subject

Article2024 January 67 1 10.1007/s11433-023-2219-7 219511 June 20, 2023
September 8, 2023 December 8, 2023

FASHI

Zhang C.-P.

Zhang C.-P., et al

# The FAST All Sky H I Survey (FASHI): the first release of catalog

Chuan-Peng Zhang  Ming Zhu  Peng Jiang  Cheng Cheng  Jing Wang  Jie Wang
Jin-Long Xu  Xiao-Lan Liu  Nai-Ping Yu  Lei Qian  Haiyang Yu  Mei Ai  Yingjie
Jing
Chen Xu  Ziming Liu  Xin Guan  Chun Sun  Qingliang Yang  Menglin Huang
Qiaoli Hao  FAST Collaboration  Guizhou Radio Astronomical Observatory,
Guizhou University, Guiyang 550000, People’s Republic of China  National
Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101,
People’s Republic of China  Chinese Academy of Sciences South America Center
for Astronomy, National Astronomical Observatories, CAS, Beijing 100101, China
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing
100871, People’s Republic of China

###### Abstract

The FAST All Sky H I survey (FASHI) was designed to cover the entire sky
observable by the Five-hundred-meter Aperture Spherical radio Telescope
(FAST), spanning approximately 22000 square degrees of declination between
$-14\degree$ and $+66\degree$, and in the frequency range of 1050-1450 MHz,
with the expectation of eventually detecting more than 100000 H I sources.
Between August 2020 and June 2023, FASHI had covered more than 7600 square
degrees, which is approximately 35% of the total sky observable by FAST. It
has a median detection sensitivity of around 0.76 ${\rm mJy~{}beam}^{-1}$ and
a spectral line velocity resolution of $\sim$6.4 ${\rm km~{}s}^{-1}$ at a
frequency of $\sim$1.4 GHz. As of now, a total of 41741 extragalactic H I
sources have been detected in the frequency range 1305.5-1419.5 MHz,
corresponding to a redshift limit of $z\lesssim 0.09$. By cross-matching FASHI
sources with the Siena Galaxy Atlas (SGA) and the Sloan Digital Sky Survey
(SDSS) catalogs, we found that 16972 (40.7%) sources have spectroscopic
redshifts and 10975 (26.3%) sources have only photometric redshifts. Most of
the remaining 13794 (33.0%) H I sources are located in the direction of the
Galactic plane, making their optical counterparts difficult to identify due to
high extinction or high contamination of Galactic stellar sources. Based on
current survey results, the FASHI survey is an unprecedented blind
extragalactic H I survey. It has higher spectral and spatial resolution and
broader coverage than the Arecibo Legacy Fast ALFA Survey (ALFALFA). When
completed, FASHI will provide the largest extragalactic H I catalog and an
objective view of H I content and large-scale structure in the local universe.

###### keywords:

Key Words: surveys, redshifts, galaxies, telescope, radio lines, H I line

95.80.+p, 98.62.Py, 98.52.-b, 95.55.Jz, 95.30.Ky, 98.58.Ge

##  1 Introduction

Neutral hydrogen (H I) is a significant component of the interstellar medium
within disk galaxies. The measurement of its abundance and kinematics, via the
21 cm emission line, has been employed to address numerous astrophysical
issues, including studies on the large-scale distribution of galaxies and
their secular evolution (e.g., Wong et al., 2006; Cheng et al., 2020; Xu et
al., 2022). The line emission from H I provides crucial information about the
distribution of neutral gas and its velocity field within a galaxy (e.g.,
Springob et al., 2005). H I line offers a way to obtain redshifts, gas masses,
and rotational widths for standard galaxies, tracing the evolution of tidal
phenomena, and furnish quantitative assessment of the potential for future
star formation through H I content. Moreover, because of its relatively simple
physics, the H I line provides a valuable means of tracing cool gas mass and
star formation potential in nearby galaxies, and enables the investigation of
low-luminosity, gas-rich object populations. Low surface brightness, dwarf,
and gas-rich galaxy populations are often underrepresented in optical or
infrared surveys. Although public wide-area optical or infrared surveys and
their associated spectroscopic surveys are good at detecting luminous
ellipticals, bright spirals, and bursting or active galaxies, they are
substantially less complete in tracing the low surface brightness, dwarf, and
gas-rich galaxy populations which actually dominate the local population.
Thus, large-scale extragalactic H I surveys remain necessary to take stock of
gas-rich galaxies in the proximity of the local universe (e.g., Koribalski et
al., 2004; Meyer et al., 2004; Wong et al., 2006; Giovanelli & Haynes, 2015;
Haynes et al., 2011, 2018).

Over the past decade, a number of significant surveys have been conducted on
the H I line providing insights into the properties of the extragalactic
population in the local universe (Giovanelli & Haynes, 2015). The Arecibo
Legacy Fast ALFA Survey (ALFALFA) was the most sensitive, large scale
extragalactic H I survey and the first of its kind (Giovanelli et al., 2005;
Haynes et al., 2018). The ALFALFA survey utilized the seven-beam Arecibo
$L$-band Feed Array (ALFA) to conduct a drift-scan survey, and covered an area
of approximately 7000 deg2 over a redshift range of $-2000<{\rm c}z<18000$
${\rm km~{}s}^{-1}$ and cataloged 31502 galaxies. Its beam size is $3\hbox
to0.0pt{.\hss}{{}^{\prime}}8\times 3\hbox to0.0pt{.\hss}{{}^{\prime}}3$, and
the final spectral resolution is 10 ${\rm km~{}s}^{-1}$ at 1420 MHz (Haynes et
al., 2011, 2018). The ALFALFA survey has provided important measurements to
determine the faint-end profile of the H I mass function (HIMF) (e.g., Martin
et al., 2010; Jones et al., 2018), the HIMF variation with the environment
(e.g., Moorman et al., 2014; Jones et al., 2016, 2018), the HI-bearing
population differing from optically selected objects (Huang et al., 2012a, b;
Gavazzi et al., 2013), and establishing metrics for the normal H I content of
galaxies (e.g., Toribio et al., 2011; Odekon et al., 2016). Another large
extragalactic H I survey before ALFALFA was the H I Parkes All Sky Survey
(HIPASS; Barnes et al., 2001). It has also made a great census of the local
extragalactic H I galaxy distribution in a coverage of about 30000 deg2,
identifying more than 5000 galaxies, which are mainly included in the HIPASS
Bright Galaxy Catalogue (BGC; Koribalski et al., 2004), the southern HIPASS
Catalogue (HICAT; Meyer et al., 2004), and the Northern HIPASS Catalogue
(NHICAT; Wong et al., 2006), respectively.

However, to deal with a number of open questions a significant improvement
over HIPASS and even ALFALFA is required. These surveys could not sample a
cosmologically fair volume since their average depth was inadequate, and their
angular and spectral resolution as well as sensitivity were limited.
Consequently, earlier surveys on H I have mainly focused on H I-rich galaxies
that are local, massive, and situated at the upper end of the HIMF. For
instance, these surveys did not detect sufficient H I components from the
population of dwarf galaxies. Typically, dwarf galaxies exhibit weak H I
signals with narrow H I widths. As a result, higher sensitivity is required
with smaller velocity bins. Furthermore, sensitivity limitations prevent the
identification of the vast H I morphology of bright local galaxies. This
understanding is critical to discern their recent formation or merger history
(e.g., Zhou et al., 2023).

The Widefield ASKAP L-band Legacy All-sky Blind Survey (WALLABY) is expected
to survey 75% of the sky in the 21-cm line of neutral atomic hydrogen gas at a
resolution of 30′′. It is projected to detect approximately half a million
galaxies in the local universe (Koribalski et al., 2020). As the next-
generation extragalactic survey in the 21-cm H I line, WALLABY is expected to
be the most powerful survey in observing the local universe. However, it will
take years to complete the WALLABY survey.

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has the
distinction of being designated as the most powerful single dish telescope. It
is located at a geographic latitude of $25^{\circ}39^{\prime}10\hbox
to0.0pt{.\hss}{{}^{\prime\prime}}6$. Its observable maximum zenith angle is
about $40^{\circ}$ (Nan et al., 2011; Jiang et al., 2019, 2020). The FAST All
Sky H I survey (FASHI) will cover the entire sky that can be detected by FAST
telescope, ranging a declination of $-14\degree$ to $+66\degree$, and has a
frequency range of $1.0-1.5$ GHz, achieves a median detection sensitivity of
$\sim$0.76 ${\rm mJy~{}beam}^{-1}$ at velocity resolution of 6.4 ${\rm
km~{}s}^{-1}$ (Jiang et al., 2019, 2020). A pilot FASHI drift scan survey
released and compared a total of 544 extragalactic H I sources with the
ALFALFA survey (Kang et al., 2022). The FASHI and ALFALFA surveys have
consistent observational parameters, including flux and velocity. However, in
sensitivity, efficiency, and resolution, the FASHI project outperforms the
ALFALFA significantly. Furthermore, the FASHI survey has successfully mapped
extragalactic H I sources, such as M106 (Zhu et al., 2021), M51 (Yu et al.,
2023a), DDO 168 (Yu et al., 2023b), M94 (Zhou et al., 2023), NGC 4490/85 (Liu
et al., 2023). This further demonstrates the significant scientific value of
FASHI data for both statistical studies and targeted investigations.

Being a blind H I survey, FASHI avoids bias towards high surface brightness
galaxies, which are usually present in optical galaxy catalogs, and offers
better angular and spectral resolution in comparison to any previous single
telescope. FASHI’s extensive coverage overlaps with several other prominent
surveys, including the Sloan Digital Sky Survey (SDSS; Abazajian et al.,
2009), the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS;
Chambers et al., 2016), the Two Micron All Sky Survey (2MASS; Skrutskie et
al., 2006), and the NRAO VLA Sky Survey (NVSS; Condon et al., 1998). The
catalog products generated by FASHI will be highly valuable for
multiwavelength data mining, extending far beyond those currently engaged in
the FASHI project itself. The program aims to provide legacy data products
that have broad applications and maximize the potential for scientific
research.

In this paper, we mainly present the FASHI project and its catalogued
extragalactic H I sources. Moreover, Section 2 explains the scientific
objectives, survey strategy, and observational setup of the FASHI project.
Section 3 discusses data reduction, which includes baseline correction, radio
frequency interference (RFI) mitigation, source extraction, and distance
calibration. Section 4 describes the FASHI catalog that has been released. It
outlines the approach used for source characterisation. It also provides an
overview of the basic parameters of the detected sources and presents the
methods used for optical counterpart identification. Section 5 comprises of
the evaluation of the reliability and completeness of the FASHI sources. It
also provides some caveats and comments for the users of the FASHI catalog. A
summary is provided in Section 6. This paper adopts the Hubble constant value
of H0 = 75 ${\rm km~{}s}^{-1}$Mpc-1, $\Omega_{\rm M}=0.3$, and
$\Omega_{\rm\Lambda}=0.7$. In addition, we adopt AB magnitude system (Oke &
Gunn, 1983) throughout this paper.

##  2 The FASHI project

Figure 1: FASHI sky distribution of currently released 41741 H I sources (in
blue points) included in Table 2 in the Galactic hemispheres, showing the
roughness of boundaries imposed by practical and scheduling constraints. For
comparison, ALFALFA $\alpha$100 (Haynes et al., 2018) and HIPASS galaxies
(Koribalski et al., 2004; Meyer et al., 2004; Wong et al., 2006) are also
indicated with red and green points, respectively. The two black dashed lines
show the position of the Galactic plane at galactic latitude $b=\pm
10^{\circ}$.

Figure 2: The distribution of large-scale structure from FASHI (in blue
points) and ALFALFA (in red points) sources at different declination (J2000)
ranges of $\rm-10^{\circ}<DEC<60^{\circ}$ and volume extending to $\sim$26323
${\rm km~{}s}^{-1}$ that corresponds to the maximum velocity of current FASHI
sources. The red dashed circle displays the maximum velocity of ALFALFA
sources in the corresponding ALFALFA images.

