# Probing the Magnetised Gas Distribution in Galaxy Groups and the Cosmic Web
with POSSUM Faraday Rotation Measures

Craig S. Anderson1, N. M. McClure-Griffiths1, L. Rudnick2, B. M.
Gaensler3,6,14, S. P. O’Sullivan4, S. Bradbury1, T. Akahori5, L. Baidoo6, M.
Bruggen7, E. Carretti8, S. Duchesne9, G. Heald9, S. L. Jung10, J.
Kaczmarek11,9, D. Leahy12, F. Loi8, Y. K. Ma1, E. Osinga6, A. Seta1, C.
Stuardi,8 A. J. M. Thomson9, C. Van Eck1, T. Vernstrom13, J. West11
1Research School of Astronomy & Astrophysics, Australian National University,
Canberra, ACT 2611, Australia
2University of Minnesota, Minneapolis, MN 55455, USA
3Department of Astronomy and Astrophysics, University of California Santa
Cruz, 1156 High Street, Santa Cruz, CA 95060, USA
4Departamento de Física de la Tierra y Astrofísica & IPARCOS-UCM, Universidad
Complutense de Madrid, 28040 Madrid, Spain
5National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo
181-8588, Japan
6Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50
St. George St, Toronto, ON M5S 3H4, Canada
7Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg, Germany
8Istituto Nazionale di Astrofisica, Via Gobetti 101, 40129 Bologna, Italy
9ATNF, CSIRO Space & Astronomy, Bentley, WA, Australia
10Department of Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
11Dominion Radio Astrophysical Observatory, Herzberg, National Research
Council, Penticton, BC V2A 6J9, Canada
12University of Calgary, Calgary, AB T2N 1N4, Canada
13University of Western Australia, 35 Stirling Highway, Crawley, WA 6009,
Australia
14David A. Dunlap Department of Astronomy and Astrophysics, University of
Toronto, 50 St. George St, Toronto, ON M5S 3H4, Canada E-mail:
<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present initial results from the Polarisation Sky Survey of the Universe’s
Magnetism (POSSUM), analyzing 22,817 Faraday Rotation Measures (RMs) with
median uncertainties of 1.2 rad m-2 across 1,520 square degrees to study
magnetised gas associated with 55 nearby galaxy groups ($z\lesssim 0.025$)
with halo masses between $10^{12.5}$ and $10^{14.0}$ M⊙. We identify two
distinct gas phases: the Intragroup Medium (IGrM) within 0–2 splashback radii
and the Warm-Hot Intergalactic Medium (WHIM) extending from 2 to 7 splashback
radii. These phases enhance the standard deviation of residual (i.e., Galactic
foreground RM-subtracted) RMs by $6.9\pm 1.8$ rad m-2 and $4.2\pm 1.2$ rad m-2
, respectively. Estimated magnetic field strengths are several $\muup$G within
the IGrM and 0.1–1 $\muup$G in the WHIM. We estimate the plasma $\beta$ in
both phases, and show that magnetic pressure might be more dynamically
important than in the ICM of more massive clusters or sparse cosmic web
filaments. Our findings indicate that ’missing baryons’ in the WHIM likely
extend beyond the gravitational radii of group-mass halos to Mpc scales,
consistent with large-scale, outflow-driven ’magnetised bubbles’ seen in
cosmological simulations. We demonstrate that RM grids are an effective method
for detecting magnetised thermal gas at galaxy group interfaces and within the
cosmic web. This approach complements X-ray and Sunyaev-Zel’dovich effect
methods, and when combined with Fast Radio Burst Dispersion Measures, data
from the full POSSUM survey — comprising approximately a million RMs — will
allow direct magnetic field measurements to further our understanding of
baryon circulation in these environments and the magnetised universe.

###### keywords:

galaxies: groups: general – intergalactic medium – magnetic fields – radio
continuum: galaxies – techniques: polarimetric

††pubyear: 2024††pagerange: Probing the Magnetised Gas Distribution in Galaxy
Groups and the Cosmic Web with POSSUM Faraday Rotation Measures–Probing the
Magnetised Gas Distribution in Galaxy Groups and the Cosmic Web with POSSUM
Faraday Rotation Measures

##  1 Introduction

Most galaxies in the local universe reside in galaxy groups, which are
themselves embedded in the sparse gaseous filaments that form the large-scale
structure of the cosmos (Haan & Braun, 2014; Bond et al., 1996; Eke et al.,
2004; Rost et al., 2024). Galaxy group environments, particularly through
their gaseous, multi-phase, magnetised Intragroup Medium (IGrM), play a
pivotal role in mediating energy and matter exchanges between galaxies and the
cosmic web, between member galaxies, and within individual galaxies, through
processes such as the transmission of shock waves, turbulence, cosmic ray
transport, gas cooling and condensation, and gas heating and redistribution
via feedback from galactic winds and active galactic nuclei (AGN) (Lovisari et
al., 2021; Oppenheimer et al., 2021; Arámburo-García et al., 2021; Ayromlou et
al., 2023).

However, capturing observational insights into the IGrM’s distribution and its
state of magnetization remains challenging. The IGrM typically consists of a
hot phase ($>10^{7}$ K) that is dominant within galaxy groups, a warm-hot
intergalactic medium (WHIM; $\sim 10^{6}$ K) that becomes dominant near the
peripheries of groups, and cooler gas pockets ($\sim 10^{5}$ K) associated
with individual group galaxies (Oppenheimer et al., 2021). While X-ray
emissions can be used to observe the hot IGrM, they are less well suited to
this task than to studies of the intracluster medium (ICM). This is due to the
sparser and cooler nature of the IGrM gas, the weakness of bremsstrahlung
compared to line emissions, and the complexity of the X-ray emissivity curve
at temperatures below 1 keV (Lovisari et al., 2021). Meanwhile, UV absorption
techniques provide rich information about cool and warm gas phases in groups
(Stocke et al., 2019; McCabe et al., 2021) but are limited by the density of
bright background quasars.

Given these limitations, Faraday Rotation Measures (RMs) emerge as a potent
alternative for mapping these elusive gas phases. When linearly polarised
electromagnetic radiation traverses magnetised plasma, it experiences rotation
in its polarization angle (PA) according to

${\rm PA}(\lambda^{2})={\rm
PA}_{0}+\left[0.81\int_{source}^{0}n_{e}(s)B_{\parallel}(s)\,{\rm
d}s\right]\cdot\lambda^{2}\equiv{\rm PA}_{0}+{\rm RM}\cdot\lambda^{2}{\rm,}$
(1)

where $\lambda$ [m] is the observed wavelength, ${\rm PA}_{0}$ [rad] is the
intrinsic PA (determined by the orientation of the magnetic field in the
synchrotron-emitting source), $s$ is the displacement along the line-of-sight
to the source [pc], $n_{e}$ [${\rm cm}^{-3}$] is the electron number density,
$B_{\parallel}$ [$\mu{\rm G}$] is the magnetic field strength along the line-
of-sight ($s$), RM [${\rm rad\,m}^{-2}$] is the Faraday Rotation Measure, and
the integral is taken along the line of sight from the location of the source
to the observer at $s=0$.

By measuring the RM towards ensembles of distant unresolved radio galaxies
(so-called ’RM grids’; Gaensler et al., 2004), it is possible to detect and
map magnetised plasma in a wide variety of foreground environments, limited
only by the factors encapsulated in Equation 1, and the sensitivity of radio
observations, which determines the density of background RMs (e.g. Loi et al.,
2019). The advent of sophisticated, wide-field survey instruments such as the
Australian Square Kilometre Array Pathfinder (ASKAP), and the Square Kilometre
Array (SKA) beyond this, has ushered in a revolution in RM grid science
(Johnston-Hollitt et al., 2015; Heald et al., 2020). In particular, ASKAP is
undertaking the POlarisation Sky Survey of the Universe’s Magnetism (POSSUM),
delivering an RM grid covering $2\pi$ steradians with 30–50 RMs per square
degree and median RM uncertainty of $\sim 1$ rad m-2 over the next few years
(Gaensler et al., 2010; Vanderwoude et al., 2024; Gaensler et al., 2024), and
the SPICE-RACS survey (Thomson et al., 2023), delivering an expected $4\pm 2$
RMs per square degree over $3\pi$ steradians even sooner.

A pivotal result from the POSSUM early science phase was the detailed RM grid
study of the Fornax Cluster (Anderson et al., 2021), which at $M\sim
6^{+3}_{-1}\times 10^{13}$ M$\odot$ (Drinkwater et al., 2001), lies on the
boundary between a large galaxy group and a poor cluster. Anderson et al.
(2021) used an RM grid with 25 RMs per square degree, which although inferior
to the full POSSUM survey, improved upon the previous state-of-the-art survey
data (Taylor et al., 2009) by a factor of $\sim 25$. In doing so, they
uncovered a substantial, previously undetected reservoir of magnetised plasma
extending well beyond the detectable X-ray emission from the cluster,
providing insights into the dynamical interactions and shock systems induced
by merging substructures. This work demonstrated that RM grids could
effectively probe gas in halos less massive than large galaxy clusters, where
their value for probing the ICM is already well-established (Clarke et al.,
2001). Motivated by these findings, this paper reports the results of our
search for signatures of magnetised gas in even lower mass halos, both within
and beyond the gravitational boundaries of a diverse sample of galaxy groups,
using initial RM data from the full POSSUM survey currently in progress. It is
organised as follows: Section 2 details the POSSUM observations and our
methods; Section 3 presents our analysis and findings; Section 4 discusses
these in the context of current astrophysical understanding. Given our focus
on the local universe ($cz<10,000$ km/s), we introduce cosmological parameters
only where necessary.

