# The second set of pulsar discoveries by CHIME/FRB/Pulsar: 14 Rotating Radio
Transients and 7 pulsars

Fengqiu Adam Dong,1 Kathryn Crowter,1 Bradley W. Meyers,1,2 Ziggy Pleunis,3
Ingrid Stairs,1Chia Min Tan,5,6 Tinyau Timothy Yu,1 Patrick J. Boyle,5,6
Amanda M. Cook,3,4 Emmanuel Fonseca,7,8Bryan M. Gaensler,3,4 Deborah C. Good
9,10 Victoria Kaspi,5,6 James W. McKee,11 Chitrang Patel,5,6Aaron B.
Pearlman5,6
1Department of Physics and Astronomy, University of British Columbia, 6224
Agricultural Road, Vancouver, BC V6T 1Z1 Canada
2International Centre for Radio Astronomy Research, Curtin University,
Bentley, WA 6102, Australia
3Dunlap Institute for Astronomy & Astrophysics, University of Toronto, 50 St.
George Street, Toronto, ON M5S 3H4, Canada
4David A. Dunlap Institute Department of Astronomy & Astrophysics, University
of Toronto, 50 St. George Street, Toronto, Ontario, Canada M5S 3H4
5Department of Physics, McGill University, 3600 rue University, Montréal, QC
H3A 2T8, Canada
6McGill Space Institute, McGill University, 3550 rue University, Montréal, QC
H3A 2A7, Canada
7Department of Physics and Astronomy, West Virginia University, Morgantown, WV
26506-6315, USA
8Center for Gravitational Waves and Cosmology, Chestnut Ridge Research
Building, Morgantown, WV 26505, USA
9Department of Physics, University of Connecticut, 196A Auditorium Rd Unit
3046, Storrs, CT, USA; Center for Computational Astrophysics,
10Flatiron Institute, 162 5th Avenue, New York, New York, 10010, USA
11The Canadian Institute for Theoretical Astrophysics,University of Toronto,
60 St. George Street, 14th floor, Toronto, ON M5S 3H8
<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

The Canadian Hydrogen Mapping Experiment (CHIME) is a radio telescope located
in British Columbia, Canada. The large field of view (FOV) of $\sim$ 200
square degrees has enabled the CHIME/FRB instrument to produce the largest FRB
catalog to date. The large FOV also allows CHIME/FRB to be an exceptional
pulsar and Rotating Radio Transient (RRAT) finding machine, despite saving
only the metadata information of incoming Galactic events. We have developed a
pipeline to search for pulsars/RRATs using DBSCAN, a clustering algorithm.
Output clusters are then inspected by a human for pulsar/RRAT candidates and
follow-up observations are scheduled with the more sensitive CHIME/Pulsar
instrument. The CHIME/Pulsar instrument is capable of a near-daily search mode
observation cadence. We have thus developed the CHIME/Pulsar Single Pulse
Pipeline to automate the processing of CHIME/Pulsar search mode data. We
report the discovery of 21 new Galactic sources, with 14 RRATs, 6 regular slow
pulsars and 1 binary system. Owing to CHIME/Pulsar’s daily observations we
have obtained timing solutions for 8 of the 14 RRATs along with all the
regular pulsars. This demonstrates CHIME/Pulsar’s ability at finding timing
solutions for transient sources.

###### keywords:

pulsars: individual: – stars: neutron – pulsars: general

††pubyear: 2015††pagerange: The second set of pulsar discoveries by
CHIME/FRB/Pulsar: 14 Rotating Radio Transients and 7 pulsars–The second set of
pulsar discoveries by CHIME/FRB/Pulsar: 14 Rotating Radio Transients and 7
pulsars

##  1 Introduction

Pulsars act as lighthouses in radio frequencies with beams that sweep over our
observatories at regular intervals. They are remnants of massive stars that
have undergone core collapse (Gold, 1968); the remains are a degenerate core
at supranuclear densities. Furthermore, pulsar emissions are observed to be
periodic, thus, timing models can precisely describe their spin evolution
(Hobbs et al., 2004; Lynch et al., 2018). Recently, subclasses of neutron
stars have provoked fervent interest in the field of transient astronomy
(Kaspi, 2010). One subclass discovered in 2006 (McLaughlin et al., 2006; Keane
et al., 2011) is the Rotating Radio Transients (RRATs); they are radio-
emitting neutron stars that by definition can only be detected in single
pulses as opposed to a fourier domain search (Keane et al., 2011; Burke-
Spolaor, 2012). RRATs have garnered more attention recently as single pulse
searches have grown in popularity. They are particularly intriguing as the
RRATs are thought to be as populous as conventional radio pulsars (McLaughlin
et al., 2006; Keane et al., 2011) and can be informative of the total Galactic
distribution of neutron stars. However, while many surveys have been
conducted, only 136 RRATs have been
found111http://astro.phys.wvu.edu/rratalog/. The incomplete knowledge of the
nulling fraction of RRATs makes population studies more difficult than
conventional pulsars. Another interesting feature of RRATs is their similar
radio presentations to Fast Radio Bursts (FRBs), extremely bright, millisecond
timescale extragalactic bursts. There have even been attempts to find FRBs in
mislabelled RRATs (Keane, 2016; Rane & Loeb, 2017). Another subclass of
neutron stars are magnetars, which are thought to be young, highly magnetised
neutron stars (Kaspi & Beloborodov, 2017) and may explain high energy
phenomena such as FRBs (CHIME Collaboration, 2020; Bochenek et al., 2020).
Therefore, finding more RRATs and magnetars can inform astronomers about the
population of Galactic pulsars, pulsar distributions and can potentially
disentangle the population of RRATs, magnetars and FRBs.

While there are over 3300 known pulsars to date (Manchester et al.,
2005)222https://www.atnf.csiro.au/research/pulsar/psrcat/ version 1.67 only
$\sim$4% of them are RRATs. The low proportion is exacerbated by the lack of
knowledge of the nulling fraction of RRATs. It is possible that large surveys
like the Parkes Multibeam Survey (Manchester et al., 2001) only captured a
fraction of the total RRATs (Mickaliger et al., 2018) encompassed in the
survey sky area, resulting in an incomplete picture of the pulsar population.
This can be remedied as next-generation and Square Kilometer Array precursor
telescopes begin large-scale pulsar and fast-transient searches (Johnston et
al., 2008; Tzioumis et al., 2010; Coenen et al., 2014; Swainston et al.,
2021). Many of the telescopes contributing to RRAT discoveries have unique
characteristics such as wide fields of view e.g. CHIME (CHIME/FRB
Collaboration, 2018; CHIME/Pulsar Collaboration, 2020), LOFAR (van Haarlem et
al., 2013), MWA (Wayth et al., 2018; Tingay et al., 2013), ASKAP (Johnston et
al., 2007; Macquart et al., 2010) or outstanding sensitivity e.g. FAST (Qian
et al., 2019). This leads to rapid coverage of the celestial sphere. The large
survey volumes necessitate the development of automated pipelines and
algorithms to identify pulsar and RRAT candidates.

Pulsar and RRAT surveys are conventionally split into two categories,
untargeted and targeted. Untargeted surveys will search a grid of celestial
positions (e.g., Manchester et al., 2001; Parent et al., 2022, 2019; Stovall
et al., 2014) while targeted surveys will have deep exposures on particularly
interesting sky locations (e.g., Pan et al., 2021; Manchester et al., 1991;
Ridolfi et al., 2021; Yan et al., 2021). In this study, we use the untargeted
CHIME telescope’s FRB instrument (CHIME/FRB), which has fixed beams on the
transiting sky. The CHIME/FRB survey is especially unique due to the $\sim
200$ square degree field of view on the transiting sky, allowing us a daily
search grid of the whole northern sky.

The primary difference in detecting RRATs and FRBs is the Galactic nature of
RRATs, quantified by their dispersion measure (DM), a measure of the time
delay in the signal because of the interstellar and intergalactic medium. The
DM follows a $f^{-2}$ law, where $f$ if observing frequency, such that lower
frequencies are delayed longer. FRBs tend to have very high DMs due to their
extragalactic nature while RRATs tend to have lower DMs. Thus, as most search
pipelines first dedisperses incoming data, the two types of radio transients
are often discovered in similar fashions (Caleb et al., 2016; Keane et al.,
2018; Bhandari et al., 2018; Ter Veen et al., 2019).

