# RAD@home citizen science discovery of an AGN spewing a large unipolar radio
bubble onto its merging companion galaxy

Ananda Hota,1,2 Pratik Dabhade,3,2 Sravani Vaddi,4,2 Chiranjib Konar,5,2
Sabyasachi Pal,6,2 Mamta Gulati,7,2 C S. Stalin,8,2 Avinash Ck,2 Avinash
Kumar,2 Megha Rajoria,2 and Arundhati Purohit2
1#eAstroLab, UM-DAE Centre for Excellence in Basic Sciences, University of
Mumbai, Santacruz-East, Mumbai-400098, India
2RAD@home Astronomy Collaboratory, Kharghar, Navi Mumbai, PIN 410210, India
3Observatoire de Paris, LERMA, Collège de France, PSL University, Sorbonne
University, 75014, Paris, France
4Arecibo Observatory, NAIC, HC3 Box 53995, Arecibo, Puerto Rico, PR 00612, USA
5Amity Institute of Applied Sciences, Amity University Uttar Pradesh,
Sector-125, Noida-201303, India
6Midnapore City College, Kuturia, Bhadutala, Paschim Medinipur, West Bengal,
721129, India
7School of Mathematics, Thapar Institute of Engineering and Technology,
Patiala, 147004, Punjab, India
8 Indian Institute of Astrophysics, Koramangala II Block, Bangalore, 560 034,
India
E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

AGN feedback during galaxy merger has been the most favoured model to explain
black hole-galaxy co-evolution. However, how the AGN-driven jet/wind/radiation
is coupled with the gas of the merging galaxies, which leads to positive
feedback, momentarily enhanced star formation, and subsequently negative
feedback, a decline in star formation, is poorly understood. Only a few cases
are known where the jet and companion galaxy interaction leads to minor off-
axis distortions in the jets and enhanced star formation in the gas-rich minor
companions. Here, we briefly report one extraordinary case, RAD12, discovered
by RAD@home citizen science collaboratory, where for the first time a radio
jet-driven bubble ($\sim$ 137 kpc) is showing a symmetric reflection after
hitting the incoming galaxy which is not a gas-rich minor but a gas-poor
early-type galaxy in a major merger. Surprisingly, neither positive feedback
nor any radio lobe on the counter jet side, if any, is detected. It is
puzzling if RAD12 is a genuine one-sided jet or a case of radio lobe trapped,
compressed and re-accelerated by shocks during the merger. This is the first
imaging study of RAD12 presenting follow-up with the GMRT, archival MeerKAT
radio data and CFHT optical data.

###### keywords:

galaxies: active – galaxies: evolution – galaxies: jets – galaxies:
interactions – (galaxies:) quasars: supermassive black holes – radio
continuum: galaxies

††pubyear: 2022††pagerange: RAD@home citizen science discovery of an AGN
spewing a large unipolar radio bubble onto its merging companion
galaxy–RAD@home citizen science discovery of an AGN spewing a large unipolar
radio bubble onto its merging companion galaxy

##  1 Introduction

The most popular model of galaxy evolution has been merger and negative
feedback of AGN activity or accretion onto the central supermassive black
hole, at the centre of all large galaxies, on the star formation in the
surrounding host galaxy (Di Matteo et al., 2005; Hopkins et al., 2008;
McNamara & Nulsen, 2012; Fabian, 2012). Theoretical as well as observational
studies have strongly supported the hypothesis of AGN feedback during the
merger of galaxies, leading to relations such as the colour-magnitude diagram,
M-$\sigma$ relation, and luminosity function of galaxies (Fabian, 2012;
Heckman & Best, 2014; Terrazas et al., 2020). Majorly there are three mediums
of feedback have been identified, namely, intense radiation from the accretion
disk, hot/cold material outflow driven outward by the AGN, and radio jets of
relativistic magnetised non-thermal plasma (Veilleux et al., 2005; Croton et
al., 2006). Various prescriptions have been proposed on how to scale the
stellar mass of the galaxy to the mass of the black hole, mass of the black
hole to the energy output in the form of radiation and then couple it to the
interstellar medium (ISM) for a mass of gas lost from the galaxy. The loss or
heating of gas in the ISM causes an eventual decrease in star formation of the
host galaxy. Several efforts are currently on-going to understand how the
radiation/wind/jet is coupled to cold gas in the galaxy so as to predict its
effect on quenching of star formation (Kormendy & Ho, 2013; King & Pounds,
2015; Mukherjee et al., 2016; Cielo et al., 2018). It is essential to
investigate the interaction between the radio jet and the ISM of a galaxy. In
classical radio galaxies, jet-ISM interaction is hardly observed because the
host galaxies are almost always gas-poor ellipticals (see a review by
Hardcastle & Croston, 2020). On the other hand, in nearby Seyfert galaxies
where the kpc-scale radio jets coincide with the narrow emission line regions
(NLR), they are often confusing if the cause is star formation (positive
feedback) or AGN-driven shocks and outflows (Nagar et al., 1999; Venturi et
al., 2021).

In an ideal and unambiguous case the radio jet hits a neighbouring galaxy and
evidence of this jet-galaxy interaction can be found in both radio and optical
bands. To date only a few cases are known where the radio jet has been clearly
observed to be triggering recent star formation. It is quite likely, that it
provides positive feedback (triggers young star formation) on a short time-
scale and leads to negative feedback, preventing star formation, which on
longer time-scales is required for the understanding of galaxy evolution. We
briefly describe five such rare cases of jet-galaxy interactions where
enhanced UV and H$\alpha$ emission and distortion of the radio jet have been
clearly observed. Understanding the interaction of the radio jet with the
stellar and gaseous component of the neighbouring galaxy is important,
especially in case of mergers, as it allows to examine the jet-ISM coupling,
responsible for positive or negative feedback.

One of the most compelling case of positive feedback is the Minkowski’s object
(Minkowski, 1958), which is a dwarf galaxy located $\sim$ 20 kpc north-east of
the radio galaxy, NGC541. At the location of interaction the jet develops a
radio knot and dwarf galaxy shows evidences of young star formation (Croft et
al., 2006; Salomé et al., 2015). 3C321 (death star galaxy) is FRII type radio
galaxy where its radio jet has been observed to interact with a minor
companion galaxy on the western side. It is noted that the jet brightens up in
radio and it develops an off-axis diversion to the north-western side
extending up to 100 kpc (Evans et al., 2008). 3C34 is a radio galaxy which
shows a typical FRII lobe on the west but its eastern lobe is observed to be
co-spatial with optically blue and emission line galaxies. It also exhibits
double hotspots suggesting jet-galaxy interaction (Best et al., 1997). 3C285
is also a FRII radio galaxy where the eastern jet passes close to a small
galaxy with dominant blue colour in optical, which is located $\sim$ 70 kpc
away from the radio core (van Breugel & Dey, 1993; Salomé et al., 2015). 3C441
is yet another case where the tip of the north-western FRII lobe appears to be
touching a face-on disk/spiral galaxy with the distortion in the radio
structure and edge-brightening of the disk galaxy on the side of the lobe
suggesting interaction (Lacy et al., 1998). The disk galaxy shows old stellar
population consistent with its red colour but also a post-starburst population
possibly due to the interaction with the radio lobe. The above-mentioned brief
summary of a few rare cases of jet-galaxy interactions clearly demonstrates
the effects of positive feedback. If the jet is observed to hit a
significantly big galaxy, as in major mergers, we may expect more exotic cases
than the ones described above with minor companions.

In this paper, we present results of RAD12, where we are witnessing a possible
case of dry and major merger of two large elliptical galaxies. This brief
report on this citizen science discovery is the very first radio-optical
imaging study of this object, unveiling a mushroom-shaped radio bubble, unlike
any of the above cases.