##  2 Methods

###  2.1 POSSUM observations

Our study uses 38 out of the planned 853 pointings for the main ‘Band
1’111POSSUM observes in ASKAP’s band 1 (800–1088 MHz) commensally with EMU
(Norris et al., 2011), but is supplemented by Band 2 (1296–1440 MHz) data from
WALLABY where available (Koribalski et al., 2020). POSSUM survey (Gaensler et
al., 2024), each observed for ten hours and covering $\sim 40$ square degrees,
totaling $\sim$1520 square degrees of sky. These observations were made with
the ASKAP radio telescope (Hotan et al., 2021) from August 2021 to August 2023
during standard survey operations. The observations recorded full polarisation
information over 800-1088 MHz, achieving typical sensitivities of 25–30
$\mu$Jy beam-1 in Stokes I (Norris et al., 2021; Vanderwoude et al., 2024) and
17 $\mu$Jy beam-1 in each of Stokes Q and U (Gaensler et al., 2024). Further
details of the polarimetric calibration and imaging with ASKAP can be found in
Chippendale & Anderson (2019); Thomson et al. (2023); Gaensler et al. (2024).
Each observation (or scheduling block; SB) underwent standardised validation
procedures described in Gaensler et al. (2024) for polarisation-specific data
products and additionally, those described in Norris et al. (2021) for total
intensity data products. We excluded SBs that failed these tests. Table 1
summarises our observations.

Table 1: Summary of observations

No. of ASKAP pointings | 38
---|---
SBIDs included in this work | 31375, 33370, 33460, 33482, 33509, 33553, 43137, 43237, 43773,
| 45761, 45781, 45811, 46925, 46943, 46946, 46951, 46962, 46966, 46971,
| 46976, 46982, 46984, 46986, 47034, 47130, 47136, 49990, 49992, 50011,
| 50230, 50413, 50415, 50423, 50538, 50787, 51431, 51574, 51818
Total period of observations | 28-08-2021 to 03-08-2023
Typical full-band sensitivity in Stokes $Q$, $U$ † | 17 $\muup$Jy beam-1 / Stokes
Total sky coverage |  $\sim 1520$ deg2
Typical Shortest baseline | 22.4 m
Typical Longest baseline | 6.4 km
Typical Angular resolution (robust $=-0.5$) | 13$\times$17 arcsec
Typical Largest recoverable angular scale‡‡ | 30 arcmin
Frequency range (central frequency) | 800–1088 MHz (288 MHz)
$\lambda^{2}$ range | 0.076–0.141 m2
RM Spread Function FWHM a | 59 rad m-2
Largest recoverable RM-scale‡‡ a | 37 rad m-2
Largest recoverable |RM| a | 8100 rad m-2
No. of RMs in final sample | 22,817
Median RM uncertainty | 1.2 rad m-2

  * •

† Measured per Stokes parameter in multi-frequency synthesis images generated
with a Briggs’ robust weighting value of -0.5. ‡ At the centre frequency of
the band. ‡‡ At greater than 50% sensitivity. a Calculated using RM-Tools
version 1.3.1222https://github.com/CIRADA-Tools/RM-Tools(Purcell et al.,
2020), quoted to 2 significant figures.

###  2.2 The RM sample

We extracted RMs by further processing the basic ASKAPsoft pipeline (Guzman et
al., 2019) outputs. We note that this does not represent the final POSSUM
survey workflow and its associated pipeline data products, which were not
available for a significant portion of the sky at the time this publication
was prepared. The ASKAPsoft pipeline uses RM synthesis (Brentjens & de Bruyn,
2005) to calculate Faraday Dispersion Functions (FDFs) from Stokes $Q/I$ and
$U/I$ spectra (formed by channel-wise division) extracted from source
components identified in total intensity mosaics of each field using Selavy
(Whiting & Humphreys, 2012). We downloaded raw FDFs from the CSIRO ASKAP
Science Data Archive333https://data.csiro.au/domain/casda (CASDA; Huynh et
al., 2020) for the SBs in Table 1, then used RM-Tools444RM-Tools version
1.3.1; https://github.com/CIRADA-Tools/RM-Tools (Purcell et al., 2020) to
extract RMs, polarised signal-to-noise ratios, and their uncertainties for all
radio source components, which are calculated using a quadratic fit to the
main peak of the FDF.

###  2.3 The galaxy group sample

We cross-matched our RMs against the combined catalogues of Makarov &
Karachentsev (2011) and Tully (2015), hereafter referred to as the ‘MKT’
catalogue of galaxy groups. This comprehensive catalogue classifies galaxies
based on parameters including mass, recession velocity (‘cz’), and proximity
to neighbouring galaxies. The Makarov & Karachentsev (2011) catalogue covers
the radial velocity range of $0<{\rm cz}<3500$ km/s, while the Tully (2015)
catalogue spans the range $3000<{\rm cz}<10000$ km/s. Both catalogues provide
integrated K-band luminosity estimates for member galaxies, enabling us to
estimate the mass and characteristic radius of each galaxy group. The grouping
procedure relies solely on these observables and covers nearly the entire
celestial sphere, so is well-suited for our study. The MKT catalogue
encompasses a wide range of group mass, from approximately $1\times 10^{11}$
M⊙ (an order of magnitude less massive than the Milky Way) to roughly $1\times
10^{15}$ M⊙ (the Coma galaxy cluster).

###  2.4 RM-Group impact parameters and associations

Our goal is to associate each POSSUM RM with its nearest MKT galaxy group by
calculating the RM impact parameter for each group (and to exclude RMs from
subsequent analysis that are most closely associated with clusters, or fail to
meet quality control criteria, as discussed in Section 2.6).

Since the MKT groups have a wide range of distances and masses, and the radius
of the IGrM will depend on these properties, we need to establish a fiducial
radius to scale the RM impact parameters. To do so, we use the integrated
K-band luminosities listed in the MKT catalogue to calculate the group halo
mass ($M_{\text{group}}$) using Equation 7 in Tully (2015):

$M_{\text{group}}=32.25\times 10^{10}(L_{10})^{1.15}~{}\text{M}_{\odot},$ (2)

where $L_{10}$ denotes the $K$-band luminosity in units of
$10^{10}~{}L_{\odot}$, assuming $H_{0}=75$ km/s/Mpc. Subsequently, we estimate
the splashback radius ($R_{spr}$) in Mpc for each group, which represents the
region enclosed by the apoapses of gravitationally accreted galaxies following
their initial transit through the group halo. The splashback radius is known
to correspond to a physical, observable cusp, beyond which matter density
decreases rapidly with the projected radius (e.g., Diemer & Kravtsov, 2014;
Adhikari et al., 2014; More et al., 2015). This relationship can be expressed
as:

$R_{spr}=0.216\times\left(\frac{M_{\text{group}}}{10^{12}~{}\text{M}_{\odot}}\right)^{1/3}~{}\text{Mpc}.$
(3)

Given that the MKT catalog provides group distances, we can convert
$R_{\text{spr}}$ to an angular scale on the sky denoted by
$\theta_{\text{spr}}$. We can also calculate the angular impact parameter
denoted $\beta_{\text{RM}}$ from the center of the group for each RM, assuming
the center to be the position of the most mass-dominant member galaxy. Using
the splashback radius as a fiducial point to scale the RM-group offsets, we
then calculate the scaled impact parameter
$\xi=\beta_{\text{RM}}/\theta_{\text{spr}}$. $\xi$ thus represents the
distance of the RM measurement from the nearest galaxy group in multiples of
the splashback radius of that group.

###  2.5 Galactic RM foreground corrections

To isolate extragalactic contributions in our POSSUM RM measurements, we must
subtract the Galactic RM foreground to derive residual RMs (RRMs) reflecting
extragalactic Faraday rotation (Kim et al., 1991). There are many possible
approaches to estimate the foreground. Models such as those by Hutschenreuter
et al. (2022) provide one option, but are based on low-resolution RM data
(about 1 RM per square degree) and so are not expected to adequately capture
the fine-scale Galactic variations that our high-resolution POSSUM data (at
least 30 RMs per square degree) can constrain.

On the other hand, L. Jung (personal communication, April 27, 2024) noted that
galaxy group halos might contribute spatially-correlated RMs of several rad
m-2 up to distances of $r_{200}$, as shown in IllustrisTNG50 data (Nelson et
al., 2019). Observational support for this comes from Anderson et al. (2021),
who documented coherent IGrM/ICM-based RM structures extending out to the
virial radius in the Fornax cluster—effectively a large galaxy group—over
scales exceeding one degree. This implies that galaxy group halos can
significantly influence the local RM field on scales that might otherwise be
assumed to be Galactic foreground structure. Clearly, it is critical to avoid
subtracting the very signal we wish to study. Anderson et al. (2021) addressed
this by fitting a linear 2D RM foreground model over a single ASKAP field,
balancing the influence of the smaller cluster RM structures with numerous
‘off-source’ RMs from the surrounding area. However, this method is unsuitable
for the present stacking experiment, not least because it could create
discontinuities in RRMs across ASKAP tile boundaries.

These challenges underscore the ongoing complexity of Galactic RM foreground
removal, which is a topic of active research for next-generation high-density
RM grid experiments like POSSUM (Khadir et al., 2024). For our purposes, we
employ an aperture-based method to subtract the foreground while retaining
group-based Faraday rotation signals:

  1. 1.

Exclusion Zone: We set a 0.4-degree exclusion radius around each RM source to
prevent subtraction of coherent IGrM-based signals.

  2. 2.

Median Estimation: We calculate the median RM of the next-nearest 40 RMs
surrounding the source, using this as the local Galactic foreground estimate.

  3. 3.

Foreground Subtraction: We subtract this estimated foreground from the
source’s RM to obtain the RRM.

The outer bound of the annulus encompassing these 40 RMs typically extends
between 0.7 degrees (10th percentile) and 1 degree (90th percentile), with an
average radius of 0.8 degrees (50th percentile). We chose a 0.4-degree
exclusion radius because it (a) corresponds to the width of coherent RM
features observed in the Fornax cluster (Anderson et al., 2021), (b) aligns
with the splashback radius of the smallest groups in our sample, and so treats
all groups consistently on this basis, and (c) corresponds to the approximate
scale where Galactic foreground contributions appear to drop below intrinsic
source contributions at mid-Galactic latitudes (Anderson et al., 2015;
Vanderwoude et al., 2024).

To quantify the uncertainty in the foreground subtraction, we used a bootstrap
resampling technique where for each RRM value we derive, we:

  1. 1.

resample the RMs in the surrounding annular region 1000 times, and calculate
the median RM for each sample.

  2. 2.

compute the standard deviation of the bootstrap medians

  3. 3.

add this bootstrap-derived uncertainty with the observed $1\sigma$ RM
uncertainty in quadrature to obtain the total uncertainty for each corrected
RRM.

The median $1\sigma$ uncertainty of the Galactic foreground RM derived in this
way is 1.2 rad m-2 , and the median $1\sigma$ total RRM uncertainty is 2.0 rad
m-2 .

