[3]Jinhyuk Yun

1]Institute for Social Data Science, Pohang University of Science and
Technology, Cheongam-ro 77, Pohang, 37673, Gyeongsangbukdo, Republic of Korea
2]Center for Global R&D Data Analysis, Korea Institute of Science and
Technology Information, Hoegi-ro 66, Dongdaemun-gu, 02456 Seoul, Republic of
Korea [3]School of AI Convergence, Soongsil University, Sangdo-ro 369,
Dongjak-gu, 06978 Seoul, Republic of Korea

# Auditing citation polarization during the COVID-19 pandemic

Taekho You  Jinseo Park  June Young Lee<EMAIL_ADDRESS>[  [  *

###### Abstract

The recent pandemic stimulated scientists to publish a significant amount of
research that created a surge of citations of COVID-19-related papers in a
short time, leading to an abrupt inflation of the journal impact factor (IF).
By auditing the complete set of COVID-19-related publications in the Web of
Science, we reveal here that COVID-19-related research worsened the
polarization of academic journals: the IF before the pandemic was proportional
to the increment of IF, which had the effect of increasing inequality while
retaining the journal rankings. We also found that the most highly cited
studies related to COVID-19 were published in prestigious journals at the
onset of the epidemic, independent of their innate importance or quality.
Through the present quantitative investigation, our findings caution against
the belief that quantitative metrics, particularly IF, can indicate the
significance of individual papers. Rather, such metrics reflect the social
attention given to a particular study.

##  1 Introduction

The recent pandemic has boosted COVID-19-related research, which has led to a
growing number of researchers publishing COVID-19-related papers [1]. During
the pandemic, as of 2021 more than 4% of published research papers focused on
COVID-19 [1]. The availability of COVID-19-related research has supported the
public to overcome the current pandemic.

The expansion of this new research field has had a substantial impact on the
scholarly publishing ecosystem. COVID-19-related papers received a large
number of citations in a short period, causing a dramatic shift in citation
counts. Specifically, some journals benefited from publishing COVID-19-related
research because it significantly increased their mean citation rate. As an
illustrative example, the Lancet more than doubled its impact factor (IF) from
$79.323$ to $202.731$, according to the 2021 Journal Citation Reports (JCR)
released in June 2022. It has been contended that COVID-19-related papers have
inflated the citation-based metrics; indeed, some journals have increased
their IF by more than tenfold [2].

Consequently, the long-lasting IF controversy has reemerged. Due to the heavy-
tailed nature of citation, which is sometimes referred to as the rich-get-
richer effect, many critics argue that IFs do not accurately reflect the
impact of scientific items because they rely solely upon mean citation counts
[3, 4]. In response, alternative metrics have been proposed [5, 6]. Moreover,
although the IF metric was designed to measure the performance of journals
rather than single papers [7], it is nevertheless frequently misunderstood to
reflect the quality of an individual paper [8]. The spreading of these
misunderstandings has increased unintended dynamics in the conduction and
evaluation of research [9, 10], even leading to cases of malpractice [11].

Resolving this IF controversy from COVID-19-related papers necessitates a deep
comprehension of citation dynamics in academia, such as the extent to which
COVID-19 publications affect journal IFs and who benefits more from publishing
COVID-19-related papers. The Matthew effect [12], also known as the rich-get-
richer effect, gives valuable insight into the accumulation of rewards in
academia [13, 14, 15, 16, 17]. Previous studies demonstrated that a little
variation in early stages leads to a substantial difference in the
productivity and citations of authors and journals in later stages [18, 14,
15]. Moreover, a paper is more likely to receive citations when published in a
prestigious journal that has a high IF [19]. Citation inequality results from
the widening gaps in return from such small, initial differences [20, 21, 17].
We believe that the emergence of the COVID-19 research field presents an
excellent opportunity to comprehend scholarly dynamics in response to external
societal influence.

In this study, we quantitatively exhibit the impact of COVID-19-related papers
on the citation ecosystem to aid in resolving the long-lasting debates on the
IF metric. For this purpose, we investigate the changes in IF by the
publication of COVID-19-related papers considering prior journal IF. We find
that COVID-19-related papers received more citations than other papers, and we
also show that although most of the citations originated from other
COVID-19-related papers, the degree of benefit to the journals differs by the
prestige of the journals reflected in their prior IFs. We reveal that the
number of COVID-19-related papers and the extent of the increase in journal IF
are nearly uncorrelated, while the IFs of prestigious journals with high IFs
increased more than those of low-IF journals. Lastly, we find that the
majority of highly cited COVID-19 publications were published during the
earliest stages of the pandemic, selected by prestige journals. Taken
together, the results demonstrate that the benefits of publishing
COVID-19-related research were granted mainly to the prestige journals, which
may aggravate citation inequality in academia.

##  2 Results

###  2.1 Citation homophily between COVID-19-related papers

Figure 1: Difference in citation distribution between COVID-19-related and
non-COVID-19-related papers. A Citation distribution of COVID-19-related and
non-COVID-19-related papers that contribute to the annual IF calculation. For
example, the distribution of citations in 2021 includes citations received for
papers published in 2019 and 2020 from the papers published in 2021. A
distribution of the yearly citation pattern is displayed in Fig. S2. B
Citation origin of COVID-19-related papers. We display both the percentage of
citations received from other COVID-19-related papers and references citing
other COVID-19 publications.

During the pandemic, COVID-19-related papers have increased their share in
academia. In 2019, only 350 papers (0.013%) were related to COVID-19, many of
which were mainly focused on other coronaviruses, based on our search query
(see Methods for step-by-step details on gathering COVID-19-related papers).
As the virus spread, their share increased to 89,112 (2.004%) and 162,256
(4.194%) in 2020 and 2021, respectively. Moreover, COVID-19-related research
occupied a major fraction of all citations across academia. Such papers
published in 2020 received 2,654,613 citations until the end of 2021, which is
13.8% among the total 19,203,421 citations in 2020 and 2021. But not only
gaining a high share, COVID-19-related publications also received immediate
citations: those published in 2021 received 787,009 citations out of the total
6,457,473 citations in 2021 (12.2%). This same trend even extended down to the
monthly citation level, as displayed in Fig. S1. After publication, 31.8% of
the citations of COVID-19-related papers arose within 6 months, while 22.2%
did so for non-COVID-19 papers. Compared to the statistics indicating that
COVID-19-related papers produced just 4.1% and 6.9% of references within the
same time period (2020 and 2021, respectively), this proportion of received
citations is high.

The increased attention given to COVID-19 research resulted in a citation
distribution with a longer tail than other research. The two-year citation
distribution shows that COVID-19-related papers received more citations than
non-COVID-19-related papers in a given year (see Fig. 1A for the merged
distribution along with Fig. S2 displaying separated distributions). When we
assume that the citation distribution follows a simple power law ($y\sim
x^{k}$), the COVID-19-related papers show $k\simeq 1.9$ and $k\simeq 2.7$ for
2020 and 2021, respectively (see Methods for the detailed computation), while
non-COVID-19-related papers have an exponent of $3.2$ and $3.3$ for 2020 and
2021, respectively. The lower exponents indicate that the proportion of
COVID-19-related papers with extremely high citation counts is greater than
that of non-COVID-19-related papers. Consequently, COVID-19-related papers
also received more citations on average. COVID-19-related research received an
average of $22.6$ (2020) and $21.8$ (2021) citations, while non-
COVID-19-related papers received $4.9$ (2020) and $5.2$ (2021) citations. This
result is consistent with a previous observation using SCOPUS [1].

We also find a homophily of citations, namely that the high citation counts of
COVID-19-related papers are attributable to other COVID-19-related papers. We
observed a high rate of citation exchange between publications related to
COVID-19, in which more than 40% of the references in these papers cite other
COVID-19-related papers, excluding 2019 (Fig. 1B). The homophily is much
stronger when we consider the received citations, where 90.9% of citations to
COVID-19-related papers published in 2020 were from other COVID-19-related
papers. This finding indicates that the rising amount of COVID-19-related
research in 2020 and 2021 resulted in a number of such papers receiving a
substantial number of citations.

###  2.2 Contribution of COVID-19-related research to IF inflation

Figure 2: Surplus impact factor (IF) by COVID-19-related publications. A
Journal impact factor increase by publishing COVID-19-related papers, where
the simple superlinear growth $y\sim x^{1.7}$ can characterize the growth
pattern (dotted line). Here, we applied a simple linear regression method to
the logarithm of the values of interest to estimate the power-law scaling
relationship between the IF and its surplus by COVID-19-related papers,
assuming a simple power-law scaling of $y=Cx^{k}$. B Increase in IF per
COVID-19-related paper in proportion to the number of COVID-19-related papers
published in journals. The increase is calculated by dividing the absolute
difference in IF between papers including and excluding COVID-19 by the number
of COVID-19-related papers in the journals. In both A and B, the red dots
represent the average value of surplus IF and the error bars show the standard
deviation in log-scale.

Several highly cited COVID-19-related studies may bolster the publishing
journals’ IF. To quantify this, we calculate the IF in terms of the existence
and number of COVID-19-related papers (see Methods for IF calculation). We
measure two different types of IFs and compare them to estimate the advantage
of publishing COVID-19-related papers: IF excluding COVID-19-related papers
and IF including them. We observe that only 763 journals (16%) among those
publishing one or more COVID-19-related papers in 2019 and 2020 dropped in IF
in 2021, while the other 4,004 journals (84%) enhanced their IFs through the
publication of COVID-19-related papers in the same period. For the former,
even though the journals decreased in IF by publishing COVID-19-related
papers, the amount of decrease was limited. Only one of these 763 journals
(CA-A CANCER JOURNAL FOR CLINICIANS) dropped in IF by more than $1$ (Fig. S3).

Individual scientists have a greater tendency to cite widespread, popular
journals than less popular journals due to psychological, sociological, and
economic factors, leading to the rich-get-richer phenomenon of citation [22].
For the COVID-19-related papers, we find that the surplus IF is proportional
to the prior IF (Fig. 2). High correlation exists between IF and its surplus
(Pearson $r=0.670$, Fig. 2A), and their relationship is even superlinear
($y=x^{1.7}$). This pattern is also verified when we consider the relative
advantage of IFs by dividing the surplus IF by the prior journal IF, which
also shows a positive correlation (Fig. S4).

However, publishing numerous papers on COVID-19 did not necessarily increase
the journal IF. Instead, as more COVID-19-related papers were published, the
gain in IF per COVID-19-related paper decreased (Fig. 2B). Journals that
published only one COVID-19-related paper in 2019 and 2020 increased their IF
by $0.12$ on average, whereas journals that published over $500$ papers in the
same period increased their IF by only $0.0009$. For example, the Lancet, the
journal with the highest IF in JCR 2021, doubled its IF (from 93.04 to 189.25)
while publishing only $46$ COVID-19-related papers (9.4% of all citable items)
in 2019 and 2020. To take one more extreme example, one journal that published
only one COVID-19-related paper in 2019 and 2020 increased its IF by $37$,
whereas the journal that published the largest number of COVID-19-related
papers (730 papers) improved its IF by only $1.02$. In other words, while a
single well-chosen paper published during the pandemic could have potentially
resulted in a significant increase in IF, publishing a large number of
COVID-19-related papers did not provide many benefits. Although allocating
more shares to COVID-19-related papers correlates positively with the rise in
the IFs of journals, the correlation is slight (Pearson $r=0.110$; see Fig.
S5).

To confirm that COVID-19 research has legitimately increased journal IFs, we
examine the correlation between IFs across years taking into account the
existence of COVID-19-related papers. When excluding COVID-19-related papers,
the correlation between two consecutive years (Pearson $r=0.957$ between 2019
and 2020 and Pearson $r=0.925$ between 2020 and 2021) is significantly high.
The correlation between the IF in 2021 excluding COVID-19-related papers and
the IF in 2020 with COVID-19-related papers is $r=0.926$. Thus, the overall
trend of journal IF without papers related to COVID-19 did not change
significantly, and this high correlation suggests the existence of a linear
relationship between the IFs for two consecutive years. Incorporating
COVID-19-related papers, however, reduces the correlation between the IFs for
2020 and 2021 (Pearson $r=0.850$). Note that we also observe a lower
correlation between the IF for 2021 with COVID-19-related papers and the IF
for 2020 without COVID-19-related papers (Pearson $r=0.849$), indicating non-
linear relationships between the IFs of the two consecutive years. Given that
journals with a higher prior IF received a greater increase in IF from
COVID-19-related papers (Fig. 2A), the publication of COVID-19 research may
contribute to the polarization of journal IFs.

###  2.3 The Matthew effect of IF polarization during the pandemic

Figure 3: Relative ratio of surplus IF from publishing COVID-19-related papers
by the 2021 journal rankings for JCR categories. The ratio was calculated by
subtracting the IF for 2021 excluding COVID-19-related papers from the IF for
2021 including COVID-19-related papers and then dividing this value by the
prior IF. The dotted line indicates the average surplus IF from publishing
COVID-19-related research (15.2%). Here, the boxes represent the quartiles of
the dataset except for points determined to be outliers.

In the preceding sections, we demonstrated that the publication of
COVID-19-related research had a positive correlation with journal IFs, while
the amount of increment had a strong correlation with the prior IFs of the
journals (Fig. 2). One may wonder how much the overall journal landscape,
i.e., the journal rankings, has changed due to the surplus IFs, or conversely,
the magnitude of the change in IF based on the journal ranking [23]. To
demonstrate the influence of COVID-19-related publications on the landscape of
JCR rankings, we compare the ratio of surplus IF in 2021 considering the
journal rank in their research categories. On average, the publication of
COVID-19-related papers increased the journal IF by 15.2% (dotted line in Fig.
3). The IFs of the top 10% prestige journals increased by 39.4%, while the IFs
of the bottom 10% journals increased by only 9.6% on average. Most journals
increased their IF by less than the average increase (15.2%) except for the
top 20%. On average, higher-ranked journals gained more citations, and this
trend is robust across all categories (Table S1).