Figure 1: RAD12 images: (a): GMRT 323 MHz colour image with contour levels of
0.0007, 0.0008, 0.0009, 0.0011, 0.0014, 0.0018, 0.0025, 0.0036, 0.0053,
0.0080, 0.0124, 0.0193, 0.0302 Jy beam-1, with beam of 10.2′′$\times$ 7.9′′,
-6.4∘. (b): MeerKAT L band image with beam of 7.7′′$\times$ 7.1′′, -8.2∘. (c):
Optical colour image from Legacy survey is superposed with 3 GHz contours
(levels: 0.0004,0.00066, 0.0013, 0.003, 0.008, 0.022, 0.057, 0.153 Jy beam-1)
from VLASS (3.77′′$\times$ 2.29′′, 20.61∘). (d): 11′′$\times$ 11′′, 0∘spectral
index map made using GMRT 323 MHz and MeerKAT L band. (e): In-band spectral
index from 900 to 1670 MHz (MeerKAT L band) with beam same as the MeerKAT map.
(f): Error in the spectral index map from GMRT and MeerKAT data. Cross mark in
green colour in sub-figures a and b marks the position of the optical nucleus
of the host galaxy of RAD12. The centre of the western optical galaxy is also
detected in both the radio bands.

##  2 Observation and Data Analysis

RAD12 was discovered by RAD@home111https://www.radathomeindia.org citizen
science research collaboratory as reported first in (Hota et al., 2014). The
innovative socio-scientific design of the RAD@home collaboratory is briefly
described in Hota et al. (2016). It is a zero-funded, zero-infrastructure,
nationwide, Inter-University network of professional astronomers and trained
citizen scientists (e-/i-astronomers) who are University students or citizens
(irrespective of their age and profession) with University-level science
education.

The collaboratory trains eligible citizens to analyse multi-wavelength (UV,
Optical, IR, radio) Red Green Blue (RGB)-contour images, and overlays of
extragalactic radio sources using simple tools like SAO ds9, NASA- Skyview and
RAD@home RGB-maker222https://www.radathomeindia.org/rgbmaker.

They are trained to find exotic radio sources similar to Speca, a rare spiral-
host episodic radio galaxy (Hota et al., 2011), and the Cosmic leaf blower
galaxy, NGC3801 which is a sub-galactic scale young radio source in a post-
merger early-type galaxy (Hota et al., 2012). Thus, this modified citizen
science with multi-wavelength approach has rich and diverse science to offer.
Following the above process, a blob of unusual radio emission in the NVSS
(Condon et al., 1998) was spotted located between two potential host galaxies,
as seen in optical images (e.g DSS), both of which are early-type galaxies.
The radio source could not be classified as FRI or FRII type. Higher
resolution image from FIRST (Becker et al., 1995) confirmed the co-incidence
of the radio core with the eastern galaxy (SDSS J004300.63–091346.3, Abolfathi
et al. 2018) seen in optical and a fainter plume like linear radio emission
was seen only on one side and extending directly towards the western galaxy
(SDSS J004254.69–091349.4) which is also at the same redshift ($z$) of 0.076.
Hota et al. (2016) suggested that it is unlike any head-tail radio galaxies or
radio bridges in interacting Taffy galaxies, and the radio jet is likely
interacting with the western galaxy but absence of the eastern jet needed
further probing.

Hence, a follow up observation was proposed to the Giant Metrewave Radio
Telescope (GMRT; Swarup et al., 1991) through the proposal titled GOOD-RAC:
GMRT Observation of Objects Discovered by RAD@home Astronomy Collaboratory,
32$\\_$093 (PI: Ananda Hota). It was observed on 11th September 2017 at 323
MHz with standard procedure of target galaxy observed in between phase and
flux density calibrators. The total on source time for the target was 3.33
hours with a bandwidth of 33.33 MHz. The data was analysed using the Source
Peeling and Atmospheric Modelling (SPAM; Intema et al., 2017) pipeline. We
also found archival MeerKAT 1283 MHz data for RAD12 as it is present in the
field of Abell 85 from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS;
Knowles et al., 2022). The deep radio images from GMRT and MeerKAT are
presented in the top panel of Fig. 1 (a and b), where the rms of GMRT 323 MHz
image is $\sim$ 90 $\muup$Jy beam-1 and $\sim$ 10 $\muup$Jy beam-1 for MeerKAT
L band image. We also created spectral index map of 11′′ resolution using GMRT
323 MHz and MeerKAT L band data, which is seen in Fig. 1d. The MGCLS data
products also provides in-band spectral index map covering the frequency range
of 900 to 1670 MHz and it is shown in Fig. 1e.

##  3 Results

In Fig. 1, we present the GMRT 323 MHz, MeerKAT L band, and their spectral
index images for the first time, revealing unprecedented details of RAD12
which are not seen in any other archival radio data. The integrated flux
densities of RAD12 at 323 MHz and 1283 MHz are 368.0$\pm$36.8 and
164.4$\pm$8.2 mJy beam-1, respectively.

The whole radio emission resembles a mushroom, rather a cross section of, and
thus various sub-components are named accordingly. As seen in Fig. 1 (panel-a
& b) a plume, poorly collimated jet, like stem of a mushroom, extends from the
bright radio core westward. The stem has two minor radio peaks: one at the
end, like a hot spot or terminal peak, and the other at the middle, like a
knot in the jet.

The observed stem like feature seen in Fig. 1 has one-to-one correspondence
with the 1.4 GHz image from FIRST as well as 3 GHz VLASS (Gordon et al., 2021)
image. The VLASS contours superposed on optical colour image from Legacy
survey333https://www.legacysurvey.org database demonstrate absence of any
compact jet or hotspot but a diffuse emission structure of the whole stem.
However, the feature resembling a cap of the mushroom is seen, both to the
north and south of the stem, in GMRT and MeerKAT images, but not detected in
FIRST and VLASS (see Fig. 1b for name of sub-structures. The stem shows a
compression at the western edge, after the terminal peak, and the emission
diffuses towards the north and south forming the cap (total N-S extent 137 kpc
or 95′′). The GMRT and MeerKAT contours are closely spaced on the western side
of the terminal peak than on its north and south sides, suggesting an abrupt
stoppage of plasma, especially in the absence of any compact feature in the
VLASS image at this location. This compression is a subtle feature and may
require further higher resolution and polarisation observations for further
probing. The diffuse emission of the cap, resembles back flow observed often
seen in typical FRII type radio lobes, extends to the east reaching almost
close to the radio core. However, the features in this radio mushroom have
subtle differences from FRII lobes, which is discussed later in the paper.

The spectral index maps (Fig. 1 d & e) display smooth variations along the
expected dynamical features, where we observe the core to be very flat, the
stem $\alpha$ $\sim$ –0.5, and the region beyond terminal peak or compressed
region has $\alpha$ more flatter than the overall stem. The diffuse emission
in the cap, as expected, is steeper with $\alpha$ ranging from $\sim$ –0.8 to
$\sim$ –1.5. Note that the errors on the spectral index, presented in the same
Figure, is around 0.15, which is much smaller than differences found in
different sub-structures. This gradual variation of spectral index from core
to stem, to terminal peak, and diffuse region of the cap show perfect textbook
example of fresh plasma supply, re-acceleration at the hot-spot and steepening
in the back flow of a typical FRII radio galaxy lobe. However, there are two
major differences, 1) instead of narrow particle beam or jet and compact
hotspot structures, RAD12 stem is not very well collimated (at least on kpc
scales) and 2. the terminal peak of the stem or the compressed region,
although shows spectral flattening, shows a concave structure (indicated with
an arrow in Fig. 1) instead of a convex structure or a bowshock structure,
which is typically seen in FRII radio galaxy lobes.

In Fig. 2 the GMRT 323 MHz radio contours are overlaid on a deep (exposure
time = 5140 secs) r-band optical image obtained from the CFHT archive
(MegaPrime instrument on 3.6m telescope). While the radio core coincide with
the nucleus of the eastern galaxy (SDSS J004300.63–091346.3) hereafter host
galaxy, a faint point radio source can be identified to coincide with the
larger and brighter western galaxy (SDSS J004254.69–091349.4) hereafter
companion galaxy. The spectral index of this point source from GMRT and
MeerKAT data is $\alpha$ $\sim$ –0.5 (see Fig. 1c) confirming its association
with the nucleus of the companion. The spectroscopic redshift of the host and
companion galaxies are 0.07628$\pm$0.00001 and 0.07618$\pm$0.00001,
respectively. To the north of the eastern galaxy, a relatively smaller galaxy
(SDSS J004259.59–091316.8) with a faint but curved stellar tail extending
northward is seen. The northern galaxy has a photometric redshift of
0.081$\pm$0.009 and is quite likely a second merging companion of RAD12.
Although there are many faint foreground/background galaxies in the region
around RAD12 and its western companion, the middle and terminal radio peaks on
the stem does not show any optical counterpart. The radio-optical overlay as
seen in Fig. 2, the stem seems to terminate, with steep gradient of radio
contours, in a region touching (in projection) the outer boundary of the
companion galaxy. Furthermore, the terminal peak has north-south orientation
unlike the middle peak on the stem. Thus, the radio mushroom seem to inflate
to both north and south direction relatively easily after getting obstructed
possibly by the gaseous halo or stellar shells (Fig. 1c and Fig. 2) of the
companion galaxy or the gas in the orbital plane of the galaxy merger.

