# The Southern-sky MWA Rapid Two-metre (SMART) pulsar survey - I. Survey
design and processing pipeline

N. D. R. Bhat  International Centre for Radio Astronomy Research, Curtin
University, Bentley, WA 6102, Australia  N. A. Swainston  International Centre
for Radio Astronomy Research, Curtin University, Bentley, WA 6102, Australia
S. J. McSweeney  International Centre for Radio Astronomy Research, Curtin
University, Bentley, WA 6102, Australia  M. Xue  National Astronomical
Observatories, Chinese Academy of Sciences, Datun Road, Chaoyang District,
Beijing 100101, China  B. W. Meyers  International Centre for Radio Astronomy
Research, Curtin University, Bentley, WA 6102, Australia  S. Kudale  National
Centre for Radio Astrophysics, Tata Institute of Fundamental Research, Pune
411 007, India  S. Dai  Western Sydney University, Locked Bag 2751, Penrith
South DC, NSW 1797, Australia  S. E. Tremblay  National Radio Astronomy
Observatory, 1003 Lopez Road, Socorro NM, 87801, USA  W. van Straten
Institute for Radio Astronomy & Space Research, Auckland University of
Technology, Private Bag 92006, Auckland 1142, New Zealand  R. M. Shannon
Centre for Astrophysics and Supercomputing, Swinburne University of
Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia  K. R. Smith
International Centre for Radio Astronomy Research, Curtin University, Bentley,
WA 6102, Australia  M. Sokolowski  International Centre for Radio Astronomy
Research, Curtin University, Bentley, WA 6102, Australia  S. M. Ord  CSIRO
Astronomy and Space Science, PO Box 76, Epping, NSW 1710, Australia  G. Sleap
International Centre for Radio Astronomy Research, Curtin University, Bentley,
WA 6102, Australia  A. Williams  International Centre for Radio Astronomy
Research, Curtin University, Bentley, WA 6102, Australia  P. J. Hancock
Curtin Institute for Computation, Curtin University, GPO Box U1987, Perth,
6845, WA, Australia  R. Lange  Curtin Institute for Computation, Curtin
University, GPO Box U1987, Perth, 6845, WA, Australia  J. Tocknell  AAO
Macquarie, Macquarie University, NSW, Australia  M. Johnston-Hollitt  Curtin
Institute for Computation, Curtin University, GPO Box U1987, Perth, 6845, WA,
Australia  D. L. Kaplan  Department of Physics, University of
Wisconsin–Milwaukee, WI 53201, USA  S. J. Tingay  International Centre for
Radio Astronomy Research, Curtin University, Bentley, WA 6102, Australia  M.
Walker  International Centre for Radio Astronomy Research, Curtin University,
Bentley, WA 6102, Australia

(dd Mmm YYYY; dd Mmm YYYY; dd Mmm YYYY)

###### Abstract

We present an overview of the Southern-sky MWA Rapid Two-metre (SMART) pulsar
survey that exploits the Murchison Widefield Array’s large field of view and
voltage capture system to survey the sky south of 30∘ in declination for
pulsars and fast transients in the 140-170 MHz band. The survey is enabled by
the advent of the Phase II MWA’s compact configuration, which offers an
enormous efficiency in beam-forming and processing costs, thereby making an
all-sky survey of this magnitude tractable with the MWA. Even with the long
dwell times employed for the survey (4800 s), data collection can be completed
in $<$100 hours of telescope time, while still retaining the ability to reach
a limiting sensitivity of $\sim$2-3 mJy (at 150 MHz, near zenith), which is
effectively 3-5 times deeper than the previous-generation low-frequency
southern-sky pulsar survey, completed in the 1990s. Each observation is
processed to generate $\sim$5000-8000 tied-array beams that tessellate the
full $\sim$610 ${\rm deg^{2}}$ field of view (at 155 MHz), which are then
processed to search for pulsars. The voltage-capture recording of the survey
also allows a multitude of post hoc processing options including the
reprocessing of data for higher time resolution and even exploring image-based
techniques for pulsar candidate identification. Due to the substantial
computational cost in pulsar searches at low frequencies, the survey data
processing is undertaken in multiple passes: in the first pass, a shallow
survey is performed, where 10 minutes of each observation is processed,
reaching about one-third of the full search sensitivity. Here we present the
system overview including details of ongoing processing and initial results.
Further details including first pulsar discoveries and a census of low-
frequency detections are presented in a companion paper. Future plans include
deeper searches to reach the full sensitivity and acceleration searches to
target binary and millisecond pulsars. Our simulation analysis forecasts
$\sim$300 new pulsars upon the completion of full processing. The SMART survey
will also generate a complete digital record of the low-frequency sky, which
will serve as a valuable reference for future pulsar searches planned with the
low-frequency Square Kilometre Array.

###### doi:

XX.XXXX/pasa.XXXX.XX

###### keywords:

surveys: sky surveys - instrumentation: interferometers – methods:
observational – pulsars: general – techniques: interferometric

Department of Physics & Astronomy, University of British Columbia, 6224
Agricultural Road, Vancouver, BC V6T 1Z1, Canada dd Mmm YYYY

##  1 INTRODUCTION

Even after five decades of productive research, pulsars continue to enable us
to push the frontiers of physics and astrophysics. These compact dense stars
harbour physical conditions that are non-existent elsewhere in the universe
(e.g., ultra-strong gravitational and magnetic fields and supra-nuclear matter
densities), which make them invaluable tools for studying extreme physics.
They are arguably amongst the most widely-exploited astrophysical objects,
with applications ranging from probing the state of ultra-dense matter to
testing strong-field gravity (e.g., Demorest et al., 2010; Kramer et al.,
2006; van Straten et al., 2001), and from probing micro-arcsecond structure
and turbulence in the interstellar medium (ISM) to complex stellar
evolutionary scenarios (e.g., Bhat et al., 2004; Archibald et al., 2009;
Bailes et al., 2011). The phenomenal impact and high-profile scientific
applications (e.g., pulsar timing arrays for the detection of nanohertz-
frequency gravitational waves) has elevated pulsar science to the ranks of a
key science for the Square Kilometre Array (SKA; e.g., Keane et al., 2015;
Janssen et al., 2015; Shao et al., 2015).

The backbone that enables this is the net result of a series of large pulsar
surveys conducted over the past five decades (e.g., Manchester et al., 2001;
Cordes et al., 2006; Keith et al., 2010; Stovall et al., 2014). Invariably,
most of them involved tessellating large parts of the sky of the instrument
and recording data at high time and frequency resolutions (i.e., large data
rates) and performing sensitive searches over the vast parameter space that is
practically feasible. Many of them were prompted by the advent of new
instrumentation or technology, and often exploited the computing affordable at
the time. They have also proven invariably rewarding in the longer term, and
often yielded a substantial increase in the pulsar population. For instance,
the Molonglo pulsar survey in the 1970s found 150 pulsars, practically
doubling the known pulsar population at the time (Manchester et al., 1978),
while the Parkes multibeam survey from the 1990s (Manchester et al., 2001)
found 742 pulsars, and discovered exotica such as the double pulsar system
J0737$-$3039A/B and the eccentric neutron star-white dwarf binary
J1141$-$6545, both of which have proven to be unique laboratories for testing
general relativity and alternate theories of gravity (Kramer et al., 2006;
Bhat et al., 2008; Venkatraman Krishnan et al., 2020; Kramer et al., 2021).
This success led to next-generation multibeam surveys at Parkes and Arecibo,
and more recently with the Five-hundred-meter Aperture Spherical radio
Telescope (FAST). Already these have collectively discovered 600 pulsars to
date. The landmark discovery of fast radio bursts (FRBs) in the Parkes high-
time resolution radio universe (HTRU) survey (Thornton et al., 2013) even
opened up an entirely new field of research. Large pulsar surveys have a
proven track record of their ability to return significant scientific
dividends in the long run, with the majority of the discoveries and spin-off
science emerging from follow-up processing over the years.

These multi-beam surveys have largely been at frequencies $\gtrsim 1$ GHz. The
past decade also witnessed a number of successful low-frequency pulsar
surveys, most of which were prompted by the advent of new-generation low-
frequency facilities (e.g., Low Frequency Array; LOFAR), or new receivers or
pulsar instrumentation at the more traditional facilities such as the Green
Bank Telescope (GBT) and the Giant Metre-wave Radio Telescope (GMRT). Notable
among these are the drift-scan surveys with the Arecibo Telescope and GBT, and
the ongoing surveys at the GMRT and GBT. The drift-scan surveys, in the
300-350 MHz range, despite their non-traditional nature, have led to $>100$
pulsar discoveries, while the highly successful Green Bank Northern Celestial
Cap (GBNCC) survey has, to date, found 160 pulsars. The net tally from the
low-frequency surveys of the past decade alone is $>$400 pulsars, including 73
pulsars by the LOFAR Tied-Array All-Sky (LOTAAS) survey (Sanidas et al.,
2019). Additionally, targeted searches have been undertaken toward
unidentified Fermi gamma-ray sources (mostly at low frequencies), leading to
$>$80 pulsars (Deneva et al., 2021, and references therein). The LOTAAS
survey, the processing of which is still ongoing, also discovered the longest-
period (23.5 s) pulsar known until recently (Tan et al., 2018b), when a 76-s
pulsar was discovered with MeerKAT (Caleb et al., 2022). In essence, surveys
at low frequencies have proven to be highly effective, particularly in
uncovering the local population of pulsars, and mapping out the high-Galactic
latitude ($b$) parts of the sky.

Surveys at low frequencies offer several benefits but they also have their
limitations. An appealing factor is the generally steep spectral nature of
most radio pulsars, where the flux density at frequency $\nu$ is
$S_{\nu}\propto\nu^{\alpha}$, where $\alpha$ is the spectral index. The
spectral index is known to vary over a wide range for pulsars,
$-4\lesssim\alpha\lesssim 0$, but the average spectral index
$\langle\alpha\rangle=-1.6\pm 0.03$ for long-period pulsars (Jankowski et al.,
2018), and is somewhat steeper ($\langle\alpha\rangle=-1.9\pm 0.1$) for MSPs
(Toscano et al., 1998; Dai et al., 2015), with a 1-$\sigma$ dispersion of
$\sim$1\. While this suggests most pulsars are significantly brighter at low
frequencies, this is more than offset by the even steeper dependence of the
sky background noise ($T_{\rm sky}\propto\nu^{-2.55}$). The sky background is
also highly direction-dependent and is typically significantly reduced toward
higher Galactic latitudes. The main benefit is the inherently larger fields-
of-view of the low-frequency telescopes, which substantially increase the
efficiency in telescope time required and hence the time for completion of
large surveys.

Amongst the multitude of other considerations are interstellar medium (ISM)
propagation effects, which tend to majorly influence low-frequency pulsar
searches; the most familiar (and significant) one is the dispersion that
manifests as frequency-dependent time delays in arrival times $\Delta
t\propto{\rm DM}\,\nu^{-2}$, where the dispersion measure (${\rm DM}$) is the
line-of-sight integral of the electron density $n_{e}$. This non-linear,
inverse dependence in frequency implies very large delays at low frequencies
($\lesssim$200 MHz); e.g., a pulsar with a DM = 100 $\rm pc\,cm^{-3}$ will
have its signal spread over ${\sim}7.5$ s in observations made over a 30 MHz
band centred at 150 MHz, as opposed to $\lesssim 0.1\,$s across a similar
(i.e., 20%) fractional bandwidth around 1.4 GHz. Circumventing this
necessitates much finer frequency resolution ($\Delta\nu$) so the residual
dispersive smearing across the finite channel width can be minimised, and
consequently requires many more channels across the recording bandwidth, and
hence a much larger data rate and substantial processing needs.