The rapid survey capability of CHIME/FRB yields has yielded $\sim 1.5\times
10^{8}$ single pulse events; many of them are false positives. For each event,
numerous statistics are generated; some useful ones are described in §2.1.
Crucially, we record the celestial locations in J2000 coordinates as Right
Ascension (RA) and Declination (Dec) along with the DM. In this study, we have
developed a search pipeline called the CHIME/FRB Metadata Clustering Analysis
(CHIMEMCA) to filter and cluster single pulse events to produce pulsar and
RRAT candidates. CHIMEMCA uses DBSCAN (Ester et al., 1996), an unsupervised
machine learning algorithm, to cluster events in RA, Dec and DM. We follow up
promising CHIMEMCA candidates with the higher sensitivity of the CHIME/Pulsar
system (§2.2, CHIME/Pulsar Collaboration (2020)). CHIME/Pulsar is a radio
instrument that allows for monitoring of 10 sources at a time with steerable
digital tracking beams. This allows almost daily coverage of the CHIMEMCA
pulsar and RRAT candidates. The high observation cadence drove the development
of a second automated pipeline, the CHIME/Pulsar Single-pulse PIPEline
(CHIPSPIPE). CHIPSPIPE is a PRESTO (Ransom, 2001, 2011) based pipeline that
also utilises SPEGID (Pang et al., 2018) and FETCH (Agarwal et al., 2020) to
process the CHIME/Pulsar search mode data stream. In this work, we present
details on 14 new RRATS and 7 new pulsars discovered using CHIMEMCA and
CHIPSPIPE.

The outline for this paper is as follows:

  * •

§2 Covers the capabilities of the various backends of the CHIME telescope,
namely, CHIME/Pulsar and CHIME/FRB.

  * •

§3 Introduces the unsupervised machine learning pipeline (CHIMEMCA) used to
find new Galactic source candidates.

  * •

§4 Introduces the automated search mode data reduction pipeline for
CHIME/Pulsar (CHIPSPIPE).

  * •

§5 Presents the timing analysis and single pulse distributions of 21 new
Galactic pulsars and RRATs.

  * •

§6 Discusses of the implications of all the CHIME/FRB pulsar and RRAT
discoveries and compares them with previously known populations.

##  2 CHIME Telescope

###  2.1 CHIME/FRB

CHIME/FRB is a backend instrument to the CHIME telescope specifically designed
to detect FRBs. CHIME/FRB receives 1024 total intensity (Stokes I) data
streams at a time resolution of 0.983ms and a frequency resolution of 24.4KHz.
Each stream represents one of the 1024 tied-array beams that are digitally
formed in the CHIME main beam (CHIME/FRB Collaboration, 2018). Furthermore,
each of the tied-array beams has a FWHM of $\sim$20’-40’.
To accommodate the large data stream CHIME/FRB continuously runs a real-time
pipeline that

  1. 1.

Dedisperses the data using the
BONSAI333https://github.com/kmsmith137/rf_pipelines tree dedispersion
algorithm

  2. 2.

Employs RFI mitigation based on running data statistics (Rafiei-Ravandi &
Smith, 2022)

  3. 3.

Groups all candidates such that a single event detected in multiple beams is
detected as one event.

  4. 4.

Grades every event using a machine learning RFI classifier called RFISifter

  5. 5.

Assigns a flag to events that are spatially (including DM) close to nearby
known pulsars. This is called the KnownSourceSifter

  6. 6.

Determines whether an incoming event has Galactic or extragalactic DM. This is
done by using the YMW16 (Yao et al., 2016) or the NE2001 (Cordes & Lazio,
2002) DM models.

  7. 7.

Issues a callback (request to save data) for total intensity and raw voltage
data if the event is extragalactic.

  8. 8.

Saves metadata for all events.

CHIME/FRB actively monitors $\sim 200$ square degrees of the sky at all times,
thus has a high chance of detecting sporadic single pulses from RRATs and
pulsars. While the total intensity is not saved for Galactic events, the
metadata provide a rich database to search for Galactic pulsars and RRATs. The
metadata include:

  * •

Right Ascension

  * •

Declination

  * •

Dispersion Measure

  * •

RFISifter grade : A grade between 1 (RFI) and 10 (astrophysical) that
RFISifter has given an event.

  * •

KnownSourceSifter associations : The association of an event to known Galactic
pulsars or repeating FRBs.

  * •

Beam activity : The number of synthesised beams that exceeded a fiducial
Signal to Noise Ratio (SNR) threshold for a given event

We feed this information into a unsupervised machine learning algorithm to
produce Galactic transient candidates. This is described in §3.

###  2.2 CHIME/Pulsar

The CHIME/Pulsar backend can observe 10 sources at one time using digitally
formed beams, and with continuous operation CHIME/Pulsar observes 400-500
known pulsars per day. The typical transit times lasts $\sim 10$ minutes,
which is sufficient to enable pulsar timing and searching observations. Each
of the instrument’s 10 tied-array beams has a FWHM of $\sim 0.25^{\circ}$ at
800MHz. CHIME/Pulsar also operates in 3 capacities: fold, search and baseband
modes. fold-mode is for pulsars with known ephemerides. Search mode records
the total intensity and is the default for surveys and new candidates. To
increase sensitivity in these two modes, we can coherently dedisperse the data
up to a DM of $\sim$500$\mathrm{pc\,cm^{-3}}$. Baseband mode is reserved for
high time resolution and polarisation studies and records the raw beam-formed
data stream. A thorough discussion of the CHIME/Pulsar system can be found in
CHIME/Pulsar Collaboration (2020).

In the this work, we use both search and fold-modes. Search mode has a time
resolution of $327.68\mu s$, greatly improving on CHIME/FRB detections and
sensitivity. All new pulsar candidates start with search mode observations.
Once detected, if the source is a conventional radio pulsar, it will be
switched to fold-mode observations.

##  3 Chime/FRB Metadata Clustering Analysis

In this section we present a repeating astrophysical source search pipeline
called the CHIME/FRB Metadata Clustering Analysis (CHIMEMCA). We use the
CHIME/FRB metadatabase of Galactic events by clustering in RA, Dec and DM. As
a result, many candidates are produced to be followed up with CHIME/Pulsar.
Due to CHIME/FRB’s sky coverage and sensitivity to single pulse events, most
Galactic discoveries detected are RRATs that have likely been missed in
previous surveys. We note that this analysis is equally adept at identifying
extragalactic transients as well as Galactic pulsars, and has been used to
identify repeating FRBs (The CHIME/FRB Collaboration in prep.)

###  3.1 DBSCAN

To systematically search our metadata database for Galactic pulsars, we use an
unsupervised machine learning algorithm called Density-based spatial
clustering of applications with noise (DBSCAN) (Ester et al., 1996). DBSCAN is
an algorithm to find clusters of points in a n-dimensional space. In our case,
this is the RA ($\alpha$), Dec ($\delta$) and DM space. DBSCAN has two
benefits over other methods such as K-means (Macqueen, 1967). Firstly, it is
able to easily trace arcs in RA-Dec space. The arcs occur because of erroneous
localisations as bright sources move into the CHIME sidelobes. However, the
sources that are known to do this need to be bright enough to overcome the
attenuation of the CHIME sidelobes (e.g. the Crab Pulsar). Secondly, DBSCAN,
unlike K-means, can form an unbounded number of clusters and therefore does
not require an arbitrary choice in the number of clusters. We use DBSCAN from
the sklearn444https://scikit-learn.org/ package in python. This version of
DBSCAN requires three parameters: $\epsilon$, the minimum distance between
cluster points, min_samples, the minimum number of events per cluster and
metric, the distance metric between events. Firstly, we choose $\epsilon$ to
be a function of the uncertainties of each event. Due to CHIME’s design, the
uncertainties in RA are not constant across our declination range. For all
declinations, the uncertainty of RA goes as
$\sigma_{\alpha}\propto\frac{1}{cos(\delta)}$, greatly inflating our
uncertainties at declinations near the pole. We gave fiducial values for
declination and DM errors, $\sigma_{\delta}=0.5\deg$,
$\sigma_{DM}=13\text{pc\,cm}^{-3}$. These were chosen because of the full
width half maximums of the beam and the largest dedispersion uncertainties
from the tree dedispersion and peak-finding algorithm. Instead of varying
$\epsilon$, we equivalently scale each parameter by their respective errors
and then set $\epsilon=1$.
A second issue is that RA ($\alpha$) is cyclic at 360∘. To deal with this, we
used a cyclic function to map RA to a value between -90 and 90 as described by
equation 1 and 2, replacing $\alpha$ with $\zeta_{1}(\alpha)$ and
$\zeta_{2}(\alpha)$. This way, events with high RA will be clustered with
those with low RA, assuming that the Dec and DM are also sufficiently close.

