eddy}=1$; see §3.1.3 and §4.1). See Fig. 12 for the description of the
analysis, color coding, and sample. Figure reproduced from Gaspari et al.
(2019).

Scaling relations allow us to predict other baryonic physics of the IGrM, such
as the extent of the IGrM multiphase condensation radius, which is shown in
the right panel of Fig. 17. As introduced in §4.1, such a radius is the locus
at which the IGrM cooling time and the turbulent eddy-turnover time match
($C\equiv t_{\rm cool}/t_{\rm eddy}=1$), both of which can be retrieved via
the X-ray scaling relations as a function of $T_{\rm x}$ and $L_{\rm x}$
(Gaspari et al. 2019). Evidently, lower mass groups have condensation radii of
less than a few kpc, while massive groups can reach $R_{\rm cond}$ of a few 10
kpc (e.g., David et al. 2017; Lakhchaura et al. 2018; Olivares et al. 2019).
Overall, scaling relations between X-ray macro-scale properties translates
into scaling relations of micro-scale properties ($M_{\rm BH}$), corroborating
a tight co-evolution between multi-scale processes in the IGrM (as depicted in
Fig. 9).

###  5.4 Impact on cosmological probes

During the past few years, it has become clear that AGN feedback will play an
important role as a leading source of systematic uncertainties for upcoming
high-profile cosmology experiments. Indeed, the energy injected by the central
AGN affects the global distribution of baryons (see §5.2), leading to local
depletion or excesses of matter with respect to the expectations of models
including dark matter only. This effect is most important in galaxy groups,
since these systems correspond to the peak of the local halo mass density and
their baryonic properties are highly sensitive to feedback. As a result, the
matter power spectrum at $z=0$ can be substantially altered by baryonic
physics, and in particular AGN feedback (e.g. van Daalen et al., 2011;
Semboloni et al., 2011). The shape of the power spectrum is strongly affected
by baryonic processes on scales $k\gtrsim 10^{-1}$ $h$/Mpc. For $10^{-1}<k<10$
$h$/Mpc most simulations predict a _deficit_ of power with respect to the
N-body case, although the actual amplitude of the effect is highly uncertain
(Chisari et al., 2019). The evacuation of baryons from the central regions of
galaxy groups under the influence of feedback is responsible for the deficit
of power on scales of $\sim 1$ Mpc, i.e. roughly the typical size of galaxy
groups. On smaller scales ($k\gtrsim 10$ $h$/Mpc), cooling and condensation of
baryons in the central regions lead to a rapidly increasing power.

Figure 18: Modification of the local matter power spectrum with respect to
pure N-body simulations in the presence of AGN feedback at the scale of galaxy
groups in the semi-analytic model of Schneider et al. (2019). The various
curves show how the power spectrum depends on the parameter $\theta_{ej}$
governing gas ejection from the central regions of groups under the influence
of AGN feedback. A strong ejection of gas from the core of halos implies
substantial modifications to the matter power spectrum on scales of $\sim 1$
Mpc. Figure reproduced with permission from Schneider et al. (2019).

Accurately predicting the shape of the matter power spectrum is crucial for
the success of future cosmic shear experiments such as _Euclid_ , which aim at
determining the growth of structures by measuring the matter power spectrum
and its evolution (Amendola et al., 2018). Semboloni et al. (2011) showed that
neglecting baryonic effects would imply important systematics on the
determination of cosmological parameters. Systematic effects can be mitigated
by excluding the small scales ($k\gtrsim 10^{-1}$ $h$/Mpc) when fitting the
measured power spectra, although that comes at the price of greatly increased
uncertainties in the resulting cosmological parameters. Constraints on
extended cosmologies such as massive neutrinos, variable dark energy equation
of state, or chameleon gravity, require sensitivity on smaller scales and
their effect is strongly degenerate with that of baryonic physics (Schneider
et al., 2020b, a).