The other significant effect is pulse broadening resulting from multipath
propagation as a consequence of scattering in the ISM, the characteristic time
for which is a nonlinear function of both DM and frequency, i.e.,
$\tau_{d}\propto{\rm DM}^{-2.2}\,\nu^{-4.4}$, under the assumption of a pure
Kolmogorov form of electron density spectrum (Cordes et al., 1985). This poses
a significant limitation in low-frequency pulsar searches, especially when the
pulse broadening time exceeds the pulsar’s spin period, i.e., $\tau_{d}\gtrsim
P$, as it results in a significant degradation or even a loss of sensitivity
to periodic emission. As with the sky background, scatter broadening is also
highly line-of-sight dependent; it is much larger in the plane, or toward the
Galactic Centre, compared to high-$|b|$ sight lines. Empirical relations exist
to guide expected broadening times as a function of DM and frequency (e.g.,
Bhat et al., 2004; Geyer et al., 2017), and can be used to guide the
observing/search strategies, e.g., $\tau_{d}\gtrsim 100$ ms at DM $\gtrsim$300
$\rm pc\,cm^{-3}$, for a line of sight as far off as $|b|\sim 5{{}^{\circ}}$
and $\sim 30{{}^{\circ}}$ away from the Galactic Centre (GC) in longitude.
This implies, at low frequencies, the search volume is largely limited to a
few kpc in the plane. However, this is not a serious limitation at higher
Galactic latitudes, where the DM tends to saturate at $\sim$20-50 $\rm
pc\,cm^{-3}$ for $|b|>15{{}^{\circ}}$. In other words, the higher survey
speeds of low-frequency surveys can be optimally exploited for covering
high-$|b|$ parts of the sky, without compromising detection sensitivity.

Yet another relevant ISM effect, especially at low frequencies, is the
modulation of apparent pulsar intensities due to scintillation effects. As
with the pulse broadening, the observable effects strongly depend on frequency
and the line of sight, as it is essentially another manifestation of multipath
propagation. For relatively nearby pulsars (DM $\lesssim$50 $\rm pc\,cm^{-3}$)
this often manifests as rapid (and very large) modulations in both time and
frequency with characteristic scales in the range $\sim$0.1-5 MHz and
$\sim$1-100 min at $\sim$150 MHz; this is diffractive scintillation (e.g.,
Rickett, 1990). Refractive scintillation also leads to intensity modulations,
but on much longer timescales of days to weeks (at low frequencies), and the
observed variations in mean flux densities can be as much as by a factor
$\sim$5-6 for low to moderate DM pulsars (e.g., Bell et al., 2016; Bhat et
al., 2018). From the perspective of candidate detection in low-frequency
searches, this sometimes results in fortuitous brightening (or inauspicious
dimming) of pulsars, which provides the opportunity to detect pulsars that
were missed earlier (e.g., owing to scintillation dimming), or to detect a
pulsar that might be below the sensitivity limit of a survey. This further
strengthens the case for low-frequency surveys.

Despite these challenges, pulsars were originally discovered at low
frequencies (at 81.5 MHz; Hewish et al. 1968) and much of the early years of
pulsar astronomy were focused at low frequencies. The eventual quest to find
them in large numbers and timing them at high precision pushed much of pulsar
astronomy (searches and timing in particular) to frequencies $\gtrsim$1 GHz.
However, the advent of several low-frequency telescopes over the past decade
and advances in affordable high-performance computing are effectively leading
to a resurgence of low frequency astronomy including large sky surveys, many
of which are conducted at frequencies $\lesssim$500 MHz.

The success of these northern surveys strongly motivates an all-sky pulsar
survey with the Murchison Widefield Array (MWA) that operates in the 80-300
MHz range in the Southern Hemisphere. The MWA, which was originally built as
an array of 128 tiles (where each tile is a 4$\times$4 dipole array) with a
maximum baseline of 3 km, is also Australia’s precursor for the low-frequency
SKA (i.e., SKA-Low; Tingay et al. 2013). Even though the MWA was not initially
designed for pulsar science, the eventual addition of a voltage capture system
(VCS; Tremblay et al. 2015) and the development of software-defined
instrumentation (for offline processing) equipped it as a pulsar-capable
facility. Notwithstanding the limitations of large data rates (28 $\rm
TB\,hr^{-1}$) and the associated data management/processing challenges, the
VCS has been exploited for wide-ranging science from studies of millisecond
pulsars to sporadic emission from pulsars (e.g. Bhat et al., 2016; Meyers et
al., 2018; Kaur et al., 2019), and from investigating the pulsar emission
physics to studying propagation effects caused by the interstellar medium
(e.g. McSweeney et al., 2017; Kaur et al., 2022). The progress in this area,
along with the array’s upgrade to Phase II (Wayth et al., 2018), whereby a
compact configuration of 128 tiles within 300 m was possible on a semi-regular
basis, has made all-sky pulsar searches tractable with this telescope.

The SMART survey described in this paper has two main objectives: (1)
performing sensitive searches for pulsars and fast transients in the sky south
of $+30{{}^{\circ}}$ in declination at 140-170 MHz; and (2) mapping the sky
for low-frequency detection of already known pulsars in the southern sky. The
main novelty of the survey is the use of a voltage-capture mode for data
recording (as opposed to the filterbank data format that has been adopted for
all past and ongoing surveys), and hence an astonishingly high survey speed
for data collection, i.e., $\sim$500 ${\rm deg^{2}\,hr^{-1}}$ in
100-$\mu$s/10-kHz resolutions). However, the computational cost of processing
(i.e., beamforming and searching) are substantial at low frequencies, and thus
drive the feasible strategies for data processing, especially at early stages.

With the large survey speed substantially reducing the demand for telescope
time for survey completion, longer dwell times become affordable, which also
increases the sensitivity to the detection of sporadic or intermittent class
of objects such as rotating radio transients (RRATs; McLaughlin et al. 2006),
intermittent or state-switching pulsars, extreme nullers etc. (e.g., Kerr et
al., 2014) among the classes of radio-emitting neutron stars, and even the
enigmatic fast radio bursts (FRBs; e.g., Thornton et al. 2013). The
detectability all of these transient class of objects is dictated by the “on-
sky” time metric $\Sigma=\Omega T$ where $\Omega$ is the instantaneous FoV and
$T$ is the time spent on sky (dwell time in the case of an all-sky survey).
Following the discussion in Sanidas et al. (2019) in the context of LOTAAS,
$\Sigma_{\rm SMART}$ = 52,735 ${\rm deg^{2}\,hr}$ , which is a factor of two
more than that of LOTAAS for which $\Sigma_{\rm LOTAAS}$ = 23,400 ${\rm
deg^{2}\,hr}$ (at 135 MHz), and indeed much larger than $\Sigma_{\rm GBNCC}$ =
1430 ${\rm deg^{2}\,hr}$ , $\Sigma_{\rm GHRSS}$ = 835 ${\rm deg^{2}\,hr}$ and
$\Sigma_{\rm AO327}$ = 132 ${\rm deg^{2}\,hr}$ (all at 300-350 MHz).

Here we present an overview of the Southern-sky MWA Rapid Two-metre (SMART)
pulsar survey. In § 2 we outline the main science goals, and describe the
observing strategy adopted for sky tessellation. Procedures for data
processing and analysis are described in § 3, and the strategies for
confirmation and initial follow-up in § 4. In § 5 we describe the survey
simulations and the expected yield. Future processing plans are outlined in §
6, followed by a summary in § 7.

##  2 Survey Description

###  2.1 Science goals and Motivation

The broader goals of the SMART survey are similar to most other large sky
surveys, i.e., exploring the new parameter space that is opened by a leap in
instrumentation, technology, or sensitivity and to uncover a large population
of previously undetected pulsars. The fact that the currently known pulsar
population ($\sim$3300, cf. the ATNF pulsar
catalogue111https://www.atnf.csiro.au/research/pulsar/psrcat/ v1.67;
Manchester et al. 2005) represents only a small fraction ($\lesssim$ 10%) of
the total expected (i.e. beamed in our direction) Galactic population (e.g.,
Keane et al., 2015, and references therein) strongly motivates such large sky
surveys. Indeed, conducting a full Galactic census of pulsars is a high-
priority science objective for the SKA. Further, given the number of broader
questions surrounding the neutron-star population (e.g., birth rates, and
comparison with rates of supernovae), the detectable pulsar population is
largely guided by the known population of pulsars at any given time. It is
therefore imperative to explore every possible avenue and steadily refine our
knowledge of pulsar population. Furthermore, the detection prospects of
pulsars in a given frequency band strongly depends on the emission and
propagation properties at those frequencies; however, the current forecast of
a detectable population in the SKA-Low band is largely guided by the pulsar
population uncovered by high-frequency surveys.

Obtaining a large body of measurements such as DM, scattering and Faraday
rotation, by using pulsars as probes of the ISM, will also enable mapping out
the distribution of magneto-ionic (and turbulent) plasma in the Galaxy, which
is steadily refined with a larger sample of measurements (e.g., Cordes &
Lazio, 2002; Bhat et al., 2004; Deller et al., 2016; Yao et al., 2017).

Finally, an underlying goal of any large-sky pulsar survey is to discover
exotic objects; while it is hard to design any particular survey specifically
for this, historical examples are abundant, e.g., the discovery of the double
pulsar in the Parkes multibeam (PMB) survey (Lyne et al., 2004), the
23.5-second period pulsar in LOTAAS (Tan et al., 2018b), and the transitional
millisecond pulsar (MSP) in the Arecibo drift-scan survey (Archibald et al.,
2009). All such broader and high-profile objectives are certainly applicable
for the SMART survey.

Figure 1:  Sky tessellation of the SMART survey. The left panels show beam
tiling patterns for two select pointings: top one a near-zenith pointing
($\delta=-28{{}^{\circ}}$), the bottom one a far southern pointing
($\delta=-70{{}^{\circ}}$). The number of tied-array beams vary from
$\sim$6000 to $\sim$8000 from near-zenith to far-zenith pointings, and the
beam shape becomes elliptical at large offsets from the zenith. The size of
the circle/ellipse indicates half power tied-array beam size; the red and blue
circles correspond to the low and high ends of the SMART band (140-170 MHz).
The right panels show the primary beam response for the same declination
pointings, at the central frequency of 155 MHz.  Table 1: Parameters of large
pulsar surveys over the past decade

Survey | Telescope | Frequency Band | Sky coverage | Time resolution | Frequency resolution | Dwell time | $S_{\rm min}$ † | Reference
---|---|---|---|---|---|---|---|---
|  | (MHz) |  | ($\mu$s) | (kHz) | (s) | (mJy) |
LOTAAS | LOFAR | 119-151 | $\delta>0{{}^{\circ}}$ | 491.52 | 12.21 | 3600 | 1-2 | SCB+19
SMART | MWA | 140-170 | $\delta<+30{{}^{\circ}}$ | 100 | 10 | 4800 | 2-3 | This work
GBNCC | GBT | 300-400 | $\delta>-40{{}^{\circ}}$ | 81.92 | 24 | 120 | 1.1 | SLR+14
GHRSS | GMRT | 306-338 | $-40{{}^{\circ}}>\delta>-54{{}^{\circ}}$ | 30.72-61.44 | 15.625-31.25 | 900, 1200 | 1.0 | BCM+16
HTRU | Parkes | 1182-1522 | $\delta<+30{{}^{\circ}}$ | 64 | 390 | 240, 540, 4200 | 0.2-0.6 | KJvS+10
GPPS | FAST | 1100-1500 | $-10{{}^{\circ}}<b<+10{{}^{\circ}}$ | 49.152 | 244.14 | 300 | 0.005 | HWW+21

  * †

Minimum detectable flux density for a 10-$\sigma$ detection, for long-period
pulsars ($P\gtrsim 0.1$ s), with small duty cycle ($W/P\sim 0.05$), and at DMs
$\lesssim$50 $\rm pc\,cm^{-3}$.

  * Notes:

Survey description reference – SCB+19: Sanidas et al. (2019) for LOTAAS;
SLR+14: Stovall et al. (2014) for GBNCC; BCM+16: Bhattacharyya et al. (2016)
for GHRSS; KJvS+10: Keith et al. (2010) for HTRU; HWW+21: Han et al. (2021)
for GPPS

The SMART pulsar survey also perfectly complements ongoing northern-sky
surveys in sky and frequency coverage (Table 1). Surveys at low frequencies
will likely be sensitive to a different pulsar population, and therefore an
all-sky survey at low frequencies is also essential to develop a comprehensive
picture of neutron-star/pulsar populations in the Galaxy. Bearing this in mind
(and as we detail in § 2.4), the survey is designed to reach a final
sensitivity comparable to that of LOTAAS, i.e., the use of long dwell times
(4800 s) to attain a limiting sensitivity (10$\sigma$) of $\sim$2-3 mJy for
long-period pulsars with small duty cycles, and assuming a spectral index
$\alpha=-1.5$ and no turnover down to $\sim$150 MHz. This is $\sim$3-5 times
deeper than the previous-generation low-frequency (70 cm) survey (Manchester
et al., 1996) in the south (and thence an accessible search volume $\sim$5-10
times larger), and $\sim$2-3 times deeper than the high-latitude segment of
the Parkes HTRU survey (Keith et al., 2010).