The FASHI project aims to observe the entire detectable sky of the FAST in the
declination range of $-14\degree$ to $+66\degree$ at a frequency between 1050
and 1450 MHz, as illustrated in Figure 1. The first data release of the FASHI
project mainly concentrates on extragalactic areas that lie predominantly
outside the observable regions of the Arecibo telescope. Between August 2020
and June 2023, the FASHI project observed an approximate area of 7600 square
degree of the sky and detected 41741 extragalactic H I sources, as shown in
Figure 1. This indicates a detection rate of approximately 5.5 sources per
square degree. Based on this detection rate, FASHI is expected to detect over
100000 extragalactic H I galaxies in the approximately 22000 square degree
region detectable by FAST, up to a redshift of approximately 0.35. Currently,
we mainly use datasets in the frequency range of 1305.5-1419.5 MHz due to
serious RFI and relatively low sensitivity at low frequency ranges. Table 1
summarizes the technical details of the FASHI project.

Table 1: FASHI project technical details.

Parameters | Values
---|---
Planed RA range |  $\rm 0^{h}\leq RA\leq 24^{h}$ (see Figure 1)
Planed DEC range |  $\rm-14\degree\lesssim DEC\lesssim+66\degree$ (see Figure 1)
Current RA range |  $\rm 0^{h}\leq RA\leq 17.3^{h}$, $\rm 22^{h}\leq RA\leq 24^{h}$
Current DEC range |  $\rm-6\degree\lesssim DEC\lesssim 0\degree$, $\rm 30\degree\lesssim DEC\lesssim 66\degree$
Receiver | 19-beam array
Polarizations per beam | 2 linear (XX, YY)
Beam size (FWHM) |  2$\hbox to0.0pt{.\hss}{{}^{\prime}}$9 at 1420 MHz
Gain |  13-17 K Jy-1
$T_{\rm{sys}}$ | 16-19 K
Total frequency coverage | 1050-1450 MHz
Released frequency range | 1305.5-1419.5 MHz (in this work)
c$z_{\odot}$ range |  200-26323 ${\rm km~{}s}^{-1}$ (in this work)
Bandwidth (total) | 400 MHz
Spectral channels | 65536 (before smoothing)
Channel spacing |  7.629 kHz or 1.6 ${\rm km~{}s}^{-1}$ at 1420 MHz
Spectral resolution |  6.4 ${\rm km~{}s}^{-1}$(after smoothing)
Spectral median rms |  $\sim$1.50 mJy at 6.4 ${\rm km~{}s}^{-1}$resolution
Map median sensitivity |  $\sim$0.76 ${\rm mJy~{}beam}^{-1}$ at 6.4 ${\rm km~{}s}^{-1}$resolution

Notes. The spectral median rms is estimated from the baseline measurement of
each integrated spectrum. The map median sensitivity of the map is estimated
from the integrated map.

###  2.1 Science goals

The primary survey strategy of the FASHI project is to utilize the high
sensitivity and rapid survey speed of FAST to perform a comprehensive
inventory of the H I gas in the neighbouring universe. Additionally, the
project aims to observe at leas 100000 H I galaxies.

The ALFALFA project identified 31502 extragalactic H I sources, determined the
HIMF of the local universe, established measurements for the typical H I
content of galaxies, and derived the clustering properties of H I galaxies
(e.g., Haynes et al., 2011; Toribio et al., 2011; Odekon et al., 2016; Jones
et al., 2018). Nevertheless, the studies concerning the low-mass end of the
HIMF from ALFALFA still face detection limitations. Increasing the H I
detection number by an order of magnitude towards low masses is necessary to
investigate the faint end of the HIFM, particularly the properties of the H I
faint targets, which include dwarf galaxies and the passive galaxy population.
According to Huang et al. (2012b), the population detected by H I surveys
presents an overall higher star formation rate at a fixed stellar mass.
However, for low-mass star-forming galaxies (blue dwarf galaxies) and
quiescent galaxies, the H I detection rate is relatively low. According to a
study by Ai & Zhu (2018) of H I gas in galaxy groups and clusters using FASHI
data, the detection rate of H I is less than 30% for member galaxies in groups
with $z>0.03$. This low detection rate poses a significant challenge to
statistical studies of H I gas properties in optically selected samples. There
is no doubt that deeper surveys, such as FASHI, can help to improve the
detection rate for H I deficient galaxies.

The FASHI project focuses on providing a more comprehensive and extensive
coverage of the local universe, as visualized in Figure 2. Additionally, it
aims to achieve a survey volume that is comparable to current optical surveys
such as the SDSS. The project would enable the study of the HIMF across
various cosmic environments, making it possible to find more low-mass dwarf
galaxies and map the local Voids accurately in order to sample multiple low-
density environments. FASHI is sensitive to large-scale diffuse gas structure,
which could be missed by interferometric surveys. One of the ultimate goals of
the FASHI project is to combine the FAST data with the data from ongoing or
planned surveys employing interferometric arrays, which will enable sampling
of the extragalactic H I sky with significantly increased resolution. This
will provide crucial information regarding the large-scale H I structures,
leading to a better understanding of the role of H I in galaxy formation and
evolution. Therefore, single-dish large-scale surveys like FASHI are still
indispensable even in the era of interferometric large-scale surveys.

It has been demonstrated by ALAFALA that a blind H I survey has the potential
to discover uncommon objects like the nearby faint dwarf Leo P (Giovanelli et
al., 2013), nearly dark galaxies, and galaxies with extraordinarily high H
I-to-stellar mass ratios (e.g., Adams et al., 2015; Janowiecki et al., 2015;
Janesh et al., 2015, 2017). In the local universe, deeper surveys of larger
volumes may detect additional such objects and even new types of species.
FAST’s sensitivity could enable the identification of H I clouds, close to
$10^{4}\,M_{\odot}$, in the Local Group, within a few minutes of integration
time. In several case studies (e.g., Zhu et al., 2021; Yu et al., 2023a, b;
Zhou et al., 2023; Liu et al., 2023), FASHI has detected intergalactic and
surrounding diffuse gas that has not been detected by other telescopes. The
work of Xu et al. (2023b) shows that FASHI can potentially discover more
candidates of “dark galaxies” that do not form any star due to low-density gas
and, therefore, reveal the mystery of dark halo substructure.

Deep H I observations using mapping techniques have the potential to reveal
filamentary gas features (e.g., Zhu et al., 2021) and large-scale
distributions (refer to Figure 2) of matter in the Local Group and nearby
universe. This can provide more insight into the distribution and properties
of dark matter. The H I gas that is faint and diffuse, and cannot be detected
by an interferometer, can be easily searched using FAST. There will be no
restriction on the survey’s depth level, and the scope of exploration will be
expanded in certain areas based on the discovery of significant
characteristics.

In addition, the FASHI project will also cover a section of the Milky Way.
This will provide an opportunity to detect numerous radio recombination lines
and high-velocity Galactic H I clouds with exceptional sensitivity.

###  2.2 Survey strategy

Survey efforts of this scale and scope require careful optimization of their
operational strategy towards achieving the science objectives within the
constraints imposed by practical observing conditions and requirements. In
this paper, the available observation time for the FASHI project mainly comes
from the schedule-filler time, i.e., the observations were carried out as a
time-filler project when there are no other programs in the FAST observing
queue. Although such type of observations cannot be planned for specified
targets, we can still do a blind search for H I emission. Our observational
setup was to fix the sky declination and do drift scans during the project
time. We also used some commissioning time to do calibration observations. In
this mode, we can not control the sky coverage uniformly, but it makes full
use of all available time to increase the sample of H I galaxies. Some areas
will be sampled multiple times and can go deeper than other areas. Such
strategy has the advantage of finding more dwarf galaxies and H I poor
galaxies. As this is the first data release of the survey, it is not a high
priority for us to achieve a uniform sky coverage. Instead, we made full use
of all available time to increase the coverage area. We plan to standardize
the survey depth for this ongoing project and will showcase the results in the
next data release.

###  2.3 Observational setup

FAST has a 19-beam receiver enabling efficient scan observations that can
cover multiple areas of observation simultaneously. The FAST spectral-line
backend is equipped with two linear polarizations, XX and YY. The spectral-
line backend has a full wideband that covers a frequency range of 1050 to 1450
MHz. The FASHI project used the low-resolution mode with 64 k channels,
resulting in a frequency resolution of 7630 Hz. The sampling time for each
spectral line in FASHI is 1 second. The FAST has an aperture of 500 m with an
effective aperture of approximately 300 m, resulting in an HPBW of 2$\hbox
to0.0pt{.\hss}{{}^{\prime}}$9 at a frequency of $\sim$1.4 GHz. The pointing
error is less than or equal to $15^{\prime\prime}$. To calibrate intensity, a
high-noise signal with an amplitude of $\sim$11 K was injected into the entire
FASHI observation for a period of 32 seconds. The factor of degrees per flux
unit (DPFU) is relevant for 19 beams, with a value of $\rm DPFU=13\sim 17$ K
Jy-1 at a frequency of 1400 MHz, as detailed in (Nan et al., 2011; Jiang et
al., 2019, 2020; Qian et al., 2020; Han et al., 2021). The technical details
for the FASHI measurements are listed in Table 1.

####  2.3.1 DecDriftWithAngle mode

Although FAST has an active reflector that enables the telescope to cover a
larger part of the sky, the motion of the reflector panels is driven by over
2000 actuators, which consume a substantial amount of electricity. The drift
mode has been extensively used at the Arecibo telescope, and the ALFALFA
survey has shown that it can deliver high-quality data with a very good
baseline. Thus, the FASHI project is predominantly performed using the drift
scan mode (DecDriftWithAngle).

The FASHI survey was mainly carried out using a drift scan observational mode
called DecDriftWithAngle, utilizing 19-beam receivers (Li et al., 2018; Jiang
et al., 2019, 2020). The drift scan method helped minimize complexities in
system control and self-generated RFI, ensuring relatively accurate pointing,
full FAST gain, and stable status for the primary surface and focal cabin
during the surveys. Throughout the survey, the telescope’s azimuth arm was
positioned on the meridian at a pre-assigned J2000 declination, with a spacing
of $21\hbox to0.0pt{.\hss}{{}^{\prime}}65$ between primary drift centers. The
feed array was rotated by $23\hbox to0.0pt{.\hss}{{}^{\circ}}4$ to enable
super-Nyquist sampling of Earth-rotation drift-scan tracks of individual beams
in the declination of J2000 coordinates, with sampling rates smaller than half
the FWHM.

####  2.3.2 MultiBeamOTF mode

Among all the FAST approved projects, the FASHI project has the lowest
priority of observation. For the FASHI project to proceed, it relies solely on
the schedule-filler observable time. Please refer to section 2.2 for details.
As a consequence of this, the observed RA ranges during the FASHI drift survey
appear relatively random. While some sky ranges have been scanned repeatedly,
some others cannot be covered at all. To cover the portions of the sky that
remained uncovered in the drift-scan mode, the MultiBeamOTF scan mode was
employed (Jiang et al., 2020). The observational setup used in the
MultiBeamOTF mode in FASHI is almost identical to that in the
DecDriftWithAngle mode (see Section 2.3.1), with the exception of a specified
declination range.

##  3 Data reduction

The FASHI data were reduced mainly using the FAST spectral data reduction
pipeline named as HiFAST (Jing et al., 2023; Xu et al., 2023a). The HiFAST
pipeline combines several useful data reduction packages including antenna
temperature correction, baseline correction, RFI mitigation, standing-waves
correction, gridding, flux correction, and fits cube generation. For the FASHI
data, the spectral resolution was smoothed to 6.4 ${\rm km~{}s}^{-1}$ per
channel, and the fits cubes were gridded into a pixel scale of 1′, meeting the
Nyquist sample criterion. The conversion from antenna temperature to flux
density was carried out using the fitted gain curves for FAST 19-beams when
gain varied with different zenith angles ($\rm ZA\lesssim 40\degree$), as
reported by Jiang et al. (2019). All other parameters used for data reduction
were set to default values as specified in the HiFAST pipeline
cookbook111https://hifast.readthedocs.io/.