Chisari et al. (2019) showed that numerical simulations have not yet converged
on the actual impact of feedback on the power spectrum (see their Fig. 3). For
instance, the very strong feedback implemented in the original Illustris
simulation, which was sufficient to completely evacuate the gas content from
most groups (see Fig. 15), leads to a very strong suppression of power
($>30\%$) on scales of a few hundred kpc. Conversely, simulations implementing
a more gentle feedback scheme (EAGLE, Horizon-AGN, MassiveBlackII) predict
small corrections with respect to the fiducial DM-only case for $k\lesssim 10$
$h$/Mpc. These simulations also predict a high gas fraction in galaxy groups
(see §5.2 and van Daalen et al., 2020, for an extensive discussion). Recently,
Schneider et al. (2019) used a semi-analytic model to predict the impact of
baryons on the matter power spectrum based on the observed gas properties of
groups. The authors modified the mass profiles of halos in large N-body
simulations to account for star formation and AGN feedback. In particular, the
semi-analytic model of Schneider et al. (2019) is highly sensitive to the
parameter $\theta_{ej}$ which governs the ejection of gas from the central
regions of the halo by AGN feedback. In Fig. 18, we show how the predicted
matter power spectrum depends on $\theta_{ej}$. With increasing feedback, a
progressively larger fraction of the gas is ejected from the halo, and thus
the expected power gets more strongly suppressed. Calibrating their semi-
analytic model on the observed gas fraction and gas density profiles of group-
scale halos, Schneider et al. (2019) provided a range of predictions matching
the existing observational constraints. High-precision measurements of the gas
density profiles in a representative sample of galaxy groups would allow us to
determine precisely the expected shape of the power spectrum (Schneider et
al., 2020a), thereby providing a key input for upcoming cosmology experiments.

In addition to the matter power spectrum, AGN feedback on the scales of galaxy
groups also affects several other cosmological observables such as the thermal
SZ power spectrum (e.g. Battaglia et al., 2012; McCarthy et al., 2014).
Indeed, AGN feedback affects the pressure profiles of halos and thus modifies
the amplitude of the power spectrum on small scales ($\ell\gtrsim 1,000$,
McCarthy et al., 2018). Ramos-Ceja et al. (2015) showed that tSZ models based
on the universal pressure profile (Arnaud et al., 2010) overpredict the power
measured by SPT and ACT on small scales. A strong feedback scenario and a low
gas fraction on group scales are needed to fit the measured power. The effect
of feedback also implies modifications to the cross-correlations between the
tSZ and other observables (e.g. Battaglia et al., 2015; Hojjati et al., 2015).
Finally, the choice of the feedback scheme affects the shape of the halo mass
function (e.g. Velliscig et al., 2014; Cui et al., 2014; Bocquet et al., 2016)
and the structure of dark-matter halos (e.g. Velliscig et al., 2014; Schaller
et al., 2015). We refer to the companion Oppenheimer et al. (2021) review for
a more general discussion of the topic.

##  6 Future observatories

###  6.1 eROSITA

At present, our knowledge of the population of galaxy groups in the local
Universe comes largely from the ROSAT All-Sky Survey (RASS). Groups identified
in the RASS (or even the Einstein slew survey) form the basis of most studies
of the mechanics of AGN feedback at this mass scale, but unfortunately groups
are at the lower limit of sensitivity for these surveys. RASS is therefore
biased toward the detection of relaxed, centrally-concentrated, cool-core
systems, with the strength of the bias increasing as mass decreases from poor
clusters to groups (Eckert et al., 2011). Searches tailored to the detection
of more extended sources in RASS reveal a population of low surface brightness
groups undetected by the original survey (Xu et al., 2018) confirming the
expected bias, while XMM-Newton observations of optically-selected groups
identify both low luminosity and disturbed systems previously not detected or
not recognised as groups in RASS (O’Sullivan et al., 2017).

