	18.8	4.3	1.3	5.7	29.0	4.2	0.80	3.9
J1313$+$0931	0.8489	12.1	0.0	2.2	71.1	19.5	2.2	42.0	$\dots$	$\dots$	$-$2.60	$\dots$	6
J1327$+$34	0.0415	4.2	0.0	0.0	121.3	108.4	1.0	108.4	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J1411$+$25	0.0625	12.4	0.1	0.9	10.3	3.8	1.4	5.3	$\dots$	$\dots$	$\dots$	$\dots$	2, 3
J1434$+$7257	0.0417	12.6	0.2	0.8	43.8	10.8	2.3	24.8	1.3	$\dots$	$\dots$	5.9	1, 3
J1518$+$4904	0.0409	11.6	0.0	2.5	50.4	16.1	3.0	47.5	8.0	$\dots$	$-$1.30	32.8	3
J1549$+$2113	1.2625	24.1	0.1	0.3	36.9	10.8	1.1	11.9	0.9	7.2	$-$1.80	8.7
J1627$+$1419	0.4909	32.2	0.0	1.1	59.2	54.7	1.2	65.1	6.1	78.0	$-$1.60	92.3
J1629$+$43	0.1812	7.3	0.0	0.2	11.4	13.1	1.0	13.2	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1645$+$1012	0.4109	36.2	0.0	1.6	49.1	28.1	1.5	42.9	2.3	69.0	$-$3.00	94.7
J1647$+$66	1.5998	22.5	0.0	0.8	25.5	7.0	1.1	7.8	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1649$+$2533	1.0153	34.5	0.1	0.7	22.8	6.6	1.5	9.6	7.4	12.0	$-$1.10	13.5
J1652$+$2651	0.9158	40.7	0.0	0.2	18.8	5.6	1.0	5.8	11.3	20.0	$-$1.00	22.2
J1706$+$59	1.4766	30.6	0.0	0.4	69.2	15.3	1.0	15.7	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1736$+$05	0.9992	38.7	0.0	2.0	15.0	10.3	1.9	19.4	$\dots$	$\dots$	$\dots$	$\dots$	3, 5, 6
J1741$+$2758	1.3607	29.1	0.0	1.9	49.4	12.4	1.7	21.6	3.0	30.0	$-$1.10	33.7
J1746$+$2540	1.0582	51.3	0.0	2.8	8.8	2.4	3.7	8.9	1.2	3.7	$-$0.60	3.9
J1752$+$2359	0.4091	36.2	0.0	1.0	21.9	6.5	1.2	7.6	3.5	5.3	$-$1.30	6.1
J1758$+$3030	0.9473	35.1	0.0	2.7	69.9	18.4	3.6	66.1	8.9	56.0	$-$1.60	66.3
J1800$+$50	0.5784	22.7	0.0	2.0	17.3	3.5	1.9	6.6	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1806$+$28	0.0151	18.7	0.1	0.4	100.2	44.2	1.1	47.8	$\dots$	$\dots$	$\dots$	$\dots$	1, 3
J1811$+$0702	0.4617	58.3	0.0	1.4	7.9	9.1	1.3	12.1	2.2	$\dots$	$\dots$	10.1	3
J1814$+$1130	0.7513	64.6	0.0	1.3	14.9	6.8	1.3	8.9	0.7	$\dots$	$\dots$	3.3	3
J1815$+$55	0.4268	59.0	0.0	0.8	15.1	3.6	1.1	3.9	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1821$+$1715	1.3667	60.2	0.0	2.0	17.0	8.2	1.9	15.5	3.7	11.0	$-$0.50	11.6
J1821$+$41	1.2619	40.7	0.0	0.5	36.2	6.6	1.0	6.9	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1822$+$0705	1.3628	62.4	0.1	0.8	19.9	14.2	1.5	21.9	3.8	$\dots$	$\dots$	17.4	3
J1832$+$27	0.6317	47.4	0.0	2.0	7.7	3.5	1.9	6.7	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 4, 6
J1836$+$51	0.6919	43.8	0.0	0.4	16.4	4.7	1.0	4.8	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J1838$+$1650	1.9020	33.1	0.0	1.6	13.9	7.1	1.5	10.5	$\dots$	$\dots$	$-$1.60	$\dots$	6
J1844$+$21	0.5946	28.8	0.0	1.0	6.3	10.0	1.2	11.7	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J1848$+$0647	0.5060	24.7	0.1	0.9	13.6	16.8	1.7	29.2	2.3	$\dots$	$\dots$	10.5	3, 5
J1849$+$2423	0.2756	62.3	0.1	0.4	11.4	7.0	1.4	9.8	2.3	8.1	$-$0.70	8.7
J1851$-$0053	1.4091	24.5	0.1	0.9	12.6	24.2	1.2	29.0	$\dots$	$\dots$	$\dots$	$\dots$	3
J1900$+$30	0.6022	71.8	0.0	1.1	16.3	5.5	1.2	6.7	$\dots$	$\dots$	$-$1.50	$\dots$	5, 6
J1906$+$1854	1.0191	156.8	0.0	1.0	10.1	7.1	1.2	8.4	4.6	18.0	$-$1.20	20.4
J1907$+$57	0.4237	54.5	0.0	0.5	13.2	3.7	1.0	3.8	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J1912$+$2525	0.6220	37.8	0.0	2.1	50.0	12.1	2.0	24.2	1.6	18.0	$-$1.90	22.0
J1921$+$42	0.5952	53.2	0.0	0.7	30.9	8.4	1.1	9.1	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1922$+$58	0.5296	53.7	0.0	1.1	30.7	7.2	1.2	8.8	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1929$+$00	1.1669	42.8	0.0	0.5	35.4	32.0	1.0	33.1	$\dots$	$\dots$	$\dots$	$\dots$	3, 5, 6
J1929$+$62	1.4561	67.8	0.0	1.0	13.8	2.3	1.2	2.6	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1930$-$01	0.5937	36.5	0.0	1.0	28.4	26.3	1.2	30.9	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J1935$+$52	0.5684	71.3	0.0	0.9	13.6	4.1	1.1	4.7	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1938$+$0650	1.1216	75.1	0.0	1.2	9.7	7.1	1.2	8.8	3.2	$\dots$	$\dots$	14.6	3, 5
J1938$+$14	2.9025	74.0	0.0	0.7	28.3	8.9	1.1	9.5	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 6
J1941$+$0121	0.2173	52.3	0.0	0.6	17.0	23.7	1.1	25.4	$\dots$	$\dots$	$\dots$	$\dots$	3
J1941$+$43	0.8409	79.3	0.0	0.9	24.9	6.8	1.1	7.6	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6

Table 7. Table A.1: Continued.