$\begin{split}\zeta_{1}(\alpha)=\begin{cases}\alpha&\alpha<90\degr\\\
180\degr-\alpha&90\degr\leq\alpha<270\degr\\\
\alpha-360\degr&270\degr\leq\alpha<360\degr\\\ \end{cases}\end{split}$ (1)

$\begin{split}\zeta_{2}(\alpha)=\begin{cases}90\degr-\alpha&\alpha<180\degr\\\
180\degr-\alpha&180\degr\leq\alpha\\\ \end{cases}\end{split}$ (2)

The errors for $\zeta_{i}$ are given by:

$\bm{\sigma}_{\zeta_{i}}^{2}=\left(\frac{\partial\zeta_{i}}{\partial\alpha}\right)^{2}\bm{\sigma}_{\alpha}^{2}$
(3)

where $\sigma_{\zeta_{i}}$ is the error of the functions described in
equations (1–2) and $\sigma_{\alpha}$ is the error in RA. The vector space fed
into DBSCAN is

$\mathbf{V}_{k}=\frac{\mathbf{v}_{k}}{\bm{\sigma}_{k}}$ (4)

where $\mathbf{v}_{k}=[\zeta_{1},\zeta_{2},\delta,{\rm DM}]$,
$\bm{\sigma_{k}}=[\sigma_{\zeta_{i}},\sigma_{\zeta_{i}},\sigma_{\delta},\sigma_{\rm
DM}]$, and $k$ subscripts each individual event.
Finally, we chose min_points=2 and metric=Chebyshev. The Chebyshev distance is
defined as

$D_{\mathrm{Chebyshev}}=\text{max}_{i}(|x_{i}-y_{i}|)$ (5)

where $i$ is indexed over $\zeta_{1}$, $\zeta_{2}$, $\delta$ and DM and $x$
and $y$ are two separate events. The choice of a Chebyshev metric was so that
we do not miss edge cases when clustering; one can choose other metrics like
Euclidean without much difference in the final results.

(a)  (b)

Figure 1: (a) Cluster number 62 (PSR J0012+5431). We find that PSR
J0012+5431’s DM is $\sim 20\%$ below the Galactic DM using both the YMW16 (Yao
et al., 2016) and NE2001 (Cordes & Lazio, 2002) models. The red ellipse gives
a scale of CHIME/FRB beam size at this declination. The points outside the red
circle are detections of PSR J0012+5431 outside the FWHM or are detections by
a neighbouring synthesized beam. The axis label for the colour bar indicates
the largest DM ($\text{pc\,cm}^{-3}$) in this direction according to the
NE2001 and YMW16 models. (b) The DM-time plot is used to identify RFI; the
horizontal blue and orange lines show pulsar transits while the red stripe
shows an RFI streak.

###  3.2 Pipeline

For this work, we queried the metadata database from 2018-10-01 to 2021-11-15.
This set of data is in excess of $1.5\times 10^{8}$ events, so RFI and known
source filtering are vital for truly unknown astrophysical candidates.
For RFI filtering; we take a variety of cuts based on the statistics in the
metadata. First we take only data with SNR$>$9; this ensures that only the
brightest bursts remain. Following that, in line with CHIME/FRB’s main
pipeline, we cut on RFISifter grade$>$5 (§2.1). Finally we cut on beam
activation (§2.1), whereby events with beam_activation$>$50 are classified as
RFI. We set this criterion on beam activation because we find that true
astrophysical pulses are not typically detected in more than 50 beams
simultaneously. Note that this should not hurt the completeness of the survey
significantly, as the single pulse luminosities of pulsars are thought to
follow log-normal distributions (Burke-Spolaor et al., 2011; Mickaliger et
al., 2018; Meyers et al., 2018, 2019) thus any astrophysical events that are
detected in more than 50 CHIME/FRB beams should be extremely rare. The final
filtering step is to remove any known Galactic pulsars via events flagged by
the KnownSourceSifter (§2.1).
Following the RFI and known source filtering process, we apply the technique
described in §3.1 to produce clusters of events that eventually become pulsar
candidates. A typical cluster identified by DBSCAN is shown in Figure 1 (a)
which depicts PSR J0012+5431, a RRAT discovered using CHIMEMCA and presented
in this work. Properties such as high positional clumping within the CHIME/FRB
beam size and low DM variation are indicative of a new Galactic source. There
are points outside of the FWHM circle; these are likely detections of PSR
J0012+5431 in a neighbouring beam or sidelobe.

To ensure that RFI has not circumvented the cuts and is not polluting the
candidates table, each cluster is inspected by eye. As each cluster comprises
many events, to check for RFI, we take each event and make a DM-time plot of
all other temporally adjacent events. RFI manifests as near vertical lines
while pulsars and FRBs are isolated points or horizontal lines. In Figure 1
(b) there is a RFI streak at $\sim$ 60s whereas all the points on the
horizontal lines belong to either PSR B2053+36 or PSR B2053+21.
After CHIMEMCA has identified a new candidate, a secondary program checks the
average cluster positions against two pulsar survey "scrapers"
555http://hosting.astro.cornell.edu/~deneva/tabscr/tabscr.php,666https://pulsar.cgca-
hub.org/. This is a check against pulsars discovered by other surveys that
have not yet been added into the KnownSourceSifter database. The clusters that
survive this vetting process are pulsar/RRAT candidates and assigned
observation time with CHIME/Pulsar.
While the majority of the discoveries presented in this work has been found
via CHIMEMCA, one pulsar and one RRAT were found serendipitously by CHIME/FRB
operators. These two sources were also identified by CHIMEMCA subsequently.

###  3.3 CHIME/FRB detection

In addition to candidate searching, we can also perform long timescale
activity analyses of RRATs using the CHIME/FRB metadata. This involves finding
all events for a source with SNR greater than 8. Following that, we show the
daily sensitivity as characterised by the detection of known pulsars and the
exposures for the source’s sky location. The same methods were used as Good et
al. (2021). All plots are hosted on https://www.chime-frb.ca/galactic.

##  4 CHIME/Pulsar Observations and the CHIME/Pulsar Single-pulse Pipeline

CHIME/Pulsar observes on a probability based schedule, depending on the
current observing load (CHIME/Pulsar Collaboration, 2020). This ensures that
new candidates can be observed even in over-subscribed sky regions. One
benefit of observing with CHIME/Pulsar is that it has higher sensitivity than
CHIME/FRB due to coherent dedispersion, greater time resolution and the
ability to track the source over the transit. Furthermore, as we do not
collect intensity data from CHIME/FRB we require a detection from CHIME/Pulsar
to claim a new pulsar discovery. A complete list of published and unpublished
pulsars is available on the CHIME/FRB Galactic sources webpage
777https://www.chime-frb.ca/galactic.

With CHIME/Pulsar, we always initially take search-mode data for every pulsar
candidate. This is quickly changed to fold-mode if we have identified that a
candidate is a pulsar; otherwise, for example for RRATs, we continue to take
search-mode observations.