Spectrum Roentgen Gamma (SRG, launched in 2019) hosts the eROSITA instrument,
a set of seven co-aligned soft X-ray telescopes covering the 0.2-10 keV band,
with a field of view of 1∘ and $\sim$15′′ spatial resolution. SRG will spend
four years surveying the whole sky once every 6 months, with eROSITA building
up a map $\sim$20$\times$ deeper than RASS in the 0.5-2 keV band (Merloni et
al., 2012). This is sufficient to detect essentially every galaxy group with a
virialized halo in the local universe (O’Sullivan et al., 2017). More massive
groups with luminosities $\sim$1042 erg s-1 should be detectable to $z\sim
0.1$ in the final eRASS:8 survey. Käfer et al. (2020) performed detailed
simulations to evaluate the sensitivity of eRASS:8 to galaxy groups. The
authors used a wavelet decomposition algorithm sensitive to large-scale
diffuse emission. In Fig. 19, we show the corresponding sensitivity curves for
two possible source detection setups: a decomposition over wavelets of scales
$1-4^{\prime}$ optimized for relatively compact sources, and the other for
scales in the range $1-16^{\prime}$ sensitive to the most extended nearby
sources. Using these setups, the authors predict eRASS:8 will detect all the
galaxy groups with $M_{500}>10^{13}M_{\odot}$ out to $z=0.05$. The most
massive groups ($\sim 10^{14}M_{\odot}$) will be detected out to $z=0.5$.
While the survey observations will typically only provide luminosity and
morphology information for individual halos, the group samples derived from
them will be a solid base from which to investigate the impact of cooling and
AGN feedback in groups, particularly when combined with radio surveys.

Pointed observations with eROSITA, possible once the survey phase is complete,
may also prove useful for studies of groups. The combination of a large field
of view and soft band effective area (roughly double that of the XMM-Newton
EPIC-pn) is well-suited to observations of the outskirts of nearby groups, and
the search for cavities or other structures associated with giant group-
central radio galaxies. Thanks to its short focal length and very stable
background (Freyberg et al., 2020), eROSITA is very sensitive to diffuse X-ray
emission below 2 keV, meaning it is well suited to study the diffuse X-ray
emission of galaxy groups.

Figure 19:  Expected sensitivity curve of the final eROSITA survey (eRASS:8)
compared to the sensitivity of existing and upcoming X-ray and SZ surveys. The
eROSITA sensitivity curve was computed from synthetic data using a wavelet
decomposition algorithm (Käfer et al., 2020) sensitive to scales of
$1-4^{\prime}$ (solid blue curve) and $1-16^{\prime}$ (dashed blue curve). For
comparison, the dashed black curve shows the sensitivity of the ROSAT all-sky
survey assuming a fixed soft X-ray flux threshold of $1.8\times 10^{-12}$ erg
s-1, which is the typical sensitivity of the REFLEX and NORAS samples (green
asterisks, Böhringer et al., 2013). The red points show the systems selected
from the second _Planck_ SZ catalogue (Planck Collaboration XXVII, 2016). The
green curve shows the expected sensitivity of the SPT-3G experiment (Benson et
al., 2014).

###  6.2 XRISM

The X-ray Imaging and Spectroscopy Mission (XRISM), expected to launch by
April 2023, will open a new era of high spectral-resolution observations of
galaxy groups. Its X-ray microcalorimeter (Resolve, XRISM Science Team, 2020)
will have a constant 7 eV energy resolution across its 0.3-12 keV band,
resolving the forest of emission lines which characterizes emission from the
IGrM. This offers opportunities in a number of important areas, including
measurements of bulk flows and turbulence in the hot gas. At present, grating
spectra only provide upper limits on the turbulence of the IGrM (Sanders &
Fabian, 2013; Pinto et al., 2015), with possible hints of asymmetries
associated with sloshing motions (Ahoranta et al., 2016). Hitomi demonstrated
the capabilities of microcalorimeters, but was only able to make a single
turbulence measurement in the Perseus cluster (Hitomi Collaboration, 2016).
XRISM should be able to measure turbulent velocities down to tens of km s-1,
providing a measure of the kinetic energy stored in the IGrM and allowing us
to determine how much of the energy of AGN outbursts can be diffused out into
the IGrM by these gas motions. Spectra from the Resolve microcalorimeter will
also provide a detailed view of shock heating and cooling, with individual
emission lines accurately tracing gas at different temperatures. Performance
verification targets for the mission include NGC 5044 and NGC 4636, and while
the spatial resolution ($>$1′) and effective area of the observatory may limit
its use to relatively bright nearby groups, its results are likely to be
ground-breaking.