PSR	$P$	$\mathrm{DM}$	${\theta }_{\mathrm{TAB}}$	${\theta }_{\mathrm{SAP}}$	$\sigma$	$S_{135}^{\mathrm{uncor}}$	$S_{135}^{\times }$	$S_{135}^{\mathrm{cor}}$	$S_{400}$	$S_{150}$	$\alpha$	$S_{135}^{\exp }$	Notes
	(s)	(pc cm-3)	(°)	(°)		(mJy)		(mJy)	(mJy)	(mJy)		(mJy)
J1942$+$81	0.2036	40.3	0.0	0.9	22.1	4.4	1.1	4.9	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J1943$+$0609	0.4462	70.7	0.0	1.5	7.4	8.1	1.4	11.4	$\dots$	$\dots$	$\dots$	$\dots$	3
J1945$+$07	1.0740	62.3	0.0	0.7	8.9	4.6	1.1	5.0	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 6
J1952$+$30	1.6657	189.8	0.0	0.5	7.9	4.4	1.0	4.6	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J1954$+$43	1.3870	130.2	0.0	0.8	6.7	5.4	1.1	6.0	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2000$+$29	3.0738	132.3	0.0	0.9	12.2	3.8	1.1	4.3	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6
J2001$+$42	0.7192	54.9	0.0	1.0	26.3	12.2	1.2	14.1	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2005$-$0020	2.2797	36.2	0.2	0.9	19.1	10.1	2.0	20.2	8.0	$\dots$	$\dots$	36.6	3
J2007$+$0809	0.3257	53.4	0.0	2.6	14.3	16.3	3.3	53.8	$\dots$	32.0	$\dots$	37.1	3
J2007$+$0910	0.4587	48.7	0.0	1.5	28.4	12.4	1.5	18.1	1.5	26.0	$-$2.70	34.6
J2008$+$2513	0.5892	60.6	0.2	0.7	19.6	7.5	1.7	13.0	2.7	3.8	$-$1.20	4.3
J2010$+$2845	0.5654	112.4	0.5	0.5	12.9	11.6	$\dots$	$\dots$	$\dots$	$\dots$	$\dots$	$\dots$	3
J2016$+$1948	0.0649	33.8	0.1	0.7	31.5	16.2	1.4	22.1	3.3	$\dots$	$\dots$	15.1	3
J2017$+$2043	0.5371	60.5	0.1	0.7	20.6	8.2	1.2	10.2	1.5	14.0	$-$1.50	16.4
J2017$+$59	0.4035	60.3	0.0	0.2	17.7	8.3	1.0	8.4	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2027$+$74	0.5152	11.6	0.0	0.4	15.9	9.2	1.0	9.5	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2030$+$55	0.5789	59.6	0.0	0.5	19.5	8.6	1.0	8.9	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J2033$+$0042	5.0134	37.8	0.1	0.8	12.6	6.2	1.4	8.9	$\dots$	$\dots$	$\dots$	$\dots$	3
J2036$+$2835	1.3587	84.3	0.1	0.5	19.9	5.6	1.4	8.1	$\dots$	9.6	$-$1.90	11.7	5
J2038$+$35	0.1602	57.9	0.0	0.4	30.1	16.8	1.0	17.2	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J2040$+$1657	0.8656	50.7	0.0	1.1	8.2	4.4	1.2	5.4	0.6	9.4	$-$2.60	12.4
J2043$+$2740	0.0961	21.0	0.0	2.2	165.5	75.5	2.2	165.6	15.0	140.0	$-$1.30	160.6
J2043$+$7045	0.5896	57.5	0.0	0.7	12.2	5.8	1.1	6.2	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J2045$+$0912	0.3956	31.4	0.0	1.6	13.1	4.0	1.5	5.8	3.5	9.6	$-$1.00	10.7
J2102$+$38	1.1899	86.2	0.0	0.4	11.5	8.2	1.0	8.4	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J2105$+$28	0.4057	62.3	0.0	0.5	16.2	5.8	1.0	6.0	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2113$+$67	0.5521	54.7	0.0	0.2	10.4	4.3	1.0	4.3	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2137$+$64	1.7510	105.9	0.0	0.9	7.8	2.6	1.1	3.0	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2139$+$2242	1.0835	44.2	0.0	1.1	75.1	33.0	1.2	40.0	$\dots$	46.0	$-$0.20	47.0
J2145$-$0750	0.0161	9.0	6.1	4.3	21.8	33.4	$\dots$	$\dots$	100.0	162.0	$-$1.80	195.8
J2155$+$2813	1.6090	77.2	0.0	1.1	22.4	4.0	1.2	4.8	2.1	9.6	$-$1.40	11.1
J2156$+$2618	0.4982	48.5	0.2	0.5	8.6	3.1	2.4	7.2	2.7	2.9	$-$1.00	3.2
J2202$+$21	1.3583	17.8	0.0	1.0	15.3	3.3	1.2	3.9	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 4, 6
J2203$+$50	0.7454	76.3	0.0	0.9	5.6	4.5	1.1	5.0	$\dots$	$\dots$	$\dots$	$\dots$	3, 5, 6
J2205$+$1444	0.9380	36.8	0.1	0.8	14.0	4.2	1.4	5.7	1.5	4.6	$-$2.10	5.7
J2207$+$40	0.6370	11.8	0.0	1.0	66.7	31.8	1.1	36.5	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2208$+$5500	0.9332	104.7	0.1	0.6	15.7	8.3	1.2	10.0	$\dots$	$\dots$	$\dots$	$\dots$	3, 5
J2215$+$1538	0.3742	29.2	0.0	1.6	27.6	11.4	1.5	16.7	3.7	6.8	$-$0.20	6.9
J2222$+$2923	0.2814	49.4	0.0	1.2	13.2	3.0	1.2	3.8	$\dots$	4.3	$-$1.10	4.8
J2222$-$0137	0.0328	3.3	0.1	1.8	19.7	5.5	2.1	11.5	$\dots$	$\dots$	$\dots$	$\dots$	3
J2227$+$30	0.8424	20.0	0.0	1.1	48.7	11.9	1.2	14.5	$\dots$	$\dots$	$\dots$	$\dots$	3, 5, 6
J2228$+$40	0.2727	74.2	0.0	0.6	13.9	5.7	1.1	6.1	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J2234$+$2114	1.3587	35.3	0.0	1.1	45.2	10.2	1.2	12.3	2.6	13.0	$-$1.30	14.9
J2235$+$1506	0.0598	18.1	0.1	0.2	17.3	6.8	1.1	7.6	3.0	6.0	$-$2.90	8.1
J2243$+$69	0.8554	67.8	0.0	0.9	13.0	4.3	1.1	4.8	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2248$-$0101	0.4772	29.0	0.0	1.2	20.8	10.5	1.2	13.0	11.0	$\dots$	$\dots$	50.3
J2253$+$1516	0.7922	29.2	0.0	1.7	26.0	9.0	1.6	14.4	2.4	6.0	$-$1.00	6.7
J2301$+$48	0.7420	72.8	0.0	0.2	14.8	4.8	1.0	4.8	$\dots$	$\dots$	$\dots$	$\dots$	3, 4, 6
J2302$+$6028	1.2064	156.7	0.0	1.8	42.3	40.1	1.7	68.5	12.0	$\dots$	$\dots$	54.9	3
J2315$+$58	1.0616	73.2	0.0	2.0	20.3	15.3	1.9	29.0	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J2316$+$69	0.8134	71.4	0.0	0.7	35.1	15.9	1.1	17.3	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2326$+$6141	0.7900	34.0	0.0	0.4	15.5	8.3	1.0	8.5	$\dots$	$\dots$	$\dots$	$\dots$	3, 6
J2340$+$08	0.3033	23.7	0.1	0.9	20.2	7.6	1.2	9.3	$\dots$	$\dots$	$\dots$	$\dots$	2, 3
J2347$+$02	1.3861	16.2	0.0	1.0	19.5	5.2	1.2	6.1	$\dots$	$\dots$	$\dots$	$\dots$	2, 3, 4, 6
J2352$+$65	1.1649	154.3	0.0	0.6	6.4	4.0	1.1	4.3	$\dots$	$\dots$	$\dots$	$\dots$	3, 5, 6
J2353$+$85	1.0117	38.2	0.0	0.4	14.2	2.4	1.0	2.4	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 6
J2356$+$22	1.8410	21.9	0.0	0.9	20.6	3.2	1.1	3.7	$\dots$	$\dots$	$\dots$	$\dots$	1, 3, 4, 6