##  4 Discussion

Below we present discussion on a few possible scenarios explaining the
observed radio-optical features of RAD12 and its companion galaxy.

Jet-galaxy interaction in a Major merger: RAD12 is unlike any of the five jet-
galaxy interactions described in the introduction where radio jet hit only a
minor gas-rich merging companion. This is possibly the first clear case of a
major merger, where the companion galaxy, which is being hit by the jet, is a
gas-poor bigger and brighter elliptical galaxy. In other examples of jet-
galaxy interactions, minor distortions (brightening of a knot, off axis
diversions, double hotspot) were observed but RAD12 is the first case of
complete halt of the jet with structural depression and spectral flattening.
Most significantly it is a symmetric and possibly experiencing complete
reflection forming a mushroom-shaped bubble. The stem is 64 kpc long (core to
terminal peak is 45′′) which is nearly half of the distance between nuclei,
128 kpc (88′′). It is puzzling what is in the middle of this gas-poor merging
ellipticals that push the jet backward. Is it the bowshock of a major merger
or magnetosphere of the incoming companion galaxy or more exotic things like
dark matter?

No sign of positive feedback: Unlike the five known cases where the jet-galaxy
interaction triggers young star formation showing up in UV-optical colours,
RAD12 does not seem to show any. Nothing is seen at the terminal peak or
contour suppression either in individual bands of SDSS or in colour image. The
u and r band SDSS magnitudes of the host galaxy are u (18.21 $\pm$ 0.03) and r
(15.36 $\pm$ 0.00), and that for the brighter ($\sim$ 1 mag.) companion galaxy
are u (17.23 $\pm$ 0.03), r (14.22 $\pm$ 0.00). Thus, the u-r colours, 2.85
and 3.01, put both of them in the typical red and dead elliptical galaxy
category. It is possible that the incoming galaxy is too big or gas-poor for
the jet to trigger visible galaxy-wide young star formation. Interestingly,
the deep CFHT optical image shows a large region ($\sim$ 25 $\times$ 25 kpc)
of faint diffuse optical emission observed close to the region of compressed
radio contours (indicated with an arrow in Fig. 2). Such optical emission is
not observed on the diametrically opposite side of the companion galaxy.
Causal connection between compressed radio emission and this asymmetric
diffuse emission in optical is unclear at the moment. Note that a small galaxy
(SDSS J004256.93–091347.1) seen in the region of compression is a background
galaxy ($z_{\rm photo}$= 0.220$\pm$0.108).

Figure 2: Optical r-band image from the CFHT of galaxies SDSS
J004300.63–091346.3, host of RAD12 (left) and companion galaxy SDSS
J004254.69–091349.4 (right) overlaid with GMRT 323 MHz contours.

Unlike a Head-Tail radio galaxy: The core-stem structure resembles a head-tail
radio galaxy, typically seen in galaxy clusters. The observed bright and
unresolved core component could possibly harbour a narrow-angle tailed radio
source. However, based on our data we observe that the core is extremely flat,
stem is also flat with freshly supplied plasma from the core, and shows no
sign of bifurcation (e.g. VLASS image) or spectral steepening in the stem.
Hence, our observational evidence disfavours the possibility of a narrow-angle
tailed radio source. Furthermore, the creation of a big radio bubble with its
spectrally distinct features encompassing the stem by a head-tail radio galaxy
is difficult to explain.

Intrinsically One-sided AGN radio jet: The L-band luminosity of the radio
source is 2.3 $\times$ 1024 W Hz-1. To put this radio loud AGN in to
perspective, one can compare its location in the power vs. projected size plot
(P-D diagram) of all classes of radio sources (see Fig. 2 of Hardcastle &
Croston 2020). RAD12 is located in between mean location of radio quiet/weak
quasars and FRI radio galaxies suggesting almost similar power but smaller jet
size. Hence, detection of only one possible radio lobe is puzzling. In the
MeerKAT image, the flux density of the whole mushroom structure is $\sim$ 140
mJy (total 164 mJy - core flux density of 24mJy) and the 3$\sigma$ upper limit
of the non-detection for the western counterpart is $\sim$ 0.03 mJy. This
gives an asymmetry by a factor of about 4660 which is extremely unusual for
asymmetric radio galaxies (Gopal-Krishna & Wiita, 2004). Brightness asymmetry
in jets can arise due to Doppler boosting, so independent clues on AGN
orientation was looked for. Faint and narrow emission lines seen in SDSS
spectrum and its location in the IR colour-colour plot (e.g. Gürkan et al.,
2014) suggest clearly that the target is a low excitation radio galaxy and not
a quasar. In other words, the radio-jet in RAD12 does not seem to be oriented
close to the line of sight. Hence, this apparently missing eastern emission in
any radio data presents a strong case for intrinsically one-sided launching of
large-scale (50 kpc) radio jet. Doppler boosting can be attributed to only
VLBI-scale (pc) jet-counter jet asymmetry and environmental asymmetry in such
early-type galaxy is not expected before the jet hits the ISM of the companion
galaxy $\sim$ 60 kpc away. Hence, the stem appears to be intrinsically
asymmetric with poorly collimated jet feature which could have grown to a
bubble or mushroom structure only after interaction with the companion galaxy.
Further detailed observations are required to confirm if RAD12 indeed has an
one-sided jet.

Merger Trapped radio bubble: Alternative to the above one-sided jet model, we
propose that RAD12 is representing a rare/transient ‘caught in the act’ phase
of merging galaxies where one of the radio lobe is trapped in between two
merging nuclei. In such a case, the radio lobe on the counter jet side
(eastern) escapes outward to a low-density environment and can miss detection
due to efficient diffusion. Due to dense inter-nuclei medium and/or shocks in
between approaching nuclei, the brightness of the trapped lobe can be enhanced
or diffuse less-efficiently. Adiabatic compression and/shock re-acceleration
of this radio plasma can be compared to that observed in a radio “Phoenix” in
clusters of galaxies (Enßlin & Gopal-Krishna, 2001).

Such well-shaped radio bubbles are known in low-power radio sources hosted in
both spiral and elliptical galaxies. The radio luminosity of RAD12 is typical
of a representative sample of radio bubbles in the nearby Universe compiled by
Hota & Saikia (2006). Beautiful example of such a radio bubble is seen in
Seyfert-Starburst composite NGC6764, where the poorly collimated nuclear jet
interacts with the gas disk of the spiral galaxy and then expands laterally to
form bipolar radio bubbles along the rotation axis of the disk (Hota & Saikia,
2006). Such bubbles can drive atomic, molecular and ionised gas outflows.
Follow up observations with Chandra showed the radio bubble to have perfect
correspondence in X-ray suggesting supersonic expansion of the bubble driven
shock waves and heating of the surrounding ISM. The total energy of the X-ray
bubble, 1056 ergs, is not only comparable to that in low-power radio galaxies
but also more than kinetic energy of the outflowing cold gas (e.g. Croston et
al., 2008). Interestingly, the dynamical age of this radio bubble also
coincide with a break in the nuclear star formation history (Hota et al.,
2012). Future study of many such intra-nuclei radio bubbles can reveal
thermostat like nature of AGN feedback-fueling cycles or when the bubble is
powerful enough to drive outflow and when it is weak to suggest trapped bubble
or inflow of gas to feed/fuel the AGN/nuclear star formation.