###  6.3 Athena

The Advanced Telescope for High Energy Astrophysics (Athena), expected to
launch in the early 2030s, represents the next generation of major X-ray
observatories, with 5′′ spatial resolution and an effective area of 1.4 m2 at
1 keV (roughly 45$\times$ that of XRISM’s Resolve instrument). It will carry a
40${}^{\prime}\times$40′ active pixel detector (the wide field imager, WFI)
providing CCD-like spectral imaging, and a 5′-diameter microcalorimeter array
(the X-ray Integral Field Unit, X-IFU) with $\leq$5′′ pixels, capable of 2.5
eV spectral resolution (see, e.g., Barret et al., 2020). The combination of
the very large collecting area with these two instruments will open up several
new fields of study for galaxy groups. The WFI survey, performed over the
first 4 years of operations, is expected to find $>$10,000 groups and clusters
at $z\geq$0.5, including $\sim$20 groups with M${}_{500}\geq$5$\times$1013 M⊙
at $z\sim 2$, and measure their temperature to better than 25% accuracy (Zhang
et al., 2020). This will provide a clear view of the evolution of AGN feedback
in groups back to the era of peak star formation and black hole growth.
Identification of cavities and spectral mapping will be possible out to
moderate redshift, showing us the impact of feedback over the past few
gigayears.

X-IFU offers capabilities similar to that of XRISM’s Resolve, but with greatly
improved spatial resolution and the ability to examine even low luminosity
systems in the local universe. It will allow mapping of turbulence and bulk
flows in the IGrM, tracing gas motions associated with mergers, sloshing,
uplift behind rising radio bubbles or AGN-driven outflows. By mapping the
kinetic and thermal energy content of the IGrM on spatial scales similar to
those at which energy is injected by radio galaxies, it will allow us to
quantify how much energy is injected into the hot gas by outbursts, determine
where and when energy is transferred out of the radio jets and lobes, and see
how it is then transported out into the surrounding halo (Croston et al.,
2013). It will also provide a clear view of the location of the coolest gas
and allow us to trace the process by which it cools out of the hot phase.

###  6.4 Lynx

The Lynx mission concept (https://www.lynxobservatory.com/report, Gaskin et
al., 2019) will, if approved, go beyond Athena, with sub-arcsecond resolution
over a 22${}^{\prime}\times$22′ field of view and an effective area of 2 m2 at
1 keV, giving 50$\times$ the throughput of Chandra. As with Athena, an active
pixel array (the high-definition X-ray imager, HDXI) would provide wide-field
CCD-like spectral imaging, but with 0.3′′ pixels to take advantage of the
exquisite spatial resolution. The Lynx X-ray Microcalorimeter (LXM) would
bring 3 eV spectral resolution on 1′′ spatial scales over a 5′ field of view,
with sub-arrays offering 0.5′′ spatial resolution or 0.3 eV spectral
resolution in 1′ fields. Much of Lynx’s proposed science relates to the
detailed physics of accretion and galaxy evolution, and the X-ray universe at
high redshift; survey observations with the HDXI would be capable of detecting
groups with masses as low as 2$\times$1013 M⊙ out to a $z\sim 3$. In low-
redshift systems, LXM could examine the conditions within individual cooling
filaments in group cores, and measure the velocities of the weak shocks and
sound waves produced during AGN outbursts. As with Athena, the observatory
would be sensitive enough to trace the IGrM out to the virial radius in a
large sample of groups, but with fine spatial resolution making identification
of structure easier, and rejection of background sources cleaner. It is
notable that Lynx would be the first mission after Chandra to be able to
provide the finely detailed images that have proved so useful in the study of
AGN feedback, reaching scales comparable to those of optical and radio
observations.

###  6.5 The Square Kilometer Array and its precursors

Radio astronomy has undergone something of a renaissance in recent years, with
new and improved capabilities coming online, e.g., the upgraded Jansky Very
Large Array (JVLA) and Giant Metrewave Radio Telescope (uGMRT), the Low-
Frequency Array (LOFAR) and the Atacama Large Milimeter Array (ALMA). These
are providing improved radio continuum surveys in the northern hemisphere and
equatorial sky. However, new telescopes in southern Africa and Australia are
opening up new opportunities, as they begin to survey the relatively poorly-
explored southern sky. These include the Murchison Widefield Array (MWA), the
Australian Square Kilometer Array Pathfinder (ASKAP), and MeerKAT in South
Africa’s Karoo region. The Galactic and Extragalactic All-sky MWA Survey
(GLEAM, Hurley-Walker et al., 2017) provides an early example, covering the
entire southern sky below Declination +30∘ at frequencies 72-231 MHz. While
its spatial resolution is modest ($\sim$100′′) its high sensitivity at low
frequency and provision of fluxes in multiple bands makes it a powerful tool
for studying radio galaxies, particularly old, fading sources. Higher
frequency ($\sim$1 GHz), higher spatial resolution (8-30′′) surveys of
continuum emission and Hi are becoming available from ASKAP (e.g., RACS,
WALLABY, McConnell et al., 2020; Koribalski et al., 2020) and MeerKAT (e.g.,
MIGHTEE, Jarvis et al., 2016), and these observatories are beginning to
produce interesting findings on, e.g., group dynamics and evolution (Oosterloo
et al., 2018; For et al., 2019) and the population of giant radio galaxies
(Delhaize et al., 2021; Pasini et al., 2020).