The SMART survey will also serve as a reference survey for future deeper
surveys at low frequencies, such as those planned with SKA-Low (Keane et al.,
2015). While the success of (and the lessons learned from) all ongoing low-
frequency surveys will indeed inform SKA-Low pulsar surveys, the SMART survey
will potentially play an additional important role, since the MWA is also the
official low-frequency precursor for SKA-Low, and is located at the same site
where SKA-Low will be built. Specifically, the sky coverage of the SMART
survey is identical to that of SKA-Low, which means a higher degree of
synergistic overlap in calibration and beamforming methodologies, than most
northern facilities. The role of reference surveys is vividly demonstrated by
the later generation multibeam surveys in the south; e.g., the PMB survey for
its successors, the HTRU pulsar survey (Keith et al., 2010) and the SUrvey for
Pulsars and Extragalactic Radio Bursts (SUPERB) (Keane et al., 2018), which
can now play a similar role for the planned surveys with MeerKAT. However,
aside from the Parkes 70 cm survey of the 1990s, the low-frequency southern-
sky remains essentially unexplored for pulsar searches, especially at
$\lesssim$300 MHz.

Aside from the aforementioned primary science goals, there are also some
auxiliary goals for the SMART survey, largely enabled by the novelty of the
data recording strategy, i.e., the use of voltage capture system and post-
processing, as opposed to the beamformed data in the filterbank format. These
not only facilitate a number of additional strategies for confirmation and
follow-up, but they can also be potentially exploited for developing and
trialling alternate strategies for pulsar searches; e.g., image-based
techniques for the identification of promising candidates that take advantage
of pulsar properties such as steep-spectrum, variability or circular
polarisation (e.g., Sett et al., 2022). These, in principle, also offer some
advantages over traditional search methods, especially for extreme pulsars
like those with sub-millisecond periods, or distant pulsars whose pulse shapes
will be significantly broadened due to multi-path scattering, but will be
sensitive primarily to very bright sources.

Notwithstanding the anticipated scientific merits of the SMART survey,
computational requirements are substantial, especially given the large data
rate of the VCS and searching at low frequencies, thereby necessitating a
multi-pass processing strategy. In the first-pass processing, we perform a
shallow survey, where 10 minutes of data from each observation are processed,
and the search is limited to basic periodicity, and DMs up to 250 $\rm
pc\,cm^{-3}$. In this paper, we outline the observing strategies employed for
the survey, and processing strategies adopted for the initial phase, and
present analysis and results to date, as well as plans and strategies for
future processing. A companion paper (hereafter Paper II) will describe the
survey status, pulsar census to date and more details on follow-up strategies
including timing and imaging follow-ups.

###  2.2 Survey strategy

The novel strategy employed for the SMART survey, i.e., the use of VCS
recording from 128 tiles, which allows high-time resolution (and
instantaneous) sampling of a very large patch of the sky (but at the expense
of a large data rate of 28 $\rm TB\,hr^{-1}$), necessitates substantial
processing to enable large-scale pulsar searching applications. Most
importantly, the voltage data from the tiles need to be coherently combined to
generate thousands of tied-array beams prior to any search processing. The
undertaking of the SMART survey is particularly enabled by the Phase II
upgrade, whereby a compact configuration of 128 tiles within $\sim$300 m
became available on a semi-regular basis. The compact configuration of Phase
II brings an enormous efficiency in terms of beamforming cost; specifically,
the number of tied-array (i.e. phased array) beams required to fill the full
FoV (at a gain level down to half power point) is reduced from $2.7\times
10^{5}$ for the Phase I array to $3.9\times 10^{3}$ for the Phase II compact
array. This reduction of more than two orders of magnitude in the
computational cost makes an all-sky high-sensitivity pulsar search tractable
(and affordable) with an interferometric array like the MWA. Thus, with the
beamforming step integrated into software-defined instrumentation, this
effectively translates into an impressively large survey speed of $\sim$450
${\rm deg^{2}\,hr^{-1}}$ , i.e., the full visible sky of the MWA
($\delta<+30{{}^{\circ}}$) can be surveyed in a modest number of VCS
pointings.

The first-pass survey strategy of processing only 10 minutes of data from each
observation (hence reaching about one-third of the full-search sensitivity)
was adopted also to boost the prospects of early pulsar discoveries. Even
though the combination of the VCS mode and the FoV provides a large survey
speed, practical considerations such as the availability of the compact
configuration necessitated multiple observing campaigns to advance the survey.
Further details including the survey status and completion plans are described
in Paper II.

###  2.3 Beamforming and Sky tessellation

The signal processing chain of the MWA including the high time resolution
system is described in a number of earlier papers (e.g., Tingay et al., 2013;
Prabu et al., 2015; Tremblay et al., 2015), and is briefly reiterated here. In
the legacy system that was employed for survey campaigns to date, the VCS sub-
system follows the second stage of channelisation in the signal path. Each
element of the array is a $4\times 4$ dipole array, called a “tile”, the
signals from which are fed to an analogue beamformer that defines the FoV. The
beamformed signals are Nyquist-sampled at 655.56 Msps and channelised (after
signal conditioning) using a polyphase filterbank (PFB) to generate
256$\times$1.28-MHz signal outputs (i.e., coarse channelisation), 24 of which
are transported to the central processing facility, where a second-stage PFB
operation is performed, resulting in 128$\times$10-kHz time series for each
coarse channel, i.e., 3072 channels across the recording 30.72 MHz bandwidth.
These voltage time series are written to an array of RAID disks by the VCS as
4+4-bit complex voltage samples. These data are recorded (up to a maximum
duration of 100 minutes) and transported to the Pawsey Supercomputing Centre
where further processing (including calibration and beamforming) is carried
out.

VCS-recorded data can be processed offline for calibration and tied-array
beamforming (Ord et al., 2019) and, optionally, can also be reprocessed to
reconstruct a higher time resolution voltage data at the native coarse channel
resolution of 0.78 $\mu{\rm s}$ (McSweeney et al., 2020). To realise the SMART
pulsar survey, this beamformer functionality was further enhanced to
optionally generate several dozens of tied-array beamformed outputs
simultaneously – i.e. the so-called _multi-pixel_ beamformer, which is
essentially the front-end of the pulsar search processing chain. The
implementation details and benchmarks are described in Swainston et al.
(2022). This software tied-array beamformer has been benchmarked on Pawsey’s
Garrawarla and Swinburne’s OzSTAR supercomputers. It performs $3\times$ faster
on the latter, which has been the primary high performance computing (HPC)
platform for much of our SMART data processing.

Thanks to the large field-of-view of the MWA ($\sim$610 ${\rm deg^{2}}$ at 155
MHz, near zenith), the entire sky south of declination
$\delta<+30{{}^{\circ}}$ can be covered in a modest number of telescope
pointings. The sky tiling strategy is shown in Fig. 1. In short, we adopted
pointings similar to that of the GaLactic and Extragalactic All-sky MWA survey
(Wayth et al., 2015), i.e., meridian drift scans optimised for maximum
sensitivity at each declination as well as for more reliable calibration
(referred to as ‘sweet spots’). In this case, the number of pointings depends
on the degree of overlap in right ascension (RA), with a minimum of 58
pointings for minimal ($1{{}^{\circ}}$) overlap and 78 for a $15{{}^{\circ}}$
overlap. A large overlap is more optimal as it effectively serves as a two-
pass strategy, which is desirable at low frequencies where intermittency (from
effects such as scintillation) tends to be more pronounced. After exploring
the full range of options, and also factoring in the available resource
constraints, we converged on a $10{{}^{\circ}}$ overlap as an acceptable
choice.222The operational constraints of the MWA limited VCS mode observations
to a maximum of 25 hours per observing semester, with the legacy system. As
shown in Fig. 1, this amounts to a total 70 pointings, i.e., 93 hr of
telescope time for the full SMART survey.

For each pointing, many tied-array beams (TABs) are formed to maximise the
sensitivity across the FoV. The tied-array beams are pointed towards fixed
right ascension and declination, with the necessary adjustments to the tile
phases made for every second of data (Ord et al., 2019). Thus, although the
observations themselves are drift scans, sources can be tracked by the same
TAB for up to the full duration of the observation.

The precise size and shape of the TABs is a non-trivial function of the tile
layout of the compact configuration, equivalent to the “Compact robust 1”
synthesised beam whose cross section is presented in Figure 7 of Wayth et al.
(2018) and discussed in Swainston et al. (2022) and in Section §3. Due to the
compact configuration’s redundant baselines (in the two “hexes”), the most
sensitive parts of the TAB consist of a main lobe whose full width half
maximum (FWHM) at 155 MHz is 23′, surrounded by a pattern of discrete grating
lobes of similar width. Although these grating lobes can be exploited for
candidate confirmation (further discussed below), we choose the TAB pointings
to form a dense (hexagonal) grid such that the main lobes overlap by
$\sim$20%, as shown in Fig. 1. This effectively Nyquist-samples the sky at a
gain of the half-power level or more. The beam shape used for this calculation
assumes that all 128 tiles are functioning, whereas, in reality, up to
$\sim$10% of tiles may be flagged in any given observation. Unless the flagged
tiles preferentially result in a reduction of the longest baselines, the
effect on the beam shape is negligible.

Tiling the FoV in this way translates to $\sim$6300 TABs for an observation
pointed toward the zenith. For pointings away from the zenith, where the beam
shape develops a significant ellipticity (e.g., at zenith angle 15∘,
ellipticity $\epsilon=\theta_{maj}/\theta_{min}=$ 1.36 where $\theta_{maj}$
and $\theta_{min}$ are the major and minor axes of the TAB), the number of TAB
pointings are in the range $\sim$4200-4500. Further, the beam size also varies
across the 20% fractional bandwidth of our survey observations; for example,
for a pointing toward the zenith (where the TAB is nearly symmetrical), the
FWHM is 25.3′ at 140 MHz but reduces to 20.7′ at 171 MHz. This further
justifies our rationale for a 20% overlap, as it ensures every single spot in
the sky is covered at a gain near or above the half power level even at the
high end of the observing band.

Finally, as with any other aperture array, the sensitivity is not uniform
across the sky and is strongly declination dependent; to first order, the loss
in sensitivity is by a factor $\cos(\theta_{z})$ where $\theta_{z}$ is the
zenith angle. In principle, this can be compensated to a certain extent by
longer integrations, though in practice, the inherent limitations of our data
recording system (VCS) limits this to no more than 90 minutes, and we
therefore use 80 minute recordings for all pointings. As such, the sensitivity
will not be uniform across the sky due other factors; e.g., the sky background
temperature $T_{\rm sky}$ is direction dependent, and the loss in sensitivity
from severe pulse broadening for distant pulsars, which applies to the sight
lines within the Galactic plane or toward the Galactic centre. Some of these
are considered in detail in § 2.4.

Figure 2:  Left: Minimum detectable flux density, $S_{\rm min}$, for the
first-pass processing of the SMART survey as a function of DM. Sensitivity
limits, assuming a 10-minute integration time, are plotted for different pulse
periods, $P=$ 1.0, 0.1, 0.01, 0.001 s, and for two different system
temperature values $T_{\rm sys}$; one corresponding to mean $T_{\rm sky}$ for
regions away from the Galactic plane, and the other for a mean $T_{\rm sky}$
in the plane, but excluding the region toward the Galactic Centre. The effect
of pulse broadening due to interstellar scattering (Bhat at el. 2004) is shown
by the dotted lines. Right: Pulse broadening (smearing) incurred by using the
first-pass processing dedispersion plan (Table 2) due to various factors such
as the finite sampling time, dispersive smearing due to the incoherent de-
dispersion algorithm used, and the effects of multi-path scattering based on
the $\tau_{d}$-DM relation from Bhat et al. (2004). The grey shaded region
denotes one order-of-magnitude larger or smaller range in the predicted
scattering.