###  4.1 CHIME/Pulsar data reduction and pipeline

Depending on the observing pressure at a particular sky location, CHIME/Pulsar
can achieve an almost daily cadence for observing pulsar/RRAT candidates.
Although some new candidates fall in oversubscribed sky regions leading to a
reduction in observations, this still yields 2466 separate search mode
pointings. Evaluating and processing extensive datasets quickly become
infeasible for a human. To automatically handle the data sets, we have
developed the open sourced CHIME/Pulsar Single-pulse PIPEline (CHIPSPIPE)
888https://github.com/CHIME-Pulsar-Timing/CHIPSPIPE-CHIme-Pulsar-Single-pulse-
PIPEline-2, a PRESTO based search pipeline that also incorporates SPEGID (Pang
et al., 2018) and FETCH (Agarwal et al., 2020). A block diagram of CHIPSPIPE
is shown in Figure 2. In addition to CHIPSPIPE, we use riptide (Morello et
al., 2020), a Fast Folding Algorithm (FFA) and prepfold, a PRESTO module, to
find the folded profiles of pulsars. For RRAT data that cannot be folded, we
use rrat_period or rrat_period_multiday included in the PRESTO package to
estimate the periods.

Figure 2: Block diagram for CHIPSPIPE. The majority of the tools used belong
to the PRESTO software suite, however, we use SPEGID to generate single pulse
candidates and FETCH to grade them on their astrophysical nature.

To handle RFI in the search mode data, we use the rfifind software included in
PRESTO to excise contaminated data. rfifind is able to remove transient narrow
band as well as periodic RFI. We also apply a static mask that will remove
most of the known bad channels at CHIME. This process reduces our band by
$\sim 30\%$. Finally, a known issue is dropped packets in the data streaming
process when the search mode observation is recorded. A dropped packet results
in a near 0 value in the search mode data. We remove this by looking for drop-
outs below 4.5$\sigma$ of the median for each frequency channel and replacing
them with the median.
When taking data, each of the 1024 frequency channels is coherently
dedispersed to the nominal DM of the candidate. The PRESTO software is then
used to search a small DM range of $\pm 20\text{pc\,cm}^{-3}$ around the
cluster DM. The DDplan.py script prepares a list of trial DMs, and the
prepsubband program is used to incoherently dedisperse the frequency channels
and generate a time series for each trial DM. As we do not need to search the
full DM range, the processing time is reduced drastically, especially for high
DM candidates.

Each time series generated by prepsubband is searched using the
single_pulse_search.py software included in PRESTO. This generates a series of
text files containing single pulse time of arrivals (TOAs), SNRs and searched
boxcar widths.
As the output for single_pulse_search.py is often time consuming for a human
to check we use SPEGID, a DBSCAN based clustering algorithm, to find single
burst candidates (Pang et al., 2018). The input to SPEGID is the text files
output by single_pulse_search.py. SPEGID then makes an assessment on whether a
series of single pulses is characteristic of an astrophysical or RFI signal,
thereby generating a list of pulse candidates.
SPEGID candidates that receive a rank of 1 or 2 (likely astrophysical) are
passed to FETCH, a deep-learning based classifier used to detect impulsive
bursts from sigproc-style data (Agarwal et al., 2020). Finally, all
astrophysical bursts are extracted for human review.
FETCH generates diagnostic waterfall plots as well as a corresponding grade. A
human then needs to review all positively graded bursts from FETCH and save
those that are truly astrophysical. In this process the human reviewer only
keeps FETCH results with SNR$>$6\.
Finally, for period extraction, rrat_period and rrat_period_multiday are used
with the timestamps of pulses that survive human vetting. Both packages are
included in PRESTO and operate on finding the largest common factor for time
in between closely spaced pulses from a single source.

###  4.2 Timing and fold-mode

We observe pulsars via CHIME/Pulsar fold-mode observation. This mode uses an
ephemeris generated by the initial filterbank detection. The folding of the
data is done via two parallel methods:

  1. 1.

We use PSRCHIVE and the suite of tools it provides like pazi to manage the RFI
and use pam to manage archives.

  2. 2.

We use PRESTO’s prepfold to fold and refine the period derived from
rrat_period.

For the sources that can be folded, the general procedure is described by Good
et al. (2021) where the Digital Signal Processing Software for Pulsar
Astronomy (Van Straten & Bailes, 2011, DSPSR) is used. RFI mitigation is done
through paz and TOAs are generated via pat for all search mode data. Finally,
we determine the timing solution using TEMPO999http://tempo.sourceforge.net/
(Nice et al., 2015) or TEMPO2101010https://bitbucket.org/psrsoft/tempo2
(Edwards et al., 2006; Hobbs et al., 2006).

For the timing of the RRAT discoveries, we use DSPSR to extract and fold 30
seconds of data around the reported detection times from SPEGID. We then
extract the sub-integration where the pulse is present, generate TOAs and
determine the timing solutions from the single pulses based on the method
above.

##  5 Results and Discoveries

Table 1: Pulsar observations table ordered by RA with RRATs followed by regular pulsars RRAT/PSR | Timing Solution Provided | # Search-mode obs | # Fold-mode obs | Type | Observation Hours
---|---|---|---|---|---
PSR J0012+5431 | Yes | 116 | - | RRAT | 46.33
PSR J0653-06 | Yes | 146 | - | RRAT | 33.92
PSR J0741+17 | No | 119 | - | RRAT | 28.73
PSR J1105+02 | Yes | 195 | - | RRAT | 44.18
PSR J1130+09 | Yes | 319 | - | RRAT | 71.08
PSR J1541+47 | Yes | 248 | - | RRAT | 79.05
PSR J2008+3758 | Yes | 65 | - | RRAT | 18.09
PSR J2113+73 | No | 73 | - | RRAT | 59.40
PSR J2138+69 | No | 84 | - | RRAT | 53.64
PSR J2215+4524 | Yes | 130 | - | RRAT | 41.11
PSR J2237+2828 | Yes | 272 | - | RRAT | 69.64
PSR J2316+75 | No | 77 | - | RRAT | 63.78
PSR J2221+81 | No | 91 | - | RRAT | 123.03
PSR J2355+1523 | Yes | 531 | - | RRAT | 101.72
PSR J0227+3356 | Yes | - | 320 | Pulsar | 83.43
PSR J0658+2936 | Yes | - | 170 | Pulsar | 46.04
PSR J1048+5349 | Yes | 241 | - | Pulsar | 92.30
PSR J1943+5815 | Yes | - | 32 | Pulsar | 13.84
PSR J2057+4701 | Yes | - | 142 | Pulsar | 47.60
PSR J2116+3701 | Yes | - | 247 | Pulsar | 72.64
PSR J2208+46 | Yes | - | 183 | Pulsar | 61.05

We present the discovery of 21 new Galactic pulsars and RRATs with timing
solutions for 15 of them. Additionally, more than half of the discovered
sources (14) were RRATs and we have successfully obtained timing solutions for
8 of them. This showcases CHIME/Pulsar’s capabilities at timing intermittent
sources due to its high observing cadence. While the majority of sources were
discovered via CHIMEMCA, PSR J1943+5815 and PSR J1130+09 were noticed
serendipitously by CHIME/FRB operators. We list all details of our
pulsars/RRATs in Table 1.

###  5.1 RRATs

In this section, we detail specific features of particular RRATs. Along with
detailed texts for the most interesting RRATs, we provide Figures 3 and 4 for
the detected single pulse distributions for peak fluxes and widths
respectively. We note that as in Good et al. (2021), the flux estimates we
provide should only be treated as lower limits. The peak flux and the width
FWHM was found by a maximum likelihood fit of a Gaussian to the pulse.
Furthermore, we show example waterfalls for each source in Figure 5, where
each source is downsampled in both frequency and time by a factor of 4,
resulting in 256 frequency channels and a time resolution of $1.31\text{ms}$.
Finally, the detection statistics for each RRAT are given in Table 2, timing
solutions are given in Table 3 and localisations are given in Table 4. Note
that the localisations given in Table 4 are obtained via metadata localisation
of CHIME/FRB data (CHIME/FRB Collaboration, 2021) and are only provided for
the cases where timing localisations are not available or only partially
available.