Equations

$$S_{\min }=\frac{\mathrm{S}/{\mathrm{N}}_{\min }{T}_{\mathrm{sys}}}{G\sqrt{n_{\mathrm{p}}{t}_{\mathrm{obs}}\Delta \nu }}\sqrt{\frac{W_{\mathrm{eff}}}{P-W_{\mathrm{eff}}}}.$$

Full text

1 Introduction

To date, there are over 2200 slow rotation-powered radio pulsars ($P_{\mathrm{spin}}\gtrsim 0.1$ s; $B_{\mathrm{surf}}\sim 10^{12}$ G) and about 360 rotation-powered radio millisecond pulsars (MSPs; $P_{\mathrm{spin}}\lesssim 30$ ms; $B_{\mathrm{surf}}\sim 10^{8}$ G) known (see ATNF catalogue111Catalogue version 1.59, http://www.atnf.csiro.au/people/pulsar/psrcat/; Manchester et al., 2005). However, these represent only a small fraction ($\lesssim 10$%) of the total expected Galactic population (e.g. Keane et al., 2015, and references therein), and there remains a strong scientific drive to find more pulsars.

Radio pulsar searches are motivated by understanding the total Galactic population of neutron stars: for example, the various classes of neutron stars (Tauris et al., 2015), their spatial distribution (Lorimer, 2011), evolution (Johnston & Karastergiou, 2017), and birth rates (Faucher-Giguère & Kaspi, 2006). Population synthesis models are based on the yields of previous pulsar surveys, and a variety of targeted, wide-field, low and high-frequency surveys are needed to constrain these models, while also averaging out the observational biases inherent to different survey approaches (e.g. Lorimer et al., 2006; Swiggum et al., 2014).

Furthermore, the discovery of individual pulsar systems has continued to provide important new insights into gravitational theories (e.g. Antoniadis et al., 2013; Archibald et al., 2018), the dense matter equation of state (e.g. Demorest et al., 2010), exotic stellar evolution (Ransom et al., 2014), accretion physics (e.g. Archibald et al., 2009) and the interstellar medium (Stinebring et al., 2001). A massive international effort is also underway with the goal of using a set of MSPs to directly detect gravitational waves (Detweiler, 1979; Hobbs et al., 2010; Verbiest et al., 2016). Pulsar discoveries thus also have a wide-reaching and significant impact outside the field of pulsar astrophysics itself; in essence, they are nature’s clocks and can be used in many applications: for example, a pulsar based-timescale (Hobbs et al., 2012) and space navigation (Ray et al., 2017).

Pulsar radio spectra are typically characterised as a power-law, where the flux density at a particular frequency, $S_{\nu}$, is proportional to the observing frequency $\nu$ to some power, i.e. $S_{\nu}\propto\nu^{\alpha}$. This is not always a good characterization of the spectrum (Lewandowski et al., 2015; Jankowski et al., 2018), however, and a low-frequency turnover around 100 to 200 MHz appears to be present for some sources (Bilous et al., 2016, and references therein). Recently, Bates et al. (2013) found that the average spectral index is $\alpha=-1.4$, with a $1\sigma$ dispersion of $\sim 1$222This is a somewhat shallower scaling compared to the $\alpha=-1.8$ found by Maron et al. (2000).. Indeed, pulsars have been observed with a wide range of spectral indices, $-4\lesssim\alpha\lesssim 0$. This variation in spectral index is not well understood. In principle, it could be intrinsic, a function of viewing geometry, and/or related to the interstellar medium (Lewandowski et al., 2015; Rajwade et al., 2016). In general, however, pulsars are steep-spectrum radio sources, meaning that they become significantly brighter towards lower radio frequencies.