###  2.4 Survey sensitivity

The sensitivity of a pulsar survey is determined by the combination of some
instrumental and processing parameters and a variety of broadening effects to
pulsar signals. Following Dewey et al. (1985), the minimum detectable flux
density for a pulsar with period $P$ and effective pulse width $W_{\rm eff}$,
down to detection significance $({\rm S/N})_{\rm min}$, i.e., minimum
detectable signal-to-noise ratio, is related to the telescope gain $G$ and
system temperature $T_{\rm sys}$, which is the sum of the receiver and sky
background temperatures, i.e.,

$S_{\rm min}=\frac{({\rm S/N})_{\rm min}(T_{\rm recv}+T_{\rm
sky})}{G\sqrt{n_{\rm pol}t_{\rm obs}B_{\rm obs}}}\sqrt{\frac{W_{\rm
eff}}{P-W_{\rm eff}}}$ (1)

where $n_{\rm pol}$ is the number of polarisations summed, $t_{\rm obs}$ is
the integration time and $B_{\rm obs}$ is the recording bandwidth. As evident
from this equation, the sensitivity is maximum for long-period pulsars with a
small duty cycle, i.e. when $W_{\rm eff}\ll P$. The gain $G=A_{\rm
eff}/2k_{B}$, where $A_{\rm eff}$ is the effective collecting area and $k_{B}$
is the Boltzmann constant. At 150 MHz, $A_{\rm eff}\approx 2750$ ${\rm m^{2}}$
for a 128-tile MWA (Tingay et al., 2013), which may imply $G\sim 1$ ${\rm
K\,Jy^{-1}}$ , however, for an aperture array such as the MWA, it is a strong
function of the zenith angle, i.e. $G(\theta_{z})=G_{\rm max}{\rm
cos}(\theta_{z})$, where $\theta_{z}$ is the zenith angle and $G_{\rm max}$ is
the gain at $\theta_{z}=0$. Moreover, for drift-scan type observations that we
employ for the SMART, $G$ depends on the offset from the phase centre, and can
be $\sim$0.5 $G_{\rm max}$ at the half power point. We therefore assume a
conservative $G\sim 0.5$ ${\rm K\,Jy^{-1}}$ for all our sensitivity
calculations. This is assuming a full coherent beam sensitivity, i.e., perfect
calibration for TAB formation and no loss of sensitivity due to flagged tiles.
In practice, a small number of tiles ($\lesssim$10) are typically flagged due
to malfunctioning, sub-optimal performance or poor calibration solution. As we
detail in §3, the strategy of observing multiple calibrators for SMART
observation allows us to perform useful cross-checks and maximise the
achievable sensitivity using the best available calibration solutions.

At the low frequencies of the MWA, the system temperature $T_{\rm sys}$ is
dominated by the sky background $T_{\rm sky}$. Both $T_{\rm recv}$ and $T_{\rm
sky}$ are frequency dependent, and $T_{\rm sky}$ is also a strong function of
the direction $(l,b)$, where $l$ and $b$ are the Galactic longitude and
latitude, respectively. We assume a mean $T_{\rm recv}$= 50 K for the 140-170
MHz band. Excluding a $\sim$10∘ cone around the Galactic centre, $T_{\rm sky}$
can vary from $\sim$200 K toward $|b|\gtrsim 60{{}^{\circ}}$ to as much as
$\sim$1200 K in the plane, toward $\gtrsim$10∘ from the Galactic centre, where
$T_{\rm sky}$ can be as large as $\sim 10^{4}$ K at 155 MHz. We use the Haslam
et al. (1982) map as the reference and assume $T_{\rm sky}\propto\nu^{-2.55}$
scaling from Lawson et al. (1987). Given this strong dependence of $T_{\rm
sky}$ with $(l,b)$, we consider two cases: (1) the sky at $|b|\lesssim
5{{}^{\circ}}$ where mean $T_{\rm sky}\sim 600$ K and (2) the sky at
$|b|\gtrsim 5{{}^{\circ}}$, where mean $T_{\rm sky}\sim 270$ K; i.e., $T_{\rm
sys}=630$ K and $300$ K, respectively, as shown in Fig. 2.

Intrinsic pulses are broadened due to a variety of effects, as discussed
earlier. As detailed in Lorimer & Kramer (2012), the total smearing time
$\tau_{\rm tot}$ is the quadratic sum of the finite sampling time $\tau_{\rm
samp}$, the residual dispersive smearing due to finite frequency channel
$\tau_{\rm chan}$, the dispersive smearing across the full recording band due
to finite DM steps in trial DM values $\tau_{\rm dm}$, and the dispersive
smearing resulting from piece-wise linear approximation of the quadratic
dispersion law in the sub-band dedispersion algorithm employed in searches
$\tau_{\rm sub}$. Fig. 2 summarises these for our current first-pass
processing. The planned second-pass search will significantly enhance the
search sensitivity by processing the full observation (4800 s) and the use of
more optimal DM steps, i.e. many more trial DMs than that used in current
search.

As evident from the figure, for our current first-pass processing, the total
smearing time is dominated by finite DM steps; this sub-optimal choice was
made in an effort to maximise the number of observations that can be processed
to completion toward a shallow all-sky survey within available computing
resources. The dedispersion plan utility used is shown in Table 2. In effect,
we progressively downsample the data five times over the DM range searched,
each time making the DM step size coarser. At ${\rm DM}\gtrsim 3$ $\rm
pc\,cm^{-3}$, the dispersive smearing time within the 10-kHz channel is larger
than the native sampling time (100 $\mu$s) but still a smaller contribution to
the total smearing time, compared to that due to the DM step size. As a
result, the net smearing time $\tau_{\rm tot}$ displays a step-wise increase
as shown in Fig. 2, given our dedispersion plan. At very low DMs $\lesssim$10
$\rm pc\,cm^{-3}$, $\tau_{\rm tot}\sim 0.7$ ms but increases to $\sim$10 ms at
DM $\sim$100 $\rm pc\,cm^{-3}$. In essence, our first-pass search severely
compromises the sensitivity to millisecond pulsars (MSPs) at larger DMs and
shorter periods, i.e., it is currently sensitive to MSPs at
$\text{DM}\lesssim$30 $\rm pc\,cm^{-3}$and $P\gtrsim$10 ms. As shown in Fig.
2, at those larger DMs, the smearing due to scattering (i.e. pulse broadening)
can also be significant. The broadening time here is based on the empirical
relation in Bhat et al. (2004), which is mostly relevant for pulsars near the
plane. As is well known, these scattering estimates can be uncertain by more
than an order of magnitude, denoted by the grey shaded region.

Table 2: Dedispersion plan for the first-pass SMART processing

${\rm DM}_{\rm min}$ | ${\rm DM}_{\rm max}$ | $\delta{\rm DM}$ | $N_{\rm DM}$ | $d_{s}$ | $\Delta t_{\rm eff}$
---|---|---|---|---|---
$(\rm pc\,cm^{-3})$ | $(\rm pc\,cm^{-3})$ | $(\rm pc\,cm^{-3})$ |  |  | (ms)
1.0 | 12.2 | 0.02 | 560 | 1 | 0.1
12.2 | 24.4 | 0.03 | 406 | 2 | 0.2
24.4 | 48.8 | 0.06 | 406 | 4 | 0.4
48.8 | 97.6 | 0.11 | 443 | 8 | 0.8
97.6 | 195.2 | 0.23 | 424 | 16 | 1.6
195.2 | 250.0 | 0.46 | 119 | 16 | 3.2

  * •

The columns 1 and 2 denote the ranges in dispersion measure, between ${\rm
DM}_{\rm min}$ and ${\rm DM}_{\rm max}$, with a DM step-size of $\delta{\rm
DM}$, resulting in $N_{\rm DM}$ trial DM values. The down sampling factor is
denoted by $d_{s}$, i.e. the factor which the temporal resolution is averaged
to yield a net resolution $\Delta t_{\rm eff}$.

The theoretical sensitivity is shown in Fig. 2 for different periods, $P=1.0$,
$0.1$, $0.01$ and $0.001$ s. In all these calculations, we have assumed a duty
cycle of 3%, i.e., $W_{\rm eff}/P=0.03$. For each period, a pair of curves are
shown: one for the best-case scenario, i.e., searches away from the plane,
where $T_{\rm sys}\sim 305$ K; and the second for the sky near the plane where
the mean $T_{\rm sys}$ is twice as high. In either case, the sensitivity is
maximum for long-period pulsars, at low to moderate DMs of $\lesssim$50 $\rm
pc\,cm^{-3}$, and toward $|b|\gtrsim 5{{}^{\circ}}$.

With our first-pass processing scheme (i.e., 10-minute integrations and a sub-
optimal dedispersion plan), we reach a limiting sensitivity of $S_{\rm
min}\sim 7\text{-}12\,\rm mJy$ for long-period pulsars, and $\sim
12\text{-}25\,\rm mJy$ for MSPs at low to moderate DMs. For the proposed deep-
pass processing (i.e., $\sim$80-minute integrations and a more granular
dedispersion plan), we can achieve a limiting sensitivity of $S_{\rm min}\sim
2\text{-}3\,\rm mJy$ for long-period pulsars and $\sim 5\text{-}10\,\rm mJy$
for MSPs at low to moderate DMs. In this case, the SMART survey sensitivity is
comparable to that of the LOTAAS survey in the northern hemisphere. While
LOTAAS can be twice as sensitive as SMART for long-period pulsars, the
sensitivity for $P\lesssim$10 ms is almost similar, owing to a lower
degradation in sensitivity in the SMART band. Compared to the Southern Pulsar
Survey of the 1990s at 430 MHz (i.e., a wavelength of 70 cm), also known as
the Parkes 70cm survey (Manchester et al., 1996), the SMART survey is
$\sim$3-5 times more sensitive, especially for pulsars at DMs $\lesssim$100
$\rm pc\,cm^{-3}$and spectral index $\alpha\lesssim-1.5$. Even the ongoing
shallow survey is comparable to the 70cm survey in theoretical sensitivity,
and if at all, slightly more sensitive for steep spectrum pulsars with no
turnover down to $\sim$100 MHz. This provides a strong motivation to undertake
a full-scale pulsar survey with the MWA.

###  2.5 Effective dwell time and sensitivity

Unlike most other pulsar surveys, where single-dish telescopes are used to
track targeted positions for small time intervals (e.g., HTRU, GBNCC), the
SMART observations are drift scans, where the primary beam is pointable but
static in horizontal coordinates (azimuth and zenith angle) once an
observation starts and the sky moves through the FoV. When forming TABs, we
track the sky position as it moves through the MWA primary beam and as a
consequence not all TABs necessarily remain within a sensitive part of the
primary beam for the full 80-minute duration.

The amount of time spent within an individual observation FWHM depends both on
the observing declination (i.e., where the primary beam is pointed) and the
target source position to be tracked with a TAB. As an example, in Fig. 3 we
plot some representative TAB pointings along with the primary beam response
for the same observations as in Fig. 1 in horizontal coordinates. As already
noted, our sensitivity drops substantially as we observe at larger zenith
angles, which we visualise by having the colour scale represent the zenith-
normalised primary beam power as a proxy for sensitivity. Secondly, the TAB
pointing directions are traced before, during, and after the 80-minute
observation, which highlights that not all targeted positions remain in a
usable part of the primary beam.

Figure 3: Tied-array beam traces through the MWA primary beam for SMART
observations. Three example pointing directions for each observation are
traced including 1 hr before and 1 hr after the 80-minute observation. The
target trace (rotating clockwise as time advances) is coloured pink to
represent the trajectory before the observation, red during the observation,
and blue after the observation is complete. North is at $0{{}^{\circ}}$ and
the azimuth angle increases to the East. The colour scales are the same for
each subplot, highlighting the sensitivity penalty incurred for observing away
from zenith.