###  3.1 The arPLS for baseline correction

In the HiFAST pipeline (Jing et al., 2023), we utilized the Asymmetrically
Reweighted Penalized Least Squares algorithm (arPLS; Baek et al., 2015) to
adjust the entire 500 MHz spectral baseline. This method permits us to modify
a parameter, “weight”, repeatedly to calculate the baseline. A significant
weight is applied automatically when the signal falls below an already fitted
baseline. Conversely, no weight or a small weight is assigned when an emission
line is present above the fitted baseline as it may be part of the spectral
peak. A preset coefficient can control the balance between fitness and
smoothness, based on the total spectral channel number. This method can
iteratively estimate the noise level and adjust the weights accordingly (Baek
et al., 2015). Thankfully, the FASHI data contains only a few instances of
weak and narrow absorption dips throughout the entire 500 MHz band. Therefore,
it is highly suitable to implement the arPLS method for reducing the FAST
spectral data. In fact, the arPLS baseline fitting method has been effectively
employed for many FAST spectral analyses (e.g., Zeng et al., 2021; Zhang et
al., 2021, 2022a). Nevertheless, there is a drawback in the arPLS algorithm
for baseline correction. Our findings revealed that a small indentation
occasionally appears in the baseline close to the line wings of the strong
emission and wide line profile with intensities over $\sim$40 mJy and
velocities over $\sim$150 ${\rm km~{}s}^{-1}$. This could lead to a slight
underestimation ($\lesssim$5%) of the integrated flux only for bright sources.
In general, arPLS is the best choice for baseline correction when reducing
broadband data from large scale surveys.

###  3.2 Radio frequency interference

The quality of antenna observational data is seriously affected by RFI.
Mitigating RFI is an essential procedure in any observation. However, strong
RFI from sources such as broadcast radio and TV, communication satellites, and
navigation satellites are extremely difficult to avoid in FASHI. As a result,
the existing RFI have made several wide frequency bands completely useless.
Zhang et al. (2022a) conducted a statistical analysis of the probabilities of
RFI distribution, derived from approximately 300 hours of blind survey data,
across the wideband FAST spectrum from 1050 to 1450 MHz. RFI is relatively
severe below 1305 MHz, whereas the frequency band above 1305 MHz is relatively
clear. At approximately 1382 MHz, a strong RFI is present due to the GPS L3
signal. This corresponds to a distance of around 112 Mpc as shown in Figure 3.
Thankfully, the probability of the strong RFI appearing is less than 10%.
These occasional RFIs have been removed reasonably well during the data
reduction process. We flag RFIs in FAST spectra employing four strategies -
flagging extremely strong RFIs, long-lasting RFIs, polarized RFIs, and beam-
combined RFIs, as detailed in Zhang et al. (2022a). Therefore, we publish only
the extragalactic sources that are located within the frequency band of
1305.5-1419.5 MHz in the first released FASHI data.

Several frequency periods ($\sim$1 MHz, $\sim$2 MHz, and $\sim$0.04 MHz) of
standing waves are inhabited in the FASHI spectral data. The spectral quality
can be significantly improved if the standing wave is completely corrected.
Standing waves are predominantly fitted and subtracted using the SW package
(Xu et al., 2023a) in the HiFAST pipeline (Jing et al., 2023). To find
standing waves at the frequency periods in the FAST spectra, the SW package
uses the Fourier transform method. To prevent confusion from the Galactic H I
emission, the velocity range of $-150<v_{\rm HI}<150$ ${\rm km~{}s}^{-1}$ was
masked and excluded from the spectral data reduction, as a further measure.
The effect obtained after implementing the SW package correction shows that
the standing wave is almost disappear. This treatment turns out to be highly
effective for both standing wave correction and source extraction.

###  3.3 Source extraction

The process of extracting sources for FASHI consists of two steps, involving
both automated and interactive procedures. The application used for finding
sources in H I data cubes is called the H I Source Finding
Application222https://github.com/SoFiA-Admin/SoFiA-2 (SoFiA) in version 2.
SoFiA is a pipeline that was initially intended for detecting and
characterising galaxies in extragalactic H I data cubes in a three-dimensional
space (Serra et al., 2015; Westmeier et al., 2021, 2022). The SoFiA source
finding algorithm involves smoothing the data on various spatial and spectral
scales specified by the user and measuring the noise level in each smoothing
iteration. Subsequently, a user-defined flux threshold is applied relative to
the noise to retain all pixels with statistically significant flux density.
SoFiA is a fully automated and reliable source finding tool, making it crucial
for the scientific success of surveys (e.g., Westmeier et al., 2022).

In the FASHI source search, we first used version 2 of SoFiA to generate a
list of initial candidates. In the SoFiA setup, the detection threshold is
4.5$\sigma$ ($\sigma$ means the local noise level within the bounding box of
the source in native units of the data cube; see the SoFiA cookbook); the
smoothing kernels are kernelsXY = 0, 3, 6 and kernelsZ = 0, 3, 7, 15
respectively; in the linking parameters, the minimum size of the sources in
the spatial dimension is 5 pixels/channel each in XY and Z space, while the
maximum size is 50 pixels in XY space, but not limited in Z space;
furthermore, we set the reliability.enable as false so that artefacts such as
RFI and bad baselines occasionally remain in the spectral data. Based on the
above setup, we found that the the source candidates found by SoFiA found
contain some false signals. Therefore, a manual interactive source extraction
has to be done, judging by the source moment maps, spectral profile, signal to
noise ratio and so on. In addition, in the cases of double or multiple sources
very close in position and redshift and low intensity level between two peaks,
SoFiA has difficulty distinguishing them and often prefers to fit them with a
wide double horn profile rather than two different single line profiles. For
the cases of bad baselines produced by the HiFAST pipeline, we used a high-
order polynomial fit to correct each source baseline again. In this step (see
also Figure 12) we extend the integrated area to $\sim$2.0-2.5 times (see
analysis in Section 4.1) the 4.5$\sigma$ region size (see above for the
definition of 4.5$\sigma$) for measuring each source integrated spectrum to
include most of the flux from H I sources. The size of the integrated area
also depends on the SNR of each H I line. If SoFiA did not fit the source
position and size well, we would manually use an ellipse or circle of an
appropriate size to measure the integrated spectrum of the source. The ellipse
or circle size would try to include $\sim$100% of each source’s integrated
flux, but a high enough SNR ($\rm SNR\gtrsim 6.5$) for each source should be
guaranteed first. Finally, we use the busy function to fit the integrated
spectra. The velocity, line width and flux density would be derived by the
busy function fitting code (Westmeier et al., 2014). Although all detections
made by SoFiA were visually inspected to discard obvious false positives
caused by noise peaks or artefacts, a small number of false detections are
likely to remain in the final catalog.

###  3.4 Source aperture

A source aperture, defined as an ellipse ($ell_{\rm maj,\,min,\,pa}$) with
major and minor radii, and a position angle, is used for measuring flux. The
entire H I flux of each source is integrated within the ellipse, providing an
estimate of the size of each H I source. Usually, the size of the ellipse is
set to be approximately 2.0-2.5 times larger than the area covered by the
4.5$\sigma$ region (refer to Section 3.3 for the definition of 4.5$\sigma$).
Approximately 95% of the measured effective diameter ($2\sqrt{ell_{\rm
maj}\times ell_{\rm min}}$) in our catalog is larger than 5$\hbox
to0.0pt{.\hss}{{}^{\prime}}$0, with a median value of 8$\hbox
to0.0pt{.\hss}{{}^{\prime}}$3 which indicates that the current ellipse size is
large enough compared to the beam size ($\sim$2$\hbox
to0.0pt{.\hss}{{}^{\prime}}$9) to encompass most of the emission from each H I
source. Furthermore, the size of the integrated area is dependent on the
signal-to-noise ratio (SNR) of each H I line. The catalog indicates that 94%
of sources have an SNR of at least 10, ensuring sufficient SNR for most
sources. Reducing the size of the ellipse would lead to the loss of flux,
whereas increasing it would introduce more noise. Our statistical analysis
shows that changing the aperture of each source in the catalog by 10% results
in a decrease or increase in the included source flux of only $\sim$2.5% or
2.0% respectively. Similarly, the flux uncertainty reduces by roughly 11.5% or
13.0%, respectively. Furthermore, the current source aperture fulfils the
criterion that the flux uncertainty is less than 10% of each source flux. An
increase of the source aperture by 10% will elevate the contribution of flux
uncertainty to roughly 12% of the total flux of each source. Overall, the
current setup of the ellipse size proves to be a highly suitable solution.

###  3.5 Distance calculation

The peculiar motions of galaxies interfere with distance calibration in the
local universe because of their gravitational interaction with the underlying
density field of matter. This can be observed in the large-scale structures
(e.g., Figure 2) of the local universe (Branchini et al., 1999; Masters, 2005;
Masters et al., 2006). Peculiar velocities of galaxies provide a powerful and
unbiased method to comprehend the dynamics and structure of the local
universe. Cosmicflows333https://edd.ifa.hawaii.edu/ is a program that compiles
galaxy distances and breaks down observed velocities into components due to
the expansion of the universe and the residual effects of gravitational
interactions (Graziani et al., 2019; Kourkchi et al., 2020). Galaxy test
particle peculiar motions can be used to derive inferences about the large-
scale structure of the universe (Tully et al., 2008, 2013, 2016). The
Cosmicflows program derives two Distance-Velocity Calculators that establish
the connections between the distances and velocities of galaxies, based on
smoothed versions of the velocity fields, with the aid of the Extragalactic
Distance Database (Tully et al., 2009). One of this calculator is limited to a
distance of 38 Mpc and is based on a fully nonlinear analysis. The other
calculation extends to a distance of 200 Mpc and is derived from an analysis
in the linear dynamical region (Graziani et al., 2019; Kourkchi et al., 2020).
The distance measurement of the sources in FASHI mainly combines two distance
calculators: the Cosmicflows-3 Distance-Velocity Calculators and the Hubble
flow distance, with H0 = 75 ${\rm km~{}s}^{-1}$Mpc-1. Details about the
combination for the distance calculators are presented in Section 4.1. The
derived distance parameters are presented in Table 2 and illustrated in Figure
3.

Figure 3: FASHI mass-distance diagram of currently released 41741 H I sources
(in blue points) listed in Table 2. The strange gaps of the FASHI targets at
$\sim$190, $\sim$210 and $\sim$266 Mpc are probably caused by relatively low
sample completeness. For comparison, the ALFALFA $\alpha$100 sources (Haynes
et al., 2018) are also shown here with red dots. The gaps of the ALFALFA
targets at $\sim$80 and $\sim$230 Mpc are caused by the known RFIs.

##  4 The FASHI extragalactic source catalog

###  4.1 Extragalactic H I catalog

Table 2: FASHI extragalactic H I source catalog.