These telescopes are the precursors of the Square Kilometer Array (SKA) a set
of next-generation telescopes expected to begin operations in the late 2020s,
combining wide frequency coverage with unprecedented sensitivity. The SKA will
be built in phases, with phase 1 consisting of two components: the SKA1-Low,
covering the 50-350 MHz band, with baselines up to 65 km providing spatial
resolution of $\sim$4′′ at 300 MHz and sensitivity a factor of 5-10 better
than LOFAR or GMRT; and the SKA1-Mid, covering 350 MHz to 15 GHz with
resolution 0.4′′ at 1.4 GHz and a sensitivity up to an order of magnitude
better than JVLA (Braun et al., 2019). The proposed phase 2 SKA would improve
sensitivity by another order of magnitude. Much of the SKA’s proposed science
relates to the early universe, but it will be an extraordinary tool for
studies of feedback in groups and clusters, tracing the entire AGN population
to high redshift, not merely the radio-loud systems that dominate current
samples (Prandoni & Seymour, 2015). SKA surveys are likely to be sensitive to
sources down to 1022 W Hz-1 out to $z$=3-4 (McAlpine et al., 2015). This
offers an opportunity to detect the radio counterparts of most galaxies
identified in current optical surveys, including essentially all group and
cluster-dominant galaxies, with sufficient resolution to allow AGN and star
formation emission to be disentangled, and with the wide frequency coverage
necessary to determine the state and age of jets and lobes. Given the very
large numbers of groups and clusters likely to be detected in the southern sky
by, e.g., eROSITA, such surveys will play an important role in identifying
systems with active cooling and feedback. Hi observations reaching low column
densities may provide another window on cooling from the IGrM. SKA will also
open up the study of diffuse radio structures and magnetic fields in groups
(e.g., Heald et al., 2020), providing constraints on rates of energy transport
and conduction in the IGrM, as well as information on gas motions (Kale et
al., 2016) and perhaps even on turbulence and shocks.

###  6.6 Upcoming SZ facilities

Upcoming surveys of the cosmic microwave background (CMB) such as CCAT-prime
(Stacey et al., 2018), Simons Observatory (Lee et al., 2019), and CMB-S4
(Abazajian et al., 2016) will likely also play a role in advancing our
understanding of AGN feedback at group scale by providing complementary
information to X-ray surveys. While the thermal SZ effect (hereafter tSZ) is a
steep function of mass ($Y_{SZ}\propto M_{500}^{5/3}$), stacking of the tSZ
effect over large samples (either X-ray or optically selected) can lead to a
detection down to $M_{500}\sim 10^{13}M_{\odot}$. As a pilot study, Planck
Collaboration et al. (2013) presented the stacked tSZ signal from a large
sample of SDSS galaxies selected to be central to their halo, and found that
the tSZ-to-mass scaling relation extends with no break all the way down to
$M_{500}\sim 10^{12.5}M_{\odot}$. However, the interpretation of the result is
rendered difficult by the large _Planck_ beam ($\sim 8^{\prime}$), which
dilutes the signal (Le Brun et al., 2015). While detecting the tSZ signal from
individual galaxy groups will be challenging for the new generation of CMB
survey instruments, the angular resolution of the foreseen facilities
($\lesssim 1^{\prime}$) will be sufficient to study the distribution of the
stacked tSZ signal and determine the origin of the signal identified by Planck
Collaboration et al. (2013). On the other hand, large single-dish facilities
such as AtLAST (Klaassen et al., 2020) will be sensitive enough to detect the
tSZ effect from galaxy groups (Mroczkowski et al., 2019).