These effects highlight at least three points for consideration: (1) it will
be an inefficient use of resources to track certain pointings for the full
observing duration, (2) tracking pointings naively for the full duration,
especially if a significant fraction of the time is spent below the 10% power
point, may actually reduce sensitivity to pulsars, and (3) the full-sky
sensitivity will be patchy regardless of TAB forming strategies (although this
is partly mitigated by having observations overlap by $\sim$20% at the central
frequency). To address (1) we can estimate the time a source remains in a
reasonable power range of the primary beam and only form TABs from the
appropriate subset of voltages recorded (e.g., while the target source is not
in a null). For (2) we must strike a careful balance between achieving maximal
sensitivity (by cutting off parts of the TAB) and dwell time (which benefits
searches for longer-period pulsars and single pulse events). The consequence
of (3) is unavoidable given the telescope configuration and observing strategy
employed, but is quantifiable.

We can evaluate the relative sensitivity (assuming a 80-minute track for a
given TAB) by summing the primary beam response power at discrete time steps,
where we use our current best Full Embedded Element (FEE) model (Sokolowski et
al., 2017), normalised to the equivalently summed power that would represent
the best possible dwell time and sensitivity combination. For our purposes we
define this quantity as the sum of the primary beam power at zenith for the
full observing duration (i.e., imagining we can track an equatorial position
with full zenith sensitivity). This is useful as it scales the effective
sensitivity to a quantity close to what a single-dish steerable telescope
could achieve. In Fig. 4 we present these effective sensitivity maps, in
equatorial coordinates, for the same example observations used in Fig. 3.

Figure 4: Effective sensitivity maps, assuming a full 80-minute tracking and
integration for a given TAB sky position. The colour map is normalised to the
best possible sensitivity (described in the text), and contours at 25, 50, and
75 per cent are drawn for clarity. Due to the drift scan nature of the
observations versus the tracking TABs, we can never achieve the best possible
sensitivity. Right Ascension and Declination are marked by the vertical and
horizontal curved grid lines, respectively. Figure 5:  Workflow diagram
illustrating the first-pass SMART processing pipeline: voltage data at
100-$\mu$s/10-kHz resolutions are recorded from 128 tiles of the array after
tile beamforming and channelisation stages, and are subsequently ported to the
Pawsey supercomputer where the initial processing including calibration,
beamforming and known pulsar detections are carried out. Search processing is
currently performed on the OzSTAR supercomputer, and is limited to basic
periodicity searches.

##  3 Data processing and analysis

In terms of data collection and processing requirements, the SMART survey is
the largest all-sky pulsar survey undertaken in the southern hemisphere, and
is only the second largest after LOTAAS. The SMART survey will accrue $\sim$3
PB of VCS data, compared to $\sim$1 PB (search mode data) by the highly
successful Parkes HTRU survey, and $\sim$8 PB (beamformed data) by LOTAAS. As
outlined earlier, the survey will cover the sky in 70 VCS pointings, each VCS
observation being 4800 s (42 TB). The management and processing of this volume
of data is non-trivial, particularly considering the computational resources
currently available. The processing software and pipelines are developed,
tested and benchmarked on Pawsey’s Galaxy/Garrawarla clusters, and
subsequently ported and benchmarked on Swinburne’s OzSTAR supercomputer. The
time on OzSTAR is secured via the merit allocation scheme under Astronomy and
Supercomputer time allocation, and is typically 0.5-0.6 million service units
(CPU core) hours per annum. These constraints largely drive the initial
processing strategies, thereby necessitating a first-pass shallow survey.

Compared to the HPC resources available at Pawsey, the processing efficiency
has been relatively higher on OzSTAR, where the current benchmarks are 2 kSU
for beamforming and 25 kSU for searching a 10-min observation (4.4 TB), where
1 kSU = 1000 service units (CPU core hours). The current allocation thus
allows processing of 9 observations (fields) per semester, where each 10-min
VCS observation is processed for $\sim$6000 tied-array beams, each of which is
then searched in 2358 trial DMs, out to 250 $\rm pc\,cm^{-3}$. The completion
of first-pass processing will thus require $\sim$2 million core hours. Scaling
from the current benchmarks, we would thus expect 1,500 kSU per full
observation for deeper searches, and 60 million core hours for full DM
searches ($\sim$10,000 searches, for a max DM of 250 $\rm pc\,cm^{-3}$),
necessitating the integration of GPU-based search processing in the future.

An overview of the processing pipeline is presented in Fig. 5, the details of
which are described in the sections below. In essence, this involves
preprocessing and beamforming of voltage data from 128 tiles of the array to
generate beamformed time series, before the data can be processed through the
search and detection pipelines. The main steps are outlined below.

###  3.1 Pre-processing and Beamforming

The main step in the preprocessing stage involves processing VCS data so they
can be calibrated and coherently combined to produce beamformed time series at
the native resolution of 100-$\mu{\rm s}$/10-kHz of the VCS. The array
calibration is performed using one of the standard calibrators (e.g., 3C444),
recorded in the visibility mode at the default 0.5-s/40-kHz resolution, where
complex gain solutions (amplitude and phase) are obtained for each of the 128
tiles, for every coarse channel (1.28 MHz wide), using the Real Time System
(RTS) software package. The procedure is essentially similar to those employed
for other VCS observations (e.g., Swainston et al., 2021). The calibration
solutions can then be used to coherently combine the voltage data in phase
using the tied-array beamformer, the conceptual details and implementation of
which are detailed in Ord et al. (2019). The functionality was enhanced, and
GPU parallelised, in preparation for SMART data processing (Swainston et al.,
2022).

The beamformed data are written as Stokes I at 100-$\mu{\rm s}$/10-kHz
resolutions. The current implementation allows processing 120 coarse-channel
beams at once, i.e., 5 full-bandwidth (30.72 MHz) beams, resulting in a data
rate of 87 GB/beam for a 10-minute observation. For each survey pointing, this
amounts to $\sim$500 TB in beam-formed data. These data are equivalent to that
would emerge from the standard pulsar backends and so can be processed using
standard pulsar search packages. Typically, data would be processed to
generate RFI masks; however the superb radio-quiet environment at the
telescope site and preferential observing during the nightly hours (and within
an hour of the source transit) make this step not essential for the SMART
data. In most cases, data are minimally affected by RFI, and consequently no
RFI-related processing is carried out in the ongoing first-pass processing.

The large FoV of the MWA means excellent prospects for detecting multiple
known pulsars within each pointing, which is also important for crucial data
quality checks and initial assessment of array calibration and tied-array
sensitivity. In short, each SMART observation is processed for known pulsars
within the primary beam ($\sim$610 ${\rm deg^{2}}$ ), using a custom pulsar
detection pipeline.

###  3.2 Search pipeline

The current SMART pipeline includes a GPU-based pipeline for front-end
processing (beamforming) and a CPU-based pipeline for downstream (search)
processing. The search pipeline is based on the Pulsar Exploration and Search
Toolkit333See https://github.com/scottransom/presto (PRESTO; Ransom, 2001,
2011) pulsar search software suite, with the addition of machine-learning (ML)
tools adopted from the LOTAAS classifier (Tan et al., 2018b). This was adopted
as a first-pass processing strategy, to ensure an end-to-end working pipeline
from the data collection and reordering stage (occuring at the observatory
site) to array calibration/quality checks (Pawsey) and search processing
(OzSTAR). To encapsulate the full search workflow, we make extensive use of
Nextflow444See https://github.com/nextflow-io/nextflow (Di Tommaso et al.,
2017) to manage data input, output, processing tasks, and intermediate or
final product creation and tracking.

In the near future, as we transition to full-sensitivity searches, the search
component will be replaced by a GPU-based implementation. Here we present a
detailed breakdown of the current SMART search pipeline, where 10-min data
(4.8 TB) are processed from each observation.

####  3.2.1 Dedispersion and periodicity search

The beamformed data are processed to create dedispersed time series for each
beam. As mentioned earlier, for the first-pass processing, maximum DM searched
is 250 $\rm pc\,cm^{-3}$. At higher DMs, scattering can be significant; e.g.,
pulse broadening times $\gtrsim$ 100 ms are expected at 155 MHz for sight
lines toward $|b|\lesssim 5{{}^{\circ}}$, and $l\gtrsim 330{{}^{\circ}}$ or
$l\lesssim 30{{}^{\circ}}$, where such high DMs can be expected. Further, even
with 10-kHz channels, DM smearing can still be significant at low frequencies.
For instance, at a frequency of 140 MHz (i.e., the low end of the SMART band),
intra-channel dispersion smearing is $\sim$1.5 ms at $\rm DM=50\,\rm
pc\,cm^{-3}$, and $\sim$10 ms at $\rm DM\sim 250\,\rm pc\,cm^{-3}$. The
dedispersion plan was created using the PRESTO DDplan.py utility, but with the
caveat that sub-optimal settings were chosen (the use of coarser DM steps) to
limit the number of DM trials to 2358, given the limitation of computational
resources. The prepsubband tool from PRESTO was used to create incoherently
dedispersed time series from the PSRFITS (i.e., search mode) files. It makes
use of the sub-band dedispersion technique, which uses a piece-wise linear
approximation to the quadratic dispersion relation. The dedispersion plan
employed in the first-pass search is shown in Table 2.

Searching for periodic signals involves computing the power spectra of the
dedispersed time series, which is performed using the realfft tool within
PRESTO, by applying Fourier transform techniques. These power spectra are then
searched for periodicities using accelsearch (Ransom et al., 2002), which
detects the most significant periodic signals and uses harmonic summing to
recover the power spectra at multiples of a given spin frequency. No
acceleration searches are performed in this first pass; i.e., searches are
only performed at zero acceleration. Acceleration searches would require
significant processing cost, given the large data rates, and the number of
trial DMs required, but will be part of the second pass search. If the
significance of any spectral bin is 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.

A sifting procedure is then performed on the list of candidates from all 2358
DM trials. We adopt a fairly standard procedure, quite similar to that
followed for LOTAAS, where candidates with $P<1$ ms or $P>30$ s are
rejected,555This period range was adopted given the minimum and maximum period
of known pulsars in the ATNF pulsar catalogue when our processing commenced,
which was 1.3 ms and 23.5 s, respectively. as well as those with $\rm DM<1$
$\rm pc\,cm^{-3}$. Candidates with similar DMs and harmonically related
periods are then grouped, and only the instance with the highest S/N is kept.
From this reduced candidate list, only those with $\gtrsim$5$\sigma$
detections are then folded.

####  3.2.2 Candidate folding

Folding of the candidates is performed using the prepfold tool, which creates
the associated candidate files and standard diagnostic plots such as those
shown in Fig. 6. Since our pipeline uses the LOTAAS classfier, the folding
analysis is carried out using the identical parameter setup as in the LOFAR
search pipeline; i.e., 100 pulse phase bins, 256 sub-bands, 120 sub-
integrations for $P>10$ ms, whereas 50 pulse phase bins and 40 sub-
integrations for $P<10$ ms. With this, the folded candidate information can be
classified and processed using the ML classifier that we have adopted from the
LOFAR search.

####  3.2.3 Single pulse search

Single pulse searches have proven to be effective for detecting the class of
pulsars that emit sporadically (e.g., RRATs, and giant-pulse emitters such as
the Crab). The basic algorithm involves trialling a range of box car widths,
$2^{n}\,t_{samp}$, where $t_{samp}$ is the sampling time resolution (100
$\mu{\rm s}$ for SMART) and $n=0,1,2,\dots,N$, where $N$ corresponds to the
maximum width searched (e.g., Cordes & McLaughlin, 2003), and detecting
‘events’ that are above a set threshold. It is not computationally demanding,
and is routinely performed in most pulsar searching. The pipeline has been
tested using a SMART observation containing the Crab pulsar, and has also
yielded a blind detection of a LOFAR-detected RRAT J0301+20 (Michilli et al.,
2018). Integrating this into the processing chain is part of our second-pass
search strategy.

(a)

(b)

(c)

(d)

Figure 6: Examples of standard PRESTO diagnostic plots of original periodic
pulsar candidate detections (left panels), and improved detection plots from
follow-up processing for confirmation (right panels). Upper panels are the
first pulsar discovered from the SMART, PSR J0036$-$1033, and the lower panels
are the second pulsar, PSR J0026$-$1955\. Initial detections are from 10-min
observing durations (first-pass processing), while the confirmation ones are
from longer durations of the same initial detection observations.