Figure 1 illustrates a histogram of uncorrected RMs versus RRMs, with RRMs
well-fit by a Student t-distribution with df=1.84±0.03, location=0.02±0.06 rad
m-2 , and scale=6.41±0.06 rad m-2 . The df value indicates tail-heavy
distribution with outliers, as expected (Taylor et al., 2009; Schnitzeler,
2010; Arámburo-García et al., 2021; Thomson et al., 2023). The scale value
closely aligns with existing measurements of RM dispersion in extragalactic
point sources (Taylor et al., 2024; Schnitzeler, 2010). Variations in the
exclusion radius (0.2 to 1 degree) and the number of RMs (20 to 50) showed
only minor effects on these results and those presented in Section 3.1, in the
latter case mainly affecting the DC offset of the RM dispersion curves we
calculate (by up to 1 rad m-2 ) but not the ‘stepped’ RM dispersion
enhancement structure of the curve that we derive our key results from.

These results affirm the effectiveness and reliability of our foreground
subtraction approach, and demonstrate robustness against methodological
variations.

###  2.6 The final RM-group sample

To ensure the reliability of our RRMs and the robustness of our findings, we
implemented several cuts to the data, _excluding_ RRMs associated with sources
that meet any of the following conditions:

  1. 1.

have linearly polarised signal-to-noise ratios $<10$, to conservatively
mitigate spurious RM measurements (e.g. Macquart et al., 2012; Anderson et
al., 2015);

  2. 2.

are located at $|b|<20^{\circ}$ to mitigate the possible impact of small-scale
RM structure in the Galactic foreground;

  3. 3.

are within $10^{\circ}$ of the Large Magellanic Cloud, where RMs may be biased
by contributions from that system (McClure-Griffiths, _in prep._);

  4. 4.

are situated in regions affected by imaging artifacts caused by bright,
resolved radio galaxies, typically within 1-degree separation;

  5. 5.

are sub-components (from source-finding) of bright, resolved radio galaxies;

  6. 6.

are associated (that is, are closest in terms of $\xi$) with halos outside the
mass range of interest (i.e., log10(M⊙)<12.5 or log10(M⊙)>14.0); or,

  7. 7.

are associated with groups having fewer than three member galaxies 555Tully
(2015) define groups with as few as a single member galaxy, explaining their
rationale therein. in the MKT catalogue;

Our final sample consists of 55 galaxy groups with redshifts in the range
$0.005\lesssim z\lesssim 0.025$, analysed using a total of 22,817 RRMs
measured towards background radio sources 666Radio sources brighter than
$\sim$1 mJy in total intensity at $\sim$GHz frequencies — satisfied by 98.5%
of our RM sources — overwhelmingly lie at greater redshifts than our sample
maximum of $z\approx 0.025$ (e.g. Magliocchetti et al., 2000; de Zotti et al.,
2010), covering a combined sky area of 1520 square degrees. It should be noted
that the RMs are apportioned across these groups unevenly, as illustrated in
Figure 2. The figure shows that 90% of the RMs are associated with half of the
groups, 50% of the RMs with only six groups, and a single group accounts for
nearly 20% of the total RMs. We further note that the distribution of RM
sampling across $\xi$ ranges for galaxy groups is also uneven, as shown in
Figure 3. This arises from present limitations in POSSUM’s areal sky coverage
coupled with the tendency of nearby or massive groups to occupy larger angular
scales. The situation will improve as POSSUM progresses towards full coverage
of the southern sky in the coming years. We characterise the potential impact
of this skewed distribution on our main findings in Section 3.4.

The groups included in our final sample are catalogued in Table
LABEL:tab:groups. The columns are Right Ascension (RA, J2000); Declination
(Decl., J2000); number of group members (N Members); Splashback Radius (Mpc);
Splashback Radius (Degrees); Distance (Mpc); Halo Mass ($\times 10^{13}$ M⊙);
Tully Nest ID; Parent SBID; Principal Galaxies Catalogue ID (PGC); Common
designation of dominant galaxy (New General/Index/Messier Catalogues).

Figure 1: Histograms of RM (blue) and RRM (orange) for our final sample. The
bin values for RRMs have been fitted with a Student’s t-distribution (red
line), as discussed in the main text. The best-fit parameter values are listed
in the yellow text box. Figure 2: Cumulative proportion of total RMs
associated with galaxy groups in our final sample, sorted by their relative
contribution to the total of 22,817 RM measurements, and truncated to include
only those 26 groups contributing the first 90% of this total. Tick marks at
0.1 intervals on the x-axis mark the cumulative proportion up to the 90%
threshold. ‘Nest’ IDs (the IDs given to individual groups and clusters in the
MKT catalogue) on the truncated y-axis identify the key contributing groups,
with ’[T15]’ indicating that the groups (and associated group identifiers) are
drawn from the Tully (2015) catalogue, and ’[MK11]’ from the Makarov &
Karachentsev (2011) catalogue. This plot highlights how our analysis and
results are weighted towards a relatively smaller number of sources from the
total sample of 55 objects.

Figure 3: Logarithm of RM count (color scale) binned by scaled impact
parameter ($\xi$;

$x$-axis; in bins of width 0.4) and galaxy group association ($y$-axis). The
latter is organised inversely according to the number of RMs associated with
each group, and the Nest IDs indicate the MKT catalogue designations,
mirroring the arrangement in Figure 2. White areas represent ranges of $\xi$
not covered by the background RMs for each galaxy group.

##  3 Analysis and Results

###  3.1 Residual RM vs. group-centric radius

In Figure 4, we present the distribution of RRMs plotted against multiples of
the splashback radius. It is first worth explaining why there are fewer
sources at large splashback radii, when the opposite might be expected. This
is mainly due to the proximity of groups to one another and our sky coverage.
That is, it becomes increasingly difficult to find regions of POSSUM survey
coverage that are far from the nearest group without encountering other
groups, especially with some nearby massive groups having large angular radii.

Visual inspection of the figure suggests that RRMs penetrating group halos at
smaller multiples of the splashback radius show greater dispersion than those
at larger multiples. To quantify this, we calculated the Median Absolute
Deviation (MAD) within a sliding window of width $\Delta\xi=1$ across the
entire range of $\xi$. Multiplying the MAD by 1.4826 provides a robust
estimate of the standard deviation (Rousseeuw & Croux, 1993) of the RRMs,
denoted as $\sigma_{\text{RRM}}$. The total uncertainty on our RRMs is
typically less than a few rad m-2 (Section 2.5), and so does not drive the
scatter seen in Figure 4. Nevertheless, we adopt a method to remove the small
impact of RRM uncertainties following Dolag et al. (2001). We compute the
corrected RM scatter ($\sigma_{\text{RM,cor}}$) by subtracting the mean
contribution of measurement errors from the observed scatter in quadrature
according to:

$\sigma_{\text{RRM,corr}}=\sqrt{\left(1.4826\times\text{MAD}\right)^{2}-\frac{\sum_{i=1}^{N}(\delta\text{RRM}_{i})^{2}}{N-1}}$
(4)

where $\delta\text{RRM}_{i}$ is the total uncertainty on the RRM of an
individual data point $i$ in the sliding window, and $N$ is the number of data
points in the sliding window. We determined 95% confidence intervals for this
statistic using $10^{4}$ bootstrap resamples within the sliding window. The
results are shown in Figure 5.

Figure 4: RRMs vs. the fiducial scaled impact parameter from their nearest
galaxy group centre ($\xi$), in units of splashback radii, for our sample. The
points are coloured by the square root of the local point density on the plot,
to better reveal structure in the distribution. The white dashed line
indicates RRM$=0$ rad m-2 . Figure 5: Analysis of RRM scatter as a function of
the fiducial scaled impact parameter $\xi$. The purple line is the calculated
$\sigma_{\text{RRM,corr}}$ value within a sliding window, the width of which
is indicated by the grey-shaded bar at lower left. Surrounding the purple
line, the dark magenta shaded region delineates the 95% confidence interval on
the statistic, obtained through bootstrap resampling. The light magenta shaded
region delineates the 95% confidence interval on the statistic under the
action of bootstrap resampling by group association, described in Section 3.4.
The red dotted line, set at $\sigma_{\text{RRM}}=6$ rad m-2 , shows the
expected standard deviation of RRM values for extragalactic point-sources as
per Taylor et al. (2024); Schnitzeler (2010). The light gray lines show the
percentile distribution (1st/99th, 5th/95th, 25th/75th, and 50th) of 1000
$\sigma_{\text{RRM}}$ curves generated identically to the main curve, but
derived from randomly re-pairing RRM values with source positions in each
case, illustrating the behaviour of the metric when the spatial relationship
between source position, RRM, and group position is broken.

The expected RM dispersion for extragalactic point-like field sources, as per
Schnitzeler (2010); Taylor et al. (2024), is $\sigma_{\text{RRM}}\approx 6$
rad m-2 , which falls within the 95% confidence interval of the MAD(RRM) curve
for all $\xi>7$ (Figure 5), where the mean measured value is
$\sigma_{\text{RRM}}=7.1$ rad m-2 . This similarity suggests that the RRM
contribution from gas associated with galaxy groups is minimal beyond seven
splashback radii, allowing us to categorise these as ’field RMs’. Conversely,
inside $\xi\sim 7$, the RRM dispersion significantly exceeds
$\sigma_{\text{RRM}}=6$ rad m-2 .

To evaluate the statistical significance of the observed differences in |RRM|
distributions between field sources and non-field sources (those inside
$\xi=7$), we employed the Kruskal-Wallis H-test, which assesses differences in
median values, and the two-sample Kolmogorov-Smirnov test, which examines the
overall distributions. The results, detailed in Table 2, strongly support the
rejection of the null hypothesis

Table 2: Summary of Statistical Test Results comparing the magnitudes of field RRMs to non-field RRMs (defined by $\xi>7$ and $\xi<7$, respectively). Results are quoted to 3 significant figures. Test Name | Test Statistic | p-value
---|---|---
Kruskal-Wallis H-test | 141 | $1.48\times 10^{-32}$
Kolmogorov-Smirnov Test | 0.0973 | $2.48\times 10^{-33}$

Visual inspection of the MAD(RRM) curve identifies two distinct increases in
value as it nears group centers from larger scaled distances: first at
$\xi\sim 7$ and then at $\xi\sim 2$. On this basis, we change our approach
from hypothesis testing—that is, whether RRM dispersion in non-field sources
is higher compared to field sources—to an exploratory analysis. We apply the
same statistical tests to compare distributions of absolute RRMs bounded by
these $\xi$ ranges identified visually. This analysis assesses whether the
observed differences could reasonably arise by chance.