Figure 3: Distributions of all the peak fluxes for the detected RRAT pulses.
Figure 4: Distributions of burst widths for the RRATs in the sample. The
widths provided are the FWHM of the pulses, assuming a gaussian burst profile.
Figure 5: Waterfall plots of all RRATs discovered in this study. The RFI bands
have been replaced with the median and the sources have been flux calibrated.

In Figure 3 we plot the peak flux of single pulses in the sample of RRATs. The
peak fluxes generally seem to follow a log-normal like distribution. This has
been well documented in other analyses of RRAT pulses such as (Cui et al.,
2017, e.g.), with others finding that fluence distributions are also
distributed log-normally (Burke-Spolaor et al., 2011; Meyers et al., 2018,
2019; Mickaliger et al., 2018). Without performing corrections for the
selection effects of CHIPSPIPE, it is difficult to disentangle the degeneracy
between the RRAT single pulse distribution and the low SNR/flux cutoff for the
detection pipeline. Thus, we defer this analysis to future publications and
note that here, we only present the characteristics of detected single pulses.

The width distribution of the single pulses, shown in Figure 4, was found by
applying a maximum likelihood fit to each pulse with a single component
gaussian. Then we found the full width half maxima using
$\text{FWHM}=2.355\sigma$. In this analysis, we do not correct for complex
structure (i.e. like those found for PSR J1105+02, PSR J1130+09, PSR J1541+47,
PSR J2008+3758, PSR J2221+81, PSR J2355+1523). This causes pulses to appear
wider as the single gaussian will try to envelop multiple peaks. Furthermore,
the widths of pulses are not consistent across the whole 400 MHz band; here we
only provide the widths of the band-averaged profiles. Lastly, the width
analysis suffers from similar shortcomings as the peak flux distributions in
that we have not corrected for the selection effects of CHIPSPIPE.
Nevertheless, we present the pulse-width distributions of all detected pulses
and note that comparisons are still useful with this sample, as similar
selection biases affect all 14 RRATs. We find that there exists large scatter
in the detected pulse widths ranging from $<$0.025$\%$ (PSR J1541+47, PSR
J2008+3758, PSR J2221+81, PSR J2237+2828, PSR J2355+1523) to 13.7$\%$ (PSR
J2355+1523) of the pulse period.

Table 2: Detection statistics for RRATs presented in this study. Included are both the CHIME/FRB and CHIME/Pulsar detection counts. The first and last detections are given in MJD. RRAT Name | FRB first det | FRB last det |  $N_{det}$ FRB | Pulsar first det | Pulsar last det |  $N_{det}$ Pulsar |  $N_{det}$ total |
---|---|---|---|---|---|---|---|---
PSR J0012+5431 | 59370 | 58528 | 549 | 58901 | 59358 | 129 | 678 |
PSR J0653-06 | 59412 | 59492 | 114 | 59191 | 59357 | 47 | 161 |
PSR J0741+17 | 59381 | 59532 | 118 | 59191 | 59357 | 107 | 225 |
PSR J1105+02 | 59447 | 59526 | 64 | 59075 | 59359 | 112 | 176 |
PSR J1130+09 | 59572 | 59378 | 8 | 58979 | 59386 | 208 | 216 |
PSR J1541+47 | 59371 | 59530 | 184 | 59076 | 59359 | 639 | 823 |
PSR J2008+3758 | 59405 | 59532 | 233 | 59078 | 59346 | 44 | 277 |
PSR J2113+73 | 58453 | 59480 | 26 | 59203 | 59339 | 21 | 47 |
PSR J2138+69 | 58444 | 59441 | 34 | 59332 | 59321 | 16 | 50 |
PSR J2215+4524 | 58401 | 59457 | 69 | 59078 | 59532 | 247 | 316 |
PSR J2221+81 | 58443 | 59495 | 17 | 58909 | 59359 | 52 | 69 |
PSR J2237+2828 | 58424 | 59525 | 56 | 59193 | 59533 | 69 | 125 |
PSR J2316+75 | 58388 | 59491 | 46 | 58968 | 59073 | 35 | 81 |
PSR J2355+1523 | 58584 | 59497 | 57 | 58663 | 59358 | 614 | 871 |
Table 3: Timing solutions for 8 RRATs. A lack of errors for some positions
indicates that there were insufficient TOAs to fit them. For sources without
(or with only partial) positional fits, the metadata localisations and
uncertainties are provided in Table 4. In these cases, the position in our
timing models do not indicate the true position of the source.
† denotes a 2 $\sigma$ lower/upper limit for $\dot{\nu}$/$\dot{P}$ respectively. Pulsar name | PSR J0012+5431 | PSR J1105+02 | PSR J1130+09 | PSR J1541+47
---|---|---|---|---
Right ascension, J2000 (h:m:s) | 00:12:23.3(1) | 11:05:32.0(2) | 11:30:55.0(5) | 15:41:05.54
Declination, J2000 (d:m:s) | +54:31:47(3) | 02:28:50.74 | +09:20:48.55 | +47:03:03.45
Pulse frequency, $\nu$ (Hz) | 0.33054565343(2) | 0.15617544155(9) | 0.2084794003(3) | 3.60099929741(4)
Pulse frequency derivative, $\dot{\nu}$ ($\rm 10^{-16}\ Hz\,s^{-1}$) |  -0.14 † |  -0.35 † | 1.2(2) | -27.2(1)
Period Epoch (MJD) | 58912 | 59216 | 59051 | 59211
Dispersion measure, DM ($\rm pc\,cm^{-3}$) | 131.3(7) | 16.5(4) | 21.0(9) | 19.4(7)
Clock standard | TT(BIPM2019) | TT(BIPM2019) | TT(BIPM2019) | TT(BIPM2019)
Ephemeris | DE440 | DE440 | DE440 | DE440
Number of TOAs | 104 | 74 | 50 | 77
Reduced $\chi^{2}$ | 0.82 | 0.8 | 0.953 | 1.3474
Derived parameters |
Pulse period, $P$ (s) | 3.0253007099 | 6.403055372 | 4.796636974(6) | 0.277700692893(3)
Pulse period derivative, $\dot{P}$ ($\rm 10^{-15}\ s\,s^{-1}$) |  1.3 † |  14.3 † | 2.9(5) | 0.2102(9)
Surface magnetic field, $B$ ($\rm 10^{11}\,G$) | - | - | 37.50 | 2.44
Characteristic age, $\tau_{c}$ (Myr) | - | - | 26.44 | 20.87
|  |  |  |
Pulsar name | PSR J2008+3758 | PSR J2215+4524 | PSR J2237+2828 | PSR J2355+1523
Right ascension, J2000 (h:m:s) | 20:07:58.7(2) | 22:15:46.57(7) | 22:37:29.41(4) | 23:55:48.62(8)
Declination, J2000 (d:m:s) | +37:58:13(1) | +45:24:44(2) | +28:28:40.8(5) | +15:23:18(2)
Pulse frequency, $\nu$ (Hz) | 0.2297634166(1) | 0.36723529308(4) | 0.9281646147(2) | 0.91374580877(9)
Pulse frequency derivative, $\dot{\nu}$ ($\rm 10^{-16}\ Hz\,s^{-1}$) |  -0.38 † | -7.5(3) | -7(2) | -3.5(2)
Period Epoch (MJD) | 59078 | 59241 | 59289 | 59092
Dispersion measure, DM ($\rm pc\,cm^{-3}$) | 143(1) | 18.5(4) | 38.1(4) | 26(1)
Clock standard | TT(BIPM2019) | TT(BIPM2019) | TT(BIPM2019) | TT(BIPM2019)
Ephemeris | DE440 | DE440 | DE440 | DE440
Number of TOAs | 39 | 103 | 22 | 128
Reduced $\chi^{2}$ | 2.2 | 0.87 | 1.97 | 1.17
Derived parameters |
Pulse period, $P$ (s) | 4.352302968 | 2.7230498235(3) | 1.0773950914(7) | 1.09439626467(5)
Pulse period derivative, $\dot{P}$ ($\rm 10^{-15}\ s\,s^{-1}$) |  7.2 † | 5.6(2) | 0.9(3) | 0.41(2)
Surface magnetic field, $B$ ($\rm 10^{11}\,G$) | - | 39.40 | 9.87 | 6.77
Characteristic age, $\tau_{c}$ (Myr) | - | 7.71 | 19.27 | 42.31

Of the 14 RRATs in this sample, we obtained timing solutions for 8 of them.
The timing models are presented in Table 3. The remaining 5 either had too few
pulses, failed to have multiple pulses within one observation, or both. We
produced the timing solution by extracting single pulse TOAs using the method
outlined in §4.2 and fit them to a timing model using the TEMPO2 software. In
the process, the epoch was always set to the centre of the time span using the
-epoch centre command of TEMPO2. The timing solution often lacks a $\dot{\nu}$
measurement and we hope that with continued monitoring $\dot{\nu}$ can
eventually be determined.