While the steep radio spectra of pulsars may at first seem to suggest that low radio frequencies (here defined as $\nu\lesssim 400$ MHz) are the best option for searches, there are a number of chromatic effects that create challenges for low-frequency pulsar surveys. Propagation through the ionised and magnetised interstellar medium (ISM) influences the observed pulsar signal via i) scintillation, a frequency and time-dependent modulation/variability of the signal strength (Rickett, 1970); ii) scattering, multi-path propagation (Rickett, 1977); iii) dispersion, a frequency-dependent light-travel time through the intervening medium (e.g. Hassall et al., 2012, and references therein); and iv) Faraday rotation, a rotation of the angle of linearly polarised emission (e.g. Manchester, 1972; Brentjens & de Bruyn, 2005; Sobey et al., 2019). A review of these propagation effects can be found in Rickett (1990). Finally, the synchrotron sky background temperature, $T_{\mathrm{sky}}$, which increases rapidly towards lower radio observing frequencies: $T_{\mathrm{sky}}\propto\nu^{-2.55}$ (Haslam et al., 1981, 1982; Lawson et al., 1987), further reduces sensitivity at low Galactic latitudes.

Most relevant to low-frequency pulsar searches are dispersion and scattering. Dispersion introduces a time delay $\Delta t\propto\mathrm{DM}\ \nu^{-2}$ (where DM is the dispersion measure, the integrated column density of free electrons along the line-of-sight). This effect can be compensated for using incoherent dedispersion, but requires high frequency resolution. However, at low observing frequencies, dispersion within a frequency channel strongly limits the effective time resolution that can be achieved, especially for relatively high DM ($>50-100$ pc cm*-3*). Therefore, searching for fast-spinning millisecond pulsars at the lowest radio frequencies requires coherent dedispersion (Bassa et al., 2017a, b).

Scattering causes a pulse broadening $\tau_{\mathrm{scat}}\propto\nu^{-4.4}$ (under the assumption of a Kolmogorov turbulence spectrum; the exact frequency scaling can deviate from $\nu^{-4.4}$) that cannot be corrected for in pulsar surveys (in practice). The magnitude of scattering depends on the distribution of the material along the line-of-sight, and is loosely correlated with DM (Bhat et al., 2004; Geyer et al., 2017). Scattering becomes a significant limitation for low-frequency pulsar searches for $\mathrm{DM}>50-100$ pc cm*-3*. DM is a proxy for distance, given a model for the free electron distribution in the Galaxy (Cordes & Lazio, 2002; Yao et al., 2017). Because of dispersive smearing and scattering, low-frequency searches are limited to detecting pulsars with $\mathrm{DM}\lesssim 100$ pc cm*-3*. This is not a major limitation for the search volume at high Galactic latitudes, but it precludes finding pulsars at large distances ($\gtrsim 3$ kpc) within the Galactic plane.

Despite these challenges, pulsars were originally discovered at 81.5 MHz (Hewish et al., 1968), and low-frequency searches continue to be fruitful. In the last decade, low-frequency pulsar surveys have discovered $\sim 300$ pulsars, and have mapped the nearby population both towards the Galactic plane and at higher Galactic latitudes. A 350-MHz survey of the northern Galactic plane with the Green Bank Telescope (GBT) discovered 33 pulsars (Hessels et al., 2008). Using a very similar observing strategy, the GBT Driftscan survey found 31 pulsars (Boyles et al., 2013; Lynch et al., 2013), including the pulsar stellar triple system PSR J0337+1715 (Ransom et al., 2014) and the transitional millisecond pulsar PSR J1023+0038 (Archibald et al., 2009). An ongoing, full-sky 350-MHz survey with the GBT, the GBNCC, has found 160 pulsars to date (Stovall et al., 2014; Kawash et al., 2018; Lynch et al., 2018). An ongoing driftscan survey with Arecibo at 327 MHz has found 82 pulsars333http://www.naic.edu/~deneva/drift-search (AO327; Deneva et al. 2013), while the GMRT High Resolution Southern Sky (GHRSS; Bhattacharyya et al. 2016) survey discovered 10 pulsars at 322 MHz in the Southern sky ($-54\degr<\delta<-40\degr$). Targeted low-frequency searches of unidentified Fermi gamma-ray sources have also discovered 62 millisecond pulsars (e.g. Hessels et al., 2011; Ray et al., 2012; Cromartie et al., 2016; Bassa et al., 2017b, 2018; Pleunis et al., 2017).

In Coenen et al. (2014) we presented a pilot survey for pulsars and fast transients using the Low-Frequency Array (LOFAR; van Haarlem et al. 2013) and its high-time-resolution ‘beam-formed’ modes (Stappers et al., 2011). We have subsequently built on those observations and started the LOFAR Tied-Array All-Sky Survey (LOTAAS). Compared with other modern, wide-field pulsar surveys, LOTAAS is novel because of its very low observing frequency ($119-151$ MHz) and long dwell time per pointing (1 hr). These characteristics were instrumental in enabling the LOTAAS discovery of 7 rotating radio transients (Michilli et al., 2018) and a 23.5-second pulsar, which is by far the slowest-spinning radio pulsar known (Tan et al., 2018a).

More broadly, the discoveries of the rotating radio transients (RRATs; McLaughlin et al., 2006), radio-emitting magnetars (Camilo et al., 2006), intermittent pulsars (Kramer et al., 2006), and the fast radio bursts (FRBs; Lorimer et al., 2007; Thornton et al., 2013; Spitler et al., 2014) have shown that radio-emitting neutron stars have a host of emission properties and can sometimes be very sporadic in their detectability. These insights strongly motivate pulsar and fast transient surveys that achieve a large ‘on-sky’ time, $\Sigma=N_{\mathrm{obs}}\Omega t_{\mathrm{obs}}$, here defined as the product of the total number of survey observations, $N_{\rm obs}$, field-of-view per pointing, $\Omega$, and dwell time per pointing, $t_{\mathrm{obs}}$. This makes LOTAAS complementary to other ongoing, low-frequency surveys, like the GBNCC and AO327 drift (Stovall et al., 2014; Deneva et al., 2013), which have higher instantaneous sensitivity but $\sim 30-60\times$ lower dwell time and instantaneous field-of-view: $\Sigma_{\mathrm{LOTAAS}}\simeq 23400$ h deg2 at 135 MHz, $\Sigma_{\mathrm{GBNCC}}\simeq 1430$ h deg2 at 350 MHz above $\delta>-40\degr$, and $\Sigma_{\mathrm{AO327}}\simeq 132$ h deg2 at 327 MHz.