###  3.3 Pulsar detection pipeline

####  3.3.1 ML classification of candidates

For each VCS survey pointing ($\sim$610 ${\rm deg^{2}}$ , which is tessellated
to $\sim$6000-8000 beams), the processing typically results in $\sim$135,000
candidates. Scaling for a significantly larger sensitivity (3$\times$) and a
larger number of DM trials ($\sim 4\times$) anticipated in full-scale deep
searches in the second pass, we may expect over 50 million candidates. Even
for the first pass, as many as 9 million candidates can be expected,
extrapolating the rate of candidates requiring scrutiny from the current
pipeline. Indeed, visual inspection of that many candidates is unrealistic,
thus necessitating the use of ML classifiers.

As an initial strategy, we have adopted the ML software that was developed for
LOTAAS. The algorithm used is described in Lyon et al. (2016) and Tan et al.
(2018a), and is summarised here. The classifiers use the statistics of the
pulse profile (i.e., mean, variance, skewness and kurtosis) and the DM curve
(i.e., S/N vs DM; see Fig. 6). As described in Tan et al. (2018a), this basic
approach is expanded by also calculating the correlation coefficient between
each sub-band of the profile, as well as correlation coefficients between each
sub-integration and the profile. In effect, the classifier uses the statistics
of correlation coefficient distributions, in addition to the statistics of the
profile and the DM curve, in order to classify the periodicity candidates.
Four standard models are used for the regression: (1) decision tree algorithm,
(2) multilayer perceptron, (3) probabilistic Bayes classifier, and (4) linear
support vector machine.

Even without being trained on MWA data, the software performs reasonably well,
with a recall rate of $\sim$83% for the worst-performing regression model.
While clearly not optimised for a MWA search, it can still provide a
significant cull on the number of candidates that require human scrutiny as
long as the number of false negatives is kept below an acceptable threshold.
To minimise the false negative rate, we use the provided “ensemble”
classifier, which labels candidates as positive if at least three models
classify them as positive. Under this criterion, the number of candidates is
cut down from the original $\sim$135,000 per pointing down to $\sim$20,000
that require human scrutiny, i.e., an efficiency of $\sim$85%. The false
negative rate can be lowered further by allowing candidates classified as
pulsars by a smaller number of regression models to be passed, but this comes
at the cost of also lowering the efficiency. For the first-pass processing, we
find the current arrangement to be an acceptable compromise, but will be
implementing an improved ML classifier for the second pass.

Of the remaining $\sim$20,000 candidates per pointing, only a small fraction
are true pulsar detections, with the vast majority of candidates consisting of
noise and RFI. Here, we are extending the definition of RFI to include any
artefact from the MWA signal path that may result in spurious detections.
Owing to the radio-quietness of the observatory site, such candidates belong
almost exclusively to this category, and almost never arise from external
sources. The most common RFI candidates are those with periods of either 1
second or with a close harmonic relationship (e.g., $0.5\,$s, $2\,$s),
relating to the division of data packets by 1-second boundaries. Such
candidates are sufficiently few (and easily identified) that we do not apply
any automatic procedure for removing them from our pool of candidates.

Figure 7: The theoretical array factor (a proxy for sensitivity) of each tied-
array beam towards the pulsar B2327$-$20, with the red cross marking the
position of the pulsar (left panel) and the beams in which the pulsar was
detected (right panel). SMART observation 1226062160 was used for the
demonstration.

####  3.3.2 Prioritisation and scrutiny of candidates

The candidates that survive the initial ML cull are still mostly dominated by
noise and RFI detections, with only a small minority being true pulsar
detections. Although all of these candidates are intended ultimately to be
visually inspected, we have developed a so-called “clustering” algorithm to
prioritise which candidates get inspected first, in order to accelerate the
detection of sufficiently bright, new pulsars.

The clustering algorithm leverages the fact that the tied-array beam of the
MWA’s compact configuration is relatively complex, with significant grating
lobes located in different parts of the primary beam. Because the spacing
between tied-array pointings is equal to the FWHM of the main lobe of the
tied-array beam, any sufficiently bright pulsar will likely be detected in
multiple beams. For instance, Fig. 7 shows a map of multiple detections of PSR
B2327$-$20 superimposed on the theoretical sensitivity of each tied-array beam
towards the pulsar, as predicted by the array factor formalism developed for
the MWA by Meyers et al. (2017). Since noise candidates will not be correlated
across different beams, prioritising similar candidates that appear in
multiple beams dramatically increases the likelihood that candidates
representing true astrophysical signals will be inspected first.666This is
counter intuitive to the case of multi-beam surveys with Parkes-like single-
dish telescopes, where similar candidates detected in multiple beams across
the sky would indicate RFI.

Candidates are considered similar if

  1. 1.

they appear in at least two adjacent beams,

  2. 2.

they have periods within 0.5% of each other, and

  3. 3.

they have DMs within 3 $\rm pc\,cm^{-3}$ of each other.

As a demonstration of the usefulness of the clustering algorithm, we show how
it would detect PSR J0026$-$1955, the second pulsar discovery in the SMART
survey (McSweeney et al., 2022). In reality, the clustering algorithm was not
implemented until after PSR J0026$-$1955 was discovered, but it is interesting
to note that the first detection (chronologically) of this pulsar was a
grating lobe detection (at the time, the candidates were being served up
randomly), which motivated the development of the clustering algorithm in the
first place.

The final set of detections of PSR J0026$-$1955 is shown in Fig. 8, on a
backdrop of the theoretical array factor (a proxy for sensitivity) towards the
pulsar assuming that our current best-fit position is correct. In this case,
three of the search beams contained the nominal pulsar position in the main
lobe, while several others positioned the pulsar in their respective grating
lobes. All of the displayed detections meet the second and third clustering
criteria (similar periods and DMs). Therefore, any pair of detections in the
same or adjacent beams are considered “clusters”, and if the clustering
algorithm was in use when this observation was processed, this pulsar would
have been picked up immediately in multiple clusters.

The clustering algorithm offers no advantage for relatively weak pulsars that
would be detected only in a single (boresight) tied-array beam. Therefore,
unclustered candidates are not deleted, only deprioritised.

####  3.3.3 Human inspection and ranking

Just as the clustering algorithm is a method for prioritising candidates for
human inspection, so too is human inspection a method for prioritising
candidates for follow-up (see §4). Users are served up candidates one at a
time and presented with the candidate’s PRESTO diagnostic plots (e.g., Fig.
6). Each candidate is given an integer rating from 1 to 5, with higher numbers
corresponding to a higher confidence that the candidate is a bona fide pulsar
detection. Clear pulsar detections are then compared to the ATNF catalogue
pulsars to check if it is a known pulsar. If a detection is unknown,
candidates listed in other surveys are then checked using the Pulsar Survey
Scraper tool.777See https://pulsar.cgca-hub.org/ If the pulsar is in either
the ATNF catalogue or in another survey’s candidate list, a note is made
against the candidate with the pulsar’s name, visible to all other users.

Figure 8: The theoretical array factor (proxy for sensitivity) in the vicinity
of PSR J0026$-$1955 for observation 1226062160, assuming a true position
(centre of image) derived from GMRT imaging (cf. Paper II for details). Red
crosses mark the position of beams in which it was detected, and the blue dot
marks the first detection. A single cross may indicate multiple detections
with slightly different periods and DMs.

Each candidate can be ranked by multiple users (but users can only rank each
candidate once). A candidate that has been rated by at least four users
becomes eligible for follow up, and the list of eligible candidates is ordered
by the average rating.

Currently, as the number of users of the system is still relatively small, the
rating of candidates is the primary bottleneck in the whole processing chain.
This means that during first-pass processing, interesting candidates have been
followed up immediately. In the future, however, as the number of users
performing the task of rating candidates grows, the pool of eligible
candidates may grow faster than the rate at which they can be followed up.
However, the above system of candidate prioritisation means that the most
interesting candidates are always followed up first.

###  3.4 Data management and web-app

The large number of generated candidates, the complex metadata associated with
them, and the desire to distribute the tasks of data processing, candidate
rating and candidate follow-up, motivated the implementation of a relational
database to track the progress of the SMART survey and coordinate processing
efforts. The database, implemented in PostgreSQL, is comprised of a set of
tables containing metadata for

  1. 1.

MWA observations (e.g., primary beams, tied-array beams, candidates);

  2. 2.

software (e.g., for beamforming, searching, ML classification), including
versioning information;

  3. 3.

candidate ratings;

  4. 4.

pulsars;

  5. 5.

users; and

  6. 6.

supercomputer facilities.

The users, along with their database access privileges and authentication, are
managed by a subset of tables which interface with website front end
implemented in Django. Both the database and the website are hosted by Data
Central.888See https://apps.datacentral.org.au/smart

Once an observation has been processed and the candidates have been subjected
to the first-pass ML cull (§3.3.1), both the metadata of the remaining
candidates as well as the candidates themselves (i.e., PRESTO .pfd files and
the associated diagnostic plots) are uploaded to Data Central. The uploaded
candidates are then available for users to rate via the web interface
(§3.3.3).

As described above, candidates can then be sorted by their average rating, and
followed up at will by any authorised user. Before following up a candidate,
the user may “claim” it by clicking a button in the candidate list. This
feature is designed to prevent multiple users from following up the same
candidate and unnecessarily duplicating effort. The decentralised design
allows members of the SMART collaboration from different research institutions
to work through the SMART data set without the need for someone to oversee and
coordinate the different groups’ activities.

##  4 Confirmation and initial follow-up of candidates

Confirmation and follow-up of promising pulsar candidates typically relies on
multiple re-observations, often requiring a significant amount of telescope
time. Fortunately, the SMART survey’s unique design, where VCS data are
retained (unlike pre-processed beamformed data), offers flexible reprocessing
options, allowing us to accelerate important confirmation and follow-up
procedures. Furthermore, a substantial amount of archival VCS data (from past
projects) are available for a large part of the MWA sky, which can also be
suitably exploited for further detection and improved localisation. These
features make the SMART survey distinct from other pulsar surveys.

In the following sections we outline the main strategies that are adopted for
confirmation and initial follow-up, including: reprocessing of the original
observation for improved detection; performing a dense grid for improved sky
localisation; and polarimetry via reprocessing the survey observation for full
Stokes information and rotation measure (RM) determination. Further detailed
follow-ups including the use of archival data for timing analysis and imaging
for improved localisation are discussed in the companion paper (Paper II).

###  4.1 Improved detection

For our ongoing shallow survey, processing the full 80-minute observation
itself readily provides an avenue for confirmation. If the source is genuine
and a steady periodic emitter, this should result in a three-fold improvement
in S/N. The improvement will be reduced if it is an intermittent source; e.g.,
a pulsar with large nulling fraction. Both these possibilities are exemplified
in Fig. 6, which shows the original discovery plots along with the improved
detections for PSRs J0036$-$1033 and J0026$-$1955\. The full 80-minute
observations (42 TB) containing the original detection can be processed and
searched over a restricted range in $P$ and ${\rm DM}$ using the PRESTO
prepfold routine. The observations were also processed using the pdmp routine
within PSRCHIVE pulsar data processing suite999See
https://sourceforge.net/projects/psrchive/ (Hotan et al., 2004; van Straten et
al., 2012), to provide a cross-check and a more accurate DM. This is
equivalent to undertaking a longer observation for confirmation. For many of
our candidates, this readily provides effective ways of confirming or
rejecting a candidate, and eliminates the need for securing additional
telescope time that most other surveys typically require.

While the long dwell time of 4800 s should in principle result in an increased
sensitivity to sporadic or intermittent pulsars, our current first-pass
processing does not necessarily benefit from this. Given this, the discovery
of PSR J0026$-$1955 in the first 10 minutes of observations, a pulsar with
long-duration nulls and a nulling fraction of $\sim$77%, was remarkably
fortuitous (see Fig. 6). Details of the discovery, including an analysis of
sub-pulse drifting, are reported in McSweeney et al. (2022). As mentioned
therein, this pulsar turned out to have already been reported as a candidate
in the GBNCC survey but was blindly (and independently) discovered in the
SMART survey data.