The majority of journals with a significant increase in IF due to
COVID-19-related publications were already high-IF journals. Among all
journals, 132 increased their IF by greater than twofold. 55.3% of these 132
journals are in the top 10% of at least one of their research categories. Only
five journals fall within the bottom 10%. In terms of research category, 102
of the 132 journals (77.3%) are classified into Clinical Medicine since the
majority of COVID-19-related papers (70.9%) were published in this category.

This proportion of highly cited articles in prestigious journals has the
potential to exacerbate citation polarization. Indeed, we found that more
COVID-19-related articles were published in prestigious journals with a high
IF than in other journals (Fig. 4A). While the top 10% ranked journals
published 26.3% (51,976) of all COVID-19-related papers from 2019 to 2021, the
bottom 90% to 100% ranked journals published only 3.5% (6,977) in the same
period. We also observe that the share of COVID-19-related papers decreases as
the journal ranking falls (Fig. 4A). Moreover, the proportion of highly cited
papers exacerbates the disparity. Eighty-four percent (86) of the 102 papers
with over 1000 citations were published by the top 10% journals, while
journals ranked 10 to 20 % published only eight of these studies. No papers
with over 1000 citations were published in the bottom 50% journals.

Citation is a stochastic multiplicative process, whereby papers with a higher
citation count are more likely to receive additional citations. Even with
similar content between papers, those published in a more prominent location
are more likely to be cited [19]. In addition, earlier works may receive more
citations because citations are cumulative. Indeed, we find that the majority
of highly cited COVID-19-related papers were published during the early stage
of the pandemic (early 2020), as shown in Fig. 4B. The number of
COVID-19-related publications gradually increased as the pandemic progressed
(see the blue line in Fig. 4B). Despite this, papers with higher numbers of
citations were generally published earlier. In conjunction with the finding
that highly cited studies were likely to be published in prestigious journals,
we may conclude that COVID-19-related studies were originally introduced in
high IF journals, and that lower IF journals then cited the previous
publications from high IF journals.

This growing pattern of citations could worsen the polarization of academic
journals. Publication of COVID-19-related research gave a significantly
greater benefit to journals with higher IFs than to those with lower IFs.
Consequently, the relative position (rank) of the majority of journals shows
only minor changes, although the overall IF of all journals tended to increase
during the pandemic. Only 39.0% of journals that published COVID-19-related
papers moved to a higher rank by including COVID-19-related research in one of
their subject categories; among them, only 51.8% changed their IF quantile to
a higher one (Fig. S6). The other 61.0% of journals maintained or decreased
their ranking. In addition, significant increases or decreases in the ranks of
journals were rarely observed (Fig. S6). The majority (90%) of the top 10%
ranked journals maintained their position regardless of COVID-19 research,
while the other 10% fell into the 10% to 20% group.

In summary, i) the IF of journals increased overall by publishing
COVID-19-related research, ii) journals with higher IFs received greater
benefits by publishing COVID-19-related research, and iii) the relative ranks
of journals did not change significantly from publishing COVID-19-related
research. These findings lead to an interesting question: Did the publication
of COVID-19-related research actually increase the polarization of journals?
To answer this, we applied the Gini coefficient [24], a well-known measure of
income inequality, to the distribution of journal IFs. In our investigation,
the Gini coefficient measures the distribution of citations across journals
within a JCR category, ranging from $0$ for the lowest heterogeneity (when all
journals receive the same average number of citations) to $1$ for the highest
heterogeneity (when only a single journal receives all citations). The trend
illustrated by the difference in the Gini coefficient as a function of the
number of COVID-19-related papers (see Figs. 4 and S7) implies that the
disparity in the number of citations between journals increases as the number
of COVID-19-related papers published increases. In conclusion, based on the
present snapshot of the Web of Science (WOS) dataset, we found that the
general pattern of heterogeneity, or polarization, among journals rises as the
number of published COVID-19-related papers increases.

Figure 4: Distribution of COVID-19-related papers and their disparities. A
Distribution of COVID-19-related research by journal ranking. As the number of
citations increases, the likelihood of papers belonging to high-IF journals
increases. 26.4% of all papers were published in the top 10% ranked journals,
whereas 85.3% of papers with more than 1000 citations were published in the
top 10% ranked journals. B Distribution of COVID-19-related papers by
publication date. From the beginning of the pandemic to mid-2020, the number
of papers increased. Approximately the same number of papers were published
between then and the end of 2021. Most of the highly cited papers ($>1000$
citations) were published in early 2020. C Plot of the Gini coefficient of the
IF distribution by JCR category. Each dot represents a JCR category. The Gini
coefficient is computed using the IF distribution of journals in a particular
category including and excluding COVID-19-related papers. Blue (orange) dots
indicate an increase (decrease) in the Gini coefficient by publishing
COVID-19-related research. The size of the dots is proportional to the number
of COVID-19-related studies published in the category.

##  3 Discussion

From the outset of the global COVID-19 pandemic, many scholars pursued the
topic and published a massive number of studies in an unprecedentedly short
period. We discovered a trend that, as a result of the intensive publication,
COVID-19-related papers acquired more citations than papers in other domains,
which reflects its considerable attention in academia. We uncovered two
significant consequences that may have led to a more severe polarization of
journals in terms of citations. First, 84% of journals that published
COVID-19-related papers in 2019 and 2020 increased their impact factors.
Second, prestigious journals were more likely to publish highly cited
COVID-19-related papers than other journals (Fig. 3).

Nonetheless, we demonstrated that publishing a large number of
COVID-19-related papers did not immediately boost a journal’s IF. Increasing
numbers of COVID-19-related papers published in a journal tended to diminish
the citation impact of a single COVID-19-related article. In addition, we
found that prestigious journals with a high prior IF gained more benefit
(increased IF) from publishing COVID-19-related research, and also that the
publications receiving the highest number of citations were predominantly
published in prestige journals during the early stages of the pandemic. Given
that not all COVID-19-related publications increased their journal’s IF, one
may assume that prestige journals simply have accepted and published more
significant research. However, considering that some papers published in
prestige journals were ultimately retracted [25, 26], the high number of
citations given to these journals is not only based on the significance of the
works but also based in part on the visibility of these journals, which can
worsen the polarization of academic publishing (Fig. 4).

As we could not explicitly assess the quality of each paper due to the scale
of the dataset, it is unclear which of the two aforementioned characteristics
(quality or visibility) has a larger impact on the current disparity in
benefit from publishing COVID-19-related research. We believe that a more in-
depth investigation of the relationship between research quality (or
significance) and citations may be necessary to increase the impact of our
findings. Also, a more detailed understanding of such correlation should form
the basis of explaining complex citation dynamics, yet we leave this for
future research.

Despite its limitations, this study can provide important insights into
citation dynamics and its effects on global events. Because of the rich-get-
richer nature of citations, papers published in prestigious journals tend to
receive more citations. As the relative ranking of the journals did not change
significantly despite the increase in the overall IFs of journals publishing
COVID-19-related research, fluctuations in IF may not well reflect the actual
impact of academic publications. This effect predominantly benefited well-
established journals, while other journals did not experience benefits to the
same extent (Fig. 4). Our research indicates that IFs are vulnerable to
external events. The majority of the recent IF changes are attributable to
citations of COVID-19-related publications; consequently, after the pandemic
is over, the majority of the journals may revert to their pre-pandemic IF
levels. It is challenging to evaluate academic journals or other participants
(researchers, institutions, etc.) using basic statistics because doing so
reflects only a portion of actual scientific achievements. Therefore, the
simplified metrics employed by some governments [27] should be accompanied by
a comprehensive and qualitative analysis of journals and individual papers for
assessment.

The use of quantitative indicators such as the IF metric has been under
debate. The San Francisco Declaration on Research Assessment (DORA), which
serves as the starting point for these discussions, explicitly states that the
use of journal-based measures (such as IFs) should be avoided to act as a
proxy for the quality of individual research publications, to evaluate the
contributions of an individual scientist, or to make hiring, promotion, or
funding choices. In practice, however, many funders and institutions employ
journal-based measures or the number of citations as markers for evaluation
rather than assessing the quality of individual papers. The polarization of
citations observed in this study demonstrates the inherent hazard of such
indicators. The IF metric is not a stable index against external shocks; it
might fluctuate temporarily and then revert following external factors. Along
with the other well-known limitations of IF, such as skewed citation
distributions within journals [28, 29], the vulnerability of the IF metric as
we found here indicates that it is increasingly inappropriate to consider
journal IF as a proxy for an individual paper’s quality.

During the current pandemic, the rapid release of COVID-19-related works
resulted in less-qualified academic outputs to the public, leading to the
retraction of many publications [30, 31]. Unfortunately, this issue happened
not only in journals with a reputation for a weak review process or low
publishing difficulty but also in prestigious journals that are widely
respected. Worse still, these retracted works earned a substantial number of
citations and extensive media attention [32]. The general public may assume
that papers published in academic journals are trustworthy and may likewise
trust secondary sources such as scientific news reporting the results of
academic findings. In the current context of appraising science and
technology, there is a chance that content published in journals with strong
indicators will be considered more reputable. Scientists must inform the
public that citation measures and journals are not equivalent to the quality
of individual research publications. In other words, the number of citations
should not be the defining characteristic of quality research. The
contemporary ecosystem of research and technology is seemingly supported by
scientists’ mutual trust and goodwill, and the public may view the scientific
community’s findings with a similar level of confidence. Combined with the
stability issue of the IF metric identified in this study, shouldn’t the
current practice of over-reliance on citation indices be discontinued so as
not to break this chain of trust? For this reason, we believe that responsible
action based on actual societal influence is essential for all members of
academia, as opposed to merely producing popular research to boost citation
impact and one’s professional reputation.

## Methods

### Data

We used publications and citation data from the XML dump of the Web of Science
Core Collection, which is dated back to 2017 and updated until the 26th week
of 2022. The data includes complete copies of Science Citation Index Expanded
(SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation
Index (AHCI), along with the Emerging Sources Citation Index (ESCI). The data
comprises 16,957,120 articles, 82,317 journals, and 116,086,223 references
retrieved from papers published between 2017 and 2022.

### COVID-19-related publications

COVID-19-related papers were retrieved from the Web of Science database (WOS,
urlhttps://www.webofscience.com/) using the following search queries provided
by Dimensions (https://www.dimensions.ai/covid19/): "2019-nCoV" OR "COVID-19"
OR “SARS-CoV-2” OR "HCoV-2019" OR "hcov" OR "NCOVID-19" OR "severe acute
respiratory syndrome coronavirus 2" OR "severe acute respiratory syndrome
corona virus 2" OR “coronavirus disease 2019” OR (("coronavirus" OR "corona
virus") AND (Wuhan OR China OR novel)). We limited the publications from 2019.
A total of $251,718$ COVID-19-related papers were collected on 4 July 2022. We
consider all other papers in the WOS that were not retrieved from the above
searching process as non-COVID-19-related papers.

### Estimation of the power-law exponent

In this study, the power-law exponents of the citation distribution in Fig. 1A
were estimated using the Python package named powerlaw [33]. Although all the
citation distributions in Fig. 1A seem to be heavy-tailed distributions, which
are commonly referred to as the power law, we verified that the distributions
sincerely follow the power law via comparison with alternative distributions
(e.g., log-normal or exponential). In the comparison with the exponential
distribution, all distributions were found to be more likely to be power-law
distributions rather than exponential ($p<0.001$). However, comparison with
the log-normal distribution was unclear. Only non-COVID-19-related papers
published in 2021 better fit the power-law distribution in a statistically
significant manner, while the other three were inconclusive ($p$ varied
$0.48$–$0.60$). In this study, we estimated the power-law exponent with the
assumption of a simple power law ($y\sim x^{k}$) regardless of the best fit
distribution, as we were more interested in comparing the thickness of the
tails than in determining the exact exponents.

### Reproduction of the journal impact factors

Although we extracted the total number of publications in the WOS with a
complete copy of the WOS provided by Clarivate, minor differences can be
presented mainly because the WOS does not report detailed methods to filter
the dataset, e.g., dump dates and the coverage of citable items. Thus, to
reproduce and estimate the journal impact factors (IFs), we followed the
method used for the JCR impact factor [2] but with an in-house XML copy of the
Web of Science, as follows:

$\mbox{IF}=\frac{\mbox{citations received by items published in the past 2
years}}{\mbox{number of citable items published in the past 2 years}}.$ (1)

We limited the citable items to those belonging to the journals indexed in
SCI-Expanded, SSCI, and A&HCI. We also considered as citable items only
articles, review papers, and proceedings papers in terms of publication type;
however, publication types were not considered when computing the number of
citations.

Note that, as of 2020, Clarivate Inc. now considers early access publications
as regular publications and includes them in the calculation of IF. For
instance, if an article is published as early access in 2020 and officially
published in 2021, then the article is counted as a citable item published in
2020, taking into account the references as the citations occurred in 2020.
The article is not considered in 2021.

With this procedure, we successfully reproduced IF scores highly correlated
with the IFs provided by Clarivate JCR (Pearson $r=0.99$; see Fig. S8). In
this study, we refer to the value computed from Eq. 1 as IF instead of the
impact factor provided by JCR unless otherwise specified. When computing the
IFs excluding COVID-19-related papers, we counted out the COVID-19-related
citable items and their references from the denominator and numerator in Eq.
1, respectively.

## Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT),
Republic of Korea, under the Innovative Human Resource Development for Local
Intellectualization support program (IITP-2022-RS-2022-00156360) supervised by
the IITP (Institute for Information & Communications Technology Planning &
Evaluation). This work was also supported by the National Research Foundation
of Korea (NRF) funded by the Korean government (grant No. NRF-2022R1C1C2004277
(T.Y.) and 2022R1A2C1091324 (J.Y.)). The Korea Institute of Science and
Technology Information (KISTI) also supported this research with grant No.
K-23-L03-C01 (J.Y.L., J.P.) and by providing KREONET, a high-speed Internet
connection. The funders had no role in the study design, data collection and
analysis, decision to publish, or preparation of the manuscript.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

## References

  * [1] Ioannidis, J. P., Bendavid, E., Salholz-Hillel, M., Boyack, K. W. & Baas, J. Massive covidization of research citations and the citation elite. Proceedings of the National Academy of Sciences of the United States of America 119(28):e2204074,119 (2022).
  * [2] McVeigh, M. Journal citation reports 2022: A preview. (2022). https://clarivate.com/blog/journal-citation-reports-2022-a-preview/
  * [3] Pendlebury, D. A. The use and misuse of journal metrics and other citation indicators. Archivum immunologiae et therapiae experimentalis 57(1):1–11 (2009).
  * [4] Larivière, V., Kiermer, V., MacCallum, C. J., McNutt, M., Patterson, M., Pulverer, B., Swaminathan, S., Taylor, S. & Curry, S. A simple proposal for the publication of journal citation distributions. Preprint at https://www.biorxiv.org/content/10.1101/062109v2.abstract (2016).
  * [5] Moed, H. F. Measuring contextual citation impact of scientific journals. Journal of Informetrics 4(3):265–277 (2010).
  * [6] Bradshaw, C. J. & Brook., B. W. How to rank journals. PLoS One 11(3):e0149,852 (2016).
  * [7] Garfield, E. Citation analysis as a tool in journal evaluation: Journals can be ranked by frequency and impact of citations for science policy studies. Science 178(4060):471–479 (1972).
  * [8] Lozano, G. A., Larivière, V. & Gingras, Y. The weakening relationship between the impact factor and papers’ citations in the digital age. Journal of the American Society for Information Science and Technology 63(11):2140–2145 (2012).
  * [9] Calcagno, V., Demoinet, E., Gollner, K., Guidi, L., Ruths, D. & de Mazancourt, C. Flows of research manuscripts among scientific journals reveal hidden submission patterns. Science 338(6110):1065–1069 (2012).
  * [10] Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P. & Stirling, A. How journal rankings can suppress interdisciplinary research: A comparison between innovation studies and business & management. Research Policy 41(7):1262–1282 (2012).
  * [11] You, T., Park. J., Lee, J. Y., Yun, J. & Jung, W.-S. Disturbance of questionable publishing to academia. Journal of Informetrics 16(2):101,294 (2022).
  * [12] Merton, R. K.: The matthew effect in science: The reward and communication systems of science are considered. Science 159(3810):56–63 (1968).
  * [13] Perc, M. The matthew effect in empirical data. Journal of The Royal Society Interface 11(98):20140,378 (2014).
  * [14] Bol, T., de Vaan, M. & van de Rijt, A. The matthew effect in science funding. Proceedings of the National Academy of Sciences of the United States of America 115(19):4887–4890 (2018).
  * [15] Nielsen, M. W. & Andersen, J.P. Global citation inequality is on the rise. Proceedings of the National Academy of Sciences of the United States of America 118(7):e2012208,118 (2021).
  * [16] Huang, J., Gates, A. J., Sinatra, R. & Barabási, A. L. Historical comparison of gender inequality in scientific careers across countries and disciplines. Proceedings of the National Academy of Sciences of the United States of America 117(9):4609–4616 (2020).
  * [17] Lawson, C., Geuna, A., Finardi, U.: The funding-productivity-gender nexus in science, a multistage analysis. Research Policy 50(3):104,182 (2021).
  * [18] Petersen, A. M., Jung, W.-S., Yang, J.-S. & Stanley, H.E. Quantitative and empirical demonstration of the matthew effect in a study of career longevity. Proceedings of the National Academy of Sciences of the United States of America 108(1):18–23 (2011).
  * [19] Larivière, V. & Gingras, Y. The impact factor’s matthew effect: A natural experiment in bibliometrics. Journal of the American Society for Information Science and Technology 61(2):424–427 (2010).
  * [20] Allison, P. D., Long, J. S. & Krauze, T. K., Cumulative advantage and inequality in science. American Sociological Review 615–62 (1982).
  * [21] Van de Rijt, A., Kang, S. M., Restivo, M. & Patil, A. Field experiments of success-breeds-success dynamics. Proceedings of the National Academy of Sciences of the United States of America 111(19):6934–6939 (2014).
  * [22] Wang, J. Unpacking the matthew effect in citations. Journal of Informetrics 8(2):329–339 (2014).
  * [23] Quaderi, N. Journal citation reports 2022: Covid-19 research continues to drive increased citation impact. Clarivate (2022). https://clarivate.com/blog/journal-citation-reports-2022-covid-19-research-continues-to-drive-increased-citation-impact/
  * [24] Gini, C Variabilità e mutabilità: contributo allo studio delle distribuzioni e delle relazioni statistiche.[Fasc. I.]. Tipogr. di P. Cuppini (1912)
  * [25] Mehra, M. R., Ruschitzka, F. & Patel, A. N. Retraction—hydroxychloroquine or chloroquine with or without a macrolide for treatment of covid-19: a multinational registry analysis (2020).
  * [26] Mehra, M. R., Desai, S. S., Kuy, S., Henry, T. D. & Patel, A. N. Retraction: cardiovascular disease, drug therapy, and mortality in covid-19. N ENGL J MED. doi:10.1056/nejmoa2007621. (2020).
  * [27] Van Dalen, H. P. & Henkens, K. Intended and unintended consequences of a publish-or-perish culture: A worldwide survey. Journal of the American Society for Information Science and Technology 63(7):1282–1293 (2012).
  * [28] Van Leeuwen, T. N. & Moed, H.F. Characteristics of journal impact factors: The effects of uncitedness and citation distribution on the understanding of journal impact factors. Scientometrics 63(2):357–371 (2005).
  * [29] Bornmann, L. & Leydesdorff, L. Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on web of science data. Journal of Informetrics 11(1):164–175 (2017).
  * [30] Quinn, T. J., Burton, J. K., Carter, B., Cooper, N., Dwan, K., Field, R., Freeman, S. C., Geue, C., Hsieh, P., McGill, K., Nevill, C. R., Rana, D., Sutton, A., Rowan, M. T. & Xin, Y. Following the science? comparison of methodological and reporting quality of covid-19 and other research from the first wave of the pandemic. BMC Medicine 19(1):1–10 (2021).
  * [31] El-Menyar, A., Mekkodathil, A., Asim, M., Consunji, R., Rizoli, S., Abdel-Aziz Bahey, A. & Al-Thani, H. Publications and retracted articles of covid-19 pharmacotherapy-related research: A systematic review. Science Progress 104(2):00368504211016,936 (2021).
  * [32] Khan, H., Gupta, P., Zimba, O. & Gupta, L. Bibliometric and altmetric analysis of retracted articles on covid-19. Journal of Korean Medical Science 37(6) (2022).
  * [33] Alstott, J., Bullmore, E. & Plenz., D. powerlaw: a python package for analysis of heavy-tailed distributions. PloS One 9(1):e85,777 (2014).

Supplementary Information for

Auditing citation polarization during the COVID-19 pandemic

Taekho You, Jinseo Park, June Young Lee, Jinhyuk Yun∗

∗Corresponding author. Email<EMAIL_ADDRESS>
This PDF file includes:

Supplementary table S1
Figures. S1 to S8

Table S1: Relative ratio of surplus impact factor (IF) from publishing COVID-19-related papers by journal category classified by JCR. The bold numbers represent the highest ratio of surplus IF in each category.  Journal Category | Top 10% | 10–20% | 20–30% | 30–40% | 40–50% | 50–60% | 60–70% | 70–80% | 80–90% | 90–100%
---|---|---|---|---|---|---|---|---|---|---
Agricultural Science | 1.664 | 1.098 | 1.111 | 1.040 | 1.002 | 1.029 | 1.078 | 1.051 | 1.039 | 1.062
Arts & Humanities, Interdisciplinary | 1.209 | 1.399 | 1.146 | 1.046 | 0.999 | 1.086 | 1.165 | 0.995 | 0.990 | 0.982
Biology & Biochemistry | 1.286 | 1.103 | 1.101 | 1.090 | 1.064 | 1.067 | 1.115 | 1.062 | 1.066 | 1.081
Chemistry | 1.104 | 1.043 | 1.040 | 1.042 | 1.038 | 1.055 | 1.063 | 1.026 | 1.046 | 1.109
Clinical Medicine | 1.508 | 1.251 | 1.189 | 1.162 | 1.152 | 1.127 | 1.142 | 1.106 | 1.102 | 1.111
Computer Science | 1.112 | 1.118 | 1.125 | 1.079 | 1.061 | 1.038 | 1.090 | 1.041 | 1.071 | 1.053
Economics & Business | 1.423 | 1.161 | 1.180 | 1.115 | 1.105 | 1.088 | 1.103 | 1.075 | 1.078 | 1.024
Engineering | 1.044 | 1.109 | 1.041 | 1.048 | 1.029 | 1.053 | 1.027 | 1.026 | 1.037 | 1.146
Environment/Ecology | 1.388 | 1.209 | 1.201 | 1.135 | 1.129 | 1.112 | 1.126 | 1.106 | 1.074 | 1.121
Geosciences | 1.024 | 1.012 | 1.037 | 1.018 | 1.018 | 1.002 | 1.021 | 1.033 | 1.041 | 1.001
History & Archaeology | 1.162 | 1.165 | 1.121 | 1.072 | 1.114 | 0.994 | 1.069 | 1.003 | 0.988 | 0.969
Literature & Language | 1.106 | 1.175 | 1.111 | 1.205 | 1.026 | 1.136 | 1.110 | 1.050 | 1.019 | 1.045
Material Science | 1.017 | 1.018 | 1.010 | 1.024 | 1.009 | 1.010 | 1.008 | 1.011 | 1.024 | 1.054
Mathematics | 1.053 | 1.046 | 1.139 | 1.039 | 1.052 | 1.062 | 1.061 | 1.016 | 1.080 | 1.013
Multidisciplinary | 1.102 | 1.086 | 1.081 | 1.081 | 1.056 | 1.066 | 1.057 | 1.048 | 1.061 | 1.030
Philosophy & Religion | 1.232 | 1.219 | 1.221 | 1.155 | 1.067 | 1.141 | 1.131 | 1.010 | 1.066 | 1.046
Physics | 1.058 | 1.057 | 1.039 | 1.025 | 1.018 | 1.030 | 1.030 | 1.028 | 1.014 | 1.073
Plant & Animal Science | 1.102 | 1.046 | 1.055 | 1.020 | 1.068 | 1.063 | 1.096 | 1.022 | 1.039 | 1.027
Psychiatry/Psychology | 1.666 | 1.341 | 1.183 | 1.153 | 1.121 | 1.227 | 1.137 | 1.176 | 1.137 | 1.136
Social Sciences, General | 1.389 | 1.218 | 1.182 | 1.147 | 1.130 | 1.073 | 1.087 | 1.070 | 1.088 | 1.033
Visual & Performing Arts | 1.088 | 1.095 | 1.069 | 1.109 | 1.049 | 1.138 | 1.038 | 1.041 | 1.025 | 1.115

Figure S1: Probability density function of the citation time difference
between the publication and the citation month of the papers. For the plot,
the COVID-19-related and non-COVID-19-related papers published in 2020 and
2021 were used. Figure S2: Citation distribution of COVID-19-related and non-
COVID-19-related papers. The citation distribution that can contribute to the
annual IF calculation (left) is the same distribution as in Fig. 1A. The
separated citation distributions in one-year (middle) and two-year (right)
time gaps show the same pattern. Both plots show that COVID-19-related papers
have a heavier-tailed distribution. Figure S3: Probability density function of
journals with IFs that increased or decreased by publishing COVID-19-related
papers. The IFs of 763 journals (16%) decreased while the IFs of 4004 journals
(84%) increased. A The pdf for the absolute increase in IF. B The pdf for the
relative increase in IF divided by the IF excluding COVID-19-related papers.
Figure S4: Relative IF increase by publishing COVID-19-related papers relative
to the journal’s IF. The extent of IF increase is divided by the IF excluding
COVID-19-related papers. The red squares and error bars respectively show the
average value and the standard deviation of the increased IF in log-scale.
Figure S5: Fraction of COVID-19-related papers published in journals by the
journal IFs. The red squares and error bars respectively show the average
value and the standard deviation of the fraction of COVID-19-related papers in
log-scale. The Pearson correlation is $0.110$. Figure S6: Change in journal
ranking by publishing COVID-19-related papers. The values show the change rate
between ranking groups by publishing COVID-19-related papers. Both Pearson and
Spearman rank correlations of the IF ranks are 0.99. A Rank change of all
journals that published COVID-19-related papers in their category. B Rank
change of the journals that published COVID-19-related papers, where less than
10% of the journals published COVID-19-related papers in their category.
Figure S7: Change in the Gini coefficients of the JCR categories by the number
of published COVID-19-related papers. A positive correlation between the
number of COVID-19-related papers and the changes in the Gini coefficients of
the categories is observed (Pearson $r=0.545$). Figure S8: Correlation between
the IFs provided by JCR and those calculated in this paper. The Pearson
correlation is high for both years 2020 and 2021.


metal-poor stars are expected to host iron-poor and water-rich planets.
Overall, both the observed densities and the previous knowledge of stellar
composition point toward a possible compositional connection between the
volatile content of rocky planets and their host stars. In order to try to
understand why metal-poor stars host both low-density and Earth-like super-
Earths, in the following we study a possible influence of the insolation flux
in the population of low-density super-Earths, which could hint at the
capability of those planets of having retained their original abundant water
reservoirs.