Here we present an overview of the LOTAAS survey and its first discoveries. In §2 we describe the novel observational setup of the survey, along with parameters such as time/frequency resolution and sensitivity. In §3 and §4 we describe the search pipeline and the results of the processing to date—in terms of pulsar discoveries and redetections. These results are discussed in §5. Lastly, we conclude in §6.

2 Survey description

2.1 Array configuration and beamforming

The LOFAR Tied-Array All-Sky Survey (LOTAAS) is an ongoing survey of the Northern sky for pulsars and transients with LOFAR. The survey uses the high-band antennas (HBAs) on the Superterp, the dense central part of the LOFAR core, since these provide the highest filling factor of LOFAR stations in the array and hence the best balance of field-of-view and raw sensitivity (van Haarlem et al., 2013; Stappers et al., 2011). This compact configuration also removes the need to compensate for differential ionospheric phase delays between stations. The HBAs on the Superterp are spread over 6 stations (CS002-007; van Haarlem et al. 2013), each of which has two sub-stations of 24 HBA tiles. Each HBA tile consists of 16 dual-polarisation antenna elements (dipoles) arranged in a $4\times 4$ pattern. Hence, the total number of dual-polarisation dipoles on the Superterp is 4608.

LOTAAS uses LOFAR’s three stages of beamforming to create tied-array beams for the HBA antennas (van Haarlem et al., 2013; Stappers et al., 2011). First, the analogue beamformer of each HBA tile forms a tile beam out of the signals from the 16 antenna elements within a tile. After digitization and coarse channelization of the signals, these tile-beams are digitally beamformed into station beams. The final stage of beamforming is performed by the central LOFAR correlator and beamformer, which combines the signals of 12 HBA sub-stations into tied-array beams (see Stappers et al. 2011 for details). At the beginning of the LOTAAS survey in December 2012 a CPU-based IBM Blue Gene/P was used as the central LOFAR correlator and beamformer (Mol & Romein, 2011). In May 2014 its functionality was transferred to Cobalt, a GPU-based correlator and beamformer (Broekema et al., 2018).

For the LOTAAS band of 119 MHz to 151 MHz (see § 2.3), the Superterp HBA station beams vary in full-width at half maximum (FWHM) at zenith from 6$\aas@@fstack{\circ}$1 at 119 MHz to 4$\aas@@fstack{\circ}$7 at 151 MHz. Tied-array beams from the 12 HBA sub-stations of the Superterp yield beams for which the FWHM varies with frequency between 0$\aas@@fstack{\circ}$41 (119 MHz) to 0$\aas@@fstack{\circ}$32 (151 MHz) at the zenith. Both the station and tied-array beams will become elongated in zenith angle when pointing away from the zenith, as the baselines between tiles in a station and the baselines between stations foreshorten.

2.2 Beam setup and tessellation

To maximise the number of tied-array beams as well as their sensitivity within station beams (sensitivity is higher towards the centre), LOTAAS forms three station beams (hereafter called sub-array pointings, or SAPs), each of which is tessellated by 61 tied-array beams (TABs). The TABs fill the central region of each SAP with a central TAB surrounded by four hexagonally filled TAB rings. The TAB rings are spaced at $0\aas@@fstack{\circ}245$ ($14\aas@@fstack{\prime}7$) separation, where the FWHM of one TAB overlaps for 28% and 14% with adjacent TABs at 119 MHz and 151 MHz, respectively. This overlap provides pseudo-Nyquist sampling of the sky. The same is true for the SAPs, which are separated by $3\aas@@fstack{\circ}82$ at the vertices of an equilateral triangle. Figure 1 shows the layout of the SAPs and TABs.

Furthermore, an additional 12 TABs are formed within each SAP. These are pointed towards known pulsars that happen to coincide with the SAP field-of-view, or at predetermined positions in the absence of known sources. Finally, the signals from the 12 HBA sub-stations per SAP are summed incoherently (Stappers et al., 2011; Broekema et al., 2018) to form an incoherent beam with a field-of-view equivalent to that of the station beam. Hence, a total of 222 beams are formed for each LOTAAS pointing. At zenith, the hexagonally tiled TABs of the three SAPs of a single pointing cover approximately 12 square degrees, while the incoherent beams cover 67 square degrees.

The setup of the tied-array and incoherent LOTAAS beams allows pointings to be efficiently tessellated to cover the entire sky. To fill in the gaps between the tied-array beams, three interleaved pointings are required to fully sample the sky, as depicted in Fig. 1 and 2. The large field-of-view of the incoherent beams has the advantage that the three pointings can be performed in three sequential passes, such that for each pass the incoherent beams completely cover the sky. As a result, each sky position is observed once with a coherently summed TAB and three times with an incoherently summed station beam. A total of 1953 individual pointings, 651 per pass, are required to cover the sky above a declination of $\delta>0\degr$. At the time of writing, January 2019, all pointings of Pass A and B have been obtained, and 43 pointings remain to be observed for Pass C. Initial processing of Pass A and B has been completed using a ‘Version 1.0’ search pipeline, and the analysis of the pass C pointings is ongoing.

2.3 Bandwidth and sampling

LOFAR stations are capable of digitizing dual-polarisation signals at 8-bit resolution for up to 488 sub-bands. Station beams can be formed for individual or groups of sub-bands. By choosing three station beams, LOTAAS can record data at a maximum of 162 sub-bands for each station beam. To optimise the LOTAAS sensitivity and TAB field-of-view we use the bottom part of the HBA band where LOFAR is most sensitive (van Haarlem et al., 2013). The 162 sub-bands of 195.3125 kHz bandwidth are centred at 135.25 MHz with a total bandwidth of 31.64 MHz. The central LOFAR correlator and beamformer uses a polyphase filter to channelise each sub-band to 16 channels, providing a total of 2592 channels over the LOTAAS band. The signals from the two linear polarisations are summed in quadrature to form total intensity (Stokes I). To comply with the network limitations between the correlator/beamformer and the central processing cluster, the data are decimated by a factor of 6 in time, to a time resolution of 491.52 $\upmu$s. Finally, time/frequency samples, represented by 32-bit floating point values, are streamed to the central processing cluster at a data rate of 37.46 Gb s*-1*.

The large instantaneous field-of-view of a single LOTAAS pointing allows for long dwell times. Each LOTAAS pointing has a 1-hr integration time, compared to the few-minute integration times used by other ongoing wide-field surveys. The long integration times increase the probability of discovering RRATs, intermittent pulsars and transient signals. With 1 hr integration times, the data volume of a single LOTAAS pointing is 16.9 TB (with 32-bit samples).