Future Direction: Using the available radio and optical archival data, it is
possible to find objects such as RAD12 and which upon further follow up
observations can be studied in depth to unravel the underlying physics
involved in the AGN feedback coupled with galaxy mergers. The GMRT and MeerKAT
data clearly demonstrate the uniqueness of RAD12 and justifies further
examination using multi-wavelength data, e.g. radio polarisation, X-ray
imaging and spectroscopy of ionised/molecular/atomic gas observations.
Particularly, studying the gas kinematics can possibly shed more light on the
interaction of a radio bubble with the ISM of a merging galaxy system and
thereby improving our understanding of AGN feedback.

Detection of large radio jets are no longer confined to classical ellipticals
but also found in many spirals and narrow-line Seyfert 1 galaxies which are
often disk galaxies (e.g Hota et al., 2011; Rakshit et al., 2018; Webster et
al., 2021). Hence, our current understanding of the prevalence of jet-mode
feedback possibly requires significant upgrade. Similarly, as this unusual
radio source could not have been correctly characterised from the NVSS, TGSS
(Intema et al., 2017), DSS like all sky survey data alone, it is likely that
such radio-mode feedbacks with non-standard radio jets and unusual spectral
nature may be more prevalent in galaxies which does not even look like
merging/interacting. Ongoing surveys like VLASS (Gordon et al., 2021) at 3 GHz
and LoTSS at 144 MHz (Shimwell et al., 2022) are ideal for citizen science
projects to discover more such cases of AGN jet feedback in merging galaxies.
Since the age of relics of past AGN activities in radio bands could be 100
million years or more compared to about 10,000 years in optical band (e.g.
Hanny’s Voorworp; Lintott et al., 2009; Keel et al., 2012), new sensitive
radio surveys will unearth significant samples. Eventually in the era of
Square Kilometre Array radio telescope, “smoking gun” evidences of AGN
feedback in a sample can be found where age of the radio relic could possibly
match the time-scale of decline in star formation in a post-merger early-type
galaxy (Hota et al., 2016).

## Acknowledgements

We thank the anonymous referee for constructive comments on the manuscript. We
thank the staff of the GMRT that made these observations possible. GMRT is run
by the National Centre for Radio Astrophysics of the Tata Institute of
Fundamental Research. Based on observations obtained with MegaPrime/MegaCam, a
joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope
(CFHT) which is operated by the National Research Council (NRC) of Canada, the
Institut National des Science de l’Univers of the Centre National de la
Recherche Scientifique (CNRS) of France, and the University of Hawaii. The
observations at the Canada-France-Hawaii Telescope were performed with care
and respect from the summit of Maunakea which is a significant cultural and
historic site. AH acknowledges University Grants Commission (UGC, Ministry of
Education, Govt. of India) for monthly salary.

The long list of national and international organisations which have helped
the establishment and growth of the first Indian astronomical citizen science
research platform RAD@home are acknowledged in detail at
https://radathomeindia.org/brochure.

## Data Availability

The GMRT data presented in this article can be accessed from the GMRT Online
Archive444 https://naps.ncra.tifr.res.in/goa/data/search. The derived data
generated in this research will be shared on reasonable request to the
corresponding author.

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# Is preprint the future of science?
A thirty year journey of online preprint services

Boya Xie  Microsoft Research<EMAIL_ADDRESS>,  Zhihong Shen
Microsoft Research<EMAIL_ADDRESS>and  Kuansan Wang  Microsoft
Research<EMAIL_ADDRESS>
(2021)

###### Abstract.

Preprint is a version of a scientific paper that is publicly distributed
preceding formal peer review. Since the launch of arXiv in 1991, preprints
have been increasingly distributed over the Internet as opposed to paper
copies. It allows open online access to disseminate the original research
within a few days, often at a very low operating cost. This work overviews how
preprint has been evolving and impacting the research community over the past
thirty years alongside the growth of the Web. In this work, we first report
that the number of preprints has exponentially increased 63 times in 30 years,
although it only accounts for 4% of research articles. Second, we quantify the
benefits that preprints bring to authors: preprints reach an audience 14
months earlier on average and associate with five times more citations
compared with a non-preprint counterpart. Last, to address the quality concern
of preprints, we discover that 41% of preprints are ultimately published at a
peer-reviewed destination, and the published venues are as influential as
papers without a preprint version. Additionally, we discuss the unprecedented
role of preprints in communicating the latest research data during recent
public health emergencies. In conclusion, we provide quantitative evidence to
unveil the positive impact of preprints on individual researchers and the
community. Preprints make scholarly communication more efficient by
disseminating scientific discoveries more rapidly and widely with the aid of
Web technologies. The measurements we present in this study can help
researchers and policymakers make informed decisions about how to effectively
use and responsibly embrace a preprint culture.

preprint analysis, citation impact, publication rate, time to publish, arXiv,
SSRN, bioRxiv

††copyright: iw3c2w3††conference: ; ††price: ;††doi:  Figure 1. Overall
preprints distribution

##  1\. Introduction

Preprint is a form of a scholarly article which is not peer-reviewed yet but
made available either as paper format or electronic copy. If the curiosity and
excitement to push forward the frontier of collective human knowledge makes
scientists desire to share their discoveries as soon as possible to as wide
audiences as possible, then online preprint could be the ultimate
manifestation of such spirit of science: accessible for anyone with the
Internet access shortly after the paper is uploaded. In the past thirty years,
the Web deeply impacts all aspects of our lives and the society, including the
academic publishing. In this work, we study the recent evolution of preprint
from 1991 to 2020 to understand whether the permeated Web technology could
help re-establish a broader adoption of the preprint tradition for a more
efficient and effective dissemination of science.

Preprints were distributed through physical mails and had been struggled with
high postage cost for decades until Paul Ginsparg at the Los Alamos National
Laboratory launched an automated email server distributing high energy physics
preprints in 1991 (Garisto, 2019; Rosenfeld et al., 1970; Ginsparg, 2011). The
email server later evolved to a web service known as arXiv, hosting preprints
in eight different fields such as physics, mathematics, computer science and
quantitative biology. Today arXiv hosts more than 1.7 million articles - one
of the largest preprint servers in size. In 1994, the most popular repository
for economics, law and the social sciences, Social Science Research Network
(SSRN) was founded by two economists (Noorden, 2016). Subsequently in 1997,
another economics preprint server, Research Papers in Economics (RePEc) was
created (RePEc, 2020). While just a small number of preprints launched from
2000 to 2010, more than 40 preprint servers have been created in the past
decade (Johnson and Chiarelli, 2019). Some are general-purpose, such as PeerJ
PrePrints, Preprints.org, viXra; some are for specific disciplines, such as
bioRxiv, medRxiv, LawArXiv, SportRxiv; and some are for specific regions or
languages, such as AfricArXiv, Arabixiv, IndiaRxiv, and Frenxiv. Figure 1
shows the preprint distribution in different disciplines and servers hosting
the articles.

In this work, we employ a large-scale multi-disciplinary preprint dataset
sourced from the Microsoft Academic Graph (MAG) (Sinha et al., 2015) to
explore the trends and impacts of preprint quantitatively. This dataset
includes 2.8 million preprints and 69.8 million peer-reviewed articles from
1991 to 2020 and it is the largest preprint dataset used for analysis to the
best of our knowledge.

  * •

Trends and adoption of preprint. To understand how different research
community and disciplines adopt the preprint culture, we analyzes the
development of preprints over the past thirty years. The annual number of
preprints has increased from a few thousands in 1991 to 227k in 2019, although
it still accounts for a small portion of all scientific papers. In the first
nine months of 2020, 192k preprints are produced, which represent 6.4% of all
papers published in the same period. When zooming in to individual domains:
physics, mathematics, and economics adopt the preprint culture the best;
increasing popularity is observed in computer science and biology in the last
5 years; and preprint is rare in other domains.

  * •

Impacts on individual researchers. The data shows when a work is documented in
preprint, it reaches an audience 14 months earlier than a peer-reviewed
counterpart. We also unveil that the early release is associated with two
times more citations for a paper.

  * •

Impacts on research communities. One of the biggest concern of adopting
preprint is the quality without a peer endorsement system in place. In this
analysis, we find preprints have sufficient quality control and the paper
quality is similar to peer-reviewed publications. We discover that 41% of
preprints ultimately have passed peer review and are published in journals or
conferences, and the destination venues are more influential than the venues
of peer-reviewed papers. Preprint shows significant advantage in timely
sharing of the latest findings during COVID-19 pandemic and it plays an
unprecedented role in the dissemination of biomedical and life science
knowledge at such public health emergencies.