[1] | [2] | [3] | [4] | [5] | [6] | [7] | [8] | [9] | [10] | [11] | [12] | [13] | [14]
---|---|---|---|---|---|---|---|---|---|---|---|---|---
FASHI ID | RA | DEC |  c$z_{\odot}$ |  ell${}_{\rm maj,\,min,\,pa}$ |  W50 |  W20 | $F_{\rm peak}$ | $\sigma_{\rm rms}$ | $S_{\rm bf}$ | $S_{\rm sum}$ | SNR | $D$ |  log$M$
| deg | deg | ${\rm km~{}s}^{-1}$ | $~{}~{}^{\prime},~{}~{}^{\prime},~{}~{}^{\circ}$ | ${\rm km~{}s}^{-1}$ | ${\rm km~{}s}^{-1}$ | mJy | mJy |  mJy$\cdot$${\rm km~{}s}^{-1}$ |  mJy$\cdot$${\rm km~{}s}^{-1}$ |  | Mpc | $M_{\odot}$
20230000823 | 166.736 | -6.193 | 6802.1 | 3.7, 3.7, 0 | 164.0 | 172.6 | 9.0 | 1.8 | 1179.4 | 1046.8 | 20.3 | 98.2 | 9.4
20230000824 | 113.607 | -6.193 | 10797.4 | 4.4, 4.4, 0 | 149.7 | 199.5 | 16.5 | 2.9 | 2221.2 | 2150.5 | 25.0 | 143.7 | 10.0
20230000825 | 166.377 | -6.187 | 7596.6 | 5.0, 4.9, 106 | 33.0 | 46.5 | 33.2 | 2.4 | 1118.9 | 1098.3 | 31.9 | 108.3 | 9.5
20230042078 | 169.097 | -6.182 | 24776.7 | 5.0, 5.0, 0 | 94.5 | 120.8 | 7.6 | 2.8 | 648.5 | 568.0 | 9.4 | 327.6 | 10.2
20230000826 | 111.686 | -6.182 | 19246.1 | 2.3, 2.3, 0 | 198.6 | 216.4 | 2.4 | 1.2 | 474.5 | 504.0 | 11.2 | 253.7 | 9.8
20230042079 | 165.515 | -6.178 | 9428.8 | 6.0, 3.8, 174 | 249.8 | 284.5 | 7.1 | 2.7 | 1403.0 | 1201.0 | 13.2 | 134.6 | 9.8
20230000828 | 163.988 | -6.174 | 10202.9 | 5.8, 5.4, 33 | 170.9 | 176.9 | 23.4 | 3.4 | 2407.1 | 2550.6 | 21.2 | 143.9 | 10.1
20230000831 | 58.707 | -6.171 | 10984.3 | 3.3, 3.3, 0 | 133.7 | 231.8 | 7.8 | 2.5 | 1022.5 | 984.2 | 13.7 | 152.0 | 9.7
20230042080 | 125.705 | -6.169 | 14644.7 | 5.5, 4.5, 142 | 70.7 | 101.2 | 13.9 | 3.6 | 994.4 | 715.8 | 13.1 | 196.1 | 9.9
20230000829 | 110.411 | -6.163 | 1641.4 | 6.7, 6.5, 109 | 183.1 | 258.9 | 16.0 | 4.2 | 3078.2 | 3119.5 | 21.4 | 24.5 | 8.6
… … |  |  |  |  |  |  |  |  |  |  |  |  |

Notes. The complete catalog containing 41741 sources can be accessed in the
online Supplementary material or at https://fast.bao.ac.cn/cms/article/271/
and https://zcp521.github.io/fashi, with additional columns available for
download.

Figure 4: Histograms of the measured spectral rms ($\sigma_{\rm rms}$),
signal-to-noise ratio (SNR), line width $W_{50}$ and $W_{20}$, heliocentric
velocity (c$z_{\odot}$) and source mass distributions for the entire released
FASHI and ALFALFA catalogs. Some useful statistical results of the FASHI
sources are given in each panel. For comparison, the source mass measured by
ALFALFA has been roughly corrected with the same Hubble constant as that used
by FASHI.

Table 2 presents the key findings of the FASHI extragalactic H I survey. The
complete contents of Table 2 can be downloaded in the online Supplementary
material or at https://fast.bao.ac.cn/cms/article/271/ and
https://zcp521.github.io/fashi. It comprises of 41741 sources having multiple
columns of observed and calculated parameters. The full online table provides
high-precision and detailed information such as the centroid coordinate
(J2000) in format of Jhhmmss.ss$\pm$ddmmss.s, spectral line central frequency
in MHz, radio-defined velocity, integrated velocity range, detection
sensitivity, and uncertainty. The introduction of each column in Table 2 and
the parameters that are only included in the online table, are all introduced
in the relevant column as follows:

  * •

Column 1: Index number for each FASHI extragalactic source. This index number
is unique to each FASHI source.

  * •

Column 2-3: Right ascension (RA) and declination (DEC) in unit deg of FASHI
source centroid (J2000). The distribution of the spatial flux of the sources
often displays an irregular structure, or RFI and nearby objects can influence
it, causing the source position offset to exceed the telescope pointing
accuracy of approximately 7.9′′ (Jiang et al., 2020). However, it should
generally be less than 1′, as evidenced by the position separation between
FASHI and SDSS sources in Figure 9. For each FASHI source, the positional
accuracy listed in the online table can be estimated by dividing the beam size
($\sim$2$\hbox to0.0pt{.\hss}{{}^{\prime}}$9) by the H I signal-to-noise ratio
(e.g., Koribalski et al., 2004). In addition, the centroid coordinate (J2000)
is only listed in the online table, formatted as Jhhmmss.ss$\pm$ddmmss.s.

  * •

Column 4: Heliocentric velocity of each H I source, c$z_{\odot}$ in unit of
${\rm km~{}s}^{-1}$. Values have been converted to the optical definition
$\delta\lambda/\lambda$ from the “radio” one ($\delta\nu/\nu$). The
c$z_{\odot}$ histogram of all FASHI sources is shown in Figure 4, where the
strange gaps of the FASHI targets at $\sim$14300 ${\rm km~{}s}^{-1}$ are
probably caused by relatively low sample completeness. The velocity
uncertainty is estimated with $\sigma({\rm c}z_{\odot})=3\sqrt{P\,\delta
v}/{\rm SNR}$, where $\delta v=6.4$ ${\rm km~{}s}^{-1}$ is the spectral
resolution, and $P=(W_{20}-W_{50})/2$ is a measure of the steepness of the
profile edges (Fouque et al., 1990; Koribalski et al., 2004). The spectral
line centre frequency, radio velocity, redshift, $z_{\odot}$ and their
uncertainties, corresponding to the heliocentric velocity, are only given in
the online table.

  * •

Column 5: Ellipse, $ell_{\rm maj,\,min,\,pa}$, with major, minor radius and
position angle, in units of arcmin, arcmin, and degree, respectively. The
ellipse indicates the measurement aperture of the integrated source in RA and
DEC space for estimating the source flux. It does not represent the properties
of the galaxy.

  * •

Column 6: Velocity width of the H I line profile, $W_{50}$ in ${\rm
km~{}s}^{-1}$, measured at 50% level of every peak by busy-function fitting.
The $W_{50}$ uncertainty is estimated with $\sigma(W_{50})=2\sigma({\rm
c}z_{\odot})$. The $W_{50}$ histogram of FASHI sources is shown in Figure 4.

  * •

Column 7: Velocity width of the H I line profile, $W_{20}$ in ${\rm
km~{}s}^{-1}$, measured at 20% level of every peak by busy-function fitting.
The $W_{20}$ uncertainty is estimated with $\sigma(W_{20})=3\sigma({\rm
c}z_{\odot})$. The $W_{20}$ histogram of FASHI sources is displayed in Figure
4.

  * •

Column 8: H I line flux intensity at the peak of each integrated spectrum,
$F_{\rm peak}$ in mJy, measured by busy-function fitting. The $F_{\rm peak}$
uncertainty is estimated with the measured $\sigma_{\rm{rms}}$ listed in
Column 9.

Figure 5: FASHI coverage and detection sensitivity. The detection sensitivity
is estimated from each detected source with the H I line noise
($\sigma_{\rm{rms}}$) in column 9 and the source size ($ell_{\rm
maj,\,min,\,pa}$) in column 5 of Section 4.1. It roughly shows the
distribution of the map rms for each velocity channel.

  * •

Column 9: H I line noise level for the spatially integrated spectral profile,
$\sigma_{\rm{rms}}$ in mJy. The measurement was taken from the signal- and
RFI-free sections of the integrated H I spectrum at a spectral resolution of
6.4 ${\rm km~{}s}^{-1}$. Figure 4 displays the $\sigma_{\rm{rms}}$ histogram
of FASHI sources. Furthermore, we can estimate the detection sensitivity based
on the H I line noise ($\sigma_{\rm{rms}}$) of each detected source. At a
spectral resolution of 6.4 ${\rm km~{}s}^{-1}$, the median detection
sensitivity is $\sim$0.76 ${\rm mJy~{}beam}^{-1}$. The detection sensitivity
at each FASHI source is listed in the online table, while the relevant image
is shown in Figure 5. As shown in Figure 5, the detection sensitivities at
different regions are currently uneven due to the constraint of the adopted
schedule-filler mode.

  * •

Column 10: Integrated H I line flux density, $S_{\rm bf}$ in ${\rm
mJy~{}km~{}s}^{-1}$ by busy-function fitting. The SNR and log$M$, respectively
in Column 12 and 14, are calculated based on $S_{\rm bf}$ in this column
rather than $S_{\rm bf}$ in Column 11. The $S_{\rm bf}$ uncertainty is
estimated with $\sigma(S_{\rm bf})=\sqrt{N}\delta v\sigma_{\rm rms}$, where
$N$ is the channel number of each spectrum only including the H I signal, and
$\delta v=6.4$ ${\rm km~{}s}^{-1}$.

Figure 6: Comparisons of the measured integrated H I line flux density by busy
function fitting ($S_{\rm bf}$) and direct summation of all velocity channels
containing signal ($S_{\rm sum}$) from each integrated spectrum. The velocity
ranges are only listed in the online table. The red dashed line indicates the
position of $S_{\rm bf}=S_{\rm sum}$. The percentages of occupied sources are
shown below and above the red dashed line.

  * •

Column 11: Integrated H I line flux density, $S_{\rm sum}$ in ${\rm
mJy~{}km~{}s}^{-1}$ by summing all velocity channels containing signal within
an appropriate velocity range from each integrated spectrum. Velocity ranges
are only listed in the online table. Both $S_{\rm sum}$ and $S_{\rm bf}$ in
Column 10 have the same level of uncertainty. Figure 6 illustrates the
comparison of $S_{\rm sum}$ and $S_{\rm bf}$ at different signal-to-noise
ratios (SNRs), revealing a noticeable systematic bias, particularly for
sources with low SNR values (SNR $\lesssim$ 15). Among all sources,
approximately one third exhibit measured flux densities ($S_{\rm bf}$) higher
than the flux densities ($S_{\rm sum}$) obtained by summing velocity channels
through busy-function fitting. The disparity among sources may have resulted
from the combined effect of signal-to-noise ratio (SNR), baseline, integrated
velocity range, and spectral profile, based on the examination of a large
sample.

  * •

Column 12: Signal-to-noise ratio (SNR) of the detection, estimated as

${\rm SNR}=\left(~{}\frac{S_{\rm
bf}}{W_{50}}\right)\frac{w_{\rm{smo}}^{1/2}}{\sigma_{\rm{rms}}}$ (1)

where $S_{\rm bf}$ is the integrated flux density in ${\rm
mJy~{}km~{}s}^{-1}$. $w_{\rm{smo}}=W_{50}/6.4$ is a smoothing width expressed
as the number of spectral resolution bins of 6.4 ${\rm km~{}s}^{-1}$ bridging
half of the signal width (e.g., Haynes et al., 2018). $\sigma_{\rm{rms}}$ is
the rms noise across the spectrum measured in mJy at 6.4 ${\rm km~{}s}^{-1}$
spectral resolution. The SNR histogram of FASHI sources is shown in Figure 4.