2.4 Survey sensitivity

Following Dewey et al. (1985), the sensitivity of the LOTAAS survey for a pulsar with period $P$ and effective pulse width $W_{\mathrm{eff}}$ down to a minimum signal-to-noise $\mathrm{S}/\mathrm{N}_{\mathrm{min}}$, relates to the gain $G$ and system temperature $T_{\mathrm{sys}}$ of the telescope, and the integration time $t_{\mathrm{obs}}$, bandwidth $\Delta\nu$ and number of summed polarisations $n_{\mathrm{p}}$ of the observation through

[TABLE]

The gain $G$ depends on the effective area $A_{\mathrm{eff}}$ of the telescope ($G=\frac{1}{2}A_{\mathrm{eff}}/k_{\mathrm{b}}$, with $k_{\mathrm{b}}$ the Boltzmann constant). For LOFAR, the number of stations and active dipoles defines the effective area, as well as the coherency of the tied-array beamforming. Following van Haarlem et al. (2013), the effective area for a HBA sub-station of 16 dipoles per tile and 24 tiles per sub-station at the central LOTAAS frequency of 135 MHz ($\lambda=2.2$ m) is $A_{\mathrm{eff}}=600$ m2. Kondratiev et al. (2016) finds that the coherent summation of $N$ stations scales as $N^{0.85}$, and typically, 5% of dipoles are not in operation. Hence, the effective area of the LOFAR Superterp is $(1-0.05)12^{0.85}600\,\mathrm{m}^{2}=4712$ m2. This yields a gain of $G=1.7$ K Jy*-1* at zenith.

The system temperature $T_{\mathrm{sys}}$ is the sum of the sky temperature $T_{\mathrm{sky}}$ and the receiver or antenna temperature $T_{\mathrm{ant}}$. The latter varies over the LOTAAS observing band, with a minimum value of 330 K at 125 MHz to 390 K at 151 MHz; we use the mean $T_{\mathrm{ant}}$ of 360 K (Kondratiev et al., 2016). At the observing frequencies of LOTAAS, the system temperature $T_{\mathrm{sys}}$ can be dominated by the sky temperature $T_{\mathrm{sky}}$. We use the reference sky temperature map at 408 MHz from Haslam et al. (1982), and the $\lambda^{2.55}$ scaling law by Lawson et al. (1987) to estimate the sky temperature at 135 MHz. The all-sky averaged sky temperature at 135 MHz is $T_{\mathrm{sky}}=510$ K, but can be as low as 200 K at high Galactic latitudes to $\ga 1000$ K in regions at low Galactic latitude towards the Galactic centre (see Fig. 2). For determining the sensitivity limits we use the best-case system temperature of $T_{\mathrm{sys}}=T_{\mathrm{ant}}+T_{\mathrm{sky}}=560$ K, as well as the all-sky averaged value of $T_{\mathrm{sys}}=870$ K.

The intrinsic pulse width is broadened by effects due to the propagation of the pulse through the interstellar medium, depending on the way these are corrected for in the analysis. As defined in Lorimer & Kramer (2012), the total temporal smearing $\tau_{\mathrm{tot}}$ is the quadratic sum of the finite sampling time $\tau_{\mathrm{samp}}$, the dispersive smearing within a single channel $\tau_{\mathrm{chan}}$, the dispersive smearing across the full bandwidth due to the finite steps in trial $\mathrm{DM}$ $\tau_{\mathrm{BW}}$, and the dispersive smearing due to the piece-wise linear approximation of the quadratic dispersion law in the sub-band dedispersion algorithm (Manchester et al., 1996) $\tau_{\mathrm{sub}}$.

The top panel of Fig. 3 shows the dispersive smearing as a function of $\mathrm{DM}$. Using the dedispersion plan described in §3.2, the dispersive smearing at low DM in LOTAAS is 0.9 ms, but rises to 20 ms at the highest-searched DM of 550 pc cm*-3*. Besides dispersive smearing, the pulsar pulses will also be smeared due to scattering in the interstellar medium. Whereas the effects of dispersion can largely be mitigated by dedispersion, scattering can not be corrected for in a blind search. Figure 3 also shows the smearing due to scattering as a function of DM, as predicted by the empirical relation from Bhat et al. (2004). Observationally, the smearing due to scattering for pulsars at the same DM but different location shows variations up to 2 or 3 orders of magnitude, and hence we plot a range of possible scattering variations. Based on this range, we expect smearing due to interstellar scattering to become dominant over dispersive smearing for DMs in the range of $20-100$ pc cm*-3*.

The sensitivity limit of the LOTAAS survey is shown in the bottom panel of Fig. 3 as a function of dispersion measure for different pulse periods $P$ and system temperature $T_{\mathrm{sys}}$ in the presence (or absence) of interstellar scattering. All these curves assume an intrinsic pulse width that is 3% of the pulse period, and a minimum detection significance of $\mathrm{S}/\mathrm{N}_{\mathrm{min}}=10$. These curves indicate that LOTAAS has a best sensitivity of about 1.2 mJy for nearby slow pulsars at high Galactic latitude ($\mathrm{DM}<50$ pc cm*-3*, $P>0.1$ s and $T_{\mathrm{sys}}=560$ K). Sensitivity to millisecond pulsars with $P<0.01$ s is limited by the coarse sampling of 491.52 $\upmu$s, though theoretically they could be detected if they are at low $\mathrm{DM}$ and relatively bright. In the presence of interstellar scattering following the Bhat et al. (2004) predictions, LOTAAS would not be able to detect pulsars with $\mathrm{DM}$s above 200 pc cm*-3*, regardless of their spin period.

2.5 Confirmation and follow-up observations

The different observing modes and station locations of LOFAR allow for flexibility when confirming pulsar candidates and performing follow-up observations. For confirming pulsar candidates, LOTAAS uses the HBA dipoles of the 24 LOFAR core stations with 15-min exposures, yielding a factor of approximately 2 increase in sensitivity over the 6 stations of the Superterp using 1-hr observations. The maximum baseline between core stations is 3.5 km, compared to 300 m for the stations on the Superterp, and hence the TABs using the core stations have a FWHM of $3\aas@@fstack{\prime}5$, a factor 7 smaller than Superterp TABs. To tile out the Superterp discovery beam, we form a single sub-array pointing and use 127 TABs in a hexagonal pattern. Depending on the accuracy of the discovery localization, the spacing between the TABs in confirmation observations can be decreased from Nyquist sampling to improve the localization by comparing the pulsar profile signal-to-noise between different beams. This approach allows the pulsar to be localised to an accuracy of about $3\arcmin$. Examples of the tied-array beam tiling in confirmation and localization observations are shown in Fig. 4.