Figure 9: MWA localisation of PSR J0026$-$1955 by performing a dense grid
around the initial pulsar position from the discovery observation. The source
position
$\rm(RA,Dec)=(00^{h}26^{m}37.5^{s},-19{{}^{\circ}}56^{\prime}24.9^{\prime\prime})$
is $\approx 32^{\prime\prime}$ offset from uGMRT-determined position (cf.
Paper II for further details). Observations were made using the extended MWA
array (Phase II, with $\sim$6 km maximum baseline). The uncertainties in the
MWA position is $\sim 12^{\prime\prime}$ (i.e., about one-tenth of the tied-
array beam size, shown as dashed circles on the left panel).

###  4.2 Improved positional determination

As outlined in §2.3, the tied-array beam size for SMART is $\sim 23^{\prime}$.
Therefore a more accurate position is essential both for improved detection
(i.e., re-beamforming on a more exact sky position) and to facilitate
effective follow-ups with other (and more sensitive) telescopes, particularly
at higher frequencies where the beams are narrower, even with single-dish
telescopes such as Parkes. This would typically involve making multiple re-
observations to form a grid around the nominal candidate position. The SMART
survey design where the sky is densely sampled (at a rate comparable to, or
slightly better than, the Nyquist; Fig. 1), allows this to be achieved via
reprocessing of the original survey observation, where a dense grid of
pointings encompassing the initial position is used for improved positional
determination. An example is shown in Fig. 9 for the case of PSR
J0026$-$1955\. In general, for an initial detection with a modest significance
of $\rm S/N\sim 10$, we may expect a positional accuracy $\sim$1-2′ through
this exercise. In practice, archival VCS data, if available, can also be
suitably exploited to progressively further improve the position. In an ideal
scenario, where data recorded from all three different configurations are
available, an improvement of the order of nearly two orders of magnitude can
be achieved through this procedure, as demonstrated in Swainston et al.
(2021).

###  4.3 Polarimetry

The VCS recording allows the reprocessing of discovery observations to
generate full polarimetric beamformed time series, which can be analysed using
standard pulsar packages such as DSPSR101010See
https://sourceforge.net/projects/dspsr/ (van Straten & Bailes, 2011) and
PSRCHIVE, for full Stokes profiles. These beamformed MWA data were obtained
using the procedures described in Ord et al. (2019) and Xue et al. (2019). The
Faraday rotation measure synthesis technique (Brentjens & de Bruyn, 2005) can
then be applied to estimate the rotation measure (RM).

As an example, Fig. 10 shows polarisation data for pulsar J0026$-$1955,
obtained by reprocessing the original discovery observation. This yielded an
RM estimate of $3.65\pm 0.09$ $\rm rad\,m^{-2}$. After correcting for Faraday
rotation, linear and circular polarisation was detected. The pulsar exhibits
significant amount of linear polarisation but only a small amount of circular
polarisation. We attempted to fit the rotating vector model (Radhakrishnan &
Cooke, 1969) to the position angle (PA) of the linear polarisation across the
on-pulse window, in order to constrain the viewing geometry, $(\alpha,\beta)$,
where $\alpha$ is the angle between the magnetic and rotation axes, and
$\beta$ is the impact angle of the magnetic axis on the line of sight. In the
absence of relativistic effects, the PA curve is expected to be steepest in
the center of the pulse profile, with slope
$d\psi/d\phi=\sin\alpha/\sin\beta\approx 2.4$, where $\psi$ is the PA at phase
$\phi$.

Figure 10: Polarimetric profiles of PSR J0026$-$1955 obtained by reprocessing
the discovery observation at 155 MHz. The black, red, and blue curves in the
lower panels show the total intensity, linear, and circular polarisation,
respectively. An RM estimate of $3.65\pm 0.09\,\rm rad\,m^{-2}$ was obtained,
and the data were corrected for Faraday rotation.

##  5 Survey simulations and forecast

The ongoing first-pass processing (i.e., essentially a shallow survey for
long-period pulsars) is limited to processing only a fraction (1/8th) of our
observation time over coarser (sub-optimal) trial DM values, out to a maximum
DM of 250 $\rm pc\,cm^{-3}$, and to basic periodicity search. In the second
pass we will extend this to full 80-min observations and employ more optimal
DM steps. Besides a three-fold increase in sensitivity expected for long-
period pulsars (by virtue of longer integration times), substantial
improvements in sensitivity is also expected to millisecond pulsars via finer
DM steps and optimal dedispersion plans to match our 100-$\mu$s/10-kHz
resolutions. These considerations motivated our simulation analysis to make
some meaningful forecast of the expected survey yield, both for long-period
pulsars and MSPs, as summarised below. They provide further justification to
undertake a full-scale search processing, planned as part of second-pass
processing.

Figure 11:  Simulated pulsars detectable (colour filled circles) in an an all-
sky high-time-resolution pulsar search with the MWA in the 140-170 MHz band.
The shaded region represents the MWA’s visible sky, i.e., the sky south of
$+30{{}^{\circ}}$ in declination. The black filled circles represent known
pulsars in the ATNF pulsar catalogue (version 1.67). The colour scale
indicates the DM in units of $\rm pc\,cm^{-3}$.  Figure 12:  Simulated pulsars
detectable in an all-sky pulsar search with the MWA’s 140-170 MHz band with a
dwell time of 4800 seconds. The shaded region represents the MWA’s visible
sky, i.e. the sky south of $+30{{}^{\circ}}$ in declination. The black filled
circles denote the long-period pulsars, whereas millisecond pulsars detectable
in high-sensitivity searches (e.g., using the CDMT) are shown as colour filled
circles. The colour scale indicates DM in units of $\rm pc\,cm^{-3}$.

###  5.1 Long-period pulsars

The discovery of two new pulsars from the processing of a small fraction of
survey data hints at the potential for many new pulsar discoveries from a
deeper survey that will take advantage of the full 80-min observation. To
estimate the survey yield, we have performed survey simulations, using the
formalism outlined in Xue et al. (2017). The analysis made use of the popular
simulation package PsrPopPy (Bates et al., 2014) that was developed from the
original pulsar simulation software PSRPOP by Lorimer et al. (2006). The
simulations take into account the sky dependence of the system temperatures at
low frequencies ($T_{sky}\propto\nu^{-2.55}$), as well as the loss in the
array gain ($G$) expected at large zenith angles, modelled as
$G(\theta_{z})=G_{\rm max}{\rm cos}(\theta_{z})$, where $\theta_{z}$ is the
zenith angle and $G_{\rm max}$ is the gain at $\theta_{z}=0$. We simulated a
population of $1.6\times 10^{5}$ Galactic canonical pulsars, extrapolated from
Parkes Multi-beam Pulsar Survey (Manchester et al., 2001) detections. The
luminosity distribution of the canonical pulsar population follows a log-
normal distribution $\langle{\rm log}_{10}L\rangle=-1.1$, $\sigma[{\rm
log}_{10}L]=0.9$ , where $L$ is the radio psuedo-luminosity in units of $\rm
mJy\,kpc^{2}$ (Faucher-Giguère & Kaspi, 2006). The Galactic radial density
distribution follows the Yusifov & Küçük (2004) model. With the caveat that
our understanding of the pulsar luminosity function and beaming fraction is
limited, we project the deep survey to reach a limiting sensitivity of
$\sim$2-3 $\rm mJy$, with a potential net yield of $310\pm 100$ new pulsar
discoveries (see Fig. 11). This projection mainly applies to the population of
long-period pulsars and does not account for other classes of pulsars such as
sporadic emitters (e.g., RRATs), or millisecond and binary pulsars, whose
populations are hard to model or simulate.

Assuming an isotropic distribution of our simulated local pulsar population
($\rm DM\lesssim 250$ $\rm pc\,cm^{-3}$), and scaling for the current (first-
pass) search sensitivity (i.e. one-third of the deep pass sensitivity), and
the fraction of data for which the candidate scrutiny has been completed
($\sim$5%), we may expect $\sim$3-5 pulsars. The detection rate at this early
stage of SMART thus appears to be in line with this general expectation. While
this may seem fortuitous, the unique advantages of the SMART pulsar survey,
especially the accessibility to the southern hemisphere, the radio-quiet
environment, and the survey parameters (e.g., long dwell times and high
time/frequency resolutions), offer excellent prospects for new pulsar
discoveries, provided the substantial processing challenges can be addressed.

###  5.2 Millisecond pulsars

Even though the detection sensitivity to MSPs is significantly reduced in our
current shallow pass of the survey (owing to the use of coarse or sub-optimal
DM step sizes; see Fig. 2), the second-pass processing, where we plan to
employ more optimal DM searches with a finer step-size in DMs, is expected to
yield a substantial improvement in sensitivity, particularly at low to
moderate DMs, out to $\lesssim$50 $\rm pc\,cm^{-3}$. At DMs $\gtrsim$70 $\rm
pc\,cm^{-3}$, and especially in regions near the Galactic plane and toward the
centre, scatter broadening is expected to result in sensitivity degradation,
given the strong frequency dependence (pulse broadening time,
$\tau_{d}\propto\nu^{-3.9}$; cf. Bhat et al., 2004), due to which
$\tau_{d}\gtrsim$10 ms, which, for millisecond pulsars, can be a substantial
fraction of the rotation period. Using PsrPoPy, we simulated a population of
$3\times 10^{4}$ MSPs with $P$ and ${\rm DM}$ distributions essentially
derived from the HTRU intermediate latitude pulsar survey (Levin et al.,
2013), and with a luminosity limit of $L_{1400}\sim 0.2\,\rm mJy\,kpc^{2}$.
This corresponds to a limiting flux density $\sim$10 $\rm mJy$ at 150 MHz,
assuming a spectral index of $\alpha=-1.8$ (and a distance of $\sim$1 kpc),
and thus in principle detectable provided there is no significant degradation
from dispersive smearing or temporal broadening from scattering.

As with the population of long-period pulsars, this analysis accounted for the
sky dependence of $T_{\rm sky}$, non-uniformity in the array gain, and strong
frequency scaling of scattering, which is especially important for MSPs. For
example, using some preliminary dedispersion plan estimates for the second
round of processing (i.e., the deep search), where we assume a typical plan
would involve DM steps of 0.01 $\rm pc\,cm^{-3}$ up to 54 $\rm pc\,cm^{-3}$
and 0.02 $\rm pc\,cm^{-3}$ out to 107 $\rm pc\,cm^{-3}$, our simulations
predict 55 detectable MSPs above our detection threshold, and hence $\sim$15
new MSP discoveries. However, a substantial increase is forecast in
simulations that closely emulate the higher sensitivity attainable through
more optimal searches that make use of _coherent dispersion measure trials_
(CDMT), which is equivalent to the use of finer DM steps of 0.002 $\rm
pc\,cm^{-3}$, and will limit residual DM smearing to $\sim 150\,\mu{\rm s}$
(comparable to $\sim 100\,\mu{\rm s}$ native resolution of the VCS). In
essence, this means that full-scale, high-sensitivity searches employing the
implementation of CDMT, if feasible for SMART, can potentially lead to the
discovery of as many as $\sim$30 MSPs.

The simulated population of $\sim$70 MSPs, along with the simulated population
of long-period pulsars (see §5.1), is shown in Fig. 12. Our simulation
analysis did not consider a large population of MSPs discovered in recent (and
highly successful) Fermi-directed targeted searches (Deneva et al., 2021, and
references therein). Even so, the detectable population of MSPs is almost
twice the currently known population within $\rm DM\lesssim 100$ $\rm
pc\,cm^{-3}$, which means a net MSP yield that is competitive to that from the
highly successful Parkes HTRU survey. Indeed, as evident from Fig. 12, the
detectable population of MSPs is limited to $\rm DM\lesssim 70$ $\rm
pc\,cm^{-3}$, which is reconcilable given the expected pulse broadening times
of $\tau_{d}\gtrsim$10 ms toward such moderate-DM pulsars at the low
frequencies of the MWA (e.g., Kirsten et al., 2019). Consequently, the vast
majority of MSPs discovered will likely be suitable for high-precision timing
applications such as pulsar timing arrays.