The results, shown in Table 3, firmly reject the null hypothesis across all
comparisons. The magnitude of the evidence is such that systematic influences
like imperfect Galactic foreground RM subtraction and cosmic variance are
likely to be more significant than raw statistical variations. These
challenges represent key areas of ongoing research in POSSUM (e.g. Khadir et
al., 2024). Nonetheless, we contend that the statistical evidence supports the
initial visual findings of distinct steps in the MAD(RRM) curve, confirming
substantial differences in the |RRM| distributions between these ranges.

Table 3: Statistical comparison of |RRM| distributions across different ranges of $\xi$. Results are quoted to 3 significant figures. Comparison | Statistic | p-value
---|---|---
Kruskal ($\xi<2$ vs. $2\lesssim\xi<7$) | 40.4 | $2.04\times 10^{-10}$
Kruskal ($\xi<2$ vs. $\xi>7$) | 179 | $9.61\times 10^{-41}$
Kruskal ($2\lesssim\xi<7$ vs. $\xi>7$) | 82.9 | $8.49\times 10^{-20}$
KS ($\xi<2$ vs. $2\lesssim\xi<7$) | 0.053 | $3.05\times 10^{-10}$
KS ($\xi<2$ vs. $\xi>7$) | 0.124 | $7.49\times 10^{-39}$
KS ($2\lesssim\xi<7$ vs. $\xi>7$) | 0.082 | $2.17\times 10^{-21}$

###  3.2 Null test to confirm RRM-group association

To verify that the observed correlation between RRM dispersion enhancements
and group-relative positions was not coincidental, we generated 1,000 MAD(RRM)
curves from RRM values that were randomly shuffled relative to their sky
positions, maintaining the original distribution of RRMs but breaking the
spatial link to group positions. The 1st/99th, 5th/95th, 25th/75th, and 50th
percentiles of these curves, calculated as a function of fiducial radius, are
depicted as gray lines in Figure 5.

The MAD(RRM) curves generated from randomly reshuffled RRMs exhibit no
significant trend with fiducial radius, consistently showing a narrow range of
RRM dispersion values centered around 8.0 rad m-2 . In contrast, the actual
MAD(RRM) curve, including its 95% confidence intervals, distinctly surpasses
the 1st/99th percentiles at $0\leq\xi<2$ and falls below at $\xi>7$. This
difference provides robust evidence linking RRM dispersion to group-centric
positions and thus gas associated with galaxy groups.

###  3.3 RRM enhancement is not driven by the circumgalactic medium of group
member galaxies

In this section, we consider whether our results could be attributed to the
circumgalactic medium of group member galaxies, which is magnetised and may
contribute measurable RM excesses of up to a few rad m-2 (e.g. Heesen et al.,
2023; Jung et al., 2023; Ramesh et al., 2023; Pakmor et al., 2024).

The scalings of mass and splashback radius with K-band luminosity described in
Section 2.4 apply to objects with K-band luminosities down to
$10^{10}~{}L_{\odot}$, typical of mid-sized spiral galaxies (Tully, 2015). In
our sample, over 95% of MKT group member galaxies have K-band luminosities
above $10^{10}~{}L_{\odot}$. Thus, we matched each RM with its closest group
galaxy, determining its fiducial radius in terms of multiples of the
splashback radius, in the same way as we did for galaxy groups themselves.

Of the RMs within the splashback radius of a galaxy group, only 78 (or 3%)
also intersect the splashback radius of a member galaxy in the galaxy group.
Upon excluding these RMs and regenerating the plots in Figures 4 and 5, along
with the statistics in Tables 2 and 3, the differences were negligible. We
conclude that the observed increase in RRM dispersion is not driven in this
case by the CGM of individual group member galaxies.

###  3.4 Variability in RRM Enhancement Results by Group Selection

We leave a comprehensive examination of RRM enhancements categorised by group
properties to future work. Nonetheless, it is crucial to determine whether the
RRM dispersion signal is primarily linked to a few individual groups or is
representative of a significant portion of the sample. The former is
plausible, because our RMs are not evenly apportioned among the galaxy groups
in our sample, nor do they consistently probe the entire range of $\xi$ for
each group (see Figures 2 and 3, and Section 2.6). This means that specific
groups could have an outsized effect on the RRM dispersion over particular
ranges of $\xi$.

To address this, we conducted a standard bootstrap resampling experiment, a
widely used method to assess the variance of a sample in statistical analysis.
In a bootstrap experiment, sampling with replacement is performed from the
full dataset to create new samples of the same size as the original dataset.
This allows for an estimation of the underlying sample variance, as some
objects may be repeated while others may be omitted in each sample.

In this case, we randomly selected 55 objects from our full sample of galaxy
groups, with replacement, 1,000 times. For each of these resampled datasets,
we recalculated the MAD(RRM) curve for the RRMs associated with these
selections. From these recalculated curves, we derived new 95% confidence
intervals for the true MAD(RRM) curve. This estimates the variance of the RRMs
with respect to the group sample. For example, if the variance structure of
the true MAD(RRM) curve is driven by only a handful of groups, the bootstrap
resamples are less likely to consistently include these groups, resulting in
larger confidence intervals centered around a value closer to 6 rad m-2 .

The newly calculated confidence intervals are depicted as the light-pink
shaded region in Figure 5. These intervals demonstrate that the core
characteristics of the true MAD(RRM) curve are preserved under this
resampling, although the 95% confidence interval becomes approximately 60%
wider than that obtained through the RM bootstrapping (dark-magenta shaded
region in Figure 5).

The results show that while the specific composition of our galaxy group
sample introduces some variance to the RRM dispersion, this effect is minor
and does not significantly alter the principal conclusions of our study.

It is also clear that the 95% confidence interval’s lower bound for $0<\xi<2$
lies well above the mean value of the true MAD(RRM) curve for $2<\xi<7$. This
implies a significant increase in RRM dispersion at $\xi<2$ among most of our
galaxy groups, in turn implying that most galaxy host a detectable, Faraday-
rotating IGrM phase. We plan to further quantify and constrain this
observation with a larger group and RM sample generated by POSSUM in future
work.

For regions $2<\xi<7$ and $\xi>7$, the MAD(RRM) curves and their 95%
confidence intervals are also inconsistent, indicating that our group sample
also demonstrate elevated RRM dispersion well beyond their splashback radius,
compared to unresolved radio galaxies whose lines of sight do not intersect
with group halos and their associated gas at all.

###  3.5 Magnitude of RRM enhancements

We propose that the enhancements in RRM dispersion detailed in Section 3.1
delineate three distinct regions of magnetised gas: (1) within groups where
$\xi<2$; (2) external to groups for $2<\xi<7$; and (3) beyond $\xi>7$, where
RRM dispersion is consistent with intrinsic contributions from unresolved
extragalactic radio sources, as discussed by Schnitzeler (2010) and Taylor et
al. (2024). The magnitude of RRM contributions can be calculated for each
region by subtracting the relevant $\sigma_{\text{RRM}}$ values in quadrature.
Specifically, we use the mean value from the MAD curve for the outer regions
and the peak value for the inner group regions (since the curve forms a
plateau in the former region, and rises to a peak in the latter, respectively,
as shown in Figure 5). Consequently, and with the caveat that the outer
regions are probed by successively smaller fractions of our total group sample
(see Figure 3), the RRM dispersion contributed by gas at $2<\xi<7$ is
calculated to be $\sigma_{\text{RRM, 2<As<7}}=4.2\pm 1.2$ rad m-2 , and by gas
within $\xi<2$, it is $6.9\pm 1.8$ rad m-2 , where the uncertainties have been
obtained by appropriately modifying the bootstrap confidence intervals
calculated in Section 3.4.

##  4 Discussion

Our findings confirm the presence of a magnetised, ionised IGrM within most
galaxy groups, detectable up to twice the splashback radius. Additionally, we
identified a second, more extended component of gas that extends out to seven
splashback radii. The associated enhancements in RRM dispersion ($6.9\pm 1.8$
rad m-2 and $4.2\pm 1.2$ rad m-2 ) are consistent with the drop in expected RM
contributions from $\sim 10$ rad m-2 within dense group environments to $\sim
1$ rad m-2 within sparser cosmic filaments, as modeled by Akahori & Ryu
(2010). This distinct transition mirrors findings by Gouin et al. (2022), who
observed a shift from predominantly hot, virialised gas in core regions of
groups to a more tenuous WHIM gas extending well beyond their virial
boundaries (also see Oppenheimer et al., 2021; Anderson et al., 2021). Indeed,
these two gas phases—the IGrM and the surrounding WHIM—are the only plausible
candidates for providing the enhanced RRM dispersions that we find in Section
3, which we discuss in more detail below. Broadly though, our findings
underscore that Faraday RM grids serve as an effective tracer of magnetised
gas in circum-halo environments.

###  4.1 The Intragroup Medium

####  4.1.1 Prevalence and detectability

We are hesitant to quantify the fraction of galaxy groups that host a
magnetised, ionised IGrM, owing to the present limited sky coverage of POSSUM
data and associated potential biases (Section 2.6). Nevertheless, a large
fraction of our sample groups contribute to RRM dispersion (Section 3.4), so
most galaxy groups must contain such a gas phase. This could represent the
hot, volume-filling gas phase implied by UV absorption IGrM studies (Stocke et
al., 2019; McCabe et al., 2021). The detection rate of this gas via Faraday
rotation likely surpasses the 10–20% of compact groups harboring relativistic
magnetised gas detected via radio synchrotron emission (Nikiel-Wroczyński et
al., 2019), and notably, the $\sim 30$% of groups/clusters whose ICM is
detected via current X-ray observations (e.g. Appendix C of Xu et al., 2022).
This may indicate that the gas illuminated by RMs is too cool or sparse to be
detected in X-rays, perhaps because the gas has not reached the virial
temperature in accreting halos (e.g. Pratt et al., 2021), or because magnetic
fields can provide pressure support that lowers the virial temperature
(Lovisari et al., 2021). S-Z observations will eventually provide another
basis for comparison (Pandey et al., 2023), but is not yet generally capable
of detecting gas in group-mass halos.