###  5.2 Complex Single Pulse Structures

Figure 6: Example waterfall plots of RRATs that show complex structure

Many of the RRATs in this sample exhibit a variety of single pulse features.
One prolific example is double or multiple peaks; examples can be found in
Figure 6. These are also often seen in other RRATs (Keane et al., 2010;
Bhattacharyya et al., 2018; Tyul’Bashev et al., 2018). In this sample, 6 of
the 14 RRATs exhibit some sort of complex structure.
PSR J1105+02 occasionally emits inter-pulses as well as double peaks. It is
unclear whether the two emission types have the same underlying mechanism.
Additionally, the second peak or inter-pulse can sometimes be brighter than
the initial pulse.
PSR J1130+09, PSR J2008+3758, PSR J2221+81, PSR J2355+1523 are the other
sources that produces double peaked pulses. Two RRATs, PSR J1130+09 and PSR
J1541+47, also produce complex single pulse structure but are discussed
separately below.

####  5.2.1 PSR J1105+02

PSR J1105+02 is a bright RRAT detected by both CHIME/FRB and CHIME/Pulsar. As
we processed the data, we found that there was a frequency dependent structure
that indicated a slightly erroneous pointing, based on the known CHIME beam
shape. We have corrected the position with a single pulse grid search whereby
six beams are placed simultaneous on the sky. The best-fit positions are
further corroborated by a timing analysis. Thus, the source is probably much
brighter than what we have shown in Figure 3. The source is among the
brightest in the sample as we still detect peak fluxes in excess of 1 Jy even
with the position offset. However, we should note that due to the position
offset, the peak fluxes shown in Figure 3 likely have greater uncertainties
than for all other sources.

####  5.2.2 PSR J1541+47

CHIME/Pulsar collected 248 search mode observations for a total of 79.05 hours
on PSR J1541+47. The initial search mode detection was on MJD 59076.
Subsequently, CHIPSPIPE found 639 bursts making PSR J1541+47 the most prolific
RRAT in the sample. Among these bursts we find some spectral variation, sub-
pulse drifting and extremely narrow pulses that are $<$0.025% of the pulse
period. Finally, this RRAT also often displays double peaks.

Table 4: Localisation of sources without robust timing solutions. The localisations are provided via the metadata localisation method (CHIME/FRB Collaboration, 2021). The metadata localisation method can often result in multiple regions, here we give only the peak of the probability distribution and quote the 95% confidence interval. Missing periods indicate insufficient days where multiple pulses were detected. Sources with only partial timing localisations are included here also with the lowest uncertainty positions listed. PSR Name | RA(h:m:s) | Dec (d:m:s) |  DM(pc/cm3) | Period (s)
---|---|---|---|---
PSR J0653-06 | 6:53(17) | -6:16(36) | 83.7 | 0.79
PSR J0741+17 | 7:41(17) | 17:03(23) | 44.3 | 1.73
PSR J1105+02 | 11:05:32.0(2) | 2:28(30) | 16.5 | 6.4
PSR J1130+09 | 11:30:55.0(5) | 9:20(30) | 21.9 | 4.79
PSR J1541+47 | 15:41(12) | 47:03(18) | 19.4 | 0.28
PSR J2113+73 | 21:13(17) | 73:37(21) | 42.4 | –
PSR J2138+69 | 21:38(14) | 69:50(16) | 46.6 | 0.22
PSR J2316+75 | 23:15(17) | 75:44(21) | 53.4 | –
PSR J2221+81 | 22:21(13) | 81:32(30) | 39 | –

###  5.3 Regular pulsars

All 7 regular pulsars in the sample are so called slow pulsars with periods
>100ms. Their timing solutions are given in Table 5. For most sources, it was
immediately obvious that the discovery was of a regularly pulsating source,
often confirmed by the prepfold software from PRESTO or a fast folding
algorithm like riptide (Morello et al., 2020). These sources were quickly
switched to fold-mode observations and their timing models were found via
TEMPO2. PSR J1048+5349 was the only exception where we did not realise that
the source was a regular pulsar, thus TOAs for PSR J1048+5349 were derived
from folded search-mode observations.

####  5.3.1 PSR J2208+46

PSR J2208+46 was initially detected as a regular pulsar with a period of 0.642
s and a DM of $\sim 63\text{pc\,cm}^{-3}$. Subsequent observations of the
pulsar showed variation in rotation properties that required modelling beyond
the first spin frequency derivative. Furthermore, the fold-mode data obtained
from the pulsar showed signs of varying spin period over time, suggesting that
the pulsar is in a binary system. Further observations revealed that the spin
variation of PSR J2208+46 is consistent with it being in a binary system with
a period of roughly 400 days. More observations are required to further refine
the properties of the orbit, in order to study the nature of the binary
system. As a result, we do not report the full timing solution here, however,
it will be reported in a subsequent publication.

Table 5: The timing solutions for discovered regular pulsars apart from J2208+46 Pulsar name | PSR J0227+3356 | PSR J0658+2936 | PSR J1048+5349
---|---|---|---
Right ascension, J2000 (h:m:s) | 02:26:57.060(1) | 06:57:44.62(8) | 10:48:06.46(5)
Declination, J2000 (d:m:s) | +33:56:27.76(7) | +29:36:29(7) | +53:49:51.9(3)
Pulse frequency, $\nu$ (Hz) | 0.806384981978(2) | 1.2140327894(6) | 0.36618479871(2)
Pulse frequency derivative, $\dot{\nu}$ ($\rm 10^{-16}\ Hz\,s^{-1}$) | -18.542(7) | -21.2(6) | -1.63(9)
Period Epoch (MJD) | 59304 | 59266 | 59216
Dispersion measure, DM ($\rm pc\,cm^{-3}$) | 27(1) | 39(1) | 27.3(9)
Clock standard | TT(BIPM2019) | TT(BIPM2019) | TT(BIPM2019)
Ephemeris | DE440 | DE440 | DE440
Number of TOAs | 314 | 141 | 215
Reduced $\chi^{2}$ | 1.3415 | 1.6144 | 1.237
Derived parameters
Pulse period, $P$ (s) | 1.2401024601760(3) | 0.8237009813(4) | 2.7308615854955(11)
Pulse period derivative, $\dot{P}$ ($\rm 10^{-15}\ s\,s^{-1}$) | 2.8515(11) | 1.44(4) | 1.18(8)
Surface magnetic field, $B$ ($\rm 10^{11}\,G$) | 19.03 | 11.02 | 18.46
Characteristic age, $\tau_{c}$ (Myr) | 6.87 | 9.04 | 35.39
Pulsar name | PSR J1943+5815 | PSR J2057+4701 | PSR J2116+3701
Right ascension, J2000 (h:m:s) | 19:43:23.175(2) | 20:57:09.335(2) | 21:16:14.304(1)
Declination, J2000 (d:m:s) | +58:15:53.08(2) | +47:01:05.22(3) | +37:01:31.13(4)
Pulse frequency, $\nu$ (Hz) | 0.786876829081(1) | 1.78707496869(2) | 6.85475255154(2)
Pulse frequency derivative, $\dot{\nu}$ ($\rm 10^{-16}\ Hz\,s^{-1}$) | -2.534(4) | -1789.66(1) | -2475(1)
Period Epoch (MJD) | 59465 | 59249 | 59219
Dispersion measure, DM ($\rm pc\,cm^{-3}$) | 71(1) | 219.0(5) | 43.7(2)
Clock standard | TT(BIPM2019) | TT(BIPM2019) | TT(BIPM2019)
Ephemeris | DE440 | DE440 | DE440
Number of TOAs | 43 | 187 | 170
Reduced $\chi^{2}$ | 0.9487 | 1.1783 | 1.545
Derived parameters
Pulse period, $P$ (s) | 1.270846926148(4) | 0.559573614715(7) | 0.1458841865523(4)
Pulse period derivative, $\dot{P}$ ($\rm 10^{-15}\ s\,s^{-1}$) | 4.073(11) | 56.0382(3) | 5.2674(3)
Surface magnetic field, $B$ ($\rm 10^{11}\,G$) | 7.30 | 56.67 | 8.87
Characteristic age, $\tau_{c}$ (Myr) | 49.06 | 0.16 | 0.44

##  6 discussion of results

Figure 7: $P-\dot{P}$ diagram that includes all pulsars on the ATNF catalog
(version 1.67) and RRATs in the RRATalog (as of August 2022). Discoveries made
by CHIME/FRB in both Good et al. (2021) and this work have been overlaid. For
3 RRATs we plot only upper limits for the measurement on $\dot{P}$. Only
RRATs/pulsars with P and $\dot{\text{P}}$ measurements are plotted in this
figure.