####  5.3.2 Density-insolation

In Fig. 13 (right-hand panel) we plot $\rho$/$\rho_{rock}$ versus the stellar
insolation flux ($S$) received by the planets in our sample. Most of the
planets receive insolations lower than 300 $\rm S_{\oplus}$. In this region,
we see that the density distribution of planets with $\rho$/$\rho_{rock}$ ¿ 1
is homogeneous across different stellar insolations. This result is to be
expected, given that the density of those planets is only determined by the
iron and silicates content of their interiors. In contrast, for planets with
$\rho$/$\rho_{rock}$ ¡ 1, their low densities must be explained through the
presence of a significant amount of volatile elements. Interestingly, the
lowest density planets (i.e., 0.4 ¡ $\rho$/$\rho_{rock}$ ¡ 0.8) tend to be
clustered in the low-insolation region of the diagram (i.e., $S$ ¡ 10 $\rm
S_{\oplus}$), while the less dense planets near the rocky planet domain tend
to receive higher insolations. Hence, this trend might indicate an influence
of the stellar irradiation on the volatile content of low-density super-
Earths.

In the highly irradiated region (i.e., 300-10 000 $\rm S_{\oplus}$), we
highlight the presence of the low-density planet TOI-561 b. With
$\rho$/$\rho_{rock}$ = 0.63 $\pm$ 0.15, it might require volatile elements in
its structure. This planet is a clear outlier in the possible density-
insolation relation discussed above. With an insolation flux of $S$ = 4745
$\pm$ 269 $\rm S_{\oplus}$, it is unlikely that it has managed to maintain a
volatile envelope. Hence, other hypothesis are needed to explain the volatile
storage mechanism, such as the magma oceans proposed by Dorn & Lichtenberg
(2021).

####  5.3.3 Synthesis

In Sect. 5.3.1 we have seen that low-density super-Earths tend to be hosted by
metal-poor stars, which we argue is to be expected since those stars are
already known to host planets with iron-poor cores (Adibekyan et al. 2021) and
are also known to have been formed in water-rich protoplanetary disks (Santos
et al. 2017). However, there also exist planets with Earth-like densities
(i.e., with a negligible water content in mass) around metal-poor stars, which
were also presumably formed in water-rich environments. Interestingly, in
Sect. 5.3.2 we found that the lowest-density planets (i.e., 0.4 ¡
$\rho$/$\rho_{rock}$ ¡ 0.8) tend to receive low insolation fluxes (i.e., $S$ ¡
10 $\rm S_{\oplus}$). These two emerging trends combined indicate that low-
irradiated super-Earths around metal-poor stars tend to have the lowest
densities, and that such low densities are not typically reached when only one
of those two factors is met (there is only the exception of TOI-561, which
consists on a metal-poor star with a highly irradiated planet). On a
compositional level, the aforementioned trend suggests that while many super-
Earths were formed on iron-poor and water-rich protoplantary disks, only the
less irradiated ones could have been able to preserve a significant amount of
water from their original reservoirs.

##  6 Summary and conclusions

We carried out an intensive radial velocity campaign with ESPRESSO in order to
confirm and precisely characterize the close-in ($P$ = 7.4 days) transiting
super-Earth-sized planet candidate TOI-244.01, which had been detected by TESS
orbiting the bright ($K$ = 7.97 mag) and nearby ($d$ = 22 pc) star GJ 1018.
The powerful combination of ESPRESSO and TESS data allowed us to determine a
very precise planetary radius and mass (8$\%$ and 11$\%$ relative
uncertainties, respectively), thus confirming the planetary nature of TOI-244
b. With a radius of $R_{\rm p}$ = 1.52 $\pm$ 0.12 $\rm R_{\oplus}$, TOI-244 b
is located amidst the lower mode of the bimodal radius distribution of small
planets, where planets are considered to be composed of iron and silicates in
a proportion similar to that of the Earth (33$\%$ Fe, and 67$\%$ $\rm
MgSiO_{3}$ in mass). However, with a measured dynamical mass of $M_{\rm p}$ =
2.66 $\pm$ 0.31 $\rm M_{\oplus}$, TOI-244 b is less dense than the majority of
super-Earths of its size ($\rho$ = 4.2 $\pm$ 1.1 $\rm g\cdot cm^{-3}$).

We investigated the possibility that a scarcity of iron in the core of TOI-244
b could contribute to the planet’s low density. To do so, we estimated the
iron-to-silicate mass fraction that would have been present in the original
protoplanetary disk ($f_{\rm iron}^{\rm star}$) based on our computed stellar
metallicity and estimated [Si/H] and [Mg/H] abundances. Unfortunately,
depending on the technique used, we found a strong discrepancy between
different metallicity computations for this M-dwarf (-0.39 $\pm$ 0.07 dex and
-0.03 $\pm$ 0.11 dex). We find $f_{\rm iron}^{\rm star}$ = 29.2 $\pm$ 4.2 $\%$
and $f_{\rm iron}^{\rm star}$ = 32.0 $\pm$ 6.5 $\%$ for the metal-poor and
solar metallicity scenarios, respectively, suggesting that TOI-244 b could
have a lower amount of iron in its core than the Earth. However, according to
the location of TOI-244 b in the mass-radius diagram, it is more likely that
its low density is related to the presence of a significant amount of volatile
elements. In that sense, we find that atmospheric loss processes may have been
very efficient to remove a potential primordial hydrogen envelope, but high
mean molecular weight volatiles such as water could have been retained. Hence,
we chose our internal structure model to be composed of an iron-rich core, a
silicate-dominated mantle, and an hydrosphere. We ran our model without
constraining the Fe/Si mode ratio, not only because of the difficulty of
obtaining an accurate metallicity for GJ 1018, but also because the assumption
that the composition of rocky planets reflects the stellar one is being
questioned. As a result, our internal structure modeling suggests that TOI-244
b has a $479^{+128}_{-96}$ km thick hydrosphere over a 1.17 $\pm$ 0.09 $\rm
R_{\oplus}$ solid structure composed of a Fe-rich core and a silicate-
dominated mantle compatible with that of the Earth.

On a population level, we find that the current population of well-
characterized low-density super-Earths tend to be found around stars with
subsolar metallicities, and that the lowest dense planets tend to receive
lower insolation fluxes than the less dense ones in the $\rho$/$\rho_{rock}$
$<$ 1 regime. However, these possible trends need to be confirmed in the
future with a larger sample of well-characterized systems.

Overall, the detection and precise characterization of the super-Earth TOI-244
b thanks to ESPRESSO and TESS data allowed us to make a first detailed
description of its composition. Given its unusual properties, the brightness
of the host star, and the likely presence of an extended atmosphere of high
mean molecular weight volatiles, TOI-244 b will probably become a key target
for future atmospheric studies.

###### Acknowledgements.

We thank the anonymous referee for the thorough report and quick response.
A.C.-G. is funded by the Spanish Ministry of Science through
MCIN/AEI/10.13039/501100011033 grant PID2019-107061GB-C61. J.L.-B. is
supported by the Spanish Ministry of Science through the Ramón y Cajal program
with code RYC2021-031640-I (funded through MCIN/AEI/10.13039/501100011033 and
the NextGenerationEU/PRTR from the European Union) and the ”la Caixa”
Foundation (ID 100010434) and from the European Unions Horizon 2020 research
and innovation programme under the Marie Sklodowska-Curie grant agreement No
847648, with fellowship code LCF/BQ/PI20/11760023. YA and JD acknowledge the
support of the Swiss National Foundation though grant 192038. This work has
been carried out within the framework of the NCCR PlanetS supported by the
Swiss National Science Foundation under grants 51NF40$\\_$182901 and
51NF40$\\_$205606\. JIGH, CAP, and ASM acknowledge financial support from the
Spanish Ministry of Science and Innovation (MICINN) project
PID2020-117493GB-I00. CAP is also thankful for funding from the Spanish
government through grants AYA2014-56359-P and AYA2017-86389-P. ASM and JIGH
also acknowledge financial support from the Government of the Canary Islands
project ProID2020010129. This material is based upon work supported by NASA’s
Interdisciplinary Consortia for Astrobiology Research (NNH19ZDA001N-ICAR)
under award number 19-ICAR19$\\_$2-0041. This work was supported by FCT -
Fundação para a Ciência e a Tecnologia through national funds and by FEDER
through COMPETE2020 - Programa Operacional Competitividade e
Internacionalização by these grants: UIDB/04434/2020; UIDP/04434/2020;
2022.06962.PTDC. NCS acknowledges support by the European Union (ERC, FIERCE,
101052347). Views and opinions expressed are however those of the author(s)
only and do not necessarily reflect those of the European Union or the
European Research Council. Neither the European Union nor the granting
authority can be held responsible for them. This work was financed by
Portuguese funds through FCT - Fundação para a Ciência e a Tecnologia in the
framework of the project 2022.04048.PTDC. CJAPM also acknowledges FCT and
POCH/FSE (EC) support through Investigador FCT Contract
2021.01214.CEECIND/CP1658/CT0001. Funding for the DPAC has been provided by
national institutions, in particular the institutions participating in the
Gaia Multilateral Agreement. The INAF authors acknowledge financial support of
the Italian Ministry of Education, University, and Research with PRIN
201278X4FL and the ”Progetti Premiali” funding scheme. This publication makes
use of VOSA, developed under the Spanish Virtual Observatory
(https://svo.cab.inta-csic.es) project funded by
MCIN/AEI/10.13039/501100011033/ through grant PID2020-112949GB-I00. This work
made use of tpfplotter by J. Lillo-Box (publicly available in
www.github.com/jlillo/tpfplotter), which also made use of the python packages
astropy, lightkurve, matplotlib and numpy. This work has made use of data from
the European Space Agency (ESA) mission Gaia
(https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and
Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). We acknowledge the use
of public TESS data from pipelines at the TESS Science Office and at the TESS
Science Processing Operations Center. Resources supporting this work were
provided by the NASA High-End Computing (HEC) Program through the NASA
Advanced Supercomputing (NAS) Division at Ames Research Center for the
production of the SPOC data products. This research has made use of the
Exoplanet Follow-up Observation Program (ExoFOP; DOI: 10.26134/ExoFOP5)
website, which is operated by the California Institute of Technology, under
contract with the National Aeronautics and Space Administration under the
Exoplanet Exploration Program. This research has made use of the NASA
Exoplanet Archive,which is operated by the California Institute of Technology,
under contract with the National Aeronautics and Space Administration under
the Exoplanet Exploration Program. This research has made use of the SIMBAD
database (Wenger et al. 2000), operated at CDS, Strasbourg, France. This work
has made use of the following software: astropy (Astropy Collaboration et al.
2022), matplotlib (Hunter 2007), numpy (Harris et al. 2020), scipy (Virtanen
et al. 2020), and lightkurve (Lightkurve Collaboration et al. 2018).