####  4.1.2 Magnetisation

Magnetic fields could play a pivotal role in shaping astrophysical processes
within galaxy groups, from modulating energy dispersal from black hole jets to
affecting pre-processing in galactic evolution (e.g. Oppenheimer et al., 2021,
and references therein). Evidence of magnetised group gas has been
increasingly observed through Faraday rotation, including in the thermal IGrM
of the Antennae system (Basu et al., 2017), the Magellanic Bridge (Kaczmarek
et al., 2017), Stephan’s Quintet (Nikiel-Wroczyński et al., 2020), and in the
(distinct) synchrotron-emitting relativistic gas phase in about 10-20% of
compact groups (Nikiel-Wroczyński et al., 2019). Notably, the estimated field
strengths suggest that magnetic fields could be dynamically important in these
contexts.

Presently, RM grid-based estimates of magnetic field strengths in cluster
environments carry fractional uncertainties of hundreds of per cent (Johnson
et al., 2020), even when the thermal particle density is well-known, which is
not the case here. Despite these uncertainties, coarse estimates of the
plausible range of magnetic field strengths required to produce the observed
RM dispersions remain valuable for testing the validity of our results, as
well as in their own right. Thus, following Gaensler et al. (2001), we have
the relation:

$\frac{\sigma_{\text{RM}}}{\text{rad/m}^{2}}=\frac{812}{2\sqrt{3}}\left(\frac{n_{e}}{\text{cm}^{-3}}\right)\left(\frac{B}{\mu\text{G}}\right)\sqrt{\left(\frac{L}{\text{kpc}}\right)\left(\frac{l}{\text{kpc}}\right)},$
(5)

where $\sigma_{\text{RM}}$ is the RM dispersion, $n_{e}$ the electron density,
$B$ the magnetic field strength, $L$ the IGrM path length, and $l$ the
magnetic field correlation scale.

A second quantity of interest is the plasma beta defined as:

$\beta=\frac{P_{\text{thermal}}}{P_{\text{magnetic}}}=\frac{nk_{B}T}{B^{2}/8\pi}$
(6)

where in turn $P_{\text{thermal}}=n_{e}k_{B}T$ is the thermal pressure,
$k_{B}$ is the Boltzmann constant, $T$ is the temperature of the plasma,
$P_{\text{magnetic}}=B^{2}/8\pi$ is the magnetic pressure (in CGS units), and
all other quantities are as previously defined. A plasma beta greater than one
($\beta>1$) indicates that thermal pressure dominates over magnetic pressure,
whereas a plasma beta less than one ($\beta<1$) implies that magnetic pressure
is dominant.

Using Monte Carlo methods, we estimated $B$ as well as $\beta$ for the IGrM of
our galaxy group sample by randomly selecting $\sigma_{\text{RM}}$ values from
a range of [9.25, 11.5] rad m-2 , representing the percentile range at the
peak of the curve in Figure 5, and then adjusted by subtracting the mean
$\sigma_{\text{RM}}$ enhancement values for $2<\xi<7$ and $\xi>7$ of 4.2 rad
m-2 and 6 rad m-2 respectively in quadrature. The electron density $n_{e}$ was
selected from [$10^{-5}$, $10^{-4}$] cm-3 (e.g. Eckert et al., 2013; Nugent et
al., 2020; Angelinelli et al., 2022; Robson & Davé, 2023a), the magnetic field
correlation scale $l$ was selected from [$1/100$,1] times the splashback
diameter $L$, which itself was randomly selected from our galaxy group sample.
Finally, $T$ was selected from [$10^{6}$, $10^{8}$] K (e.g. see Figure 1 of
Gouin et al., 2022).

Randomly sampling these prior distributions $10^{7}$ times and solving for
$B$, we obtain the distribution shown in Figure 6. The mode and mean of the
$B$ distribution is 1.6 $\muup$G and 5.0 $\muup$G, respectively, with a long
tail extending to $\sim 20\muup$G. The magnetic reversal scale, a critical yet
less certain parameter, affects these estimations quite strongly. Restricting
the number of line-of-sight reversals to a maximum of 10 alters the mode and
mean to 0.6 $\muup$G and 1.7 $\muup$G, respectively. In any case, the total
magnetic field strength broadly agrees with estimates cited in the IGrM
studies listed above—typically $\sim$ a few $\muup$G.

Our estimate of the plasma $\beta$ comes with several caveats and should be
considered indicative only. Astrophysically, even in single galaxy groups, the
$\beta$ value is likely sensitive to local conditions and varies significantly
throughout the group environment. However, our derivation is based on a
stacking experiment that encompasses a broad range of masses, averaged out to
twice the splashback radius, and does not account for potential astrophysical
correlations among the parameters involved. Moreover, these values are drawn
from ranges selected to be only broadly representative of group environments.
Despite these limitations, our Monte Carlo sampling yields a median $\beta$
value of 0.94, with 10th and 90th percentile values of 0.03 and 9.8,
respectively. The $\beta$ value exceeds 1 in 49% of the samples.

These values are generally smaller (i.e., more magnetically dominated) than is
typical in massive galaxy cluster ICMs (where $\beta$ is more typically
10–100; e.g., Vazza et al., 2021), driven primarily by the lower electron
densities and temperatures in our groups compared to clusters, while still
supporting the microgauss-level magnetic field strengths cited above. This, in
turn, may be related to the fundamentally lesser influence of gravity compared
to other baryonic processes at work in groups, which we discuss in more detail
in Section 4.2.1 below. It may also suggest that RM enhancements are efficient
tracers of low-$\beta$ plasma regions and processes, potentially biasing the
value we derive as a result. Regardless, the fact that the percentile values
straddle a ratio of 1 suggest that magnetic fields may play a significant role
in the dynamics of the IGrM.

Figure 6: Histogram of $B$ values described in Section 4.1.2.

###  4.2 The Cosmic Web

####  4.2.1 Extent of magnetised thermal gas

Our observations reveal that the magnetised thermal gas associated with galaxy
groups extends well beyond their splashback radii—that is, their effective
gravitational boundaries—with significant $\sigma_{RRM}$enhancements
persisting up to seven splashback radii before tapering off to the expected
background level of $\sim 6$ rad m-2 . Such gas must be in a dynamic rather
than bound state, likely being accreted from the surrounding cosmic web or
expelled towards it.

Other findings in the recent literature also hint at the presence of gas at
the periphery of group- and cluster-mass halos. Anderson et al. (2021) used
pilot POSSUM data to show that RM dispersion enhancements illuminated cosmic
gas substantially further out in the Fornax cluster than existing X-ray
observations, towards the virial radius, and additionally highlighted features
of the gas related to the dynamics of the cluster including shocks and a
swept-back tails induced by merging substructures. Deviations from self-
similar scaling relations at the group scale, notably the reduced gas
fractions, further underline the dynamic nature of group environments, and
suggest that gas is being systematically expelled from group centres
(Angelinelli et al., 2022; Ayromlou et al., 2023; Robson & Davé, 2023b). UV
absorption studies have detected ionised gas far beyond the virial radii of
galaxy groups (Stocke et al., 2019; McCabe et al., 2021), and new groups and
clusters with extended, flattened X-ray profiles have been reported,
suggesting the presence of significant ionised gas reservoirs in halo
outskirts (Xu et al., 2022). Similarly, the detection of magnetised
‘megahalos’ around cluster systems by Cuciti et al. (2022), although involving
more massive systems than considered here, suggests that magnetised gas at
halo boundaries might be more widespread than anticipated.

With respect to our results specifically—that is, the RRM dispersion
enhancement extending from 2 to 7 splashback radii (or $\sim$ 3 to 7 Mpc) that
we attribute to the WHIM—we point out that galaxy groups are thought to be
embedded in cosmic filaments (of typical width $\sim 3$ Mpc; e.g. O’Sullivan
et al., 2019; Carretti et al., 2022) and magnetised bubbles that span between
1 and 10 Mpc in diameter; Arámburo-García et al., 2021). Simulations further
suggest that the cosmic budget of ‘missing baryons’ becomes closed at fiducial
separations of between and few and 10 times the splashback radius after being
ejected by AGN activity (Ayromlou et al., 2023). Thus, we claim to have
detected such material directly, as other recent studies have also focused on
achieving with LOFAR RM data (Blunier & Neronov, 2024; Bondarenko et al.,
2024). Future more detailed comparisons of POSSUM and LOFAR data with
simulations will be fruitful to understand the relative strengths of each
dataset, and to help constrain baryonic feedback and flow models in
simulations.

####  4.2.2 Magnetisation

We can repeat the analysis of Section 4.1.2, but here substitute numbers
appropriate for the cosmic web WHIM. Specifically, we select
$\sigma_{\text{RM}}$ values from a range of [7.0, 9.0] rad m-2 , representing
the percentile range of the plateau in Figure 5, and then adjusted by
subtracting the expected background RM dispersion of 6 rad m-2 in quadrature.
The electron density $n_{e}$ was selected from [$10^{-5.5}$, $10^{-4.5}$] cm-3
(e.g. Akahori & Ryu, 2010, 2011; O’Sullivan et al., 2019; Carretti et al.,
2022), the total filament width $L$ was taken to be seven times the splashback
diameter of the groups in our sample, which was randomly selected for each
Monte Carlo iteration, and the magnetic field reversal scale $l$ was selected
from [0.1,1] times the filament width, reflecting the relatively more ordered
fields expected in WHIM filaments (Akahori & Ryu, 2010). Finally, $T$ was
selected from [$10^{5.5}$, $10^{7}$] K (see Figure 1 of Gouin et al., 2022),
and we assume the plasma is fully ionised.