Overall, our findings demonstrate that with the penetration of Web
technologies, online preprint servers enable early release and attract more
citations for individual researchers. Preprints also benefit the research
community by hosting adequate-quality papers with prompt open access. These
benefits might be overlooked before due to the lack of quantitative
measurements. The endeavour of scientific communication aims to spread
knowledge as rapidly, as widely, and as cost-effectively as possible. This
work provides evidence that we could achieve this goal better at this Web era
with a responsible adoption of the preprint culture and, ultimately, to
collectively advance the frontier of human knowledge and make it available to
public usage more effectively.

##  2\. Data and Method

In order to have a holistic view of online preprint and its evolution in
different disciplines, we design our analysis to include as many major
preprint servers as possible, and cover fields from natural science to social
science. MAG as a Web-scale scholarly dataset serves the purpose well. This
section describes how the preprint dataset is generated from MAG, and what are
the assumptions and known limitations.

###  2.1. Microsoft academic graph

MAG is a heterogeneous graph containing scientific publications, citation
relationships, as well as other academic entities such as authors,
institutions, journals, conferences, and fields of study. The graph is updated
weekly to include newly released scholarly articles on the Web. As of October
2020, there are over 240 million scientific documents from 1800 to 2020 in MAG
with eight different types: journal, conference, book, patent, book chapter,
repository, thesis, and other.111All data presented in this study are based on
MAG 2020-10-01 version. The preprint servers covered in this work are listed
in Table 1.

Totally 2.8 million preprints are found in MAG based on document types and URL
domains, among which 1.7 million are contributed by arXiv, followed by SSRN.
SSRN claims to host 0.95 million abstracts and 0.82 million full text papers
(SSRN, 2020a). However MAG only contains 0.76 million preprints sourced from
SSRN. We notice there is a significant drop in the number of SSRN papers
posted online after 2016 in MAG, coincident with Elsevier’s acquisition of
SSRN. Since we don’t find any other sources providing downloadable SSRN data,
MAG SSRN data post 2016 is treated as incomplete, related evaluations are
affected accordingly. Besides SSRN, the collections for Keldysh Institute
preprint, PsyArXiv, and SocArXiv are also incomplete in MAG. There are other
smaller preprint servers not being covered in this study, but we believe the
major players in the preprint services are included and the data is sufficient
to unveil the key characteristics of the preprint landscape.

Online preprints started in 1991 with the launch of arXiv server. There are
less than 1% preprints prior to 1991 in MAG. Therefore this study only focuses
on preprints starting from 1991. In order to compare preprints with peer-
reviewed papers, journal and conference papers from 1991 to 2020 are also
included. We exclude other types of documents (e.g. technical reports) with
the assumption that a vast majority of journal and conference papers are peer-
reviewed while other types of documents may not go through a rigorous peer
endorsement process. All analysis performed in this study are based on past 30
years data unless otherwise specified.

Server |  Self-reported size |  Size in MAG | Year range
---|---|---|---
arXiv | 1,777,025 222https://arxiv.org/ accessed Oct. 14, 2020 | 1,753,132 | 1992-present
SSRN | 950,733333https://papers.ssrn.com/sol3/DisplayAbstractSearch.cfm accessed Oct. 14, 2020 | 759,075 | 1994-present
RePEc | N.A. | 100,284 | 1993-present
bioRxiv | 98,301444 http://api.biorxiv.org/reports/content_summary | 100,109 | 2013-present
viXra | 35,893 555https://vixra.org/ accessed Oct. 14, 2020 | 31,497 | 2007-present
NBER Working Paper | 24,000+666https://www.nber.org/pubs.html#:~:text=The%20NBER%20working%20paper%20series,includes%20more%20than%2024%2C000%20papers. | 31,128 | 1973-present
IACR ePrint | N.A. | 23,291 | 1996-present
Preprints.org | 16,951777https://www.preprints.org/ accessed Oct. 14, 2020 | 17,337 | 2016-present
medRxiv | N.A. | 11,568 | 2019-present
IMF Working Paper | 7,008888https://www.imf.org/en/publications/search?when=After&series=IMF+Working+Papers accessed Oct. 14, 2020 | 8,016 | 1986-present
Peerj preprints | 5,068999https://peerj.com/preprints/ | 4,066 | 2012-2019
ACS PETR | N.A. | 3,028 | 1920-2012
Nature Precedings | N.A. | 2,986 | 2007-2012
Polymer Preprints | N.A. | 1,214 | 1966-2014
Keldysh Preprint | 2,849101010http://www.mathnet.ru/php/journal.phtml?jrnid=ipmp&option_lang=eng accessed Oct. 14, 2020 | 797 | 1967-present
EconStor Preprints | 713111111https://www.econstor.eu/handle/10419/30002 accessed Oct. 14, 2020 | 721 | 1986-present
SocArXiv | 6,439121212https://osf.io/preprints/socarxiv accessed Oct. 14, 2020 | 564 | 2016-present
chemRxiv | N.A. | 307 | 2017-present
PsyArXiv | 11,959131313https://psyarxiv.com/ accessed Oct. 14, 2020 | 28 | 2016-present
Table 1. Online preprint servers covered in this study

###  2.2. Experiment design

Figure 2. Paper groups and sizes used in this analysis

To facilitate evaluations, we split preprints, journal and conference papers
from 1991 to 2020 into three mutually exclusive groups (P-only, JC-only, P-JC)
based on whether they are submitted to preprints or are published at a peer-
reviewed journal or conference. In total, five groups are defined below and we
summarize their relationships and sizes in Figure 2 and their usages in
subsequent analysis in Table 2.

  * •

P-only: papers only submitted to preprints;

  * •

JC-only: papers only published;

  * •

P-JC: papers submitted to preprints and published;

  * •

P-all (P-only + P-JC): all preprints;

  * •

JC-all (JC-only + P-JC): all papers published.

###  2.3. Assumptions and limitations

Preprint and post-print. Authors normally upload papers to preprint servers
either without submitting to a journal, or while waiting for the peer review
result or for the official publishing. However, there are also cases that
papers are uploaded to repositories after they are published in journals or
conferences, which are called post-prints. Some servers restrict the
submission to pre-publication only, while others host both. ArXiv and SSRN
allow both preprint and post-print. Since there isn’t a clear signal to
differentiate post-prints from preprints, and we estimate141414Post-print is
assumed if the paper’s publication date is prior to the preprint online date.
there are about 4% post-print in arXiv and 2% in SSRN, we treat all articles
in arXiv and SSRN as preprints in this study.

Section | Figure |  Usage |  Measurement
---|---|---|---
1 & 3.2 | 1 |  Preprint distribution |  P-all
3.1 | 3 |  Trend |  P-all and P-all/(P-all + JC-all)
3.2 | 4 |  Trend by domain |  P-all/(P-all + JC-all)
4.1 | 5 |  Days to publish by server |  P-JC(publishDate - onlineDate)
4.1 | 6 |  Days to publish by domain |  P-JC(publishDate - onlineDate)
4.2 | 7 |  Citation impact |  P-JC.citation and JC-only.citation
4.2 | 8 |  Citation impact by domain |  P-JC.citation - JC-only.citation
5.2 | 9 |  Publish rate by domain |  P-JC/P-all
5.2 | 10 |  Publish rate by server |  P-JC/P-all
5.3 | 11 |  IF distribution |  JC-all and P-JC
5.3 | 12 |  IF distribution by domain |  JC-all and P-JC
Table 2. Online preprint servers covered in this study

Preprint and working paper. Multiple economics paper servers, such as RePEc,
NBER, and IMF, refer to unpublished articles as working papers. A working
paper shares many things in common with preprints: both are non-peer-reviewed,
both are made available online preceding publication to reach audiences
earlier. Sometimes working paper could be preliminary results and less mature
than preprint, and other times working paper and preprint are used
interchangeably. Here we treat working papers from the above mentioned three
servers as preprints.