## References

  * Abdurro’uf et al. (2022) Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35
  * Acuña et al. (2021) Acuña, L., Deleuil, M., Mousis, O., et al. 2021, A&A, 647, A53
  * Acuna et al. (2023) Acuna, L., Deleuil, M., & Mousis, O. 2023, arXiv e-prints, arXiv:2305.01250
  * Adibekyan et al. (2021) Adibekyan, V., Dorn, C., Sousa, S. G., et al. 2021, Science, 374, 330
  * Aguichine et al. (2021) Aguichine, A., Mousis, O., Deleuil, M., & Marcq, E. 2021, ApJ, 914, 84
  * Aguichine et al. (2020) Aguichine, A., Mousis, O., Devouard, B., & Ronnet, T. 2020, ApJ, 901, 97
  * Akeson et al. (2013) Akeson, R. L., Chen, X., Ciardi, D., et al. 2013, PASP, 125, 989
  * Allard et al. (2003) Allard, F., Guillot, T., Ludwig, H.-G., et al. 2003, in Brown Dwarfs, ed. E. Martín, Vol. 211, 325
  * Allard et al. (2012) Allard, F., Homeier, D., & Freytag, B. 2012, Philosophical Transactions of the Royal Society of London Series A, 370, 2765
  * Aller et al. (2020) Aller, A., Lillo-Box, J., Jones, D., Miranda, L. F., & Barceló Forteza, S. 2020, A&A, 635, A128
  * Almenara et al. (2018) Almenara, J. M., Díaz, R. F., Dorn, C., Bonfils, X., & Udry, S. 2018, MNRAS, 478, 460
  * Ambikasaran et al. (2015) Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., & O’Neil, M. 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 252
  * Angus et al. (2018) Angus, R., Morton, T., Aigrain, S., Foreman-Mackey, D., & Rajpaul, V. 2018, MNRAS, 474, 2094
  * Angus et al. (2019) Angus, R., Morton, T. D., Foreman-Mackey, D., et al. 2019, AJ, 158, 173
  * Antoniadis-Karnavas et al. (2020) Antoniadis-Karnavas, A., Sousa, S. G., Delgado-Mena, E., et al. 2020, A&A, 636 [arXiv:2002.09367]
  * Astropy Collaboration et al. (2022) Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167
  * Astudillo-Defru et al. (2020) Astudillo-Defru, N., Cloutier, R., Wang, S. X., et al. 2020, A&A, 636, A58
  * Baranne et al. (1996) Baranne, A., Queloz, D., Mayor, M., et al. 1996, A&AS, 119, 373
  * Barros et al. (2022) Barros, S. C. C., Demangeon, O. D. S., Alibert, Y., et al. 2022, A&A, 665, A154
  * Batalha et al. (2019) Batalha, N. E., Lewis, T., Fortney, J. J., et al. 2019, ApJ, 885, L25
  * Batalha et al. (2013) Batalha, N. M., Rowe, J. F., Bryson, S. T., et al. 2013, ApJS, 204, 24
  * Bayo et al. (2008) Bayo, A., Rodrigo, C., Barrado Y Navascués, D., et al. 2008, A&A, 492, 277
  * Bensby et al. (2003) Bensby, T., Feltzing, S., & Lundström, I. 2003, A&A, 410, 527
  * Berger et al. (2018) Berger, T. A., Huber, D., Gaidos, E., & van Saders, J. L. 2018, ApJ, 866, 99
  * Bianchi et al. (2017) Bianchi, L., Shiao, B., & Thilker, D. 2017, ApJS, 230, 24
  * Bogdan et al. (2023) Bogdan, T., Pillich, C., Landers, J., Wende, H., & Wurm, G. 2023, A&A, 670, A6
  * Bonomo et al. (2019) Bonomo, A. S., Zeng, L., Damasso, M., et al. 2019, Nature Astronomy, 3, 416
  * Borucki et al. (2010) Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977
  * Brinkman et al. (2023) Brinkman, C. L., Weiss, L. M., Dai, F., et al. 2023, AJ, 165, 88
  * Brugger et al. (2017) Brugger, B., Mousis, O., Deleuil, M., & Deschamps, F. 2017, The Astrophysical Journal, 850, 93
  * Brugger et al. (2016) Brugger, B., Mousis, O., Deleuil, M., & Lunine, J. I. 2016, The Astrophysical Journal, 831, L16
  * Bryson et al. (2010) Bryson, S. T., Jenkins, J. M., Klaus, T. C., et al. 2010, in Software and Cyberinfrastructure for Astronomy, ed. N. M. Radziwill & A. Bridger, Vol. 7740, International Society for Optics and Photonics (SPIE), 77401D
  * Burke et al. (2015) Burke, C. J., Christiansen, J. L., Mullally, F., et al. 2015, ApJ, 809, 8
  * Castro-González et al. (2022) Castro-González, A., Díez Alonso, E., Menéndez Blanco, J., et al. 2022, MNRAS, 509, 1075
  * Castro González et al. (2020) Castro González, A., Díez Alonso, E., Menéndez Blanco, J., et al. 2020, MNRAS, 499, 5416
  * Catling & Kasting (2017) Catling, D. C. & Kasting, J. F. 2017, Atmospheric Evolution on Inhabited and Lifeless Worlds
  * Chen & Rogers (2016) Chen, H. & Rogers, L. A. 2016, ApJ, 831, 180
  * Christiansen et al. (2022) Christiansen, J. L., Bhure, S., Zink, J. K., et al. 2022, AJ, 163, 244
  * Cloutier et al. (2019) Cloutier, R., Astudillo-Defru, N., Bonfils, X., et al. 2019, A&A, 629, A111
  * Crossfield et al. (2016) Crossfield, I. J. M., Ciardi, D. R., Petigura, E. A., et al. 2016, ApJS, 226, 7
  * Crossfield et al. (2022) Crossfield, I. J. M., Malik, M., Hill, M. L., et al. 2022, ApJ, 937, L17
  * Cushing et al. (2005) Cushing, M. C., Rayner, J. T., & Vacca, W. D. 2005, ApJ, 623, 1115
  * Damasso et al. (2020) Damasso, M., Sozzetti, A., Lovis, C., et al. 2020, A&A, 642, A31
  * de Leon et al. (2021) de Leon, J. P., Livingston, J., Endl, M., et al. 2021, MNRAS, 508, 195
  * Delrez et al. (2021) Delrez, L., Ehrenreich, D., Alibert, Y., et al. 2021, Nature Astronomy, 5, 775
  * Demangeon et al. (2021) Demangeon, O. D. S., Zapatero Osorio, M. R., Alibert, Y., et al. 2021, A&A, 653, A41
  * Díaz et al. (2016) Díaz, R. F., Ségransan, D., Udry, S., et al. 2016, A&A, 585, A134
  * Dorn et al. (2015) Dorn, C., Khan, A., Heng, K., et al. 2015, A&A, 577, A83
  * Dorn & Lichtenberg (2021) Dorn, C. & Lichtenberg, T. 2021, ApJ, 922, L4
  * Dorn et al. (2017) Dorn, C., Venturini, J., Khan, A., et al. 2017, A&A, 597, A37
  * Dressing & Charbonneau (2015) Dressing, C. D. & Charbonneau, D. 2015, ApJ, 807, 45
  * Ducati (2002) Ducati, J. R. 2002, VizieR Online Data Catalog
  * Erkaev et al. (2007) Erkaev, N. V., Kulikov, Y. N., Lammer, H., et al. 2007, A&A, 472, 329
  * Espinoza et al. (2020) Espinoza, N., Brahm, R., Henning, T., et al. 2020, MNRAS, 491, 2982
  * Faria et al. (2016) Faria, J. P., Haywood, R. D., Brewer, B. J., et al. 2016, A&A, 588, A31
  * Flewelling et al. (2020) Flewelling, H. A., Magnier, E. A., Chambers, K. C., et al. 2020, ApJS, 251, 7
  * Foreman-Mackey (2018) Foreman-Mackey, D. 2018, Research Notes of the American Astronomical Society, 2, 31
  * Foreman-Mackey et al. (2017) Foreman-Mackey, D., Agol, E., Ambikasaran, S., & Angus, R. 2017, AJ, 154, 220
  * Foreman-Mackey et al. (2013) Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306
  * Fossati et al. (2017) Fossati, L., Erkaev, N. V., Lammer, H., et al. 2017, A&A, 598, A90
  * Fulton et al. (2018) Fulton, B. J., Petigura, E. A., Blunt, S., & Sinukoff, E. 2018, PASP, 130, 044504
  * Fulton et al. (2017) Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109
  * Gaia Collaboration et al. (2021) Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2021, A&A, 649, A1
  * Gaia Collaboration et al. (2022) Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2022, arXiv e-prints, arXiv:2208.00211
  * Gajdoš et al. (2019) Gajdoš, P., Vaňko, M., & Parimucha, Š. 2019, Research in Astronomy and Astrophysics, 19, 041
  * Ginzburg et al. (2018) Ginzburg, S., Schlichting, H. E., & Sari, R. 2018, MNRAS, 476, 759
  * Gliese & Jahreiß (1991) Gliese, W. & Jahreiß, H. 1991, Preliminary Version of the Third Catalogue of Nearby Stars, On: The Astronomical Data Center CD-ROM: Selected Astronomical Catalogs, Vol. I; L.E. Brotzmann, S.E. Gesser (eds.), NASA/Astronomical Data Center, Goddard Space Flight Center, Greenbelt, MD
  * Goodman & Weare (2010) Goodman, J. & Weare, J. 2010, Communications in Applied Mathematics and Computational Science, 5, 65
  * Guerrero et al. (2021) Guerrero, N. M., Seager, S., Huang, C. X., et al. 2021, ApJS, 254, 39
  * Gupta & Schlichting (2019) Gupta, A. & Schlichting, H. E. 2019, MNRAS, 487, 24
  * Hadden & Lithwick (2014) Hadden, S. & Lithwick, Y. 2014, ApJ, 787, 80
  * Harris et al. (2020) Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357
  * Haywood et al. (2014) Haywood, R. D., Collier Cameron, A., Queloz, D., et al. 2014, MNRAS, 443, 2517
  * Henden (2019) Henden, A. A. 2019, Journal of the American Association of Variable Star Observers, 47, 130
  * Henden et al. (2012) Henden, A. A., Levine, S. E., Terrell, D., Smith, T. C., & Welch, D. 2012, Journal of the American Association of Variable Star Observers, 40, 430
  * Hippke et al. (2019) Hippke, M., David, T. J., Mulders, G. D., & Heller, R. 2019, AJ, 158, 143
  * Hippke & Heller (2019) Hippke, M. & Heller, R. 2019, A&A, 623, A39
  * Holczer et al. (2016) Holczer, T., Mazeh, T., Nachmani, G., et al. 2016, ApJS, 225, 9
  * Howard et al. (2012) Howard, A. W., Marcy, G. W., Bryson, S. T., et al. 2012, ApJS, 201, 15
  * Howell et al. (2014) Howell, S. B., Sobeck, C., Haas, M., et al. 2014, PASP, 126, 398
  * Hunter (2007) Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90
  * Husser et al. (2013) Husser, T. O., Wende-von Berg, S., Dreizler, S., et al. 2013, A&A, 553, A6
  * Jenkins (2002) Jenkins, J. M. 2002, ApJ, 575, 493
  * Jenkins et al. (2010) Jenkins, J. M., Chandrasekaran, H., McCauliff, S. D., et al. 2010, in Software and Cyberinfrastructure for Astronomy, ed. N. M. Radziwill & A. Bridger, Vol. 7740, International Society for Optics and Photonics (SPIE), 77400D
  * Jenkins et al. (2020) Jenkins, J. M., Tenenbaum, P., Seader, S., et al. 2020, Kepler Data Processing Handbook: Transiting Planet Search, Kepler Science Document KSCI-19081-003, id. 9. Edited by Jon M. Jenkins.
  * Jenkins et al. (2016) Jenkins, J. M., Twicken, J. D., McCauliff, S., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9913, Software and Cyberinfrastructure for Astronomy IV, ed. G. Chiozzi & J. C. Guzman, 99133E
  * Jin et al. (2014) Jin, S., Mordasini, C., Parmentier, V., et al. 2014, ApJ, 795, 65
  * Johansen & Dorn (2022) Johansen, A. & Dorn, C. 2022, A&A, 662, A19
  * Jontof-Hutter et al. (2015) Jontof-Hutter, D., Rowe, J. F., Lissauer, J. J., Fabrycky, D. C., & Ford, E. B. 2015, Nature, 522, 321
  * Khata et al. (2021) Khata, D., Mondal, S., Das, R., & Baug, T. 2021, MNRAS, 507, 1869
  * Kipping et al. (2014) Kipping, D. M., Nesvorný, D., Buchhave, L. A., et al. 2014, ApJ, 784, 28
  * Kite & Barnett (2020) Kite, E. S. & Barnett, M. N. 2020, Proceedings of the National Academy of Science, 117, 18264
  * Kochanek et al. (2017) Kochanek, C. S., Shappee, B. J., Stanek, K. Z., et al. 2017, PASP, 129, 104502
  * Kopparapu et al. (2013) Kopparapu, R. K., Ramirez, R., Kasting, J. F., et al. 2013, ApJ, 765, 131
  * Kostov et al. (2019) Kostov, V. B., Schlieder, J. E., Barclay, T., et al. 2019, AJ, 158, 32
  * Kreidberg (2015) Kreidberg, L. 2015, PASP, 127, 1161
  * Lacedelli et al. (2021) Lacedelli, G., Malavolta, L., Borsato, L., et al. 2021, MNRAS, 501, 4148
  * Lacedelli et al. (2022) Lacedelli, G., Wilson, T. G., Malavolta, L., et al. 2022, MNRAS, 511, 4551