  * •

Column 13: Distance, $D$ in Mpc. For FASHI sources with c$z_{\odot}\geq 15000$
${\rm km~{}s}^{-1}$, their distance are estimated as
c$z_{\rm{cmb}}/\rm{H_{0}}$. Here, c$z_{\rm{cmb}}$ is the recessional velocity
measured in the Cosmic Microwave Background reference frame (Lineweaver et
al., 1996) and H0 is the Hubble constant, which is adopted to be 75 ${\rm
km~{}s}^{-1}$ Mpc-1. For objects with $2400\leq{\rm c}z_{\rm{\odot}}<15000$
${\rm km~{}s}^{-1}$, we use the CF3 calculator (Graziani et al., 2019;
Kourkchi et al., 2020). For objects with ${\rm c}z_{\rm{\odot}}<2400$ ${\rm
km~{}s}^{-1}$, we utilized the NAM calculator (Graziani et al., 2019; Kourkchi
et al., 2020). The Cosmicflows-3 Distance-Velocity Calculator employs a
hierarchical Bayesian model to deduce the peculiar velocity field and matter
distribution within $z\sim 0.054$ using the Cosmicflows-3 galaxy distance
catalog (Graziani et al., 2019). Additionally, there are 869 nearby galaxies
with individual distance measurements within 11 Mpc or corrected radial
velocities $V_{\rm LG}<600$ ${\rm km~{}s}^{-1}$ (Karachentsev et al., 2013),
which was applied to the distance estimate with the highest priority. The
uncertainty in distance is estimated at 5% of the distance, with a focus on
the Hubble constant’s uncertainty. Figure 3 shows the distance distribution of
FASHI sources.

  * •

Column 14: Logarithm of the H I mass, log$M$ in unit of solar mass, computed
via the formula (e.g., Meyer et al., 2017; Catinella et al., 2010)

$M_{\rm HI}=\frac{2.356\times 10^{5}}{1+z_{\odot}}D^{2}S_{\rm bf},$ (2)

where the adopted distance $D$ is given in Column 13 of Table 2, and the
factor $1+z_{\odot}$ is applied for cosmological corrections (e.g., Haynes et
al., 2018). The error on the mass of H I is estimated by combining the
uncertainties in distance and integrated flux density. Figure 4 shows the mass
histogram of FASHI sources.

###  4.2 Comparison with ALFALFA sources

The spatial distribution of the FASHI, ALFALFA, and HIPASS sources is shown in
Figure 1. The current FASHI project focuses on the unobserved sky that the
ALFALFA survey did not cover (see also Figure 2). Currently, the FASHI survey
has detected a total of 41741 sources in $\sim$7600 deg2 (or $\sim$5.5 sources
per deg2), while the ALFALFA survey observed 31502 objects in 7000 deg2 (or
$\sim$4.5 sources per deg2). Although HIPASS has covered a larger area of the
sky compared to FASHI, it has only detected 5374 H I sources. The FASHI and
ALFALFA mass-distance diagrams are presented in Figure 3. The FASHI has
detected sources located at greater distances (see also Figure 2) and sources
with lower masses than the ones detected by ALFALFA at different distances
(see also Figure 4). This indicates that FASHI has better detection
sensitivity compared to the ALFALFA H I survey.

Figure 2 shows the distribution of large-scale structures from FASHI and
ALFALFA sources in different declination ranges. It covers
$\rm-6^{\circ}\lesssim DEC\lesssim 66^{\circ}$ and extends to a c$z_{\odot}$
range of $\sim$26323 ${\rm km~{}s}^{-1}$. The FASHI data shows clearer
structures from void to filament compared to the ALFALFA data. The FASHI
filamentary structure is much more elongated than that of the ALFALFA in
different declination spaces due to the better RA coverage in FASHI. The new
FASHI data would contribute important clues to investigate the large-scale
process of local universe evolution.

The histograms of the measured spectral noise level ($\sigma_{\rm rms}$),
signal to noise ratio (SNR), line width ($W_{50}$ and $W_{20}$), heliocentric
velocity (c$z_{\odot}$) and source mass distributions for the released FASHI
and ALFALFA catalogs are displayed in Figure 4. Some useful statistical
results of the FASHI sources are given in each panel. For comparison, the
source mass measured by ALFALFA has been roughly corrected with the same
Hubble constant as that used by FASHI. The velocity and mass histograms
suggest a little lower source count for FASHI compared to ALFALFA in certain
velocity and mass ($\lesssim 10^{8}M_{\odot}$) ranges. This discrepancy may be
due to the removal of 2000 candidates from the current published catalogue due
to severe RFI on some frequencies and unreliable sources. Li et al. (2022)
shows that ALFALFA could underestimate the number of low HI mass galaxies
below $10^{9}\,M_{\odot}$ due to cosmic variance, while the FASHI has detected
more sources in the mass range of $10^{8}-10^{9}M_{\odot}$ than the ALFALFA.
FASHI can detect more low mass galaxies and improve this. The FASHI will
eventually provide a larger H I population of low-mass galaxies than the
ALFALFA when the 2000 candidates are confirmed with high detection
sensitivities.

Figure 7: Comparisons of all the FASHI and ALFALFA detected source
distributions within the ranges of $10^{h}<{\rm RA}<15^{h}$ and
$30^{\circ}<{\rm DEC}<36^{\circ}$. The yellow rectangles indicate the FASHI
uncovered areas. The FASHI and ALFALFA sources are indicated with blue dot and
red cross, and the source number is 2631 and 1695, respectively.

The comparison of all the FASHI and ALFALFA detected source distributions
within the ranges of $10^{h}<{\rm RA}<15^{h}$ and $30^{\circ}<{\rm
DEC}<36^{\circ}$ is shown in Figure 7. The FASHI and ALFALFA detected source
numbers are 2631 and 1695, respectively. This indicates that within the same
region, the FASHI detected source number is approximately 1.5 times higher
than the ALFALFA source number. However, we must acknowledge that the current
FASHI catalog also fails to detect some sources. For instance, within the
ranges of $10^{h}<{\rm RA}<15^{h}$ and $30^{\circ}<{\rm DEC}<36^{\circ}$, the
FASHI catalog fails to detect approximately 120 sources. This accounts for
around 7.0% of the total sources and is mainly due to three reasons. Firstly,
FASHI was unable to detect faint sources due to a high level of noise in
certain areas. Such unreliable and low-quality sources were removed from the
current FASHI catalog. These sources require additional verification through
future optical and H I observations. Secondly, FASHI observations have not yet
effectively covered some of ALFALFA’s regions due to serious RFI and limited
observation time. Thirdly, there is a very small number ($\lesssim$1%) of
ALFALFA catalog samples that even at high sensitivity, FASHI was unable to
detect.

Table 3: FASHI and ALFALFA cross-matching catalog.

FASHI | ALFALFA
---|---
FASHI ID | RA | DEC |  c$z_{\odot}$ | AGCNr | RA | DEC |  c$z_{\odot}$
| deg | deg | ${\rm km~{}s}^{-1}$ |  | deg | deg | ${\rm km~{}s}^{-1}$
20230013594 | 162.753 | 36.195 | 7131.45 | 5951 | 162.766 | 36.179 | 7109
20230013570 | 213.934 | 36.177 | 7408.96 | 245480 | 213.943 | 36.172 | 7427
20230013560 | 8.515 | 36.171 | 4809.24 | 104543 | 8.502 | 36.165 | 4809
20230013506 | 167.184 | 36.169 | 8246.72 | 216420 | 167.186 | 36.170 | 8177
20230058952 | 41.349 | 36.163 | 14472.05 | 124857 | 41.349 | 36.163 | 14466
20230054697 | 45.278 | 36.162 | 8158.04 | 132238 | 45.288 | 36.162 | 8207
20230052360 | 210.480 | 36.162 | 5123.18 | 245319 | 210.491 | 36.171 | 5141
20230013536 | 36.907 | 36.157 | 10740.81 | 1919 | 36.904 | 36.156 | 10755
20230013521 | 146.329 | 36.151 | 6555.41 | 190478 | 146.345 | 36.157 | 6537
20230013518 | 214.888 | 36.149 | 7419.97 | 9173 | 214.900 | 36.151 | 7426
… … |  |  |  |  |  |  |

Notes. The full catalog including 3620 sources could be downloaded in the
online Supplementary material or at https://fast.bao.ac.cn/cms/article/271/
and https://zcp521.github.io/fashi.

Figure 8: Comparisons of the measured rms, SNR, integrated flux, and distance
parameters using the cross-matched 3620 FASHI and ALFALFA sources under a
condition of $\rm\delta_{RA}\leq 3^{\prime}$, $\rm\delta_{DEC}\leq 3^{\prime}$
and $\rm\delta_{velocity}\leq 100$ ${\rm km~{}s}^{-1}$ listed in Table 3. The
red dashed line indicates the position of the linear relation of $y_{\rm
FASHI}=x_{\rm ALFALFA}$ in each panel. The percentage of source occupied is
also shown in rms and SNR panels, respectively. In $\sigma_{\rm rms}$ panel,
the FASHI rms is normalised to the same velocity bin with a spectral
resolution of 10 ${\rm km~{}s}^{-1}$, as the ALFALFA’s.

Figure 9: The position and velocity separations between the FASHI and SGA &
SDSS spectroscopic sources obtained from the cross-matched samples (16972
sources) under a condition of $\rm\delta_{RA}\leq 3^{\prime}$,
$\rm\delta_{DEC}\leq 3^{\prime}$ and $\rm\delta_{velocity}\leq 1000$ ${\rm
km~{}s}^{-1}$ listed in Tables 4 and 5. The occupied percentage within some
separation ranges is also shown in each panel.

Through cross-matching FASHI and ALFALFA sources while satisfying the
condition of $\rm\delta_{RA}\leq 3^{\prime}$, $\rm\delta_{DEC}\leq 3^{\prime}$
and $\rm\delta_{velocity}\leq 100$ ${\rm km~{}s}^{-1}$, we obtained a total of
3620 sources. The cross-matching results are presented in Table 3. The entire
catalog is available for download online. This sample is sufficiently large
for comparing the parameters between FASHI and ALFALFA sources. Figure 8
displays the comparison of the measured rms, SNR, integrated flux, and
distance parameters between FASHI and ALFALFA sources using the cross-matched
sample. According to Figure 8, it can be observed that a lower noise level is
found in 88.3% of FASHI sources, and 98.3% of FASHI sources have higher SNR
than cross-matched ALFALFA sources with the same spectral resolution of 10
${\rm km~{}s}^{-1}$. Furthermore, we find that the integrated flux
distributions of the two survey sources are in good agreement. We also
compared the integrated H I lines of these bright sources ($\gtrsim 7.0\,{\rm
Jy~{}km~{}s}^{-1}$) with those obtained from ALFALFA. The FASHI’s integrated
bright point sources show a flux excess of approximately 10% compared to the
ALFALFA sources for sources with $\rm SNR_{FASHI}\gtrsim 40$. Perhaps the
FASHI sources, due to their extremely high sensitivity, include more tidal
clouds and can provide a more accurate measurement for bright sources than the
ALFALFA according to Hoffman et al. (2019). For sources with relatively low
SNR ($\rm SNR_{FASHI}\lesssim 20$), the FASHI’s measured flux distribution is
lower than that of the ALFALFA’s. Sources with low SNR are typically
associated with very faint sources. Due to 98.3% of FASHI sources having
better SNR compared to ALFALFA sources, the latter will have significantly
lower SNR than the faint sources in FASHI. Measuring a very faint source often
leads to high levels of uncertainties. It is possible that FASHI provides more
accurate measurements than ALFALFA for faint sources (see also analysis in
Jing et al., 2023).

In addition, the measured distances in Figure 8 show a consistent relationship
after correction with $D_{\rm FASHI}=\frac{70}{75}D_{\rm ALFALFA}$ between
FASHI and ALFALFA, because ALFALFA source distances are estimated using a
different Hubble constant H0 = 70 ${\rm km~{}s}^{-1}$Mpc-1 from FASHI (see
details for distance calculation in Sections 3.5 and 4.1). This further
suggests that the released FASHI catalogs are highly reliable.

###  4.3 Optical counterparts

Due to the discrepancy between the spatial resolution of the optical image
(typically 1′′) and the large beam size ($\sim$2$\hbox
to0.0pt{.\hss}{{}^{\prime}}$9) of FAST, it may be difficult to detect
trustworthy optical counterparts, especially for merging systems where only
one H I target is present in the FASHI catalog but there are several optical
galaxies at similar redshifts. Furthermore, the long extended H I tail of
galaxies can be regarded as one H I bright target, which lacks clear optical
counterparts. In order to identify reliable optical counterparts, conducting
high-resolution H I follow-up observations is necessary. Nonetheless, this is
excessively costly for large blind H I survey projects like FASHI. To gain an
initial understanding of the FASHI galaxy properties, we cross-matched the
FASHI targets with the findings of optical galaxy surveys.