Paper discipline categorization. To perform the analysis on preprints in
individual disciplines, we leverage the domain categorization of each paper in
MAG (Shen et al., 2018). MAG has a high-quality scientific concept (or field
of study) taxonomy constructed semi-automatically. As of October 2020, there
are more than 740K concepts organized into a six level hierarchy with 19 top
level domains, such as: biology, computer science, economics, etc. Each paper
is assigned with at least one top level domain. Interdisciplinary papers are
labeled with multiple top level domains, e.g. a biochemical paper is labeled
with both biology and chemistry. For all domain-related analysis,
interdisciplinary papers are counted once towards each of its belonging
domains. Physics, mathematics, computer science, economics, and biology are
selected for disciplinary analysis because they either have the most number of
preprints or are fast evolving.

##  3\. Preprint Trend

In this section, we provide an overview of the preprint general trends and
development in different disciplines. The annual preprint volume and ratio are
evaluated.

###  3.1. Overall trend

The number of preprints uploaded online per year has been increasing steadily
in the past decades, from 3k in 1991 to 227k in 2019. There are at least 192k
new preprints made available online in the first nine months of 2020.
Previously, the volume of overall scientific literature are found to be
growing exponentially and double every 15 years on average (Fortunato et al.,
2018). The preprint follows a similar growth trajectory but doubles in less
than 10 years. Despite the increasing number, the ratio of preprints to all
scientific articles is still very low, only 6.4% in 2020, as shown in Figure
3. ArXiv is a dominant player in preprint services, where its change is
illustrated in the bottom area of the figure. The volume growth in arXiv
defines the overall trend of all preprints. As described in the previous
section, SSRN volume is expected to be higher post 2015. BioRxiv becomes an
important contributor starting from 2015 as shown in the red area. It has
generated the third largest volume in the past five years.

Figure 3. Annual number of preprints (P-all) and reprints(P-all)/all-
papers(P-all+JC-all) rate growth

###  3.2. Trend by domain

The development of preprint is different in each domain: physics and
mathematics are the pioneers as people in these communities are more intimate
with Web technologies since early days; some are new active players within the
past 10 years, such as computer science and biology. Figure 1 shows the
preprint distribution in various disciplines and servers hosting the articles.
Among all preprints, half are physics and mathematics papers, where 96% of
physics and mathematics preprints are uploaded to arXiv. Considering the
history of online preprints, it is not surprising to find that 31% of all
preprints are physics papers. Publishing papers as a preprint is the norm in
physics. Figure 4 exhibits that three out of ten physics papers were preprints
in the past twenty years. On the other hand, mathematics is where we see the
second largest volume, and there has been increasing level of interest of
using preprints from mathematicians. Although computer science has the third
largest number of preprints, majority (95%) of computer science papers are
non-preprints due to the high throughput of computer science literature.
Besides physics and mathematics which have a high ratio of preprints to all-
papers at 27.7% and 15.7% respectively, economics has a relatively high
average ratio at 20.8% from 1991 to 2015. SSRN hosts the most economics
preprints, as well as other social sciences such as business and political
science, shown in Figure 1. While many other disciplines have less than 2%
preprints, biology has been promoting preprint since 2013, and a good momentum
is observed in the past five years. Biology has 6.5% preprints in 2020 and
majority of them are in bioRxiv.

Figure 4. Yearly preprints(P-all)/all-papers(P+JC) trend by domain

##  4\. Impact on individual researcher

Multiple articles discussed the concerns that authors might have over
submitting preprints: scooping, being precluded from journal publication, and
low quality implication, etc.(Bourne et al., 2017; Kaiser, 2017; Clemens,
2020; Sarabipour et al., 2019). On the other hand, the benefits of preprints
are frequently mentioned as well. Since it is not straight-forward to design
quantitative measurements from the publishing data of above mentioned
pitfalls. Instead, we exam some claimed benefits of preprints for individual
researchers.

###  4.1. Preprints provide early visibility

Presenting scientific findings through paper puts a timestamp on the work. The
lengthy peer review and publishing process could take months or even years. On
the contrary, uploading a preprint paper to get a permanent open accessed
timestamp only takes a few days. In addition to this unbeatable advantage of
time-stamping a paper, preprints enable the work to receive credits (in the
form of citations) faster, which is crucial for young researchers whose career
progression heavily depends on a timely recognition of their work (Sarabipour
et al., 2019).

Figure 5. Days to publish by preprint server Figure 6. Days to publish by
domain

Here, we analyze the duration between preprints’ initial online date and the
journal/conference official publication date to evaluate how much the preprint
expedites an article to reach an audience to gain the potential earlier
recognition. The analysis is conducted from two perspectives: days difference
in each preprint server, and with different disciplines. Figure 5 exhibits the
measurement for six major preprint servers and the average of all preprints.
The average days to publish of different preprint servers are listed in the
legend of Figure 5. ArXiv has similar days to publish to the overall average
of all servers. Both are around 400 days. It takes much longer to publish for
papers from NBER, SSRN, and RePEc (shown with dotted lines in Figure 5), which
is 0.5 to 1.1 times longer compared to the average. These three servers are
where most economics preprints submit to. This result demonstrates a huge
benefit that preprints bring to economics researchers for earlier visibility.
We assume that a first version preprint is similar to a ready-for-peer-review
paper, and both take similar amount of time to be published in peer-reviewed
destinations. Figure 6 implies economics papers take the longest time to
publish, at 822 days. This finding is consistent with the observation of a
prolonged peer review process in economics (Moore, 1965; Ozler, 2011). By
submitting to preprint servers, an economic researcher can get recognition
2.25 years earlier. Biology has the shortest lag, about 7 months. Figure 5
shows preprints in medRxiv only take 103 days to publish. However, medRxiv
just launched in June 2019. Many papers in medRxiv might still undergo the
review and publishing process right now thus being excluded from the plots in
Figure 5. The measurement for medRxiv will be more accurate when there is more
data available over time to overcome the current bias from the early published
"survivors".

To conclude, preprints help papers become accessible 7 months to 2.25 years
earlier than peer-reviewed counterparts, and economics researchers would
benefit from it the most.

###  4.2. Preprints correlate with more citations

The early visibility provided by preprints generates opportunities for
scientific work to make more impacts. One of the most direct and recognized
measurement of research impact is paper citations. The citation is found to
follow power-law distribution (Golosovsky and Solomon, 2012; Stegehuis et al.,
2012; Waltman et al., 2012) and the dynamics is universal for all disciplines
(Fortunato et al., 2018). The preprint is no exception, Appendix A shows the
preprint citation follows a power-law distribution. To illustrate whether
preprints is associated with more citations, we compare the papers’ citation
of published papers with (P-JC) and without (JC-only) a preprint version.

Figure 7. Overall citation impact

The yearly median citations of the four groups of papers: P-JC, JC-only,
P-all, and JC-all, are illustrated in Figure 7. It manifests that papers with
preprints have more citations in any year regardless whether they have a
published version. On average of all years, a journal or conference paper with
a preprint version (P-JC) has median citation of 14.8, while a non-preprint
counterpart (JC-only) receives 2.6 citations, which results in 12.2 citation
difference (five times more). All preprints (P-all), regardless whether they
have been published in journal/conference, have 0.7 more citations than papers
without a preprint version (JC-only).

Figure 8 demonstrates the citation difference between papers with and without
preprints in different domains. The citation difference is defined as the
yearly average citation per published paper with preprints (P-JC) subtracting
the yearly average citation per paper without preprints (JC-only). The
citation difference is positive and it indicates that published papers with a
preprint version (P-JC group) have bigger impacts as the citation of P-JC
group is more than the citation of JC-only group. Here we reveal that preprint
has positive citation impact in all domains, in which economics has the most
citation difference (6.9-fold more) and mathematics has the least (0.95-fold
more). The number of citations and citation differences for each discipline
are listed in Table 3.

Figure 8. Citation impact by domain (citation of published paper with preprint (P-JC) subtract citation of published paper without preprint (JC-only)) Domain |  Paper with preprint citations (P-JC) |  Paper without preprint citations (JC-only) |  Citation ratio (P-JC/JC-only)
---|---|---|---
Economics | 220 | 28 | 7.86
CS | 116 | 21 | 5.52
Physics | 50 | 18 | 2.78
Others | 72 | 31 | 2.32
Biology | 68 | 31 | 2.19
Mathematics | 43 | 22 | 1.95
Overall | 74 | 24 | 3.08
Table 3. Citation impact

By evaluating 69.4 million papers published from 1991 to 2020 over all
disciplines, we uncover that preprints have significant positive contribution
to paper citations. This result is consistent with previous studies(Serghiou
et al., 2018; Gentil-Beccot et al., 2010) at a dataset which is hundreds times
larger with more comprehensive coverage on all disciplines and a longer time
span.