  * Lavie et al. (2022) Lavie, B., Bouchy, F., Lovis, C., et al. 2022, arXiv e-prints, arXiv:2210.09713
  * Li et al. (2019) Li, J., Tenenbaum, P., Twicken, J. D., et al. 2019, PASP, 131, 024506
  * Lightkurve Collaboration et al. (2018) Lightkurve Collaboration, Cardoso, J. V. d. M., Hedges, C., et al. 2018, Lightkurve: Kepler and TESS time series analysis in Python, Astrophysics Source Code Library, record ascl:1812.013
  * Lillo-Box et al. (2014) Lillo-Box, J., Barrado, D., Moya, A., et al. 2014, A&A, 562, A109
  * Lillo-Box et al. (2020a) Lillo-Box, J., Figueira, P., Leleu, A., et al. 2020a, A&A, 642, A121
  * Lillo-Box et al. (2020b) Lillo-Box, J., Lopez, T. A., Santerne, A., et al. 2020b, A&A, 640, A48
  * Lillo-Box et al. (2022) Lillo-Box, J., Santos, N. C., Santerne, A., et al. 2022, A&A, 667, A102
  * Livingston et al. (2018a) Livingston, J. H., Crossfield, I. J. M., Petigura, E. A., et al. 2018a, AJ, 156, 277
  * Livingston et al. (2018b) Livingston, J. H., Endl, M., Dai, F., et al. 2018b, AJ, 156, 78
  * Lodders (2003) Lodders, K. 2003, ApJ, 591, 1220
  * Lopez & Fortney (2014) Lopez, E. D. & Fortney, J. J. 2014, ApJ, 792, 1
  * Luque et al. (2022) Luque, R., Fulton, B. J., Kunimoto, M., et al. 2022, A&A, 664, A199
  * Luque & Pallé (2022) Luque, R. & Pallé, E. 2022, Science, 377, 1211
  * Luyten (1979) Luyten, W. J. 1979, New Luyten catalogue of stars with proper motions larger than two tenths of an arcsecond; and first supplement; NLTT. (Minneapolis (1979)); Label 12 = short description; Label 13 = documentation by Warren; Label 14 = catalogue
  * Maldonado et al. (2020) Maldonado, J., Micela, G., Baratella, M., et al. 2020, A&A, 644, A68
  * Mandel & Agol (2002) Mandel, K. & Agol, E. 2002, ApJ, 580, L171
  * Mann et al. (2019) Mann, A. W., Dupuy, T., Kraus, A. L., et al. 2019, ApJ, 871, 63
  * Mann et al. (2017) Mann, A. W., Dupuy, T., Muirhead, P. S., et al. 2017, AJ, 153, 267
  * Mann et al. (2013) Mann, A. W., Gaidos, E., & Ansdell, M. 2013, ApJ, 779, 188
  * Marcus et al. (2010) Marcus, R. A., Sasselov, D., Hernquist, L., & Stewart, S. T. 2010, ApJ, 712, L73
  * Marfil et al. (2021a) Marfil, E., Tabernero, H. M., Montes, D., et al. 2021a, A&A, 656, A162
  * Marfil et al. (2021b) Marfil, E., Tabernero, H. M., Montes, D., et al. 2021b, A&A, 656, A162
  * Mayo et al. (2018) Mayo, A. W., Vanderburg, A., Latham, D. W., et al. 2018, AJ, 155, 136
  * Mayor et al. (2011) Mayor, M., Marmier, M., Lovis, C., et al. 2011, arXiv e-prints, arXiv:1109.2497
  * Mayor & Queloz (1995) Mayor, M. & Queloz, D. 1995, Nature, 378, 355
  * Morello et al. (2022) Morello, G., Parviainen, H., Murgas, F., et al. 2022, arXiv e-prints, arXiv:2201.13274
  * Mori et al. (2022) Mori, M., Livingston, J. H., Leon, J. d., et al. 2022, AJ, 163, 298
  * Morton et al. (2016) Morton, T. D., Bryson, S. T., Coughlin, J. L., et al. 2016, ApJ, 822, 86
  * Mousis et al. (2020a) Mousis, O., Deleuil, M., Aguichine, A., et al. 2020a, ApJ, 896, L22
  * Mousis et al. (2020b) Mousis, O., Deleuil, M., Aguichine, A., et al. 2020b, ApJ, 896, L22
  * Muirhead et al. (2012) Muirhead, P. S., Hamren, K., Schlawin, E., et al. 2012, ApJ, 750, L37
  * Murgas et al. (2023) Murgas, F., Castro-González, A., Pallé, E., et al. 2023, arXiv e-prints, arXiv:2304.09220
  * Neves et al. (2012) Neves, V., Bonfils, X., Santos, N. C., et al. 2012, A&A, 538, A25
  * Neves et al. (2014) Neves, V., Bonfils, X., Santos, N. C., et al. 2014, A&A, 568, A121
  * Oddo et al. (2023) Oddo, D., Dragomir, D., Brandeker, A., et al. 2023, AJ, 165, 134
  * Owen & Wu (2013) Owen, J. E. & Wu, Y. 2013, ApJ, 775, 105
  * Palle et al. (2023) Palle, E., Orell-Miquel, J., Brady, M., et al. 2023, arXiv e-prints, arXiv:2301.06873
  * Parviainen & Aigrain (2015) Parviainen, H. & Aigrain, S. 2015, MNRAS, 453, 3821
  * Passegger et al. (2022) Passegger, V. M., Bello-García, A., Ordieres-Meré, J., et al. 2022, A&A, 658, A194
  * Patel & Espinoza (2022) Patel, J. A. & Espinoza, N. 2022, AJ, 163, 228
  * Pepe et al. (2021) Pepe, F., Cristiani, S., Rebolo, R., et al. 2021, A&A, 645, A96
  * Pepe et al. (2002) Pepe, F., Mayor, M., Galland, F., et al. 2002, A&A, 388, 632
  * Pepper et al. (2017) Pepper, J., Gillen, E., Parviainen, H., et al. 2017, AJ, 153, 177
  * Petigura et al. (2013) Petigura, E. A., Howard, A. W., & Marcy, G. W. 2013, Proceedings of the National Academy of Science, 110, 19273
  * Piaulet et al. (2023) Piaulet, C., Benneke, B., Almenara, J. M., et al. 2023, Nature Astronomy, 7, 206
  * Pizzolato et al. (2003) Pizzolato, N., Maggio, A., Micela, G., Sciortino, S., & Ventura, P. 2003, A&A, 397, 147
  * Plez (2012) Plez, B. 2012, Turbospectrum: Code for spectral synthesis, Astrophysics Source Code Library
  * Plotnykov & Valencia (2020) Plotnykov, M. & Valencia, D. 2020, MNRAS, 499, 932
  * Rabus et al. (2019) Rabus, M., Lachaume, R., Jordán, A., et al. 2019, MNRAS, 484, 2674
  * Rasmussen & Williams (2006) Rasmussen, C. E. & Williams, C. K. I. 2006, Gaussian Processes for Machine Learning
  * Ricker et al. (2015) Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, Journal of Astronomical Telescopes, Instruments, and Systems, 1, 014003
  * Roberts et al. (2012) Roberts, S., Osborne, M., Ebden, M., et al. 2012, Philosophical Transactions of the Royal Society of London Series A, 371, 20110550
  * Robin et al. (2003) Robin, A. C., Reylé, C., Derrière, S., & Picaud, S. 2003, A&A, 409, 523
  * Rodgers & Nicewander (1988) Rodgers, J. L. & Nicewander, W. A. 1988, The American Statistician, 42, 59
  * Rogers et al. (2023a) Rogers, J. G., Schlichting, H. E., & Owen, J. E. 2023a, arXiv e-prints, arXiv:2301.04321
  * Rogers et al. (2023b) Rogers, J. G., Schlichting, H. E., & Owen, J. E. 2023b, arXiv e-prints, arXiv:2301.04321
  * Rowe et al. (2014) Rowe, J. F., Bryson, S. T., Marcy, G. W., et al. 2014, ApJ, 784, 45
  * Ryabchikova et al. (2015) Ryabchikova, T., Piskunov, N., Kurucz, R. L., et al. 2015, Phys. Scr, 90, 054005
  * Santerne et al. (2018) Santerne, A., Brugger, B., Armstrong, D. J., et al. 2018, Nature Astronomy, 2, 393
  * Santos et al. (2017) Santos, N. C., Adibekyan, V., Dorn, C., et al. 2017, A&A, 608, A94
  * Santos et al. (2015) Santos, N. C., Adibekyan, V., Mordasini, C., et al. 2015, A&A, 580, L13
  * Sanz-Forcada et al. (2011) Sanz-Forcada, J., Micela, G., Ribas, I., et al. 2011, A&A, 532, A6
  * Schweitzer et al. (2019) Schweitzer, A., Passegger, V. M., Cifuentes, C., et al. 2019, A&A, 625, A68
  * Scora et al. (2020) Scora, J., Valencia, D., Morbidelli, A., & Jacobson, S. 2020, MNRAS, 493, 4910
  * Seager et al. (2007) Seager, S., Kuchner, M., Hier-Majumder, C. A., & Militzer, B. 2007, ApJ, 669, 1279
  * Shappee et al. (2014) Shappee, B. J., Prieto, J. L., Grupe, D., et al. 2014, ApJ, 788, 48
  * Shporer et al. (2020) Shporer, A., Collins, K. A., Astudillo-Defru, N., et al. 2020, ApJ, 890, L7
  * Siverd et al. (2012) Siverd, R. J., Beatty, T. G., Pepper, J., et al. 2012, ApJ, 761, 123
  * Skrutskie et al. (2006) Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163
  * Smith et al. (2020) Smith, J. C., Morris, R. L., Jenkins, J. M., et al. 2020, Kepler Data Processing Handbook: Finding Optimal Apertures in Kepler Data, Kepler Science Document KSCI-19081-003, id. 7. Edited by Jon M. Jenkins.
  * Smith et al. (2012) Smith, J. C., Stumpe, M. C., Van Cleve, J. E., et al. 2012, PASP, 124, 1000
  * Sozzetti et al. (2021) Sozzetti, A., Damasso, M., Bonomo, A. S., et al. 2021, A&A, 648, A75
  * Stassun et al. (2019) Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138
  * Stefánsson et al. (2020) Stefánsson, G., Kopparapu, R., Lin, A., et al. 2020, AJ, 160, 259
  * Stumpe et al. (2014) Stumpe, M. C., Smith, J. C., Catanzarite, J. H., et al. 2014, PASP, 126, 100
  * Stumpe et al. (2012) Stumpe, M. C., Smith, J. C., Van Cleve, J. E., et al. 2012, PASP, 124, 985
  * Suárez Mascareño et al. (2020) Suárez Mascareño, A., Faria, J. P., Figueira, P., et al. 2020, A&A, 639, A77
  * Suárez Mascareño et al. (2022) Suárez Mascareño, A., González-Álvarez, E., Zapatero Osorio, M. R., et al. 2022, arXiv e-prints, arXiv:2212.07332
  * Swift et al. (2012) Swift, D. C., Eggert, J. H., Hicks, D. G., et al. 2012, ApJ, 744, 59
  * Tabernero et al. (2022) Tabernero, H. M., Marfil, E., Montes, D., & González Hernández, J. I. 2022, A&A, 657, A66
  * Terada et al. (2007) Terada, H., Tokunaga, A. T., Kobayashi, N., et al. 2007, ApJ, 667, 303
  * Tian & Heng (2023) Tian, M. & Heng, K. 2023, arXiv e-prints, arXiv:2301.10217
  * Tody (1986) Tody, D. 1986, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 627, Instrumentation in astronomy VI, ed. D. L. Crawford, 733
  * Toledo-Padrón et al. (2020) Toledo-Padrón, B., Lovis, C., Suárez Mascareño, A., et al. 2020, A&A, 641, A92
  * Trifonov et al. (2019) Trifonov, T., Rybizki, J., & Kürster, M. 2019, A&A, 622, L7
  * Trotta (2008) Trotta, R. 2008, Contemporary Physics, 49, 71
  * Turbet et al. (2020) Turbet, M., Bolmont, E., Ehrenreich, D., et al. 2020, A&A, 638, A41
  * Twicken et al. (2018) Twicken, J. D., Catanzarite, J. H., Clarke, B. D., et al. 2018, PASP, 130, 064502
  * Unterborn et al. (2016) Unterborn, C. T., Dismukes, E. E., & Panero, W. R. 2016, The Astrophysical Journal, 819, 32
  * Vanderburg et al. (2019) Vanderburg, A., Huang, C. X., Rodriguez, J. E., et al. 2019, ApJ, 881, L19
  * Venturini et al. (2020) Venturini, J., Guilera, O. M., Haldemann, J., Ronco, M. P., & Mordasini, C. 2020, A&A, 643, L1
  * Virtanen et al. (2020) Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261
  * Weiss et al. (2021) Weiss, L. M., Dai, F., Huber, D., et al. 2021, AJ, 161, 56
  * Wells et al. (2018) Wells, R., Poppenhaeger, K., & Watson, C. A. 2018, MNRAS, 473, L131
  * Wenger et al. (2000) Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9
  * Wildi et al. (2010) Wildi, F., Pepe, F., Chazelas, B., Lo Curto, G., & Lovis, C. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 77354X
  * Wolszczan & Frail (1992) Wolszczan, A. & Frail, D. A. 1992, Nature, 355, 145
  * Wright et al. (2010) Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868
  * Xie (2014) Xie, J.-W. 2014, ApJS, 210, 25
  * York et al. (2000) York, D. G., Adelman, J., Anderson, John E., J., et al. 2000, AJ, 120, 1579
  * Zechmeister & Kürster (2009) Zechmeister, M. & Kürster, M. 2009, A&A, 496, 577
  * Zeng et al. (2019) Zeng, L., Jacobsen, S. B., Sasselov, D. D., et al. 2019, Proceedings of the National Academy of Science, 116, 9723
  * Zeng et al. (2016) Zeng, L., Sasselov, D. D., & Jacobsen, S. B. 2016, ApJ, 819, 127