The resulting distribution for $B$ is shown in Figure 7. In this case, the
mode and mean of the $B$ distribution is 0.2 $\muup$G and 0.6 $\muup$G,
respectively, with a tail extending to $\sim 3\muup$G. Constraining the number
of line-of-sight reversals has relatively little impact here. These values are
at the upper end of predictions for individual cosmic web filaments by e.g.
Akahori & Ryu (2010), and about an order-of-magnitude higher than the more
typical predicted value of 10 nG, and initial RM-based measurements of $\sim
30$ nG (Carretti et al., 2022), in such environments. We do not see these
results as being in tension: The Carretti et al. (2023) observations mainly
probe regions between 3 and 20 splashback radii 777This assumes a conservative
conversion here of $r_{\text{splashback}}\approx 2\times r_{200c}$ (More et
al., 2015) — the standard fiducial cluster boundary chosen by Carretti et al.
(2022) from massive clusters by design, which is a very different environment
from the group-centric environment that we probe here. That is, we believe we
are probing denser environments in the WHIM that build up around groups,
whether by accretion or outflow.

Under our Monte Carlo sampling, the plasma $\beta$ (again with the caveats
cited in Section 4.1.2) yields a median value of 3.1, with 10th and 90th
percentile values of 0.2 and 26.5, respectively. $\beta$ exceeds 1 in 68% of
the samples, but the fact that a significant fraction of values do not
indicates that the magnetic fields may be strong enough to be dynamically
important in this context. For comparison, the recent detection of magnetic
fields of strength $\sim 10$–84 nG in cosmic web filaments with overdensities
ranging from $\sim 10$–60 (Carretti et al., 2023) produces $\beta$ values (we
estimate here) of $\sim 150$ down to $\sim 10$. Thus, while the plasma $\beta$
in the region of the cosmic web near galaxy groups probed in this work
overlaps with that of the sparser cosmic web filaments probed by Carretti et
al. (2023), it appears to be more magnetically dominated on average. We
speculate this could be the result of AGN expelling magnetised gas into the
region around the groups, as we also invoked to explain the extent of this gas
above.

Figure 7: Histogram of $B$ values described in Section 4.2.2.

###  4.3 RM Grids as a Prime Observational Tracer of Circum-Halo environments

Our results underscore the effectiveness of using cosmic magnetic fields as
in-situ probes to reveal the presence, structure, and dynamics of cosmic gas
in the vicinity of galaxy groups, building on work by e.g. Anderson et al.
(2021) in the poor Fornax cluster. This highlights the value of RM analysis in
this specific context of uncovering new aspects of the magnetised thermal IGrM
and beyond, particularly with the dense RM grid of the POSSUM survey, and
future RM grids of the SKA era (Johnston-Hollitt et al., 2015; Heald et al.,
2020).

RM grids thus appear to be a promising and relatively untapped resource for
hunting and studying gas at halo peripheries, adding to their long-established
value in the context of the canonical ICM present in massive cluster cores
(e.g. Clarke et al., 2001). Possible high-value targets in these locations
include accretion shocks and streams, merger shocks, and links between the
IGrM/ICM and WHIM gas in the broader cosmic web.

Since Rotation Measures (RMs) depend on electron density, magnetic field
strength, and path length (refer to Equation 1), integrating RM data with
observations from X-ray emissions (e.g. Bulbul et al., 2024), the Sunyaev-
Zel’dovich Effect (e.g. Pandey et al., 2023), and now Fast Radio Burst
Dispersion Measures (Connor & Ravi, 2022; Medlock & Cen, 2021; Rafiei-Ravandi
et al., 2024; Medlock et al., 2024; Theis et al., 2024) will eventually enable
the measurement of the strength and orientation of magnetic fields associated
with galaxy group gas. Given that magnetic fields typically dragged along with
plasma flows in astrophysical environments (that is, when plasma $\beta>1$),
they naturally serve as in situ probes for detecting the presence, flow
characteristics, and behavior of intergalactic gas. This approach can be
likened to conducting a cosmic magnetic "dye tracer" experiment, albeit in a
limited and metaphorical sense, offering a unique window into the dynamics of
cosmic baryons in this environment.

###  4.4 Conclusions and future work

Our work significantly advances our understanding of the magnetised Intragroup
Medium (IGrM) and its extension into the cosmic web. Using data from the
Polarisation Sky Survey of the Universe’s Magnetism (POSSUM), we analyzed
22,817 Residual Faraday rotation measures (RRMs) across 55 diverse galaxy
groups with halo masses between $10^{12.5}$ and $10^{14.0}$ M⊙. Our key
findings are:

  * •

An Extended Magnetised IGrM: We confirm the presence of a magnetised IGrM
extending up to twice the splashback radius for most galaxy groups,
complemented by a secondary gas component—the Warm-Hot Intergalactic Medium
(WHIM)—extending from 2 to 7 splashback radii. These components enhances RM
dispersion by $6.9\pm 1.8$ rad m-2 within the IGrM core and $4.2\pm 1.2$ rad
m-2 in the WHIM, relative to previously established RRM dispersion
contributions produced local to the RM sources of 6 rad m-2 . This discovery
aligns with and expands upon recent theories and simulations _vis-a-vis_ the
likely spatial distribution of ‘missing baryons’, and suggests that a broader
spatial distribution of ionised and magnetised gas around galaxy groups exists
than has previously been recognised.

  * •

A High Prevalence of Magnetised IGrM: A large fraction of our sample groups
contribute to the RRM dipserion inside the splahsback radius, and thus show
evidence of a magnetised IGrM. This detection rate appears to be higher than
X-rays currently suggest.

  * •

Diagnostic Value of RMs: Faraday Rotation Measures (RMs) have proven to be
effective at detecting gas in low-density environments surrounding group-mass
halos, pushing down in mass from the rich clusters that have more typically
been used to probe. Beyond measuring magnetic fields, RMs can illuminate the
structure and extent of the magnetised thermal IGrM and the cosmic web.

  * •

Implications for Cosmic Web Studies: The detection of magnetised gas extending
well beyond the groups’ splashback radii into the cosmic web underscores the
dynamic state of galaxy groups, either accreting onto or being expelled from
these structures. We appear to trace a denser phase of gas than previous
studies focused on WHIM filaments (Carretti et al., 2023). These may represent
the magnetised bubbles driven by galactic outflows that appear in cosmological
simulations.

  * •

Our results bridge the ranges of density and environments where RM grids have
previously been found to provide useful constraints on magnetised gas
properties. These experiments can now probe magnetised gas from galactic scale
(e.g. Heesen et al., 2023) to groups (the present work), poor clusters
(Anderson et al., 2021) and rich clusters (e.g. Clarke et al., 2001), and
beyond these structures into surrounding magnetised bubbles (present work,
also Bondarenko et al., 2024) to sparse cosmic web filaments (e.g. Carretti et
al., 2023; Blunier & Neronov, 2024). RMs therefore appear to provide excellent
tracers of magnetised gas throughout the cosmic cascade of baryons, from the
largest scales down to galaxies.

Our study does have limitations: the RM data are not evenly distributed across
galaxy groups and do not cover all scaled separations from each group.
However, this analysis is based on data that covers only $\sim 5$% of the
$2\pi$ steradians of sky that POSSUM will survey in the next few years with
30–50 RMs per square degree (Vanderwoude et al., 2024), Gaensler _et al. in
preparation_ , and only $\sim 3$% of the $3\pi$ steradians that SPICE-RACS
will observe with $\sim 5$ RMs per square degree this year (Thomson et al.,
2023). Consequently, the number of available RMs will greatly increase in the
next few years, and sky coverage will expand dramatically over an even shorter
timescale. While these improvements will not completely eliminate our current
challenges, they will enhance the overall quality of RM grid experiments by
increasing RM grid density and improving the uniformity and comprehensiveness
of coverage. This will also enable more complex experiments in the future,
such as examining magnetised gas in galaxy groups categorised by various
properties.

Further advances will come from combining RM data with multi-wavelength
observations such as X-ray emission, the Sunyaev-Zel’dovich Effect, and Fast
Radio Burst dispersion measures. This will help clarify the role of magnetic
fields in the cosmic baryon cycle around galaxy groups and how they influence
the flow of energy and materials through this environment. The Square
Kilometre Array (SKA), the Next Generation Very Large Array (ngVLA), and
DSA-2000 will also in future provide denser RM grids through more sensitive
measurements, broadening the scope of RM grid experiments as probe of group
environments. In all of this, comprehensive magnetohydrodynamic simulations
that predict magnetic field properties and RM signals in group environments
will be critical, providing a theoretical framework to interpret observational
data, and which can be refined through RM data to constrain feedback models
(e.g. Blunier & Neronov, 2024; Bondarenko et al., 2024).

## acknowledgement

We thank the reviewer, Russ Taylor, for his time and comments, which have
improved several important aspects of this work.

This scientific work uses data obtained from Inyarrimanha Ilgari Bundara / the
Murchison Radio-astronomy Observatory. We acknowledge the Wajarri Yamaji
People as the Traditional Owners and native title holders of the Observatory
site. CSIRO’s ASKAP radio telescope is part of the Australia Telescope
National Facility 888https://ror.org/05qajvd42. Operation of ASKAP is funded
by the Australian Government with support from the National Collaborative
Research Infrastructure Strategy. ASKAP uses the resources of the Pawsey
Supercomputing Research Centre. Establishment of ASKAP, Inyarrimanha Ilgari
Bundara, the CSIRO Murchison Radio-astronomy Observatory and the Pawsey
Supercomputing Research Centre are initiatives of the Australian Government,
with support from the Government of Western Australia and the Science and
Industry Endowment Fund. The POSSUM project has been made possible through
funding from the Australian Research Council, the Natural Sciences and
Engineering Research Council of Canada, the Canada Research Chairs Program,
and the Canada Foundation for Innovation. The Dunlap Institute is funded
through an endowment established by the David Dunlap family and the University
of Toronto. This work was partially funded by the Australian Government
through an Australian Research Council Australian Laureate Fellowship (project
number FL210100039) to N.Mc-G.. B.M.G. acknowledges the support of the Natural
Sciences and Engineering Research Council of Canada (NSERC) through grant
RGPIN-2015-05948, and of the Canada Research Chairs program. CIRADA is funded
by a grant from the Canada Foundation for Innovation 2017 Innovation Fund
(Project 35999), as well as by the Provinces of Ontario, British Columbia,
Alberta, Manitoba and Quebec. TA was supported in part by JSPS KAKENHI Grant
Number, JP21H01135. SPO acknowledges support from the Comunidad de Madrid
Atracción de Talento program via grant 2022-T1/TIC-23797.