To identify bright galaxy counterparts that may be saturated or masked in
wide-field galaxy surveys such as the SDSS, we first compared the FASHI
catalog with the local galaxy sample Siena Galaxy
Atlas444https://www.legacysurvey.org/sga/sga2020/ (SGA), which contains 383620
galaxies with $D_{25}>20^{\prime\prime}$. For each FASHI H I target, we
searched for SGA counterparts under the condition $\rm\delta_{RA}\leq
3^{\prime}$, $\rm\delta_{DEC}\leq 3^{\prime}$ and $\rm\delta_{speed}\leq 1000$
${\rm km~{}s}^{-1}$. If more than one candidate galaxy was present, we
selected the optical counterpart with the shortest distance. From the SGA we
obtained 14072 counterparts, which are listed in Table 4. For the other 27669
H I targets, we cross-matched with the SDSS spectroscopic redshift catalog
(Abazajian et al., 2009) using the same strategy as for the SGA cross-match,
yielding 2900 targets, which are listed in Table 5. In total, we found 16972
optical counterparts from the SGA and SDSS spectroscopic redshift catalogs.

Figure 9 reports the measured position and velocity separations along with
statistical results of the cross-matched sample of 16972 sources from the
FASHI and SGA & SDSS projects. The statistical results indicate that the
position and absolute velocity separations of 87.7% and 87.6%, respectively,
of the FASHI sources are less than 60′′ and 50 ${\rm km~{}s}^{-1}$ compared to
the cross-matched SGA & SDSS sources. The small offset between the FASHI
sources and the optical counterparts suggests that the nearest candidates are
statistically appropriate for our purpose. Both separations are consistent
with the cross-match results obtained by the SGA and SDSS. The SGA and SDSS
spectroscopic data are deemed highly reliable. This observation implies that
FASHI sources have more accurate position and velocity measurements.

To obtain complete optical counterparts for all FASHI targets, we performed a
further cross-match with the SDSS photometric catalog using
CasJobs555http://mastweb.stsci.edu/mcasjobs/ to identify all SDSS galaxies
within 3′ of each FASHI target. For each SDSS galaxy within the FAST beam
(assuming the r-band Petrosian magnitude $m_{r}$ and the distance to the H I
center is $D_{i}$ arcsec), we considered the probability of the serendipitous
counterparts based on the surface number density in the r band, which is
$P_{\rm mag}=e^{-\pi D_{i}^{2}n(m<m_{r})}$, where $n(m<m_{r})$ is the surface
number density brighter than $m_{r}$ (Downes et al., 1986; Zhang et al.,
2022b). If the SDSS target is faint, the expected number of galaxies that
randomly appear in the area of $\pi D_{i}^{2}$ would be high. Therefore, such
a faint galaxy would be more likely to be randomly associated with the H I
target and, thus, less likely to be the counterpart. Moreover, the
distribution of the RA and DEC offset also shows that it is more likely to
find counterparts with less center offset. Therefore, we treated the offset as
a probability $P_{\delta\rm RA}\times P_{\delta\rm DEC}$, assuming a Gaussian
distribution of the offsets, with the center and $\sigma$ from the SGA offset
results. Thus, the optical counterpart probability ($P_{\rm OC}$) of each SDSS
galaxy within the beam is $P_{\rm OC}=P_{\rm mag}\times P_{\delta\rm RA}\times
P_{\delta\rm DEC}$. We emphasize that this is a relative probability used to
filter out unlikely optical counterparts within the FAST beam.

Using the probability method above, we will always find an SDSS target as the
most likely counterpart to the H I target. However, for the optically faint
galaxies (Leisman et al., 2017; Xu et al., 2023b; Gray et al., 2023), the SDSS
image is not deep enough to detect the targets, or the low surface brightness
galaxies are not detected by the SDSS pipeline. On the other hand, for a H I
target with SDSS coverage but no SDSS spec-$z$ counterparts, the optical
counterpart would be fainter than $m_{r}\sim 17.7$. Finally, cross-matching
with SDSS photometric data yielded 10975 optical counterparts. The derived
results of the cross-match with the photometric catalog are listed in Table 6.

The FASHI coverage includes part of the Galactic plane where there is little
SDSS coverage. Although these regions ($20^{\circ}<\rm RA<120^{\circ}$) are
covered by the Pan-STARRS project, the crowded foreground star overlap and the
strong dust extinction from the Milky Way limit the accuracy of the Galactic
photometry. In this paper we have only matched the FASHI targets with SGA and
SDSS, and have not identified optical counterparts for the remaining 13794
FASHI targets.

Table 4: The identified optical counterparts from the SGA catalog.

FASHI | SGA
---|---
FASHI ID | RA | DEC | $z_{\odot}$ | SGA | RA | DEC | $z_{\odot}$
| deg | deg |  |  | deg | deg |
20230000823 | 166.736 | -6.193 | 0.02269 | PGC1035830 | 166.738 | -6.183 | 0.02290
20230042079 | 165.515 | -6.178 | 0.03145 | PGC1035861 | 165.504 | -6.180 | 0.03160
20230060002 | 356.034 | -6.162 | 0.00704 | PGC072252 | 356.044 | -6.170 | 0.00704
20230000831 | 58.707 | -6.171 | 0.03664 | PGC1036048 | 58.703 | -6.163 | 0.03644
20230000832 | 169.267 | -6.141 | 0.04476 | PGC1036233 | 169.281 | -6.148 | 0.04532
20230000833 | 171.782 | -6.140 | 0.02999 | PGC035230 | 171.789 | -6.139 | 0.02995
20230000834 | 168.912 | -6.137 | 0.01945 | PGC1036364 | 168.917 | -6.137 | 0.02020
… … |  |  |  |  |  |  |

Notes. The complete catalog including 14072 sources could be downloaded in the
online Supplementary material or at https://fast.bao.ac.cn/cms/article/271/
and https://zcp521.github.io/fashi.

Table 5: The identified optical counterparts from the SDSS spectroscopic
catalog.

FASHI | SDSS
---|---
FASHI ID | RA | DEC | $z_{\odot}$ | OBJID | RA | DEC | $z_{\odot}$
| deg | deg |  |  | deg | deg |
20230053284 | 48.425 | -6.073 | 0.03144 | 1237649964065161287 | 48.429 | -6.084 | 0.03125
20230060070 | 37.368 | -5.847 | 0.04766 | 1237679439889301650 | 37.364 | -5.848 | 0.04810
20230060087 | 36.523 | -5.777 | 0.03241 | 1237679439888908518 | 36.530 | -5.781 | 0.03209
20230060106 | 35.641 | -5.708 | 0.03285 | 1237679341105971489 | 35.638 | -5.713 | 0.03276
20230001011 | 54.560 | -5.652 | 0.02203 | 1237652901845598378 | 54.566 | -5.659 | 0.02183
20230001046 | 54.542 | -5.535 | 0.02211 | 1237652901845598221 | 54.554 | -5.540 | 0.02215
20230060210 | 58.242 | -5.077 | 0.01724 | 1237652901847171254 | 58.241 | -5.080 | 0.01735
… … |  |  |  |  |  |  |

Notes. The complete catalog including 2900 sources could be downloaded in the
online Supplementary material or at https://fast.bao.ac.cn/cms/article/271/
and https://zcp521.github.io/fashi.

Table 6: The identified optical counterparts from the SDSS photometric
catalog.

FASHI | SDSS
---|---
FASHI ID | RA | DEC | $z_{\odot}$ | OBJID | RA | DEC | $P_{\rm mag}$
| deg | deg |  |  | deg | deg |
20230042080 | 125.705 | -6.169 | 0.04885 | 1237674460407923463 | 125.731 | -6.163 | 0.848
20230042081 | 126.961 | -6.160 | 0.02162 | 1237673708252234978 | 126.971 | -6.155 | 0.936
20230000830 | 172.136 | -6.156 | 0.01670 | 1237671142014648688 | 172.146 | -6.146 | 0.994
20230000845 | 44.275 | -6.107 | 0.03168 | 1237679438818574478 | 44.286 | -6.143 | 0.969
20230000841 | 122.921 | -6.115 | 0.03842 | 1237673710935081236 | 122.928 | -6.125 | 0.996
20230060006 | 191.710 | -6.096 | 0.01264 | 1237671763173310839 | 191.721 | -6.116 | 0.860
20230042084 | 46.376 | -6.103 | 0.02957 | 1237679339500011708 | 46.377 | -6.101 | 0.999
… … |  |  |  |  |  |  |

Notes. The complete catalog including 10975 sources could be downloaded in the
online Supplementary material or at https://fast.bao.ac.cn/cms/article/271/
and https://zcp521.github.io/fashi.

Figure 10: The corresponding recovery rate from the photometry matching
results (listed in Table 6) having both the SDSS spectroscopic and photometric
data, as well as the r-band cmodel magnitude from the SDSS. The recovery rate
is high for bright targets, but drops to about 80% at the faint end.
Unfortunately, we do not have the recovery rate for the counterparts only from
the SDSS photometric catalog, but we hope that the future spectroscopic
redshift survey projects, such as DESI and PFS, will help to identify more
optical counterparts.

To validate our matching method, we applied it to match the FASHI H I catalog
to the SDSS spectroscopic redshift and SDSS photometric catalogs. We used the
same methods as for the SGA catalog match and the SDSS photometry match. Since
the spectroscopic redshift results are more reliable for the optical
counterparts, we selected sources that have both photometry data and
spectroscopic redshift. We then compared the photometry matching results with
the spectroscopic redshift catalog. The derived distribution of the recovery
rate of the counterparts is shown in Figure 10. We found that the recovery
rate is about 80% for targets with $r\simeq 17$ AB mag, and the rate decreases
for fainter targets.

Figure 11: The H I mass and the stellar mass of the counterparts distribution.
The SGA catalog counterparts are in green, and the SDSS spectroscopic and the
photometric counterparts are in red and blue, respectively.

To provide a first overview of the optical properties of the FASHI sample, we
present the H I and stellar mass diagram in Figure 11. The stellar mass was
estimated using the mass-to-light ratio from Bell et al. (2003), with a factor
of 1.6 applied to convert the Salpeter initial mass function (IMF) to the
Chabrier IMF (Chabrier, 2003). As expected, the SGA counterparts are
associated with more massive galaxies, while the SDSS photometric counterparts
are mainly associated with less massive galaxies.

Since the SDSS photometric counterparts are mainly associated with low-mass
galaxies, whose H I mass is also subject to detection reliability, it may be
more effective to use future spectroscopic redshift survey projects, such as
DESI (DESI Collaboration et al., 2022) and PFS (Wang et al., 2020), to
identify fainter optical counterparts and explore their optical properties,
rather than relying solely on improving the algorithm for selecting optical
counterparts from the photometric catalog.