##  Appendix A Additional tables

Table 7: TESS photometry of GJ 1018. The detrended light curve was obtained by dividing the PDCSAP by the trend obtained through the biweight method (see Section 2.1). The complete table is accessible through CDS (see link in the main title caption). BJD (days) | PDCSAP ($\rm e^{-}s^{-1}$) | Detrended PDCSAP | Sector
---|---|---|---
2458354.113 | $10687\pm 14$ | $0.9997\pm 0.0013$ | TESS02
2458354.114 | $10699\pm 14$ | $1.0009\pm 0.0013$ | TESS02
… | … | … | …
2459088.244 | $10517\pm 13$ | $0.9990\pm 0.0013$ | TESS29
2459088.246 | $10515\pm 13$ | $0.9988\pm 0.0013$ | TESS29
… | … | … | …
Table 8: Complete ESPRESSO radial velocities and activity indicators of GJ 1018 acquired between 9 October 2021 and 2 October 2022 under the programs IDs 108.2254.002, 108.2254.005, and 108.2254.006. This table is accessible through CDS (see link in the main title caption). RJD (days) |  RV ($\rm m\,s^{-1}$) |  FWHM ($\rm m\,s^{-1}$) | S-index (x1000) |  Contrast ($\%$) |  $\rm H_{\alpha}$ (x1000) | NaD (x1000) | BIS
---|---|---|---|---|---|---|---
59496.698 | $15139.30\pm 0.73$ | $2747.3\pm 1.5$ | $665.4\pm 3.5$ | $34.337\pm 0.018$ | $610.36\pm 0.20$ | $70.79\pm 0.12$ | $252.0\pm 1.5$
59499.536 | $15139.54\pm 0.79$ | $2748.4\pm 1.6$ | $683.5\pm 4.1$ | $34.228\pm 0.020$ | $618.31\pm 0.20$ | $72.83\pm 0.13$ | $324.6\pm 1.6$
59514.774 | $15140.54\pm 0.88$ | $2758.0\pm 1.8$ | $820.7\pm 5.1$ | $34.181\pm 0.022$ | $601.96\pm 0.23$ | $78.85\pm 0.16$ | $135.3\pm 1.8$
59517.632 | $15142.17\pm 0.62$ | $2759.3\pm 1.2$ | $812.0\pm 2.6$ | $34.223\pm 0.015$ | $598.70\pm 0.15$ | $78.210\pm 0.098$ | $225.1\pm 1.2$
59520.697 | $15141.96\pm 0.67$ | $2755.6\pm 1.3$ | $745.0\pm 3.0$ | $34.225\pm 0.017$ | $588.32\pm 0.17$ | $74.92\pm 0.11$ | $252.0\pm 1.3$
59523.770 | $15143.70\pm 0.60$ | $2752.6\pm 1.2$ | $691.3\pm 2.6$ | $34.223\pm 0.015$ | $570.90\pm 0.14$ | $72.540\pm 0.090$ | $185.1\pm 1.2$
59529.697 | $15138.04\pm 0.87$ | $2748.6\pm 1.7$ | $662.5\pm 4.8$ | $34.275\pm 0.022$ | $567.23\pm 0.23$ | $70.66\pm 0.16$ | $270.6\pm 1.7$
59532.530 | $15143.77\pm 0.52$ | $2747.1\pm 1.0$ | $633.8\pm 1.9$ | $34.301\pm 0.013$ | $580.01\pm 0.13$ | $71.428\pm 0.078$ | $238.2\pm 1.0$
59535.617 | $15142.10\pm 0.51$ | $2745.1\pm 1.0$ | $613.5\pm 1.8$ | $34.340\pm 0.013$ | $572.37\pm 0.12$ | $71.102\pm 0.076$ | $229.7\pm 1.0$
59538.593 | $15143.86\pm 0.56$ | $2746.9\pm 1.1$ | $627.4\pm 2.1$ | $34.323\pm 0.014$ | $589.11\pm 0.14$ | $70.274\pm 0.085$ | $190.7\pm 1.1$
59541.659 | $15138.98\pm 0.76$ | $2743.1\pm 1.5$ | $630.8\pm 3.5$ | $34.306\pm 0.019$ | $586.31\pm 0.20$ | $70.14\pm 0.13$ | $329.2\pm 1.5$
59544.621 | $15136.91\pm 0.52$ | $2744.6\pm 1.0$ | $641.2\pm 1.9$ | $34.310\pm 0.013$ | $601.20\pm 0.13$ | $71.598\pm 0.078$ | $225.4\pm 1.0$
59547.661 | $15137.17\pm 0.74$ | $2745.2\pm 1.5$ | $621.0\pm 3.5$ | $34.299\pm 0.018$ | $595.90\pm 0.19$ | $70.72\pm 0.12$ | $212.6\pm 1.5$
59550.695 | $15138.33\pm 0.53$ | $2745.8\pm 1.1$ | $674.3\pm 2.0$ | $34.292\pm 0.013$ | $592.22\pm 0.12$ | $69.985\pm 0.078$ | $214.5\pm 1.1$
59553.568 | $15144.63\pm 0.55$ | $2747.1\pm 1.1$ | $713.2\pm 2.1$ | $34.275\pm 0.014$ | $605.78\pm 0.13$ | $72.017\pm 0.081$ | $273.4\pm 1.1$
59556.631 | $15142.67\pm 0.72$ | $2749.6\pm 1.4$ | $868.8\pm 3.5$ | $34.260\pm 0.018$ | $661.87\pm 0.19$ | $82.32\pm 0.12$ | $250.5\pm 1.4$
59559.584 | $15142.83\pm 0.98$ | $2750.4\pm 2.0$ | $758.5\pm 5.6$ | $34.271\pm 0.024$ | $604.98\pm 0.27$ | $75.09\pm 0.18$ | $332.5\pm 2.0$
59562.558 | $15142.17\pm 0.83$ | $2755.2\pm 1.7$ | $787.5\pm 4.5$ | $34.245\pm 0.021$ | $611.19\pm 0.22$ | $73.98\pm 0.14$ | $253.2\pm 1.7$
59580.587 | $15138.64\pm 0.61$ | $2749.3\pm 1.2$ | $791.4\pm 2.6$ | $34.228\pm 0.015$ | $593.87\pm 0.15$ | $77.662\pm 0.095$ | $230.3\pm 1.2$
59583.554 | $15141.88\pm 0.57$ | $2750.0\pm 1.1$ | $752.6\pm 2.3$ | $34.257\pm 0.014$ | $584.92\pm 0.14$ | $74.288\pm 0.087$ | $230.0\pm 1.1$
59744.857 | $15138.99\pm 0.82$ | $2749.5\pm 1.6$ | $647.6\pm 4.3$ | $34.191\pm 0.020$ | $588.62\pm 0.21$ | $71.17\pm 0.15$ | $198.2\pm 1.6$
59760.864 | $15144.56\pm 0.52$ | $2750.6\pm 1.0$ | $680.0\pm 1.8$ | $34.172\pm 0.013$ | $582.72\pm 0.12$ | $73.414\pm 0.079$ | $205.0\pm 1.0$
59786.712 | $15144.9\pm 1.1$ | $2750.0\pm 2.1$ | $771.8\pm 6.3$ | $34.195\pm 0.027$ | $589.10\pm 0.28$ | $73.44\pm 0.21$ | $286.0\pm 2.1$
59788.710 | $15144.87\pm 0.69$ | $2752.1\pm 1.4$ | $737.6\pm 3.0$ | $34.199\pm 0.017$ | $581.69\pm 0.17$ | $73.32\pm 0.11$ | $216.2\pm 1.4$
59790.806 | $15144.61\pm 0.63$ | $2751.2\pm 1.3$ | $789.5\pm 2.4$ | $34.213\pm 0.016$ | $599.45\pm 0.15$ | $76.86\pm 0.10$ | $221.3\pm 1.3$
59792.691 | $15140.70\pm 0.92$ | $2751.7\pm 1.8$ | $730.3\pm 4.9$ | $34.236\pm 0.023$ | $584.17\pm 0.23$ | $73.20\pm 0.17$ | $284.6\pm 1.8$
59795.744 | $15138.93\pm 0.82$ | $2754.0\pm 1.6$ | $774.2\pm 3.8$ | $34.229\pm 0.020$ | $607.84\pm 0.22$ | $77.12\pm 0.15$ | $303.3\pm 1.6$
59797.895 | $15139.5\pm 1.1$ | $2755.8\pm 2.3$ | $720.3\pm 6.2$ | $34.190\pm 0.028$ | $608.82\pm 0.33$ | $76.21\pm 0.24$ | $323.9\pm 2.3$
59798.682 | $15141.05\pm 0.75$ | $2751.9\pm 1.5$ | $738.9\pm 3.5$ | $34.240\pm 0.019$ | $608.29\pm 0.19$ | $76.36\pm 0.13$ | $282.6\pm 1.5$
59800.814 | $15140.88\pm 0.53$ | $2754.3\pm 1.1$ | $737.1\pm 1.7$ | $34.203\pm 0.013$ | $615.84\pm 0.13$ | $75.087\pm 0.082$ | $252.2\pm 1.1$
59802.806 | $15142.63\pm 0.54$ | $2751.0\pm 1.1$ | $866.4\pm 1.8$ | $34.181\pm 0.013$ | $679.96\pm 0.14$ | $83.944\pm 0.085$ | $258.2\pm 1.1$
59804.676 | $15142.86\pm 0.71$ | $2752.6\pm 1.4$ | $669.5\pm 2.9$ | $34.178\pm 0.018$ | $603.54\pm 0.18$ | $75.88\pm 0.12$ | $217.0\pm 1.4$
59805.877 | $15141.89\pm 0.77$ | $2751.7\pm 1.5$ | $752.9\pm 3.3$ | $34.214\pm 0.019$ | $611.17\pm 0.21$ | $76.52\pm 0.14$ | $273.8\pm 1.5$
59807.711 | $15137.99\pm 0.82$ | $2750.6\pm 1.6$ | $687.6\pm 3.8$ | $34.184\pm 0.020$ | $611.05\pm 0.22$ | $74.17\pm 0.15$ | $318.6\pm 1.6$
59811.828 | $15139.34\pm 0.53$ | $2750.1\pm 1.1$ | $740.5\pm 1.8$ | $34.225\pm 0.013$ | $610.03\pm 0.13$ | $75.519\pm 0.083$ | $215.1\pm 1.1$
59813.666 | $15139.80\pm 0.96$ | $2751.8\pm 1.9$ | $702.9\pm 5.3$ | $34.222\pm 0.024$ | $590.24\pm 0.26$ | $82.35\pm 0.18$ | $202.3\pm 1.9$
59815.623 | $15137.99\pm 0.89$ | $2752.5\pm 1.8$ | $620.0\pm 4.8$ | $34.206\pm 0.022$ | $567.41\pm 0.22$ | $87.95\pm 0.16$ | $287.2\pm 1.8$
59817.629 | $15140.49\pm 0.61$ | $2749.0\pm 1.2$ | $696.2\pm 2.4$ | $34.256\pm 0.015$ | $566.88\pm 0.14$ | $73.399\pm 0.096$ | $260.5\pm 1.2$
59817.705 | $15141.42\pm 0.60$ | $2749.0\pm 1.2$ | $718.7\pm 2.2$ | $34.284\pm 0.015$ | $577.86\pm 0.15$ | $74.411\pm 0.099$ | $220.2\pm 1.2$
59818.893 | $15142.96\pm 0.66$ | $2753.4\pm 1.3$ | $669.7\pm 2.5$ | $34.214\pm 0.016$ | $573.71\pm 0.17$ | $72.94\pm 0.11$ | $346.4\pm 1.3$
59823.850 | $15139.40\pm 0.91$ | $2749.2\pm 1.8$ | $669.6\pm 4.4$ | $34.259\pm 0.023$ | $566.65\pm 0.25$ | $74.01\pm 0.17$ | $239.7\pm 1.8$
59827.827 | $15138.08\pm 0.71$ | $2750.2\pm 1.4$ | $688.1\pm 2.9$ | $34.255\pm 0.018$ | $580.15\pm 0.19$ | $74.33\pm 0.12$ | $183.0\pm 1.4$
59828.825 | $15136.77\pm 0.66$ | $2746.6\pm 1.3$ | $645.8\pm 2.5$ | $34.275\pm 0.016$ | $564.14\pm 0.17$ | $72.49\pm 0.11$ | $189.7\pm 1.3$
59830.775 | $15134.42\pm 0.58$ | $2748.3\pm 1.2$ | $827.7\pm 2.0$ | $34.253\pm 0.014$ | $630.62\pm 0.15$ | $81.738\pm 0.094$ | $253.0\pm 1.2$
59832.849 | $15136.20\pm 0.98$ | $2745.3\pm 2.0$ | $654.7\pm 4.9$ | $34.141\pm 0.024$ | $577.67\pm 0.27$ | $72.18\pm 0.19$ | $331.4\pm 2.0$
59834.713 | $15142.24\pm 0.65$ | $2748.2\pm 1.3$ | $759.9\pm 2.5$ | $34.251\pm 0.016$ | $611.63\pm 0.17$ | $77.76\pm 0.11$ | $227.2\pm 1.3$
59836.668 | $15144.5\pm 1.0$ | $2753.3\pm 2.0$ | $684.3\pm 5.3$ | $34.220\pm 0.025$ | $577.48\pm 0.28$ | $74.86\pm 0.20$ | $278.6\pm 2.0$
59839.738 | $15144.60\pm 0.85$ | $2753.5\pm 1.7$ | $734.0\pm 4.0$ | $34.245\pm 0.021$ | $577.22\pm 0.23$ | $75.10\pm 0.15$ | $215.0\pm 1.7$
59841.810 | $15146.51\pm 0.94$ | $2755.1\pm 1.9$ | $754.1\pm 4.7$ | $34.241\pm 0.023$ | $575.28\pm 0.26$ | $75.27\pm 0.18$ | $372.5\pm 1.9$
59842.586 | $15146.60\pm 0.81$ | $2755.7\pm 1.6$ | $698.5\pm 3.8$ | $34.224\pm 0.020$ | $582.39\pm 0.21$ | $74.69\pm 0.14$ | $231.0\pm 1.6$
59844.569 | $15143.30\pm 0.89$ | $2753.6\pm 1.8$ | $784.1\pm 4.7$ | $34.226\pm 0.022$ | $581.37\pm 0.23$ | $76.93\pm 0.16$ | $260.2\pm 1.8$
59846.556 | $15139.44\pm 0.87$ | $2757.2\pm 1.7$ | $876.1\pm 4.5$ | $34.166\pm 0.021$ | $624.29\pm 0.23$ | $82.24\pm 0.16$ | $281.8\pm 1.7$
59848.563 | $15143.03\pm 0.90$ | $2758.3\pm 1.8$ | $794.7\pm 4.7$ | $34.178\pm 0.022$ | $581.16\pm 0.24$ | $76.12\pm 0.16$ | $295.8\pm 1.8$
59850.543 | $15141.32\pm 0.96$ | $2759.0\pm 1.9$ | $776.4\pm 5.4$ | $34.157\pm 0.024$ | $572.63\pm 0.25$ | $75.76\pm 0.17$ | $165.4\pm 1.9$
59852.544 | $15139.75\pm 0.72$ | $2754.6\pm 1.4$ | $777.6\pm 3.2$ | $34.215\pm 0.018$ | $596.29\pm 0.18$ | $78.35\pm 0.12$ | $237.3\pm 1.4$
59852.749 | $15139.74\pm 0.66$ | $2758.4\pm 1.3$ | $782.7\pm 2.7$ | $34.199\pm 0.016$ | $588.54\pm 0.17$ | $77.55\pm 0.11$ | $294.7\pm 1.3$
59854.545 | $15140.52\pm 0.85$ | $2754.3\pm 1.7$ | $770.0\pm 4.2$ | $34.199\pm 0.021$ | $594.01\pm 0.23$ | $76.82\pm 0.15$ | $231.0\pm 1.7$