## Data Availability

This paper uses raw data products that are publicly available on the CSIRO
ASKAP Science Data Archive (CASDA; Chapman et al., 2017; Huynh et al., 2020).
A table of our RM data and galaxy group associations is available at CDS via
anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via
https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS at J/MNRAS/Vol/Page upon
publication.

## References

  * Adhikari et al. (2014) Adhikari S., Dalal N., Chamberlain R. T., 2014, J. Cosmology Astropart. Phys., 2014, 019
  * Akahori & Ryu (2010) Akahori T., Ryu D., 2010, ApJ, 723, 476
  * Akahori & Ryu (2011) Akahori T., Ryu D., 2011, ApJ, 738, 134
  * Anderson et al. (2015) Anderson C. S., Gaensler B. M., Feain I. J., Franzen T. M. O., 2015, ApJ, 815, 49
  * Anderson et al. (2021) Anderson C. S., et al., 2021, Publ. Astron. Soc. Australia, 38, e020
  * Angelinelli et al. (2022) Angelinelli M., Ettori S., Dolag K., Vazza F., Ragagnin A., 2022, A&A, 663, L6
  * Arámburo-García et al. (2021) Arámburo-García A., Bondarenko K., Boyarsky A., Nelson D., Pillepich A., Sokolenko A., 2021, MNRAS, 505, 5038
  * Ayromlou et al. (2023) Ayromlou M., Nelson D., Pillepich A., 2023, MNRAS, 524, 5391
  * Basu et al. (2017) Basu A., Mao S. A., Kepley A. A., Robishaw T., Zweibel E. G., Gallagher John. S. I., 2017, MNRAS, 464, 1003
  * Blunier & Neronov (2024) Blunier J., Neronov A., 2024, arXiv e-prints, p. arXiv:2403.13418
  * Bond et al. (1996) Bond J. R., Kofman L., Pogosyan D., 1996, Nature, 380, 603
  * Bondarenko et al. (2024) Bondarenko K., Boyarsky A., Sokolenko A., Vovk I., 2024, arXiv e-prints, p. arXiv:2404.17402
  * Brentjens & de Bruyn (2005) Brentjens M. A., de Bruyn A. G., 2005, A&A, 441, 1217
  * Bulbul et al. (2024) Bulbul E., et al., 2024, A&A, 685, A106
  * Carretti et al. (2022) Carretti E., et al., 2022, MNRAS, 512, 945
  * Carretti et al. (2023) Carretti E., O’Sullivan S. P., Vacca V., Vazza F., Gheller C., Vernstrom T., Bonafede A., 2023, MNRAS, 518, 2273
  * Chapman et al. (2017) Chapman J. M., Dempsey J., Miller D., Heywood I., Pritchard J., Sangster E., Whiting M., Dart M., 2017, in Lorente N. P. F., Shortridge K., Wayth R., eds, Astronomical Society of the Pacific Conference Series Vol. 512, Astronomical Data Analysis Software and Systems XXV. p. 73
  * Chippendale & Anderson (2019) Chippendale A., Anderson C., 2019, 019, On-Dish Calibration of XY Phase for ASKAP’s Phased Array Feeds, https://www.atnf.csiro.au/projects/askap/ACES-memos. Commonwealth Scientific and Industrial Research Organisation, https://www.atnf.csiro.au/projects/askap/ACES-memos
  * Clarke et al. (2001) Clarke T. E., Kronberg P. P., Böhringer H., 2001, ApJ, 547, L111
  * Connor & Ravi (2022) Connor L., Ravi V., 2022, Nature Astronomy, 6, 1035
  * Cuciti et al. (2022) Cuciti V., et al., 2022, Nature, 609, 911
  * Diemer & Kravtsov (2014) Diemer B., Kravtsov A. V., 2014, ApJ, 789, 1
  * Dolag et al. (2001) Dolag K., Schindler S., Govoni F., Feretti L., 2001, A&A, 378, 777
  * Drinkwater et al. (2001) Drinkwater M. J., Gregg M. D., Colless M., 2001, ApJ, 548, L139
  * Eckert et al. (2013) Eckert D., Molendi S., Vazza F., Ettori S., Paltani S., 2013, A&A, 551, A22
  * Eke et al. (2004) Eke V. R., et al., 2004, MNRAS, 348, 866
  * Gaensler et al. (2001) Gaensler B. M., Dickey J. M., McClure-Griffiths N. M., Green A. J., Wieringa M. H., Haynes R. F., 2001, ApJ, 549, 959
  * Gaensler et al. (2004) Gaensler B. M., Beck R., Feretti L., 2004, New Astron. Rev., 48, 1003
  * Gaensler et al. (2010) Gaensler B. M., Landecker T. L., Taylor A. R., POSSUM Collaboration 2010, in American Astronomical Society Meeting Abstracts #215. p. 515
  * Gaensler et al. (2024) Gaensler B., et al., 2024, The POSSUM Survey, In preparation
  * Gouin et al. (2022) Gouin C., Gallo S., Aghanim N., 2022, A&A, 664, A198
  * Guzman et al. (2019) Guzman J., et al., 2019, ASKAPsoft: ASKAP science data processor software (ascl:1912.003)
  * Haan & Braun (2014) Haan S., Braun R., 2014, MNRAS, 440, L21
  * Heald et al. (2020) Heald G., et al., 2020, Galaxies, 8, 53
  * Heesen et al. (2023) Heesen V., et al., 2023, A&A, 670, L23
  * Hotan et al. (2021) Hotan A. W., et al., 2021, Publ. Astron. Soc. Australia, 38, e009
  * Hutschenreuter et al. (2022) Hutschenreuter S., et al., 2022, A&A, 657, A43
  * Huynh et al. (2020) Huynh M., Dempsey J., Whiting M. T., Ophel M., 2020, in Ballester P., Ibsen J., Solar M., Shortridge K., eds, Astronomical Society of the Pacific Conference Series Vol. 522, Astronomical Data Analysis Software and Systems XXVII. p. 263
  * Johnson et al. (2020) Johnson A. R., Rudnick L., Jones T. W., Mendygral P. J., Dolag K., 2020, ApJ, 888, 101
  * Johnston-Hollitt et al. (2015) Johnston-Hollitt M., et al., 2015, Advancing Astrophysics with the Square Kilometre Array (AASKA14), p. 92
  * Jung et al. (2023) Jung S. L., McClure-Griffiths N. M., Pakmor R., Ma Y. K., Hill A. S., Van Eck C. L., Anderson C. S., 2023, MNRAS, 526, 836
  * Kaczmarek et al. (2017) Kaczmarek J. F., Purcell C. R., Gaensler B. M., McClure-Griffiths N. M., Stevens J., 2017, MNRAS, 467, 1776
  * Khadir et al. (2024) Khadir A., et al., 2024, Choosing interpolation techniques for reconstructing Galactic Faraday rotation, manuscript in preparation
  * Kim et al. (1991) Kim K. T., Tribble P. C., Kronberg P. P., 1991, ApJ, 379, 80
  * Koribalski et al. (2020) Koribalski B. S., et al., 2020, Ap&SS, 365, 118
  * Loi et al. (2019) Loi F., Murgia M., Govoni F., Vacca V., Prandoni I., Bonafede A., Feretti L., 2019, MNRAS, 485, 5285
  * Lovisari et al. (2021) Lovisari L., Ettori S., Gaspari M., Giles P. A., 2021, Universe, 7, 139
  * Macquart et al. (2012) Macquart J.-P., Ekers R. D., Feain I., Johnston-Hollitt M., 2012, ApJ, 750, 139
  * Magliocchetti et al. (2000) Magliocchetti M., Maddox S. J., Wall J. V., Benn C. R., Cotter G., 2000, MNRAS, 318, 1047
  * Makarov & Karachentsev (2011) Makarov D., Karachentsev I., 2011, MNRAS, 412, 2498
  * McCabe et al. (2021) McCabe T., Borthakur S., Heckman T., Tumlinson J., Bordoloi R., Dave R., 2021, ApJ, 923, 189
  * Medlock & Cen (2021) Medlock I., Cen R., 2021, MNRAS, 502, 3664
  * Medlock et al. (2024) Medlock I., Nagai D., Singh P., Oppenheimer B., Anglés Alcázar D., Villaescusa-Navarro F., 2024, arXiv e-prints, p. arXiv:2403.02313
  * More et al. (2015) More S., Diemer B., Kravtsov A. V., 2015, ApJ, 810, 36
  * Nelson et al. (2019) Nelson D., et al., 2019, MNRAS, 490, 3234
  * Nikiel-Wroczyński et al. (2019) Nikiel-Wroczyński B., et al., 2019, A&A, 622, A23
  * Nikiel-Wroczyński et al. (2020) Nikiel-Wroczyński B., Soida M., Heald G., Urbanik M., 2020, ApJ, 898, 110
  * Norris et al. (2011) Norris R. P., et al., 2011, Publ. Astron. Soc. Australia, 28, 215
  * Norris et al. (2021) Norris R. P., et al., 2021, Publ. Astron. Soc. Australia, 38, e046
  * Nugent et al. (2020) Nugent J. M., Dai X., Sun M., 2020, ApJ, 899, 160
  * O’Sullivan et al. (2019) O’Sullivan S. P., et al., 2019, A&A, 622, A16
  * Oppenheimer et al. (2021) Oppenheimer B. D., Babul A., Bahé Y., Butsky I. S., McCarthy I. G., 2021, Universe, 7, 209
  * Pakmor et al. (2024) Pakmor R., et al., 2024, MNRAS, 528, 2308
  * Pandey et al. (2023) Pandey S., et al., 2023, MNRAS, 525, 1779
  * Pratt et al. (2021) Pratt C. T., Qu Z., Bregman J. N., 2021, ApJ, 920, 104
  * Purcell et al. (2020) Purcell C. R., Van Eck C. L., West J., Sun X. H., Gaensler B. M., 2020, RM-Tools: Rotation measure (RM) synthesis and Stokes QU-fitting, Astrophysics Source Code Library, record ascl:2005.003 (ascl:2005.003)
  * Rafiei-Ravandi et al. (2024) Rafiei-Ravandi M., et al., 2024, ApJ, 961, 177
  * Ramesh et al. (2023) Ramesh R., Nelson D., Heesen V., Brüggen M., 2023, MNRAS, 526, 5483
  * Robson & Davé (2023a) Robson D., Davé R., 2023a, MNRAS, 518, 5826
  * Robson & Davé (2023b) Robson D., Davé R., 2023b, MNRAS, 518, 5826
  * Rost et al. (2024) Rost A. M., et al., 2024, MNRAS, 527, 1301
  * Rousseeuw & Croux (1993) Rousseeuw P. J., Croux C., 1993, Journal of the American Statistical Association, 88, 1273
  * Schnitzeler (2010) Schnitzeler D. H. F. M., 2010, MNRAS, 409, L99
  * Stocke et al. (2019) Stocke J. T., Keeney B. A., Danforth C. W., Oppenheimer B. D., Pratt C. T., Berlind A. A., Impey C., Jannuzi B., 2019, ApJS, 240, 15
  * Taylor et al. (2009) Taylor A. R., Stil J. M., Sunstrum C., 2009, ApJ, 702, 1230
  * Taylor et al. (2024) Taylor A. R., et al., 2024, MNRAS, 528, 2511
  * Theis et al. (2024) Theis A., Hagstotz S., Reischke R., Weller J., 2024, arXiv e-prints, p. arXiv:2403.08611
  * Thomson et al. (2023) Thomson A. J. M., et al., 2023, Publ. Astron. Soc. Australia, 40, e040
  * Tully (2015) Tully R. B., 2015, AJ, 149, 171
  * Vanderwoude et al. (2024) Vanderwoude S., et al., 2024, AJ, 167, 226
  * Vazza et al. (2021) Vazza F., et al., 2021, Galaxies, 9, 109
  * Whiting & Humphreys (2012) Whiting M., Humphreys B., 2012, Publ. Astron. Soc. Australia, 29, 371
  * Xu et al. (2022) Xu W., Ramos-Ceja M. E., Pacaud F., Reiprich T. H., Erben T., 2022, A&A, 658, A59
  * de Zotti et al. (2010) de Zotti G., Massardi M., Negrello M., Wall J., 2010, A&ARv, 18, 1