In this section, we quantify the positive impacts that preprints bring to
individual researchers in terms of days to publish and the citation
difference. Preprints make papers available to public 14 months earlier
compared to peer-reviewed papers. It is most beneficial to economic
researchers who usually undergo a prolonged publishing process up to 3 years.
Preprints correlate with two times more citations on average, and always have
positive effect on citations of papers in all domains that we evaluate.

##  5\. Impact on the research community

Is preprint contributing to the research community similar to peer-reviewed
articles or is it diluting the novel findings with inaccurate or even
incorrect conclusions? Do preprints serve better in certain scenarios where
peer-reviewed journals or conferences have limitations? In this section, we
address the quality concerns of preprints by first reviewing and summarizing
the established ways of quality control on preprint servers. We then assess
the population of preprints ultimately being published in a peer-reviewed
venue and evaluate the impact factors of these venues to further provide the
quantitative evidence of the preprints’ quality. At last, we review and
discuss the important role of preprints in recent public health emergencies
and how preprints help to speed up the dissemination of biomedical knowledge
in a global pandemic.

###  5.1. No peer review is not equivalent to no quality control

Preprints would not be valuable to the research community if the content were
substandard. The quality of preprints is one of the major concerns of preprint
consumers as there is no formal peer review process in place. However there
are other processes implemented at different servers to help the quality
control.

  * •

Author control: arXiv requires author registration and endorsement for new
users (arXiv, 2020); IMF working paper (IMF, 2020) and Keldysh Institute
Preprints (of Applied Mathematics, 2020) only include works from its own
employees or members.

  * •

Screening process: many preprint servers perform checks on completeness,
relevance, plagiarism, appropriate language and contents, as well as the
ethical and legal compliance (arXiv, 2019; Laboratory, 2020a; SSRN, 2020b;
Kirkham et al., 2020).

  * •

Extended content review: bioRxiv and medRxiv reject materials that might pose
a health or biosecurity risk (Laboratory, 2020a, b). Additionally preprint
platforms are taking extra cautions when the paper might have an immediate
impact on public health. ArXiv and ChemRxiv have enhanced their screening on
COVID-19 related articles and bioRxiv has stopped accepting articles "making
predictions about treatments for COVID-19 solely on the basis of computational
work" (Kwon, 2020).

The peer review process functions as an error and fraud capturing tool,
therefore peer-reviewed papers are perceived as quality guaranteed today.
Oftentimes, the value of a paper is linked to the prestige of the publication
venue, especially when it comes to grant application or career progression.
Nevertheless, peer review has its own limitations. Besides issues such as
sustainability, the peer review fraud, unfair reviews, and incompetent
reviewers (Alberts et al., 2008; Stahel and Moore, 2014; Fountain, 2014),
several studies have shown that peer reviews do not always detect errors, and
some peer-review-rejected research works are not necessarily low quality, some
are even Nobel Prize winning discoveries (Smith, 2006; Allison et al., 2016;
Balietti, 2016; Xiao, 2020).

###  5.2. Adequate amount of preprints do pass peer review

Put the criticisms of peer review aside, publishing at a well branded journal
or conference is still one of the most common ways to certify the quality of
an article. Evaluating 2.8 million preprints in MAG, we find that 41% of them
eventually are published at peer-reviewed venues, shown in Figure 9. Physics
and mathematics are the top two domains having most preprints published in
journals or conferences, 69% and 51% respectively. Comparably stable
publication rates are observed in all domains except biology, which jumps from
23% in 2009 to over 80% in 2017. Notice the new player bioRxiv has the highest
publication rate among all preprint servers shown in Figure 10. The
publication rate of bioRxiv in our result is consistent with the value
reported by Abdill and Blekhman (Abdill and Blekhman, 2019). BioRxiv is the
major contributor to the elevated preprint publication rate in biology in
recent years. MedRxiv is a new medical preprint server launched by the same
founder as bioRxiv, Cold Spring Harbor Laboratory, in June 2019. Within one
year, medRxiv already has 32% of papers published. This number is likely to
grow higher over time as some papers might be waiting to be published. The
three dashed lines in Figure 10 represent the publication rate for three most
used economics preprint servers. The NBER has the best rate among the three,
close to arXiv. SSRN comes second, but has less than half of the NBER
publication rate. RePEc have further more portions of preprints stay
unpublished, with less than 10% published.

Figure 9. Preprint publication rate (P-JC/P-all) by domain Figure 10. Preprint
publication rate (P-JC/P-all) by server

###  5.3. Preprint is published in venues with higher impact factor

With the cohort of published preprints (J-PC group), we further analyze their
destination journals and conferences. To check whether preprints tend to be
accepted by less influential venues, we use the 2019 two-year impact factor as
the impact indicator and plot the number of published preprints (P-JC) and the
number of all published papers (JC-all) with respect to their destination
impact factors in Figure 11 and 12. The 2019 impact factor is calculated as
the average citation received in 2019 for papers published in 2017 and 2018
151515https://en.wikipedia.org/wiki/Impact_factor using MAG data:

$IF_{2019}=\frac{Citations_{2019}}{Publication_{2018}+Publication_{2017}}$

Since the calculation of the impact factor is based on publications in 2017
and 2018, only papers published in these two years account for this
distribution analysis. There are 2,278,117 papers published in 2017 and 2018,
and among them 152,799 papers have a preprint version. Figure 11 illustrates
the paper distributions over venue impact factors, where solid blue line
denotes published preprints, i.e. P-JC papers published in 2017 and 2018, and
dashed black line represents all papers, i.e. JC-all papers published in 2017
and 2018. The distributions of the two groups are closely aligned with each
other and both are right skewed with similar means. The published preprints
are accepted by venues with higher impacts (impact factor 5.2 and 4.2 for P-JC
and JC-all groups respectively). When we take a closer look at different
domains, in physics, computer science, and biology, published preprints shift
more towards higher impact factors in Figure 12. To showcase the popular
preprint destination venues, we present twenty-four top journals and fourteen
top conferences with the highest numbers of preprint papers in Appendix B.
Four top journals from each domain are listed. Since there are not many
disciplines which regard peer-reviewed conferences as primary publishing
venues except for computer science, the top conferences listed are all in
computer science domain.

Figure 11. 17’-18’ published paper (JC-all) and published preprint (P-JC)
distribution over venue impact factors ( * calculated with MAG data) Figure
12. 17’-18’ published paper (JC-all) and published preprint (P-JC)
distribution over venue impact factors by domain ( * calculated with MAG data)

###  5.4. Preprint for disease outbreak response

In September 2015, a statement from a WHO Consultation urged a timely share of
pre-publication data during public health emergencies (Organization, 2015). In
the following year, Wellcome Trust, together with 30 other global health
bodies, called for rapid and open access for Zika research data (Trust, 2016).
Both statements emphasized the importance and necessity of sharing preliminary
data faster in such disease outbreaks. The COVID-19 pandemic that started in
December 2019 has infected more than 34.8 million people and caused 1 million
deaths around the globe by October 2020 according to the WHO situation reports
(Organization, 2020). Compared with other outbreaks in the 21st century, such
as SARS, H1N1 swine flu, MERS, Ebola, and Zika, scientists respond to COVID-19
much faster this time. The number of papers related to COVID-19, especially
preprint papers, has surpassed any other outbreak topics. Preprint has been
unprecedentedly utilized by researchers to share their studies on
pathogenesis, epidemiological characteristic and estimation, treatment,
vaccine, etc. Fraser et al. (Fraser et al., 2020) reported that 37.5% of
COVID-19 papers are hosted on preprint servers and the weekly new COVID-19
preprints is as many as 250. The top preprint articles have been reported in
more than 300 news and tweeted over 10 thousand times. The swift release
process of preprints not only benefits researchers to access the latest
findings earlier but also bridges the communication between laboratories and
the public more efficiently at this unique challenging time.