Table 9: Complete HARPS RVs and FWHMs of GJ 1018 acquired between 15 December 2018 and 8 January 2019 under the program ID 1102.C-0339. This table is accessible through CDS (see link in the main title caption). RJD (days) | RV ($\rm m\,s^{-1}$) | FWHM ($\rm m\,s^{-1}$)
---|---|---
58467.550 | $15127.3\pm 1.8$ | $2960.4\pm 1.8$
58467.635 | $15131.3\pm 2.4$ | $2956.1\pm 2.4$
58468.566 | $15126.5\pm 1.9$ | $2959.4\pm 1.9$
58469.627 | $15126.6\pm 2.1$ | $2961.8\pm 2.1$
58471.576 | $15131.1\pm 2.0$ | $2968.6\pm 2.0$
58474.620 | $15127.7\pm 2.3$ | $2955.3\pm 2.3$
58475.641 | $15134.9\pm 3.0$ | $2954.2\pm 3.0$
58477.600 | $15129.2\pm 2.1$ | $2951.3\pm 2.1$
58478.603 | $15129.2\pm 2.1$ | $2953.5\pm 2.1$
58479.601 | $15126.5\pm 1.7$ | $2958.9\pm 1.7$
58484.586 | $15130.8\pm 1.8$ | $2969.1\pm 1.8$
58487.555 | $15133.5\pm 2.0$ | $2962.2\pm 2.0$
58488.590 | $15136.7\pm 2.2$ | $2964.9\pm 2.2$
58489.573 | $15128.6\pm 2.6$ | $2964.9\pm 2.6$
58491.613 | $15117.8\pm 2.8$ | $2956.2\pm 2.8$
Table 10: ASAS-SN photometry (Shappee et al. 2014) of GJ 1018 computed by shifting the photometric aperture every 1 October in order to ensure the target centering. The complete table is accessible through CDS (see link in the main title caption). HJD (days) | Flux (mJy) | Camera | Filter
---|---|---|---
2456788.907 | $32.464\pm 0.080$ | bf | V
2456789.906 | $32.402\pm 0.075$ | bf | V
2456794.907 | $32.41\pm 0.13$ | bf | V
2456805.887 | $31.331\pm 0.066$ | bf | V
2456808.883 | $31.868\pm 0.049$ | bf | V
2456809.884 | $30.971\pm 0.072$ | bf | V
2456810.872 | $32.599\pm 0.085$ | bf | V
… | … | … | …
2458014.683 | $16.229\pm 0.029$ | bj | g
2458015.680 | $16.303\pm 0.028$ | bj | g
2458016.678 | $16.282\pm 0.049$ | bj | g
2458018.673 | $16.214\pm 0.028$ | bj | g
2458019.670 | $16.314\pm 0.028$ | bj | g
2458020.663 | $16.236\pm 0.031$ | bj | g
2458021.656 | $16.289\pm 0.026$ | bj | g
… | … | … | …
2458068.440 | $16.650\pm 0.023$ | bn | g
2458076.419 | $16.700\pm 0.020$ | bn | g
2458080.404 | $16.618\pm 0.020$ | bn | g
2458081.402 | $16.404\pm 0.028$ | bn | g
2458082.380 | $16.461\pm 0.031$ | bn | g
2458084.287 | $16.641\pm 0.035$ | bn | g
2458085.268 | $16.782\pm 0.064$ | bn | g
… | … | … | …
2458398.671 | $16.381\pm 0.027$ | bF | g
2458404.736 | $16.470\pm 0.033$ | bF | g
2458406.512 | $16.181\pm 0.043$ | bF | g
2458407.551 | $16.134\pm 0.034$ | bF | g
2458408.512 | $16.305\pm 0.038$ | bF | g
2458413.678 | $15.659\pm 0.065$ | bF | g
2458415.775 | $16.02\pm 0.12$ | bF | g
… | … | … | …
Table 11: Absolute fluxes in different filters that make up the SED of GJ 1018. All the catalogs were accessed through the Spanish Virtual Observatory (SVO; Bayo et al. 2008). The complete table is accessible through CDS (see link in the main title caption). Filter ID | Abs. Flux ($\rm erg\,s^{-1}cm^{-2}\AA^{-1}$)
---|---
GALEX/GALEX.NUV | $\left(7.6\pm 3.1\right)\times 10^{-17}$
Misc/APASS.B | $\left(6.48\pm 0.15\right)\times 10^{-14}$
Generic/Johnson.B | $\left(6.503\pm 0.057\right)\times 10^{-14}$
SLOAN/SDSS.g | $\left(9.85\pm 0.18\right)\times 10^{-14}$
GAIA/GAIA3.Gbp | $\left(1.3338\pm 0.0038\right)\times 10^{-13}$
Misc/APASS.V | $\left(1.527\pm 0.039\right)\times 10^{-13}$
Generic/Johnson.V | $\left(1.5008\pm 0.0054\right)\times 10^{-13}$
HST/ACS$\\_$WFC.F606W | $\left(1.9170\pm 0.0036\right)\times 10^{-13}$
GAIA/GAIA3.G | $\left(2.9298\pm 0.0076\right)\times 10^{-13}$
SLOAN/SDSS.r | $\left(2.038\pm 0.057\right)\times 10^{-13}$
Generic/Johnson.R | $\left(2.5561\pm 0.0038\right)\times 10^{-13}$
SLOAN/SDSS.i | $\left(4.331\pm 0.084\right)\times 10^{-13}$
GAIA/GAIA3.Grp | $\left(4.327\pm 0.015\right)\times 10^{-13}$
HST/ACS$\\_$WFC.F814W | $\left(4.8812\pm 0.0055\right)\times 10^{-13}$
Generic/Johnson.I | $\left(4.9304\pm 0.0062\right)\times 10^{-13}$
GAIA/GAIA3.Grvs | $\left(5.109\pm 0.032\right)\times 10^{-13}$
SLOAN/SDSS.z | $\left(5.5047\pm 0.0052\right)\times 10^{-13}$
PAN-STARRS/PS1.y | $\left(5.5937\pm 0.0075\right)\times 10^{-13}$
2MASS/2MASS.J | $\left(4.492\pm 0.095\right)\times 10^{-13}$
2MASS/2MASS.H | $\left(2.765\pm 0.084\right)\times 10^{-13}$
2MASS/2MASS.Ks | $\left(1.354\pm 0.036\right)\times 10^{-13}$
WISE/WISE.W1 | $\left(3.013\pm 0.063\right)\times 10^{-14}$
WISE/WISE.W2 | $\left(9.75\pm 0.16\right)\times 10^{-15}$
WISE/WISE.W3 | $\left(2.880\pm 0.050\right)\times 10^{-16}$
WISE/WISE.W4 | $\left(2.46\pm 0.30\right)\times 10^{-17}$
OAJ/JPAS.J0400 | $\left(3.494\pm 0.091\right)\times 10^{-14}$
OAJ/JPLUS.J0410 | $\left(4.262\pm 0.066\right)\times 10^{-14}$
OAJ/JPAS.J0410 | $\left(4.427\pm 0.077\right)\times 10^{-14}$
OAJ/JPAS.J0420 | $\left(3.706\pm 0.077\right)\times 10^{-14}$
OAJ/JPLUS.J0430 | $\left(4.107\pm 0.067\right)\times 10^{-14}$
OAJ/JPAS.J0430 | $\left(4.154\pm 0.082\right)\times 10^{-14}$
OAJ/JPAS.J0440 | $\left(5.994\pm 0.094\right)\times 10^{-14}$
OAJ/JPAS.J0450 | $\left(8.53\pm 0.11\right)\times 10^{-14}$
OAJ/JPAS.J0460 | $\left(1.017\pm 0.012\right)\times 10^{-13}$
OAJ/JPAS.J0470 | $\left(9.77\pm 0.13\right)\times 10^{-14}$
OAJ/JPAS.gSDSS | $\left(1.0074\pm 0.0019\right)\times 10^{-13}$
OAJ/JPLUS.gSDSS | $\left(1.0286\pm 0.0019\right)\times 10^{-13}$
OAJ/JPAS.J0480 | $\left(9.29\pm 0.13\right)\times 10^{-14}$
OAJ/JPAS.J0490 | $\left(9.90\pm 0.13\right)\times 10^{-14}$
OAJ/JPAS.J0500 | $\left(1.052\pm 0.013\right)\times 10^{-13}$
OAJ/JPAS.J0510 | $\left(1.009\pm 0.014\right)\times 10^{-13}$
OAJ/JPLUS.J0515 | $\left(1.079\pm 0.0098\right)\times 10^{-13}$
OAJ/JPAS.J0520 | $\left(1.228\pm 0.015\right)\times 10^{-13}$
OAJ/JPAS.J0530 | $\left(1.547\pm 0.015\right)\times 10^{-13}$
OAJ/JPAS.J0540 | $\left(1.616\pm 0.016\right)\times 10^{-13}$
OAJ/JPAS.J0550 | $\left(1.601\pm 0.016\right)\times 10^{-13}$
OAJ/JPAS.J0560 | $\left(1.609\pm 0.016\right)\times 10^{-13}$
OAJ/JPAS.J0570 | $\left(1.629\pm 0.017\right)\times 10^{-13}$
OAJ/JPAS.J0580 | $\left(1.657\pm 0.016\right)\times 10^{-13}$
OAJ/JPAS.J0590 | $\left(1.602\pm 0.015\right)\times 10^{-13}$
OAJ/JPAS.J0600 | $\left(1.547\pm 0.014\right)\times 10^{-13}$
OAJ/JPAS.J0610 | $\left(1.582\pm 0.015\right)\times 10^{-13}$
OAJ/JPAS.rSDSS | $\left(2.1212\pm 0.0024\right)\times 10^{-13}$
… | …
Table 12: Inferred parameters of TOI-244 b obtained from the TESS light curve analysis, ESPRESSO and HARPS radial velocities analysis, and the final joint analysis based on the TESS, ESPRESSO and HARPS data.  Parameter | TESS photometry | ESPRESSO and HARPS RVs | Joint fit
---|---|---|---
Orbital parameters
$P$ [days] | $7.397225^{+0.000026}_{-0.000024}$ | $7.39714^{+0.00074}_{-0.00075}$ | $7.397225^{+0.000026}_{-0.000023}$
$T_{\rm 0}$ [JD] | 2458357.3627 $\pm$ 0.0020 | 2458357.3622 $\pm$ 0.017 | 2458357.3627 $\pm$ 0.0020
$i$ [degrees] | 88.32 $\pm$ 0.16 | – | 88.32 $\pm$ 0.16
Planet parameters |
$R_{\rm p}$/$R_{\rm\star}$ | $0.0326^{+0.0017}_{-0.0018}$ | – | $0.0326^{+0.0017}_{-0.0018}$
$K$ [$\rm m/s$] | – | 1.54 $\pm$ 0.16 | 1.55 $\pm$ 0.16
Stellar parameters |
$M_{\rm\star}$ $[\rm M_{\odot}]$ | 0.428 $\pm$ 0.029 | – | 0.428 $\pm$ 0.029
$R_{\rm\star}$ $[\rm R_{\odot}]$ | 0.426 $\pm$ 0.025 | – | 0.426 $\pm$ 0.025
$u1$ | 0.2204 $\pm$ 0.0011 | – | 0.2204 $\pm$ 0.0011
$u2$ | 0.4112 $\pm$ 0.0017 | – | 0.4112 $\pm$ 0.0017
Matérn-3/2 GP hyperparameters |
$\eta_{\sigma_{S2}}$ [$\rm e^{-}/s$] | $0.88^{+0.42}_{-0.22}$ | – | $0.87^{+0.36}_{-0.21}$
$\eta_{\rho_{S2}}$ [days] | $1.13^{+0.95}_{-0.45}$ | – | $1.09^{+0.79}_{-0.45}$
$\eta_{\sigma_{S29}}$ [$\rm e^{-}/s$] | $1.80^{+0.63}_{-0.34}$ | – | $1.79^{+0.63}_{-0.34}$
$\eta_{\rho_{S29}}$ [days] | $0.79^{+0.43}_{-0.25}$ | – | $1.09^{+0.44}_{-0.24}$
Quasiperiodic GP hyperparameters |
$\eta_{\rm 1,RV}$ [$\rm m/s$] | – | $3.5^{+1.0}_{-0.7}$ | $3.5^{+1.0}_{-0.7}$
$\eta_{\rm 2,RV}$ [days] | – | $86^{+26}_{-23}$ | $85^{+26}_{-23}$
$\eta_{\rm 3,RV}$ [days] | – | $53.2^{+1.3}_{-1.1}$ | $53.3^{+1.2}_{-1.1}$
$\eta_{\rm 4,RV}$ | – | $0.62^{+0.11}_{-0.09}$ | $0.62^{+0.11}_{-0.09}$
$\eta_{\rm 1,FWHM}$ [$\rm m/s$] | – | $3.34^{+0.70}_{-0.53}$ | $3.35^{+0.70}_{-0.54}$
Instrument-dependent parameters (TESS) |
$F_{\rm 0,S2}$ [$\rm e^{-}/s$] | $0.04^{+0.31}_{-0.32}$ | – | $0.05^{+0.29}_{-0.30}$
$F_{\rm 0,S29}$ [$\rm e^{-}/s$] | $0.17^{+0.56}_{-0.54}$ | – | $0.18^{+0.54}_{-0.55}$
$\sigma_{\rm TESS,S2}$ [$\rm e^{-}/s$] | $0.21^{+0.23}_{-0.15}$ | – | $0.20^{+0.23}_{-0.15}$
$\sigma_{\rm TESS,S29}$ [$\rm e^{-}/s$] | $1.90^{+0.53}_{-0.76}$ | – | $1.92^{+0.51}_{-0.74}$
Instrument-dependent parameters (ESPRESSO and HARPS) |
$\gamma_{\rm ESPR,RV}$ [$\rm m/s$] | – | $15141.0^{+1.2}_{-1.3}$ | $15141.0^{+1.2}_{-1.3}$
$\gamma_{\rm HAR,RV}$ [$\rm m/s$] | – | $15128.5^{+2.5}_{-2.6}$ | $15128.5^{+2.4}_{-2.6}$
$\gamma_{\rm ESPR,FWHM}$ [$\rm m/s$] | – | 2750.9 $\pm$ 1.2 | 2750.9 $\pm$ 1.2
$\gamma_{\rm HAR,FWHM}$ [$\rm m/s$] | – | 2959.9 $\pm$ 2.6 | 2959.9 $\pm$ 2.6
$\sigma_{\rm ESPR,RV}$ [$\rm m/s$] | – | $0.19^{+0.21}_{-0.13}$ | $0.19^{+0.20}_{-0.13}$
$\sigma_{\rm HAR,RV}$ [$\rm m/s$] | – | $2.9^{+1.3}_{-1.1}$ | $2.9^{+1.3}_{-1.1}$
$\sigma_{\rm ESPR,FWHM}$ [$\rm m/s$] | – | $0.55^{+0.39}_{-0.36}$ | $0.56^{+0.39}_{-0.37}$
$\sigma_{\rm HAR,FWHM}$ [$\rm m/s$] | – | $4.4^{+1.5}_{-1.2}$ | $4.4^{+1.5}_{-1.2}$