Besides the larger number of stations and shorter integration time, confirmation observations use the same observational setup as the search observations. Hence, 162 sub-bands cover 31.64 MHz of bandwidth over the same frequency range, channelised to 2592 channels, with polarisations summed to form Stokes I and downsampled to a sampling time of 491.52 $\upmu$s.

Once a newly discovered pulsar is confirmed and localised, the timing programme is started. The follow-up timing observations again use all the HBA dipoles of the 24 LOFAR core stations, but now only a single tied-array beam is formed. For this tied-array beam, dual-polarisation complex voltages, sampled at the 5.12 $\upmu$s Nyquist rate of a 195.3125 kHz sub-band are recorded for 400 sub-bands. Hence, 78.125 MHz of bandwidth is recorded between frequencies of 110 MHz to 188 MHz. The complex voltages allow phase-coherent dedispersion removal with dspsr (van Straten & Bailes, 2011). Subsequent timing analysis is performed with psrchive (Hotan et al., 2004) and tempo2 (Hobbs et al., 2006; Edwards et al., 2006).

To constrain their radio spectra, newly discovered pulsars are also observed using the 76-m Lovell telescope at Jodrell Bank at 1532 MHz and occasionally at 330 MHz. For these observations the ROACH backend (Bassa et al., 2016) is used, providing 400 MHz of bandwidth centred at 1532 MHz and 64 MHz of bandwidth at 330 MHz. Pulsars that are visible at 1532 MHz are observed as part of the regular pulsar timing programme of the Lovell telescope. For some pulsars, follow-up observations at 1.4 GHz were also obtained with the Nançay Radio Telescope (Guillemot et al., 2016; Cognard et al., 2017).

3 Analysis

In terms of data volume, the LOTAAS survey is the largest pulsar survey performed to date, as the 1953 LOTAAS pointings produce 8 PB of raw 8-bit archived data. The management and the processing of such a huge amount of data is not a trivial task, and can pose a challenge for even the largest high performance computing (HPC) facilities available worldwide. For this reason, although at the beginning of the survey the HPC cluster of the Jodrell Bank pulsar group was used (HYDRUS; 552 CPU cores, 728 GB RAM), the main workhorse of the survey’s processing since 2014 has been the Dutch National Supercomputer Cartesius444https://userinfo.surfsara.nl/systems/cartesius (over 44500 CPU cores, 115 TB RAM), managed by SURFSara555https://www.surf.nl/en/about-surf/subsidiaries/surfsara. The storage of the raw data and the processed results uses the SARA Long Term Archive (LTA), where a significant fraction of the LOFAR LTA is also hosted. The co-locality of the SARA LTA and Cartesius provides increased efficiency, required for various reprocessing runs of the stored data, with transfer speeds over 1 GB s*-1*.

The processing time required to reduce that amount of raw data is also immense. For the first processing runs, $10\mathrm{M}$ CPU-hours were requested in 2013 in order to streamline the LOTAAS pipeline and get first results. This was followed by two $25\mathrm{M}$ CPU-hours requests, in 2015 and 2017 respectively. These $60\mathrm{M}$ CPU-hours have been granted by the Netherlands Organisation for Scientific Research (NWO) via proposal submission. Out of these $50\mathrm{M}$ CPU-hours, $30\mathrm{M}$ have been used to process all acquired data at the time of writing (January 2019), and reprocess the early LOTAAS data (acquired before May 2015), which had been processed only with the prototype LOTAAS pipeline.

The LOTAAS processing pipeline is based on the presto666https://www.cv.nrao.edu/~sransom/presto (Ransom, 2001) pulsar search software suite, with additional code written by members of the LOTAAS group for single-pulse searches (Michilli et al., 2018) and candidate classification (Lyon et al., 2016; Tan et al., 2018b). Currently it is a purely CPU-based pipeline; however, in the near future some parts will be replaced by GPU implementations. Here we present a detailed breakdown of the LOTAAS pipeline.

3.1 Pre-processing

The 16.9 TB of raw data for each 1-hr LOTAAS pointing are stored in HDF5 format777https://github.com/nextgen-astrodata/DAL on the central processing cluster. Here, the LOFAR Pulsar Pipeline (PuLP; Kondratiev et al. 2016) requantises the raw data from 32-bit floating point values to scaled and offset 8-bit integers, while also storing the scales and offsets. This step reduces the data volume by a factor of 4. The requantised output is stored in PSRFITS format (Hotan et al., 2004), yielding an 18 GB file for each of the 222 beams. The pipeline then identifies and creates radio frequency interference (RFI) masks, and performs a series of tests concerning the data quality (i.e. RFI statistics, packet loss in the correlator and beamformer). Finally, the pipeline folds the data for the TABs that contain known radio pulsars. The requantised PSRFITS files, RFI masks, and associated metadata are then ingested into the LTA. Observations for which the data quality tests fail are marked for re-observation at the end of the survey. Once the data have been validated to be on the LTA, they become available for downloading to Cartesius.

On the Cartesius supercomputer, the PSRFITS files are converted to SIGPROC filterbank format using an adapted version of presto’s psrfits2fil.py. This version was tailored to LOTAAS data in order to control the desired block size (the minimum amount of data that can be processed in presto), which was set to 512 spectral channels per sub-integration. This particular block size was selected in order to minimise the effects of dispersive smearing, which lowers the detection signal-to-noise for pulsars at higher $\mathrm{DM}$s. Subsequently, new RFI masks are created using 2.5 s time integrations and the default settings of rfifind (for more details, see Cooper 2017). The size of these time segments was selected based on the typical RFI properties, in order to avoid excessive data masking. Typically, the fraction of data flagged as RFI is 10% for the TABs, and 15% for the incoherently summed station beams.