Recently, the kinetic SZ effect (kSZ), i.e. the Doppler shift of the CMB
spectrum induced by moving electron clouds, has emerged as a promising tool
for studying the baryon content of galaxy groups (Hill et al., 2016; Ferraro
et al., 2016). The kSZ signal is independent of the gas temperature, which
makes it in principle more suitable than the tSZ for the study of low-mass
systems. The kSZ signal cannot be detected directly by stacking CMB
observations, given that the average velocity of structures with respect to
the CMB rest frame vanishes. However, the kSZ signal can be measured by cross-
correlating CMB maps with spectroscopic galaxy surveys, thereby fixing each
system’s velocity; this technique is known as the _pairwise_ kSZ. Several
recent studies reported low-significance detections of the kSZ with this
technique (De Bernardis et al., 2017; Calafut et al., 2021; Planck
Collaboration et al., 2016). These early results may indicate that the flat
gas density profiles inferred from X-ray data (see §3.1.4) extend far beyond
the halo’s virial radius, which, if confirmed, provides a detection of the gas
expelled from the central regions of halos by AGN feedback. Cross-correlating
the kSZ data from future CMB experiments with large spectroscopic surveys like
DESI may yield a detection of the pairwise kSZ at high significance (Battaglia
et al., 2017) and possibly out to high redshifts (Chaves-Montero et al.,
2019).

DE: lead author, Sect. 1, 2, 3.1.3, 3.1.4, 5.2, 5.3, 5.4, 6.1, 6.6; MG: Sect.
3.1.3, 4, 5.3; FG: Sect. 3.1.1, 3.1.2; AMCLB: Sect. 2, 5.1, 5.2; EOS: Sect.
3.2, 3.3, 6

MG acknowledges partial support by NASA Chandra GO8-19104X/GO9-20114X and HST
GO-15890.020-A grants. AMCLB is supported by a fellowship of PSL University at
the Paris Observatory. EOS acknowledges support from NASA through XMM-Newton
award number 80NSSC19K1056 and Chandra award number GO8-19112A.

###### Acknowledgements.

We deeply thank Yannick Bahé, Weigang Cui, Marco De Petris, Yohan Dubois,
Scott Kay, Ian McCarthy, Alisson Pellissier, Ewald Puchwein, Elena Rasia,
Dylan Robson, Marcel van Daalen, Federico Sembolini, Mark Vogelsberger and
Rainer Weinberger for providing data from their simulations for inclusion in
Fig. 14 and 15, even if some could not be included in the end. We also thank
Paul Nulsen for providing cavity parameters for inclusion in Table
LABEL:tab:cavity_properties, and the anonymous referees for useful comments.
We thank Ming Sun, Mark Voit, Iurii Babyk, Trevor Ponman and Aurel Schneider
for granting us permission to reprint some of their figures. EOS thanks G.
Schellenberger and K. Kolokythas for useful conversations. DE thanks Alexis
Finoguenov for useful discussions. DE and AMCLB thank Benjamin Oppenheimer for
useful discussions. We are also grateful to Tony Mroczkowski, Joop Schaye and
Ming Sun for sending comments on the accepted version while we were checking
the proofs, which were taken into account for the published version. The
authors declare no conflict of interest. no

##  Appendix A List of the properties of detected AGN cavities in galaxy
groups. Only quantities available from the literature are included, thus for
some systems the listing will be incomplete.