Throughout this work and together with that reported by Good et al. (2021) we
have shown that CHIME/FRB is an good tool for finding new pulsars and RRATs.
Furthermore, CHIME/FRB’s daily cadence of observations of the northern sky and
exceptional exposure have poised it for population studies of RRATs. Employing
unsupervised machine learning we have found an order of magnitude more pulsars
than relying on serendipitous human discoveries. Other wide field-of-view
telescopes (i.e. CRAFT, LOFAR) could recruit CHIMEMCA-like analyses to aid
their searches.
We have placed the RRATs and pulsars in a $P-\dot{P}$ diagram in Figure 7; all
the discovered sources lie in the region of regular slow pulsars. Among the
discovered sources, most were simple-peaked isolated pulsars, however a few of
the RRATs displayed interesting single pulse properties such as double peaks
and spectral variation. This could motivate further investigation for RRATs
such as PSR J1541+47. Furthermore, one source is a detached binary with an
unusually long period, highlighting CHIME/FRB/Pulsar potential for discoveries
of interesting sources.

As the CHIME/FRB survey progresses, we will begin to map a large portion of
the RRATs above CHIME/FRB’s sensitivity threshold in the northern hemisphere.
Due to CHIME/FRB’s exposure time, we have an opportunity to perform detailed
statistics on the RRAT population and their distributions that has been
difficult to achieve using single-dished telescopes.

###  6.1 Comparison of new CHIME sources to the known RRAT population

We compare the properties of the newly discovered RRATs to that of the broader
population using the same method as outlined by Good et al. (2021), and again
note that no attempt to account for differences in telescope sensitivities,
survey completeness or data analysis workflows has been made. Due to this, we
emphasise that the results are purely observational. The period, DM, burst
rate, and pulse width distributions for both known RRATs and CHIME sources are
shown in Figure 8. We include only those sources presented in Section 5.1.
Given the relatively small sample size, we bootstrap the Kruskal-Willis
H-test111111The null hypothesis for this test is, essentially, that the two
samples are drawn from a distribution with the same median. We use Python’s
‘scipy.stats.kruskal‘ implementation. statistic computation, resampling both
the CHIME and known RRAT samples with replacement and iterating $10^{4}$ times
for each distribution.

The 95-percentile range of p-values from these tests (see Table 6) are then
used to decide whether the null hypothesis can be rejected – we reject the
null hypothesis only if the upper confidence bounds are below 0.05. We note
that, due to the widely varying telescope and survey configurations used to
find the compared samples, the meaning of any “significance” is weak. To truly
explore these distributions rigorously would require knowing the biases for
each survey and correcting the samples on a case-by-case basis, at which point
we would also be dealing with small-number statistics. For that reason, we
take this analysis as a naive sanity check.

Figure 8: Rotation period (top left), dispersion measure (top right), burst rate (bottom left), and pulse width (bottom right) distributions of known RRATs (grey-hatched histogram) and the CHIME candidates presented here (blue histogram). For the width distributions, we have scaled all widths to 600 MHz following the procedure of Good et al. (2021). Distribution sizes and a summary of their statistical comparison are given in Table 6. Table 6: Sample sizes and 95-percentile p-value ranges from the bootstrapped Kruskal-Willis H-tests, comparing the CHIME and known RRAT distributions . Parameter | $N_{\rm CHIME}$ |  $N_{\rm RRATalog}$ (As of August 2022) | p-value range
---|---|---|---
Period | 11 | 98 | 0.013 | 0.98
DM | 14 | 122 | $1.9\times 10^{-4}$ | 0.39
Burst rate | 14 | 84 | $3.6\times 10^{-6}$ | 0.01
Burst width | 14 | 84 | $1.6\times 10^{-4}$ | 0.20

The only notable result is that these new candidates appear to have a slightly
lower median burst rate than the broader population. This is unsurprising
considering the dwell time on each source achievable with CHIME/FRB and
CHIME/Pulsar ($\sim$10 minutes per observation, many observations) compared to
other telescopes and surveys (typically a few minutes, only a handful of
observations at most). The brightest and highest burst-rate RRATs will have
been predominantly discovered by earlier large sky-area pulsar/fast-transient
surveys. We would, therefore, expect that our discoveries would tend towards
being sources with much lower burst rates and/or higher burst-rate
irregularities.

##  7 Conclusion

This work reports on the discovery of 21 new Galactic pulsars and RRATs. We
again show that CHIME/FRB, although primarily a FRB machine, is a powerful
instrument for detecting RRATs even without total intensity data. To improve
the completeness of the RRAT/Pulsar survey of CHIME/FRB we have developed
CHIMEMCA, a technique using the metadata collected by CHIME/FRB. Candidates
identified with CHIMEMCA are followed up with CHIME/Pulsar with near-daily
observations, enabling detections into the low burst-rate parameter space of
RRATs. The high data rate makes processing the search mode data too labour
intensive, thus we have developed an automated pipeline, CHIPSPIPE, comprising
current state-of-the-art software. With CHIMEMCA and CHIPSPIPE working
synergistically, we have discovered 13 new RRATs and 6 new pulsars. In
addition we also report the discovery of 1 new RRAT and 1 new pulsar
serendipitously.
The development of CHIPSPIPE and CHIME/Pulsar’s high observation cadence opens
the door for large scale studies on RRAT properties and statistics (Meyers et
al. in prep). This technical development could lend itself to other telescopes
hoping to carry out similar FRB and Pulsar surveys.
By obtaining timing solutions for 8 out of the 14 RRATs discovered, we show
that CHIME/Pulsar is capable of timing intermittent sources. Robust timing
solutions become possible even for RRATs with low burst rates. We plan to
continue the long term monitoring of RRATs discovered in this campaign along
with those discovered by Good et al. (2021). In the future, we will test
automated methods of obtaining the timing solution for RRATs.
As the CHIME/FRB experiment continues, we will pursue all the Galactic
candidates that CHIMEMCA produces. In time, this will elucidate various
aspects of the mysterious RRAT population. Not only will we increase the
number of extant RRATs, but we will likely be able to discover a large portion
of the RRATs above CHIME/FRB’s sensitivity threshold in the northern
hemisphere.

##  8 acknowledgments

We acknowledge that CHIME is located on the traditional, ancestral, and
unceded territory of the Syilx/Okanagan people. We are grateful to the staff
of the Dominion Radio Astrophysical Observatory, which is operated by the
National Research Council of Canada. CHIME is funded by a grant from the
Canada Foundation for Innovation (CFI) 2012 Leading Edge Fund (Project 31170)
and by contributions from the provinces of British Columbia, Québec and
Ontario. The CHIME/FRB Project, which enabled development in common with the
CHIME/Pulsar instrument, is funded by a grant from the CFI 2015 Innovation
Fund (Project 33213) and by contributions from the provinces of British
Columbia and Québec, and by the Dunlap Institute for Astronomy and
Astrophysics at the University of Toronto. Additional support was provided by
the Canadian Institute for Advanced Research (CIFAR), McGill University and
the McGill Space Institute thanks to the Trottier Family Foundation, and the
University of British Columbia. The CHIME/Pulsar instrument hardware was
funded by NSERC RTI-1 grant EQPEQ 458893-2014.