###  4.4 Extragalactic OH megamasers

OH Megamasers (OHM) are powerful sources of line emission detected at L band,
originating from the starburst nuclei in merging galaxy systems (Darling &
Giovanelli, 2002, 2006; Giovanelli & Haynes, 2015). Approximately 130 such
sources have been discovered to date (e.g., Darling & Giovanelli, 2002; Haynes
et al., 2018; Roberts et al., 2021; Suess et al., 2016). OHM lines shifted
from their rest frequencies, which are 1612.231, 1665.402, 1667.359, and
1720.530 MHz, into the targeted H I bandpass may contaminate blind H I line
surveys of the local universe. The FAST with its Ultra-Wide Bandwidth (UWB)
receiver can simultaneously cover frequencies ranging from 500 to 3300 MHz and
is expected to be an influential instrument for detecting OH emission from the
Milky Way and extragalaxies (Zhang et al., 2023). For FASHI, the redshift
range of the 1667.359 MHz OHM line corresponds to 0.150 $<z<$ 0.278 within a
frequency range of 1305-1450 MHz. Essentially, the extragalactic H I and OHM
lines exhibit similar spectral profiles at various redshifts. Moreover, the
1612.231 and 1720.530 MHz lines are consistently too weak to be recognized by
FASHI, and the 1665.402 and 1667.359 MHz lines blend together due to their
broad line width. Therefore, it is impossible to distinguish them based solely
on the line profile for large sample identifications. In cases where optical
redshifts are provided, the OHM can be easily identified among the extracted H
I candidates (refer to the OHM sample in Figure 12). Alternatively, we are
considering employing WISE infrared colours and magnitudes to distinguish OHMs
from H I line emitters (refer to Suess et al., 2016). The identified OHM
candidates with optical redshifts have been removed from the FASHI catalog,
and their details will be presented in a forthcoming work. It is likely to
that there still exist OHM sources remaining in the current FASHI catalog.

###  4.5 Radio recombination lines

Radio recombination lines (RRLs) originate from gas ionized by young massive
stars in the Galactic H II regions (Zhang et al., 2014, 2021; Hou et al.,
2022). Galactic RRLs typically have narrower line widths (FWHM $<$ 50 ${\rm
km~{}s}^{-1}$) than most extragalactic H I spectral lines (see RRL sample in
Figure 12). The frequency band of FAST (1050-1450 MHz) can simultaneously
cover at most 23 RRLs for H$n\alpha$, C$n\alpha$, and He$n\alpha$ (principal
quantum number $n=164-186$), respectively. According to Zhang et al. (2021),
21 RRLs of H$n\alpha$ and C$n\alpha$ at 1050-1450 MHz have been simultaneously
detected with strong emission signals. The FASHI coverage towards the Galactic
direction can aid in building a more expansive sample of RRLs from the Milky
Way. The RRLs from the FASHI data have been identified using the criteria that
the same spatial position should have consistent flux density and line width
at different transition frequencies. The line frequencies of 1326.792,
1350.414, 1399.368, and 1374.600 MHz are relevant within the frequency range
of the currently released data (1305.5-1419.5 MHz). As the first released
FASHI catalog concentrates on the extragalactic H I source, we have excluded
the Galactic RRL targets from the catalog.

Figure 12: Interactive interface for the FASHI source finder. In each panel,
the upper blue spectrum represents the raw line extracted from data fits cube
produced from the HiFAST pipeline (Jing et al., 2023), the dash-dot line
displays the fitted baseline, while the vertical and horizontal dashed green
lines roughly indicate the line center velocity and line width; the middle
green spectrum is the baseline removed data, and the red line represents busy-
function fit; the lower-left image displays the moment 0 map; the lower-right
section presents the source information, assuming all the emission lines are H
I. Top left: a normal double-horn galaxy. Top right: a normal galaxy with RFI,
located at the around 9000-9500 ${\rm km~{}s}^{-1}$. Bottom left: a radio
recombination line. Bottom right: a new recently discovered OHM (Zhang et al.
in prep.) that corresponds to IRAS 10106+4708 with a redshift of $z_{\rm
OH}=0.20586$ (Abazajian et al., 2009).

###  4.6 Galactic high velocity cloud

Galactic high velocity clouds (HVCs) are gas clouds located nearby the
Galactic plane, which move at speeds ($\gtrsim 90$ ${\rm km~{}s}^{-1}$)
substantially different from the rotation of the disk of the Milky Way galaxy
(Adams et al., 2013, 2016; Moss et al., 2013; Verschuur, 1973). HVCs are
thought to be clouds of material falling into the Galaxy from the outside the
Galactic plane (Wakker & van Woerden, 1997). Additionally, HVCs might
potentially have extragalactic origins and they may be very similar to the
low-mass population of failed galaxies. HVCs share some features with the
nearby extragalactic sources (Haynes et al., 2018). And it is not possible to
identify any stellar counterparts in HVCs. Therefore, the first released FASHI
catalog includes the HVCs, although these are not identified.

###  4.7 Accessing FASHI data

In addition to the catalog listed in Table 2, the corresponding integrated 1D
H I line spectra and 2D moment maps for all sources have been extracted from
the 3D fits cube over at least a 400 velocity channel window. The entire FASHI
H I catalog will be made directly available to any user upon publication of
this paper. The reduced 1D cubelets may be accessed reasonably by contacting
the corresponding authors. The reduced 2D and 3D data cubes are currently
provided only to internal users. Furthermore, all the raw data of the FASHI
project will be freely accessible to any user after a twelve-month privileged
period666The twelve-month privileged period is after each scan observation,
not after the entire survey is completed. as per the FAST data release
policy777See details in https://fast.bao.ac.cn/cms/article/129/.

##  5 Discussion

###  5.1 Reliability

The released catalog (Table 2) contains sources extracted using SoFiA at a
4.5$\sigma$ threshold. The sources were inspected through interactive manual
source extraction for reliability, based on the 0th, 1st, and 2nd moments,
integrated spectral profile, and SNR ($\rm SNR>5.0$). Human intervention was
used to optimize the measurement accuracy and improve the catalog’s
reliability by rejecting spurious detection that corresponds to low-level RFI,
poorly sampled data, and residual baseline fluctuations. The optical
counterpart of each source was identified and listed in Tables 4, 5, and 6
(see Section 4.3 for details). Sources located near the cube’s edge, blanked
pixels, or frequency were removed. As a result, all published FASHI sources
are of relatively high confidence.

###  5.2 Completeness

In the first released FASHI H I catalog, the sensitivities for detection
displayed in Figure 5 are uneven across different regions. This is because
FASHI could only use the schedule-filler time, as detailed in Section 2.2. As
a result, the completeness becomes relatively low at a high detection
sensitivity or at a relatively high redshift. Additionally, the strange gaps
of the FASHI targets at $\sim$190, $\sim$210 and $\sim$266 Mpc are also
resulted in a relatively low completeness. Moreover, approximately 2000
sources, which have low reliability due to low signal-to-noise ratio ($<5.0$)
or being located at the edge of velocity (channel) or spatial (pixel), have
been removed from FASHI catalog and need more time for identification. Thus,
if completeness analysis is needed, the completeness levels should be
estimated at different detection sensitivity levels.

###  5.3 Caveats and comments

Despite the high quality of most FASHI data, caution should be exercised when
using the source catalog presented in Table 2. Some of caveats and comments
are listed below:

  * •

Caveats: In some double peak spectra in FASHI data, the W50 values measured by
busy-function are multiple times lower than the W20 values when the lower peak
intensity is less than 50% of the higher peak intensity. If using such W50
values for calculations and statistics, they should be further corrected.
There are approximately 2000 sources that have low reliability due to low
signal-to-noise ratio ($<5.0$) or being located at the edge of velocity
(channel) or spatial (pixel). Low-reliability sources have been excluded from
the current FASHI catalog. Future optical and H I observations need to be
conducted to ensure their accuracy. Some negative sources (absorption lines)
have been detected, but they are not included in this released catalog. More
time is required for the data reduction of the negative H I sources.

  * •

The FASHI project covers certain regions of the Milky Way galaxy. Despite our
best efforts, some of the Galactic RRLs might still be present in the catalog.
Additionally, the extragalactic OHMs have been excluded from the catalog only
if the SDSS catalog contains a matching known spectroscopic redshift.

##  6 Summary

The FASHI project intends to scan the entire sky visible from the FAST
observatory, which covers an area of approximately 22000 square degrees across
a declination range of $-14\degree$ to $+66\degree$. The FASHI project is
expected to detect over 105 H I sources across the portion of the FAST sky
with a frequency range of 1050-1450 MHz. This detection was possible up to a
redshift of $z\approx 0.35$. FASHI has surveyed an area of approximately 7600
square degrees from August 2020 to June 2023. The median detection sensitivity
achieved was $\sim$0.76 ${\rm mJy~{}beam}^{-1}$ at a resolution of $\sim$6.4
${\rm km~{}s}^{-1}$ at $\sim$1.4 GHz. The frequency range used for the search
was approximately 1305.5-1419.5 MHz. In total, 41741 H I sources were detected
and identified. Our detection rate is $\sim$5.5 sources per deg2, which is
slightly higher than that of the ALFALFA survey. This is thanks to the higher
sensitivity of FAST. FASHI is sampling a wide range of hosts galaxies from
local, very low H I mass dwarfs to gas-rich massive galaxies seen to $z\sim
0.09$. The FASHI survey provides the largest extragalactic H I catalog,
offering an unbiased view of H I content and structures in the universe. Up to
now, FASHI is the most sensitive, comprehensive, and previously unheard of
blind extragalactic H I survey of the FAST sky, with a higher spectral and
spatial resolution, and broader coverage than the ALFALFA. We cross-matched
SGA and SDSS spectroscopic redshift data, together with SDSS photometric data,
to identify optical counterparts for 27947 FASHI sources. Approximately 59.3%
of the complete FASHI catalog lacks coverage by any spectroscopic redshift
survey. This presents a large number of candidates for future spectroscopic
redshift and photometric surveys, such as DESI and PFS.

## Acknowledgements

This work is supported by the National Key R&D Program of China (Nos.
2018YFE0202900 and 2022YFA1602901). CPZ acknowledges support by the West Light
Foundation of the Chinese Academy of Sciences (CAS). CC is supported by the
National Natural Science Foundation of China, Nos. 11803044, 11933003, and
12173045. This work is sponsored (in part) by the Chinese Academy of Sciences
(CAS), through a grant to the CAS South America Center for Astronomy
(CASSACA). We acknowledge the science research grants from the China Manned
Space Project with No. CMS-CSST-2021-A05. FAST is a Chinese national mega-
science facility, operated by the National Astronomical Observatories of
Chinese Academy of Sciences (NAOC). The Siena Galaxy Atlas was made possible
by funding support from the U.S. Department of Energy, Office of Science,
Office of High Energy Physics under Award Number DE-SC0020086 and from the
National Science Foundation under grant AST-1616414. Funding for the Sloan
Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the
U.S. Department of Energy Office of Science, and the Participating
Institutions. SDSS acknowledges support and resources from the Center for
High-Performance Computing at the University of Utah. The SDSS web site is
www.sdss4.org. SDSS is managed by the Astrophysical Research Consortium for
the Participating Institutions of the SDSS Collaboration including the
Brazilian Participation Group, the Carnegie Institution for Science, Carnegie
Mellon University, Center for Astrophysics — Harvard & Smithsonian (CfA), the
Chilean Participation Group, the French Participation Group, Instituto de
Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the
Physics and Mathematics of the Universe (IPMU) / University of Tokyo, the
Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz
Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie
(MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-
Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical
Observatories of China, New Mexico State University, New York University,
University of Notre Dame, Observatório Nacional / MCTI, The Ohio State
University, Pennsylvania State University, Shanghai Astronomical Observatory,
United Kingdom Participation Group, Universidad Nacional Autónoma de México,
University of Arizona, University of Colorado Boulder, University of Oxford,
University of Portsmouth, University of Utah, University of Virginia,
University of Washington, University of Wisconsin, Vanderbilt University, and
Yale University.

The authors declare that they have no conflict of interest.