3.2 Dedispersion

The next step is the creation of the dedispersed time series for each beam. The DM range $0-546.5$ pc cm*-3* is searched; higher DMs are not searched because of the significant dispersion smearing and scattering at these low observing frequencies. At a frequency of 135 MHz, which is the centre frequency of the survey, the intra-channel dispersion smearing at a $\mathrm{DM}=50$ pc cm*-3* is $\sim 2$ ms, whereas for $\mathrm{DM}>100$ pc cm*-3* scattering likely dominates (see Fig. 3). The dedispersion plan was created using the presto DDplan.py tool and consists of 10120 DM-trials (Table 1). This dedispersion plan is aimed at minimizing the dispersive smearing over the selected DM range, while optimizing the processing efficiency when using parallelised dedispersion routines. The mpiprepsubband tool from presto is used to create incoherently dedispersed time-series from the RFI masked filterbank files. This tool uses the sub-band dedispersion technique, which uses a piece-wise linear approximation to the quadratic dispersion law.

3.3 Periodicity search

In order to look for periodic signals within the dedispersed time series, their power spectra are computed by applying a discrete fast Fourier transform (FFT) using the realfft tool. In order to improve our sensitivity to long-period pulsars, a red noise removal procedure is also applied to the power spectra using rednoise. The whitened power spectra are now searched for periodicities using accelsearch (Ransom et al., 2002), which detects the most significant periodic signals and uses harmonic summing to recover the power at multiples of the spin frequency. No acceleration searches are currently performed; accelsearch only searches at zero acceleration, due to the significant processing time that would otherwise be required. LOTAAS is thus not sensitive to pulsars in tight binary systems, unless these are fortuitously observed at a preferential, low-acceleration orbital phase. If a frequency has spectral significance in excess of $2\sigma$, it is marked as a candidate and the corresponding harmonics up to the 16th are summed to increase the detection significance.

Once the list of candidates for all the DM-trials has been compiled, a sifting procedure to reduce the number of candidates by selecting only the most likely is performed. The sifting strategy in place is the one developed for the LOTAS pilot survey (Coenen et al., 2014) and is described in detail in Coenen (2013). Candidates with $P<2$ ms or $P>15$ s, along with candidates with $\mathrm{DM}<0.5$ pc cm*-3* are rejected. We note that PSR J0250+5854, which has $P=23.5$ s (Tan et al., 2018a), was discovered through its $P<15$ s harmonics. Also rejected are those candidates that are not detected in neighbouring DM trials. Candidates with similar DMs and harmonically related periods are grouped, and only the instance with the highest signal-to-noise is kept. From the significantly reduced candidate list, only those with $5\sigma$ detections are folded.

3.4 Candidate folding

Folding is performed using the prepfold tool, which creates candidate files and diagnostic plots such as those shown in Fig. 5 for further inspection. In the folding analysis we use 100 pulse phase bins (50 for $P<50$ ms), 288 sub-bands, 40 sub-integrations, and a running mean subtraction applied on the input filterbank file. The latter assisted in the discovery of PSR J0317+13, the observation of which was characterised by strong broadband and narrowband RFI, but had as a side effect the creation of artefacts during the folding: in other words, signal ‘dips’ around the profiles of bright sources with narrow profile widths, which changes the detection significance (see e.g. PSR J1635+23 in Fig. 5).

3.5 Candidate classification

Each pointing generates approximately 20000 candidates, and on completion the survey will have produced approximately 40 million candidates. Since visual inspection of all the candidates would be impossible, the machine learning classifiers described in Lyon et al. (2016) and Tan et al. (2018b) are used to reduce the candidate list to a reasonable number for visual inspection. These classifiers use the statistics (mean, variance, skewness and kurtosis) of the pulse profile and the DM curve (see Fig. 5; signal-to-noise as a function of DM). The Tan et al. (2018b) classifier expands on this approach by calculating the correlation coefficient between each sub-band and the profile, as well as the correlation coefficient between each sub-integration and the profile. The classifier uses the statistics of correlation coefficient distributions, in addition to the statistics of the profile and DM curve, to automatically classify periodicity candidates. This approach reduces the number of candidates per pointing to $\sim 220$, i.e. one per beam on average.

3.6 Single-pulse search

A search for bright impulsive signals in the dedispersed time series is performed using single_pulse_search.py from presto. The script convolves box-car functions of variable widths between $0.5-100$ ms with the time series at different DMs. A value for the signal-to-noise is calculated for each bin of the convolved time series. If the signal-to-noise of one bin within a set of user specified time ranges is above a threshold of $5\sigma$, the characteristics of the event are stored – i.e. arrival time with respect to the beginning of the time series, DM, width and signal-to-noise. Due to the large parameter space searched and the geographical area where the Superterp is located, a large number of events correspond to RFI. During a typical 1-hr LOTAAS observation, $\sim 10^{8}$ events are detected above a signal-to-noise ratio of 5. In order to lower the number of events generated by RFI, the L-sps automated classifier has been developed (Michilli et al., 2018). It is a machine-learning-based classifier that uses the same Very Fast Decision Tree algorithm as the classifier for the periodicity search (Tan et al., 2018b). The classifier uses 5 scores based on the width and signal-to-noise of the signal as a function of DM. At the end of the classification, it produces detailed diagnostic plots to help quickly identify real signals. Typically, a few tens of diagnostic plots need to be visually inspected for each pointing.

4 Results

4.1 Discoveries

As of January 2019, the LOTAAS survey has discovered and confirmed 73 pulsars; in this overview paper, we present their properties. An up to date list of LOTAAS discoveries is maintained online888http://www.astron.nl/lotaas.

The pulse profiles of these 73 pulsars are shown in Fig. 6. The profiles are from the discovery observations, and hence representative of the data used to search for and find these pulsars. Throughout the paper, we will refer to the LOTAAS discoveries by their discovery name, which is based on the right ascension and declination determined from the follow-up gridding observations. We report the pulsar name with two digits in declination. In the future, when timing solutions for these pulsars are presented, these names will be superseded by names based on their more precise timing positions.

The properties of the LOTAAS discovered pulsars are listed in Table 4.1. The discovery spin period $P$, $\mathrm{DM}$ and the position ($\alpha_{\mathrm{J2000}}$, $\delta_{\mathrm{J2000}}$) based on the best localization from the confirmation observations using the full LOFAR core. Here, we have assumed a positional uncertainty of $3\arcmin$ in both $\alpha_{\mathrm{J2000}}$ and $\delta_{\mathrm{J2000}}$, which should encompass possible systematic uncertainties of order $1\arcmin$ due to ionospheric beam jitter, as well as the localization uncertainty due to gridding. Distance estimates based on the dispersion measure are computed using the NE2001 model for the Galactic electron distribution (Cordes & Lazio, 2002).