In conclusion, preprints have implemented several screening processes for the
quality control. 41% of preprints are published in peer reviewed journals or
conferences, and the destination venues are as influential as papers without a
preprint version, if not more. Preprint contributes to the research community
by facilitating rapid open access of research results which is highly valuable
during public health emergencies.

##  6\. Related Work

To the best of our knowledge, there are more than sixty online preprint
servers active today. Despite the absence of an overview which covers all
disciplines with large scale data, various evaluations have been carried out
with a specific subject area or server. The most frequently evaluated preprint
servers are arXiv and bioRxiv; popular evaluated subject areas are high energy
physics, biomedical, economics, and COVID-19. Gentil-Beccot et al. (Gentil-
Beccot et al., 2010) analyzed the citation and reading behavior in arXiv with
286,180 high energy physics journal articles and preprints which appeared from
1991 to 2007. Kelk and Devine (Kelk and Devine, 2012) did scienceographic
comparison between arXiv and viXra by sampling 20 physics papers appeared
between 2007 and 2012 from each of the two servers. A detailed overview of all
37,648 preprints uploaded to bioRxiv from 2013 to 2018 were performed by
Abdill and Belkham (Abdill and Blekhman, 2019). Serghio et al. (Serghiou et
al., 2018) assessed 776 bioRxiv preprints posted between 2013 and 2017 to
measure their Altmetric scores and citations. A similar work assessed 5,405
bioRxiv preprint was done by Fu and Hughey (Fu and Hughey, 2019). Baumann and
Wohlrabe (Baumann and Wohlrabe, 2020) investigated four working paper series
in RePEc to estimate the publish rate of economics working papers. Li et al.
(Li et al., 2015) compared the citations received between four repositories:
arXiv, RePEc, SSRN and PMC by sampling 384 papers. While the COVID-19 pandemic
is affecting the whole world, vast number of studies related to this disease
were carried out, including analysis on the preprints’ performance during the
pandemic (Fraser et al., 2020; Gianola et al., 2020; Biotechnology, 2020;
Kwon, 2020). Key characteristics and policies of 44 biomedical preprint
platforms were reviewed in (Kirkham et al., 2020). Besides data analysis,
there were also many discussions related to preprints, such as the reasons to
use and not to use preprints and the value to early career researchers
(Sarabipour et al., 2019; Kaiser, 2017; Mayo-Yánez, 2020; Bourne et al.,
2017).

##  7\. Conclusion

In this work, we provide a quantitative overview of preprints from all
disciplines in the past thirty years. Despite the increasing number of
preprint servers and effort from major research funders adopting policies to
promote preprints, the preprint has not been a popular form for most
scientists. Generally physics and mathematics are the two disciplines that
adopt preprint culture the best. They have long history of using online
preprint archives, produce the most number of preprints (50% of all
preprints), and have high preprints to all-paper ratios as well as highest
preprint publication rates. Biology has significant increase in recent years,
both in the preprints size and the publication rate.

One major advantage of preprint is its immediate online release in contrast to
the long peer-reviewed publishing process. This study shows that by using
preprints, individual researchers communicate their findings to the audiences
14 months ahead of peer-reviewed papers, and the work is cited five times more
compare to the articles only published in peer-reviewed venues without a
preprint version. Researchers in economics gain the most time and citation
advantage when they utilize preprints.

Besides the benefit to individual researchers, preprints also contribute to
the research community by providing a platform which shares valuable research
results in a timely fashion. The quality of preprint papers is assessed
through the discussion of existing quality control strategies, the preprint
publication rate, and the quality of their destination venues. The 41%
publication rate and the similar destination impact factor distribution
indicate the quality of preprints is on par with peer-reviewed papers. Last
but not least, we emphasize the important role that preprint plays in response
to disease outbreaks, and its swift publishing mechanism shows unbeatable
advantage in such time sensitive events.

In the past thirty years, our analysis has shown that the format of scholarly
communication has evolved together with the rapid Web technological
development. The scientific publishing landscape has been gradually shifted to
embrace the preprint culture. The ultimate goal of scholarly communication is
to disseminate the knowledge and new discoveries more rapidly, widely, and
cost-effectively. Our work provides quantitative evidence to demonstrate that
online preprint services could make us one step closer to this grand goal. We
have witnessed the fast adoption and flourish of the preprint culture in
biomedical domains in the past five years with the promotional efforts from
research organizations, publishers, and high profile researchers. Despite our
extensive analysis of the positive impacts of preprints, there are still
uncharacterized facets of the reserved attitudes towards preprints among many
researchers. How to better promote preprints in different communities will
need a closer look by domain experts and to integrate other data besides
scholarly publications themselves.

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##  Appendix A Preprint citation distribution

##  Appendix B Select journals and conferences

Journal | Domain | Paper count with preprint | Journal paper count | Preprint rate | Citation per paper
---|---|---|---|---|---
Physical Review D | Physics | 61,584 | 87,816 | 0.70 | 34
Physical Review B | Physics | 52,946 | 163,086 | 0.32 | 36
The Astrophysical Journal | Physics | 51,359 | 86,688 | 0.59 | 62
Physical Review Letters | Physics | 40,496 | 104,261 | 0.39 | 82
Journal of Algebra | Mathematics | 3,959 | 12,604 | 0.31 | 14
Advances in Mathematics | Mathematics | 3,468 | 6,795 | 0.51 | 25
J. Stat. Mech. Theory Exp. | Mathematics | 3,222 | 4,955 | 0.65 | 20
Trans Am Math Soc | Mathematics | 2,992 | 8,094 | 0.37 | 24
IEEE Trans Wirel Commun | CS | 1,450 | 9,541 | 0.15 | 47
IEEE Trans Commun | CS | 1,200 | 10,353 | 0.12 | 51
IEEE Trans. Inf. Theory | CS | 4,109 | 11,844 | 0.35 | 76
IEEE Trans. Signal Process. | CS | 1,666 | 15,349 | 0.11 | 61
J Bank Financ | Economics | 2,171 | 4,970 | 0.44 | 88
Am Econ Rev | Economics | 2,050 | 5,677 | 0.36 | 278
Journal of Financial Economics | Economics | 1,969 | 2,881 | 0.68 | 310
Management Science | Economics | 1,945 | 8,285 | 0.23 | 94
eLife | Biology | 2,044 | 18,958 | 0.11 | 15
PLoS Computational Biology | Biology | 1,339 | 8,183 | 0.16 | 43
Journal of Theoretical Biology | Biology | 722 | 9,986 | 0.07 | 35
PLoS Genetics | Biology | 672 | 9,479 | 0.07 | 63
Scientific Reports | Multidisciplinary | 5,947 | 119,208 | 0.05 | 15
PLoS ONE | Multidisciplinary | 4,551 | 257,798 | 0.02 | 24
Nature Communications | Multidisciplinary | 4,277 | 33,712 | 0.13 | 53
Nature | Multidisciplinary | 1,949 | 95,790 | 0.02 | 169
Table 4. Top journals with high number of papers having preprint version (1991-2020 Sep.) Conference | Sub-domain | Paper # with reprint | Conference paper # | Preprint rate | Citation per paper
---|---|---|---|---|---
CVPR | Computer Vision | 4,187 | 17,065 | 0.25 | 109
NeurIPS | Machine Learning | 3,273 | 10,367 | 0.32 | 92
ICML | Machine Learning | 2,793 | 8,798 | 0.32 | 73
AAAI | Artificial Intelligence | 2,650 | 17,874 | 0.15 | 25
ACL | Natural Language Processing | 2,008 | 9,906 | 0.20 | 49
ICLR | Machine Learning | 1,887 | 2,557 | 0.74 | 77
ICCV | Computer Vision | 1,607 | 8,180 | 0.20 | 88
EMNLP | Natural Language Processing | 903 | 4,339 | 0.21 | 64
KDD | Data Mining | 623 | 6,463 | 0.10 | 80
WWW | World Wide Web, Information Retrieval | 622 | 6,751 | 0.09 | 69
SIGIR | Information Retrieval | 350 | 5,903 | 0.06 | 56
WSDM | Data Mining, Information Retrieval | 141 | 1,145 | 0.12 | 58
Table 5. Top conferences with high number of papers having preprint version
(1991-2020 Sep.)