This research was enabled in part by support provided by the BC Digital
Research Infrastructure Group and the Digital Research Alliance of Canada
(alliancecan.ca).
A.M.C is supported by the Ontario Graduate Scholarship
K.C. is supported by a UBC Four Year Fellowship (6456)
F.A.D is supported by a UBC Four Year Fellowship (6456)
B.M.G. is supported by an NSERC Discovery Grant (RGPIN-2022-03163), and by the
Canada Research Chairs (CRC) program.
J. W. McKee is a CITA Postdoctoral Fellow: This work was supported by Ontario
Research Fund—research Excellence Program (ORF-RE) and the Natural Sciences
and Engineering Research Council of Canada (NSERC) [funding reference CRD
523638-18]
A.B.P. is a McGill Space Institute (MSI) Fellow and a Fonds de Recherche du
Quebec – Nature et Technologies (FRQNT) postdoctoral fellow.
Z.P. is a Dunlap Fellow.
V.M.K. holds the Lorne Trottier Chair in Astrophysics & Cosmology,a
Distinguished James McGill Professorship, and receives support from an NSERC
Discovery grant (RGPIN 228738-13), from an R. Howard Webster Foundation
Fellowship from CIFAR, and from the FRQNT CRAQ.
Pulsar and FRB research at UBC are supported by an NSERC Discovery Grant and
by the Canadian Institute for Advanced Research

Software: PRESTO, SPEGID, FETCH, PSRCHIVE, DSPSR, TEMPO, TEMPO2

##  9 Data Availability

The data underlying this article will be shared on reasonable request to the
corresponding author.

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# Predicting Citi Bike Demand Evolution Using Dynamic Graphs
Note: Names in alphabetical order. Project links in Section 5

Alexander Saff<EMAIL_ADDRESS>Mayur Bhandary<EMAIL_ADDRESS>Siddharth Srivastava<EMAIL_ADDRESS>
###### Abstract

Bike sharing systems often suffer from poor capacity management as a result of
variable demand. These bike sharing systems would benefit from models to
predict demand in order to moderate the number of bikes stored at each
station. In this paper, we attempt to apply a graph neural network model to
predict bike demand in the New York City, Citi Bike dataset.

##  I Introduction

In recent years, last mile transportation solutions such as bicycle sharing
systems have become popular in major cities throughout the United States.
These solutions seek to offer low cost and efficient modes of transport in
urban areas without creating a substantial carbon footprint. Despite the
environmental and economic benefits of bike sharing systems, administrators
have struggled to manage variable demand for bikes. Our paper attempts to
apply a graph neural network to predict bike demand in the Citi Bike network.
We aim to adapt an existing model for predicting vehicle traffic from Yu et al
[1]. The results of these predictions can be used to inform restocking
decisions across the Citi Bike network.

##  II Problem Formulation

We model our problem as predicting the evolution of a dynamic graph. The Citi
Bike dataset [4] can be viewed as a graph where each node is a station and the
distances between the nodes are the edges. Ideally, the number of bikes at the
station represents the nodes value, however, due to the limitations of this
dataset, we sum the number of outbound bikes and inbound bikes instead. This
sum represents the traffic that a particular station received. Station traffic
changes over time with bike demand which results in a dynamic graph. There are
1475 stations and we have data for the bikes at each station in 30 minutes
time steps. We aim to predict the traffic at each station to inform restocking
decisions and create better rental experiences.

##  III Approach

We use a Spatio-Temporal Graph Convolutional Network to predict the traffic of
Citi Bikes (departures and arrivals) [1]. In our architecture, we only used
one spatio-temporal convolution (ST-Conv) block because using 2 led to
overfitting during our training. Subsequently there is an output layer with a
final temporal convolution and a fully-connected layer [3]. The ST-Conv block
consists of a spatial graph convolution between two temporal convolution
layers. Temporal convolution layers work like time series prediction models,
which applies a function to each of the values in the window of these time
steps. The output of this function is passed to a Gated Linear Unit (GLU) [2],
which selects features that are relevant for subsequent predictions. Using our
Citi Bike model as context, traffic information of these bikes at one station
will influence the traffic information at nearby stations (essentially
”spreading” information across nodes).

##  IV Analysis and Numerical Results

###  IV-A Preprocessing

We focus our initial analysis on prepossessing to ensure that our data is
compatible with the model that we intend to use. In Figure 1 we visualize the
mean outbound bikes across all stations in order to understand general trends
in outbound flows across the network. We can see that the data appears to be
periodic and that there are generally few departures at any given time. This
plot confirms that our dataset is consistent across time and that it does not
include any outliers. Additionally, we suspect that the periodic spikes make
this dataset suitable for inference.

Figure 1: Average traffic for each station (inbound and outbound traffic)

We also plotted the frequency of the average number of departures across all
stations for 30 minute intervals below. This plot shows that lower departure
numbers occur more frequently than higher departure numbers and that there is
a peak between 1 and 2 departures during 30 minute intervals.

###  IV-B Model Output and Results

In Figure 1, we show the average traffic (inbound + outbound bikes) for each
station. We find that a lot of stations don’t have any traffic at a give time-
step, which is to be expected.

In Figure 2 we also plot the average traffic at all stations for one time-
step. This shows the distribution of activity across the entire network,
collapsed on the time-step column. Most stations don’t have any traffic, but
the second peak tells us that the most traffic on average at a given time-step
at the stations was around one-two bikes. This basically displays two
underlying distributions - a distribution when there’s no traffic, and the
distribution of what the ”peak” traffic is on average.

Figure 2: Average traffic across the network for a time-step (collapsed on the
time-step axis)

We also create a matrix of the stations’ distance from each other and plot
this pairwise distance in Figure 3.

Figure 3: Pairwise distance between stations

The model overfit slightly, despite shrinking the number of parameters and
reducing training time. This can be shown in Table 4, where the best
validation loss is higher than the final validation loss, and the best
validation loss is still higher than the best (and final) training loss.
Despite this, the model performs quite well. It achieved a mean average error
of 1.84, and a root mean squared error of 3.08. The mean average percent error
shown in 4 is a bad indicator of model accuracy as our ground truth data is
heavily zero biased. Since the ground truth value is in the denominator of the
MAPE calculation, this explodes the MAPE values.

Figure 4: Final training, validation, and test results

Figure 5 shows the predicted average traffic (inbound + outbound bikes) for
the last 3 days in June for each station overlayed on the ground-truth values.
These three days were the hold-out test set, so the model never saw the
ground-truth values during training. By visual inspection, the predicted
values are quite close.

Figure 5: Prediction of our model vs Ground Truth

Figure 6 shows the predicted average traffic at all stations for the last 3
days in June for one time-step overlayed on the ground-truth data. This shows
the distribution of activity across the entire network, collapsed on the time-
step column. These three days were the hold-out test set, so the model never
saw the ground-truth values during training. By visual inspection, the
predicted distribution is quite close.

Figure 6: Model’s prediction of average traffic across the entire network for
a time-step

Given more training time to find better hyperparameters, more powerful compute
to operate on larger historical samples for each prediction, and a larger
dataset, results could likely be improved even further.

##  V Project Links

Github (code):  Github repo

Data:  Data

Slides:  Slides

## References

  * [1] Yu, B., Yin, H., and Zhu, Z. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. arXiv e-prints (Sept. 2017), arXiv:1709.04875.
  * [2] Gallicchio, C., Micheli, A., I& Pedrelli, L. (2018). Comparison between DeepESNs and gated RNNs on multivariate time-series prediction. arXiv preprint arXiv:1812.11527.
  * [3] Tassos Sapalidis and Andrea Vallebueno (2022). Predicting Evolution of Dynamic Graphs. (https://medium.com/stanford-cs224w/predicting-evolution-of-dynamic-graphs-7688eca1daf8)
  * [4] https://ride.citibikenyc.com/system-data