Table 1: Properties of the cavities detected in groups. 1 Projected semi-major axis of the cavity 2 Projected semi-minor axis of the cavity 3 Projected distance from the cavity center to the core 4 Ages are reported as $t_{s}$-$t_{buoy}$-$t_{refill}$. Where only a single value is reported, this is $t_{s}$. 5 Bolometric luminosity between 0.001 and 100 keV inside $r_{cool}$, where the cooling time is less than 7.7 Gyr References: 1 Bîrzan et al. (2004); 2 Rafferty et al. (2006); 3 Gitti et al. (2010); 4 Liu et al. (2019); 5 Lal et al. (2013); 6 Pandge et al. (2019); 7 Randall et al. (2011); 8 Grossová et al. (2019); 9 Schellenberger et al. (2017); 10 O’Sullivan et al. (2011a); 11 Kolokythas et al. (2020); 12 O’Sullivan et al. (2017); Kolokythas et al. (2018); 13 Machacek et al. (2007); 14 Randall et al. (2009); 15 O’Sullivan et al. (2018b); 16 O’Sullivan et al. (2019); 17 Machacek et al. (2010); 18 Pandge et al. (2013); 19 Dong et al. (2010); 20 Shin et al. (2016); 21 David et al. (2017); Schellenberger et al. (2021); 22 Panagoulia et al. (2014b); 23 Cavagnolo et al. (2010); 24 Nulsen, P., priv. comm.; 25 Blanton et al. (2004) Source | $a^{1}$ | $b^{2}$ | $R^{3}$ | $pV$ | Age4 | $L_{cool}^{5}$ | Ref.
---|---|---|---|---|---|---|---
| (kpc) | (kpc) | (kpc) | ($10^{56}$ ergs) | (Myr) | ($10^{42}$ ergs/s) |
HCG 62 N | 5.0 | 4.3 | 8.4 | $2.9^{+4.1}_{-1.5}$ | 18-15-31 | $1.8\pm 0.2$ | 1,2,3
HCG 62 S | 4.0 | 4.0 | 8.6 | $2.1^{+3.7}_{-1.3}$ | 19-16-29 |  |
3C88 E | 23 | 23 | 28 | 95 | 60 |  | 4
3C449 S | 13 | 13 | 39 | 14.6 | 70 |  | 5
IC1262 N | 2.2 | 1.5 | 6.5 | 58.0 | 17-24-52 | $3.3^{+0.2}_{-0.3}$ | 6
IC1262 S | 4 | 2 | 6.1 | 50.1 | 12-21-42 |  |
NGC 5813 in SW | 0.95 | 0.95 | 1.3 | 0.11 | 1.2 |  | 7
NGC 5813 in NE | 1.03 | 0.93 | 1.4 | 0.15 | 1.4 |  |
NGC 5813 mid SW | 3.9 | 3.9 | 7.7 | 1.53 | 7.2 |  |
NGC 5813 mid-1 NE | 2.9 | 2.2 | 4.9 | 0.93 | 4.6 |  |
NGC 5813 mid-2 NE | 2.8 | 2.4 | 9.3 | 0.41 | 8.8 |  |
NGC 5813 out SW | 5.2 | 3.0 | 22.2 | 0.6 | 20.8 |  |
NGC 5813 out NE | 8.0 | 4.4 | 18.0 | 2.6 | 17.0 |  |
IC 4296 NW | 80 | 80 | 230 | 920 | 220 |  | 8
NGC 741 W | 8 | 8 | 16 | $12.2\pm 1.2$ | 30 |  | 9
NGC 193 | 63.4 | 47.2 | 0.0 | 22.7${}^{+17.3}_{-17.7}$ | 44.2-20.4-76.9 | 0.11$\pm$0.01 | 10
NGC 507 E | 21.7 | 8.7 | 22.1 | 308${}^{+494}_{-63}$ | 48.4-229-38.9 | 1.37$\pm$0.02 | 10
NGC 507 W | 13.4 | 5.0 | 11.7 | 90${}^{+254}_{-30}$ | 22.2-17.4-38.9 |  |