## References

  * Abazajian et al. (2009) Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543
  * Adams et al. (2013) Adams, E. A. K., Giovanelli, R., & Haynes, M. P. 2013, ApJ, 768, 77
  * Adams et al. (2016) Adams, E. A. K., Oosterloo, T. A., Cannon, J. M., Giovanelli, R., & Haynes, M. P. 2016, A&A, 596, A117
  * Adams et al. (2015) Adams, E. A. K., Faerman, Y., Janesh, W. F., et al. 2015, A&A, 573, L3
  * Ai & Zhu (2018) Ai, M., & Zhu, M. 2018, ApJ, 862, 48
  * Baek et al. (2015) Baek, S.-J., Park, A., Ahn, Y.-J., & Choo, J. 2015, The Analyst, 140 1, 250
  * Barnes et al. (2001) Barnes, D. G., Staveley-Smith, L., de Blok, W. J. G., et al. 2001, MNRAS, 322, 486
  * Bell et al. (2003) Bell, E. F., McIntosh, D. H., Katz, N., & Weinberg, M. D. 2003, ApJS, 149, 289
  * Branchini et al. (1999) Branchini, E., Teodoro, L., Frenk, C. S., et al. 1999, MNRAS, 308, 1
  * Catinella et al. (2010) Catinella, B., Schiminovich, D., Kauffmann, G., et al. 2010, MNRAS, 403, 683
  * Chabrier (2003) Chabrier, G. 2003, PASP, 115, 763
  * Chambers et al. (2016) Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560
  * Cheng et al. (2020) Cheng, C., Ibar, E., Du, W., et al. 2020, A&A, 638, L14
  * Condon et al. (1998) Condon, J. J., Cotton, W. D., Greisen, E. W., et al. 1998, AJ, 115, 1693
  * Darling & Giovanelli (2002) Darling, J., & Giovanelli, R. 2002, AJ, 124, 100
  * Darling & Giovanelli (2006) Darling, J., & Giovanelli, R. 2006, AJ, 132, 2596
  * DESI Collaboration et al. (2022) DESI Collaboration, Abareshi, B., Aguilar, J., et al. 2022, AJ, 164, 207
  * Downes et al. (1986) Downes, A. J. B., Peacock, J. A., Savage, A., & Carrie, D. R. 1986, MNRAS, 218, 31
  * Fouque et al. (1990) Fouque, P., Durand, N., Bottinelli, L., Gouguenheim, L., & Paturel, G. 1990, A&As, 86, 473
  * Gavazzi et al. (2013) Gavazzi, G., Fumagalli, M., Fossati, M., et al. 2013, A&A, 553, A89
  * Giovanelli & Haynes (2015) Giovanelli, R., & Haynes, M. P. 2015, A&A Rev., 24, 1
  * Giovanelli et al. (2005) Giovanelli, R., Haynes, M. P., Kent, B. R., et al. 2005, AJ, 130, 2598
  * Giovanelli et al. (2013) Giovanelli, R., Haynes, M. P., Adams, E. A. K., et al. 2013, AJ, 146, 15
  * Gray et al. (2023) Gray, L. M., Rhode, K. L., Leisman, L., et al. 2023, AJ, 165, 197
  * Graziani et al. (2019) Graziani, R., Courtois, H. M., Lavaux, G., et al. 2019, MNRAS, 488, 5438
  * Han et al. (2021) Han, J. L., Wang, C., Wang, P. F., et al. 2021, Research in Astronomy and Astrophysics, 21, 107
  * Haynes et al. (2011) Haynes, M. P., Giovanelli, R., Martin, A. M., et al. 2011, AJ, 142, 170
  * Haynes et al. (2018) Haynes, M. P., Giovanelli, R., Kent, B. R., et al. 2018, ApJ, 861, 49
  * Hoffman et al. (2019) Hoffman, G. L., Dowell, J., Haynes, M. P., & Giovanelli, R. 2019, AJ, 157, 194
  * Hou et al. (2022) Hou, L., Han, J., Hong, T., Gao, X., & Wang, C. 2022, Science China Physics, Mechanics, and Astronomy, 65, 129703
  * Huang et al. (2012b) Huang, S., Haynes, M. P., Giovanelli, R., & Brinchmann, J. 2012b, ApJ, 756, 113
  * Huang et al. (2012a) Huang, S., Haynes, M. P., Giovanelli, R., et al. 2012a, AJ, 143, 133
  * Janesh et al. (2017) Janesh, W., Rhode, K. L., Salzer, J. J., et al. 2017, ApJ, 837, L16
  * Janesh et al. (2015) Janesh, W., Rhode, K. L., Salzer, J. J., et al. 2015, ApJ, 811, 35
  * Janowiecki et al. (2015) Janowiecki, S., Leisman, L., Józsa, G., et al. 2015, ApJ, 801, 96
  * Jiang et al. (2019) Jiang, P., Yue, Y., Gan, H., et al. 2019, Science China Physics, Mechanics, and Astronomy, 62, 959502
  * Jiang et al. (2020) Jiang, P., Tang, N.-Y., Hou, L.-G., et al. 2020, Research in Astronomy and Astrophysics, 20, 064
  * Jing et al. (2023) Jing, Y., Wang, J., Liu, Z., Zhu, M., & Jiang, P. 2023, SCPMA, prep, prep
  * Jones et al. (2018) Jones, M. G., Haynes, M. P., Giovanelli, R., & Moorman, C. 2018, MNRAS, 477, 2
  * Jones et al. (2016) Jones, M. G., Papastergis, E., Haynes, M. P., & Giovanelli, R. 2016, MNRAS, 457, 4393
  * Kang et al. (2022) Kang, J., Zhu, M., Ai, M., Yu, H., & Sun, C. 2022, Research in Astronomy and Astrophysics, 22, 065019
  * Karachentsev et al. (2013) Karachentsev, I. D., Makarov, D. I., & Kaisina, E. I. 2013, AJ, 145, 101
  * Koribalski et al. (2004) Koribalski, B. S., Staveley-Smith, L., Kilborn, V. A., et al. 2004, AJ, 128, 16
  * Koribalski et al. (2020) Koribalski, B. S., Staveley-Smith, L., Westmeier, T., et al. 2020, Ap&SS, 365, 118
  * Kourkchi et al. (2020) Kourkchi, E., Courtois, H. M., Graziani, R., et al. 2020, AJ, 159, 67
  * Leisman et al. (2017) Leisman, L., Haynes, M. P., Janowiecki, S., et al. 2017, ApJ, 842, 133
  * Li et al. (2018) Li, D., Wang, P., Qian, L., et al. 2018, IEEE Microwave Magazine, 19, 112
  * Li et al. (2022) Li, X., Li, C., Mo, H. J., Xiao, T., & Wang, J. 2022, ApJ, 941, 48
  * Lineweaver et al. (1996) Lineweaver, C. H., Tenorio, L., Smoot, G. F., et al. 1996, ApJ, 470, 38
  * Liu et al. (2023) Liu, Y., Zhu, M., Yu, H., et al. 2023, MNRAS, 523, 3905
  * Martin et al. (2010) Martin, A. M., Papastergis, E., Giovanelli, R., et al. 2010, ApJ, 723, 1359
  * Masters (2005) Masters, K. L. 2005, Galaxy flows in and around the Local Supercluster, PhD thesis, Cornell University, New York
  * Masters et al. (2006) Masters, K. L., Springob, C. M., Haynes, M. P., & Giovanelli, R. 2006, ApJ, 653, 861
  * Meyer et al. (2004) Meyer, M. J., Zwaan, M. A., Webster, R. L., et al. 2004, MNRAS, 350, 1195
  * Meyer et al. (2017) Meyer, M., Robotham, A., Obreschkow, D., et al. 2017, PASP, 34, 52
  * Moorman et al. (2014) Moorman, C. M., Vogeley, M. S., Hoyle, F., et al. 2014, MNRAS, 444, 3559
  * Moss et al. (2013) Moss, V. A., McClure-Griffiths, N. M., Murphy, T., et al. 2013, ApJS, 209, 12
  * Nan et al. (2011) Nan, R., Li, D., Jin, C., et al. 2011, International Journal of Modern Physics D, 20, 989
  * Odekon et al. (2016) Odekon, M. C., Koopmann, R. A., Haynes, M. P., et al. 2016, ApJ, 824, 110
  * Oke & Gunn (1983) Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713
  * Qian et al. (2020) Qian, L., Yao, R., Sun, J., et al. 2020, The Innovation, 1, 100053
  * Roberts et al. (2021) Roberts, H., Darling, J., & Baker, A. J. 2021, ApJ, 911, 38
  * Serra et al. (2015) Serra, P., Westmeier, T., Giese, N., et al. 2015, MNRAS, 448, 1922
  * Skrutskie et al. (2006) Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163
  * Springob et al. (2005) Springob, C. M., Haynes, M. P., Giovanelli, R., & Kent, B. R. 2005, ApJS, 160, 149
  * Suess et al. (2016) Suess, K. A., Darling, J., Haynes, M. P., & Giovanelli, R. 2016, MNRAS, 459, 220
  * Toribio et al. (2011) Toribio, M. C., Solanes, J. M., Giovanelli, R., Haynes, M. P., & Martin, A. M. 2011, ApJ, 732, 93
  * Tully et al. (2016) Tully, R. B., Courtois, H. M., & Sorce, J. G. 2016, AJ, 152, 50
  * Tully et al. (2009) Tully, R. B., Rizzi, L., Shaya, E. J., et al. 2009, AJ, 138, 323
  * Tully et al. (2008) Tully, R. B., Shaya, E. J., Karachentsev, I. D., et al. 2008, ApJ, 676, 184
  * Tully et al. (2013) Tully, R. B., Courtois, H. M., Dolphin, A. E., et al. 2013, AJ, 146, 86
  * Verschuur (1973) Verschuur, G. L. 1973, A&A, 22, 139
  * Wakker & van Woerden (1997) Wakker, B. P., & van Woerden, H. 1997, ARA&A, 35, 217
  * Wang et al. (2020) Wang, S.-Y., Huang, P.-J., Chen, H.-Y., et al. 2020, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 11447, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 114477V
  * Westmeier et al. (2014) Westmeier, T., Jurek, R., Obreschkow, D., Koribalski, B. S., & Staveley-Smith, L. 2014, MNRAS, 438, 1176
  * Westmeier et al. (2021) Westmeier, T., Kitaeff, S., Pallot, D., et al. 2021, MNRAS, 506, 3962
  * Westmeier et al. (2022) Westmeier, T., Deg, N., Spekkens, K., et al. 2022, PASA, 39, e058
  * Wong et al. (2006) Wong, O. I., Ryan-Weber, E. V., Garcia-Appadoo, D. A., et al. 2006, MNRAS, 371, 1855
  * Xu et al. (2023a) Xu, C., Jing, Y., Wang, J., et al. 2023a, RAA, prep, prep
  * Xu et al. (2022) Xu, C. K., Cheng, C., Appleton, P. N., et al. 2022, Nature, 610, 461
  * Xu et al. (2023b) Xu, J.-L., Zhu, M., Yu, N., et al. 2023b, ApJ, 944, L40
  * Yu et al. (2023a) Yu, H., Zhu, M., Xu, J.-L., et al. 2023a, MNRAS, 521, 2719
  * Yu et al. (2023b) Yu, N.-P., Zhu, M., Xu, J.-L., et al. 2023b, MNRAS, 521, 737
  * Zeng et al. (2021) Zeng, Q., Chen, X., Li, X., et al. 2021, MNRAS, 500, 2969
  * Zhang et al. (2014) Zhang, C.-P., Wang, J.-J., Xu, J.-L., Wyrowski, F., & Menten, K. M. 2014, ApJ, 784, 107
  * Zhang et al. (2021) Zhang, C.-P., Xu, J.-L., Li, G.-X., et al. 2021, Research in Astronomy and Astrophysics, 21, 209
  * Zhang et al. (2022a) Zhang, C.-P., Xu, J.-L., Wang, J., et al. 2022a, Research in Astronomy and Astrophysics, 22, 025015
  * Zhang et al. (2023) Zhang, C.-P., Jiang, P., Zhu, M., et al. 2023, Research in Astronomy and Astrophysics, 23, 075016
  * Zhang et al. (2022b) Zhang, L., Vieira, J. D., Ajello, M., et al. 2022b, ApJ, 939, 117
  * Zhou et al. (2023) Zhou, R., Zhu, M., Yang, Y., et al. 2023, ApJ, 952, 130
  * Zhu et al. (2021) Zhu, M., Yu, H., Wang, J., et al. 2021, ApJ, 922, L21