##  6 Future processing plans

The planned second-pass survey will extend the processing to the full 80-min
observations and carry out more optimal searches in the DM parameter space,
while incorporating searches for both long-period pulsars and millisecond
pulsars. As such, the long dwell times of SMART (4800 s) can be exploited to
search for pulsars with very long periods, like those discovered by LOFAR and
MeerKAT (Tan et al., 2018b; Caleb et al., 2022), and provide increased
sensitivity to objects that emit intermittently, e.g. pulsars with long null
duraions such as PSR J0026$-$1955 (McSweeney et al., 2022). In addition, the
adopted strategy to archive recorded voltages offers additional avenues for
future processing; e.g. searches for millisecond pulsars through the
application of novel hybrid dedispersion approaches that involve the use of
coherent dispersion measure trials (CDMT), which was demonstrated by the LOFAR
through the discovery of PSR J0952$-$0607 (Bassa et al., 2017). Below we
outline our processing plans and strategies in the near-term and highlight
some of the computational challenges and other considerations in planning this
second-pass processing.

###  6.1 Beamforming and sensitivity optimisation

As discussed earlier in §2.5, the tied-array beamforming strategy warrants
some more careful thought in order to maximise sensitivity while also reducing
needless processing. Inevitably, this produces an uneven sensitivity threshold
across the sky due to both primary beam pointing effects and effective dwell
time. These considerations are also important when estimating survey-wide
statistics. We are formulating a more efficient beamforming scheme that takes
into account these technical details, which will be presented in a subsequent
paper detailing the second-pass survey processing.

###  6.2 Dedispersion planning and RFI mitigation strategies

For the first-pass survey processing described in this paper, the dedispersion
plan outlined in Table 2 is adequate for all observations. In contrast, a
slightly more sophisticated plan may be required for the second-pass
processing to accommodate the eight-fold increase in observation length and to
provide increased sensitivity to shorter-period pulsars. We are actively
developing a sensible strategy that balances our sensitivity goals and the
relatively large computational costs associated with dedispersing MWA VCS
data, especially since we would essentially be producing $\sim$10$\times$ as
many DM trials.

In addition to revisiting the dedispersion plan, we will also incorporate a
more careful approach to excising or mitigating RFI (both periodic and
impulsive). The observatory site is exceptionally RFI-quiet (owing to the
geographical location and radio-quiet zone status), hence the first-pass
processing did not include any active RFI mitigation other than what is
naturally gained by forming TABs (where off-axis RFI is “phased out”). We are
currently examining the periodic RFI environment by processing observations
taken throughout the SMART observing semesters and using a standard PRESTO-
based approach to find bright, common terrestrial signals by searching for
periodic “candidates” in the zero-DM topocentric time series data. Once we
collect this information, we will apply the masks (after appropriate
barycentric corrections are made) during the periodicity search pipeline.
Additionally, there can occasionally be bursts of narrow-band interference
(e.g., air-craft and satellites in TAB grating lobes) that could severely
affect our data quality for short periods of time. There are several software
pre-processing solutions to this kind of RFI (e.g., Eatough et al., 2009; Men
et al., 2019; Morello et al., 2022), which we will explore in parallel to the
periodic RFI mitigation strategies. Empirically, VCS data are remarkably clear
of impulsive/narrowband RFI in the SMART observing band, and data excision is
$\ll$10% for a typical observation.

###  6.3 Searches for long-period pulsars and sporadic emitters

The long dwell times of SMART make it particularly amenable to the application
of fast-folding algorithms that offer significantly higher sensitivity to
pulsars with rotation periods $\gtrsim$10 s (e.g., Morello et al., 2020). Such
slow-spinning pulsars are likely to be near the radio emission ‘death lines’
so can be invaluable in gaining useful insights into the intricacies of the
pulsar radio emission process. Recent applications of this algorithm in Parkes
and Arecibo searches have led to the discoveries of pulsars with $P>$10 s and
(Morello et al., 2020), or very weak pulsars ($S_{1400}\sim 10$ $\mu$Jy) with
a $\sim$2% duty cycle (Parent et al., 2018). These, and other recent
discoveries such as a 76-s pulsar with MeerKAT (Caleb et al., 2022), provide a
strong motivation for undertaking fast-folding searches. The low levels of RFI
at the observatory site are particularly advantageous for this.

The SMART survey dwell time is substantially longer than those of previous-
generation southern-sky surveys, particularly at high-$|b|$ parts of the sky,
where it is 20 times longer than the HTRU survey (Keith et al., 2010) and 40
times longer than the southern pulsar survey (Manchester et al., 1996). It is
also 40 times longer than that of the ongoing GBNCC survey (Stovall et al.,
2014) that covers the sky north of $-55{{}^{\circ}}$ in declination (Table 1).
Considering this, detection prospects are promising, especially given the
$\sim$2-3 $\rm mJy$ limiting sensitivity that the SMART can attain for long-
period pulsars (§2.4) and negligible degradation in signal strength due to
dispersion and pulse broadening effects.

As described earlier, the long dwell times also increase the search
sensitivity to objects that emit sporadically, such as RRATs and giant-pulse
emitters (e.g., the Crab pulsar), which can be more effectively detected by
searching for individual dispersed pulses, and will be part of the second-pass
processing.

###  6.4 Searches for binary and millisecond pulsars

The long dwell times, and high time and frequency resolutions, of the SMART
can also be exploited, in principle, to search for binary and millisecond
pulsars. However, a full-scale acceleration search can be prohibitively
expensive at the low frequencies, given the very large number of DM and
acceleration trials that are required (e.g., typically $\sim 10^{4}$ up to 250
$\rm pc\,cm^{-3}$, and $\sim 2400$ across $\pm 100\,\rm m\,s^{-2}$). Compared
to the HTRU-south low-latitude survey, which has been successful in finding
such systems (e.g., Cameron et al., 2020), the cost of searching SMART data
can be more than an order of magnitude greater. The successful detection of
several MSPs and the double pulsar in our initial census (cf. Paper II) makes
such searches worthwhile.

An inherent limitation in the searches for such short-period pulsars is the
significant degradation in sensitivity due to substantial dispersion smearing
(relative to rotation periods) despite our 10-kHz channels. Fortunately, this
can be alleviated by using CDMT-based searches (Bassa et al., 2017). Recording
in 24$\times$1.28-MHz channels makes the SMART data highly amenable to the
application of CDMT searches, and can result in a substantial increase in
detection sensitivity to short-period millisecond pulsars. Integration of this
novel method, and benchmarking on prospective HPC clusters with significant
computational resources (e.g., Pawsey’s emerging Setonix cluster) is also part
of our future processing plans, although a full-scale processing may have to
await access to sufficient computational resources. We are also exploring
publicly available, GPU-enabled Fourier domain acceleration search software
(e.g., AstroAccelerate; Armour et al., 2020) as a drop-in replacement for
PRESTO’s CPU-based accelsearch.

Regardless, the high cost of such computationally-intensive searches will
likely necessitate a multi-pass processing strategy; for instance, an initial
pass involving acceleration searches, but limited to a modest number of
acceleration trials (e.g., $\sim$150 to cover $\pm 6\,\rm m\,s^{-2}$), thereby
retaining sensitivity to short-period objects ($P\lesssim 10$ ms) but with the
binary orbital period, $P_{b}\gtrsim 5$ days (i.e., with low-mass white-dwarf
type companions). Full-scale acceleration searches that target binary systems
such as PSR J0737$-$3039 or PSR J1757$-$1854 with $P_{b}\lesssim 5$ hr (i.e.,
requiring $\sim$2400 trials spanning across $\pm 100\,\rm m\,s^{-2}$) are
hence deferred to the longer-term future. Such searches will be primarily
limited to the regions around the Galactic plane, at least initially, thus
processing only a fraction of the SMART data (e.g., sky within $|b|\lesssim
5{{}^{\circ}}$). Such a multi-pass strategy is also motivated by the
demonstrated success of HTRU-south, which has led to the discovery of exotic
systems such as PSR J1757$-$1854 (Cameron et al., 2018) and wide-orbit double
neutron-star system (Sengar et al., 2022). In any case, notwithstanding the
high computational cost, the high-profile scientific applications of such rare
systems make similar full-scale acceleration searches scientifically
compelling for the SMART data. The long-term scientific dividends of such
systems are vividly demonstrated by Kramer et al. (2021) through the 16-year
timing analysis of the double pulsar, enabling the most stringent tests of
general relativity and alternative theories of gravity.

##  7 Summary and conclusions

With its novel features such as voltage recording and long dwell times, and
access to the pristine radio-quiet environment in the southern hemisphere, the
SMART survey is well positioned to play an impactful role in the exploration
of the southern, low-frequency sky for pulsar surveys and science. Since the
MWA is a precursor for SKA-Low, the SMART survey will also serve as an
important preparatory step for pulsar surveys planned with SKA-Low.
Additionally, it will map out the southern sky for low-frequency detections of
many pulsars that were originally discovered at frequencies $\gtrsim$400 MHz.

The survey is enabled by the advent of the Phase II upgrade of the MWA, the
compact configuration of which offers an enormous gain in the beamforming and
processing cost, thereby making large all-sky pulsar surveys tractable with
large-FoV interferomtric arrays such as the MWA. The combination of voltage
recording and the FoV brings a survey efficiency of $\sim$450 ${\rm
deg^{2}\,hr^{-1}}$ , but at the expense of large data rates of 28 $\rm
TB\,hr^{-1}$. Consequently, $\sim$3 PB of (VCS mode) data for the full survey
and significant processing costs.

Due to the substantial computational cost involved in searching at low
frequencies, the processing is undertaken in multiple passes. In the ongoing
first-pass processing, 10 min of data from each observation are processed in
2358 trial DMs, out to a maximum DM of 250 $\rm pc\,cm^{-3}$, thereby reaching
about one-third of the sensitivity that will eventually be attainable in full
observation processing.

The voltage recording strategy adopted for the SMART survey enables a
multitude of avenues for follow-ups and confirmations, including improved
detection, initial polarimetry and arcminute-level positional determination –
all by reprocessing the original observation and, where possible, also
archival VCS data. This also facilitates timely follow-up studies using more
sensitive telescopes such as Parkes and uGMRT that operate at frequencies
$\gtrsim$300 MHz.

With the recent development of a web-app for facilitating efficient scrutiny
of candidate analysis, including classification and ranking for identifying
promising ones to follow-up, we anticipate the discovery rate to increase in
the coming years. As software tools mature and the search pipelines are
expanded to include acceleration trials and fast-folding based algorithms, and
additional computational resources become available, it will become possible
to extend the processing to include searches for binary and millisecond
pulsars, and those with very long periods or even sporadic emitters. Our
simulation analysis forecasts a survey yield of $\sim$300 long-period pulsars
and $\sim$30 millisecond pulsars by the completion of full processing. The
SMART survey data will serve as a complete digital record of the low-frequency
southern sky, and an important reference for even more ambitious surveys
planned with the SKA-Low.

We thank an anonymous referee for several useful comments that helped to
improve the content and presentation of this paper. The scientific work made
use of Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy
Observatory. We acknowledge the Wajarri Yamaji people as the traditional
owners of the Observatory site. This work was supported by resources provided
by the Pawsey Supercomputing Centre with funding from the Australian
Government and the Government of Western Australia. This work was also
supported by resources awarded under Astronomy Australia Ltd’s ASTAC merit
allocation scheme on the OzSTAR national facility at the Swinburne University
of Technology. The OzSTAR program receives funding in part from the Astronomy
National Collaborative Research Infrastructure Strategy (NCRIS) allocation
provided by the Australian Government. The development of SMART webapp was
facilitated by the software support scheme of ADACS. We thank Simon O’Toole
for help with the migration of webapp to Data Central. The GMRT is run by the
National Centre for Radio Astrophysics of the Tata Institute of Fundamental
Research, India. The Parkes (_Murriyang_) radio telescope is part of the
Australia Telescope National Facility which is funded by the Australian
Government for operation as a National Facility managed by CSIRO. We thank L.
Levin for help with the simulation analysis of MSPs.

Software: We acknowledge the use of the following software/packages for this
work: CASA (McMullin et al., 2007), DSPSR (van Straten & Bailes, 2010, 2011),
PRESTO (Ransom, 2001, 2011), PSRCHIVE (Hotan et al., 2004; van Straten et al.,
2011), Tempo2 (Hobbs et al., 2006; Hobbs & Edwards, 2012), PINT (Luo et al.,
2019, 2021), PsrPopPy (Bates et al., 2014), Nextflow (Di Tommaso et al.,
2017).

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