Table 4: Galaxy groups in present work Tully Nest ID | NCG/IC/Messier | PGC | RA | Decl. | N Members | Splashback Radius | Splashback Angular Radius | Distance | Halo Mass | Parent SBID
---|---|---|---|---|---|---|---|---|---|---
|  |  | [Deg.] | [Deg.] |  | [Mpc] | [Deg.] | [Mpc] | $\mathrm{M_{\odot}}$ |
200705 |  | 2443 | 10.193 | -38.343 | 4 | 0.68 | 1.11 | 70.0 | 2.5 | 46971
201359 | NGC238 | 2595 | 10.857 | -50.183 | 3 | 0.79 | 1.04 | 87.0 | 4.0 | 46951
200600 |  | 2948 | 12.674 | -52.22 | 4 | 0.75 | 1.07 | 80.0 | 3.4 | 46951
201311 | NGC409 | 4132 | 17.388 | -35.806 | 3 | 0.6 | 1.06 | 65.0 | 1.7 | 46986
200348 | NGC527 | 5128 | 20.992 | -35.115 | 6 | 0.69 | 1.39 | 56.0 | 2.6 | 46986
200258 | NGC549 | 5278 | 21.369 | -38.267 | 7 | 0.75 | 1.44 | 60.0 | 3.4 | 46986
201129 | NGC568 | 5468 | 21.988 | -35.718 | 3 | 0.58 | 1.22 | 55.0 | 1.6 | 46986
200165 |  | 6104 | 24.744 | -46.573 | 9 | 0.85 | 1.51 | 65.0 | 5.0 | 46966
201241 |  | 6131 | 24.853 | -49.382 | 3 | 0.71 | 0.89 | 91.0 | 2.9 | 46966, 47136
201112 |  | 6994 | 28.302 | -49.56 | 3 | 0.72 | 1.32 | 62.0 | 3.0 | 47136
200261 | NGC745 | 7054 | 28.532 | -56.694 | 7 | 0.78 | 1.51 | 59.0 | 3.8 | 46946
200327 |  | 3628333 | 29.418 | -57.79 | 6 | 0.78 | 1.42 | 63.0 | 3.8 | 46946
200448 |  | 8012 | 31.515 | -55.194 | 5 | 0.69 | 1.3 | 61.0 | 2.7 | 46946
201362 | NGC822 | 8055 | 31.663 | -41.157 | 3 | 0.44 | 0.96 | 52.0 | 0.68 | 50415
200506 | NGC835 | 8228 | 32.352 | -10.136 | 5 | 0.6 | 1.82 | 37.0 | 1.7 | 33370
200771 | NGC842 | 8258 | 32.462 | -7.762 | 4 | 0.46 | 1.41 | 37.0 | 0.77 | 33370
200984 |  | 8311 | 32.633 | -53.837 | 3 | 0.5 | 0.95 | 60.0 | 1.0 | 46946, 47034, 47136
201282 |  | 8341 | 32.728 | -39.366 | 3 | 0.53 | 1.19 | 52.0 | 1.2 | 50415
200783 |  | 8581 | 33.608 | -7.368 | 4 | 0.46 | 1.13 | 47.0 | 0.81 | 33370
201051 | NGC883 | 8841 | 34.772 | -6.791 | 3 | 0.7 | 1.59 | 50.0 | 2.8 | 33370, 33460, 33482, 33509, 33553
200640 |  | 9124 | 36.095 | -58.397 | 4 | 0.96 | 1.18 | 93.0 | 7.1 | 46946, 49990
200271 | NGC945 | 9426 | 37.156 | -10.539 | 7 | 0.59 | 1.52 | 44.0 | 1.7 | 33482, 33553
200602 |  | 9585 | 37.789 | -57.918 | 4 | 1.01 | 1.23 | 94.0 | 8.4 | 49990
200740 |  | 9800 | 38.602 | -10.844 | 4 | 0.45 | 1.16 | 44.0 | 0.73 | 33482, 33553
300025 | NGC 1068 |  | 38.866 | -9.356 | 22 | 0.6 | 3.5 | 20.0 | 1.8 | 33482, 33553
201187 | IC243 | 10009 | 39.634 | -6.902 | 3 | 0.55 | 0.91 | 70.0 | 1.4 | 33460, 33482, 33509, 33553
200685 | IC247 | 10100 | 40.037 | -11.734 | 4 | 0.75 | 1.05 | 82.0 | 3.4 | 33482, 33553
200541 | NGC1045 | 10129 | 40.121 | -11.277 | 5 | 0.57 | 1.44 | 45.0 | 1.5 | 33482, 33553
200503 | NGC1244 | 11659 | 46.63 | -66.776 | 5 | 0.66 | 1.34 | 56.0 | 2.3 | 46982
200663 |  | 12231 | 49.384 | -54.358 | 4 | 0.87 | 1.16 | 86.0 | 5.3 | 50011
200445 |  | 3095684 | 66.806 | -62.781 | 5 | 0.61 | 1.25 | 56.0 | 1.8 | 50230
201088 |  | 15172 | 67.0 | -47.913 | 3 | 0.57 | 1.31 | 50.0 | 1.5 | 51431
200155 |  | 16971 | 78.65 | -61.481 | 9 | 0.73 | 1.7 | 49.0 | 3.1 | 50538
300277 | NGC 5078 | 0046304 | 199.428 | -32.102 | 4 | 0.38 | 1.51 | 29.0 | 0.44 | 43137
300279 | NGC 5078 | 0046490 | 199.958 | -27.41 | 26 | 0.76 | 3.42 | 25.0 | 3.5 | 43137
100394 | NGC5094 | 46580 | 200.195 | -14.081 | 5 | 0.86 | 1.41 | 70.0 | 5.2 | 31375
100392 |  | 46747 | 200.737 | -32.728 | 5 | 0.97 | 1.23 | 90.0 | 7.4 | 43137
100074 | NGC5135 | 46974 | 201.434 | -29.834 | 13 | 0.9 | 2.3 | 45.0 | 5.9 | 43137
100391 |  | 47023 | 201.571 | -19.635 | 7 | 0.96 | 1.4 | 78.0 | 7.1 | 31375
100341 |  | 48161 | 204.489 | -30.932 | 6 | 0.55 | 1.35 | 47.0 | 1.4 | 43137
100179 |  | 50096 | 210.899 | -25.428 | 8 | 0.99 | 1.64 | 69.0 | 7.8 | 50413
101226 |  | 715794 | 212.255 | -30.559 | 3 | 0.62 | 0.95 | 74.0 | 1.9 | 50413
101276 |  | 184531 | 212.927 | -30.233 | 3 | 0.71 | 0.91 | 89.0 | 2.9 | 50413
100753 |  | 50762 | 213.234 | -28.774 | 4 | 0.7 | 0.97 | 83.0 | 2.8 | 50413
100361 | NGC5626 | 51794 | 217.454 | -29.748 | 6 | 0.88 | 1.41 | 71.0 | 5.5 | 46943
101029 | IC4453 | 52084 | 218.619 | -27.519 | 3 | 0.52 | 1.5 | 40.0 | 1.2 | 46943
100993 |  | 738224 | 219.365 | -28.724 | 3 | 0.62 | 1.04 | 68.0 | 2.0 | 46943
201214 |  | 3902372 | 283.404 | -56.719 | 3 | 0.48 | 0.91 | 61.0 | 0.9 | 51574
300356 | IC4797 | 0062589 | 284.124 | -54.306 | 8 | 0.65 | 2.04 | 36.0 | 2.2 | 51574
200982 |  | 62713 | 285.823 | -53.206 | 3 | 0.72 | 0.91 | 91.0 | 3.0 | 51574
200757 |  | 62721 | 285.914 | -53.934 | 4 | 0.55 | 1.36 | 47.0 | 1.4 | 51574
200083 | NGC6753 | 62870 | 287.848 | -57.05 | 13 | 0.8 | 2.74 | 33.0 | 4.1 | 51574
200765 | NGC6812 | 63625 | 296.351 | -55.347 | 4 | 0.56 | 1.42 | 45.0 | 1.4 | 51818
201283 | IC4994 | 64489 | 304.935 | -53.447 | 3 | 0.54 | 1.09 | 57.0 | 1.3 | 51818
201240 |  | 67429 | 327.453 | -59.323 | 3 | 0.76 | 1.05 | 83.0 | 3.6 | 45761