NGC 1550 E | 5.96 | 2.31 | 9.0 | 6.70${}^{+10.7}_{-1.98}$ | 12.8-27.9-37.0 | 2.79${}^{+0.03}_{-0.01}$ | 11
NGC 1550 W | 4.09 | 1.88 | 14.65 | 2.00${}^{+2.45}_{-0.68}$ | 19.8-52.6-33.0 |  |
NGC 4261 E | 20.94 | 15.51 | 24.82 | 31.02${}^{+11.98}_{-6.15}$ | 40.5-36.6-105.3 | 0.11${}^{+0.08}_{-0.01}$ | 12
NGC 4261 W | 18.62 | 16.91 | 21.72 | 32.78${}^{+6.30}_{-6.52}$ | 35.4-31.8-99.4 |  |
NGC 4636 NE | 2.67 | 1.11 | 3.25 | 0.28${}^{+0.40}_{-0.06}$ | 8.8-20.2-46.8 | 0.18$\pm$0.01 | 10
NGC 4636 SE | 2.40 | 1.52 | 2.80 | 0.47${}^{+0.28}_{-0.10}$ | 7.6-17.0-47.2 |  |
NGC 4636 SW | 2.78 | 1.88 | 4.62 | 0.88${}^{+0.48}_{-0.31}$ | 11.7-31.2-62.1 |  |
NGC 4636 NW | 2.53 | 1.32 | 3.33 | 0.38${}^{+0.36}_{-0.08}$ | 9.1-21.5-49.7 |  |
NGC 4782 | 10.7 | 10.7 | 23.0 | 11.0 | 35-54- |  | 13
NGC 5044 SW | 6.54 | 2.84 | 8.60 | 2.17${}^{+2.87}_{-0.49}$ | 21.1-14.4-35.5 | 4.72$\pm$0.01 | 12
NGC 5044 NW | 3.04 | 2.34 | 4.85 | 0.62${}^{+0.22}_{-0.13}$ | 11.4-16.4-35.5 |  |
NGC 5044 in SW | 0.15 | 0.15 | 0.45 | 0.0007 | 1 |  | 21
NGC 5044 in NE | 0.15 | 0.15 | 0.45 | 0.0007 | 1 |  | 21
NGC 5098 N | 3.0 | 1.6 | 2.97 | 7.0 | 18 |  | 14
NGC 5098 S | 3.0 | 1.6 | 2.97 | 7.0 | 18 |  |
NGC 5846 N | 0.74 | 0.58 | 0.64 | 0.35${}^{+0.15}_{-0.13}$ | 1.7-1.2-4.6 | 0.27$\pm$0.01 | 10
NGC 5846 S | 0.74 | 0.58 | 0.68 | 0.35${}^{+0.15}_{-0.13}$ | 1.8-1.4-4.8 |  |
NGC 5903 | 16.0 | 13.0 | 24.6 | 2.3$\pm$0.10 | 82.5 | 0.0047$\pm$0.0005 | 15
NGC 6269 N | 5.2 | 5.2 | 10.7 | 22.4${}^{+7.2}_{-9.0}$ | 14.0-14.3-26.2 | 0.78${}^{+0.04}_{-0.03}$ | 10
NGC 6269 S | 5.5 | 5.5 | 12.3 | 27.2${}^{+8.2}_{-10.5}$ | 16.1-17.1-28.9 |  |
NGC 6338 in NE | 4.60 | 3.22 | 3.96 | 20.38${}^{+9.65}_{-4.83}$ | 7.3-5.6-19.3 | 4.56${}^{+0.08}_{-0.06}$ | 16
NGC 6338 in SW | 4.22 | 3.22 | 6.34 | 10.20${}^{+3.85}_{-2.53}$ | 11.4-14.7-30.8 |  |
NGC 6338 outer | 6.49 | 4.01 | 18.21 | 12.98${}^{+8.38}_{-2.88}$ | 25.8-33.5-32.7 |  |
VII Zw 700 NE | 3.96 | 2.43 | 5.54 | 0.24${}^{+0.17}_{-0.11}$ | 44.0-41.7-95.5 | 0.54$\pm$0.03 | 16
VII Zw 700 SW | 4.70 | 1.90 | 3.43 | 0.43${}^{+0.65}_{-0.19}$ | 27.3-29.3-82.8 |  |
NGC 6868 NW | 11.7 | 11.7 | 38.7 | 1.48 | 88-107-119 |  | 17
NGC 6868 SE | 8.14 | 8.14 | 25.3 | 1.0 | 55 |  |
A 1991 N | 16.8 | 5.5 | 12.4 | 496 | 18-28-68 | 60.4 | 18
A 1991 S | 13.3 | 6.2 | 11.5 | 535 | 18-29-59 |  |
A 3581 1 | 7.9 | 3.8 | 3.1 |  |  |  | 19
A 3581 2 | 3.4 | 3.7 | 3.1 |  |  |  | 19
NGC 533 1 | 2.2 | 1.3 | 1.2 |  |  |  | 19
NGC 533 2 | 3.1 | 1.6 | 1.6 |  |  |  | 19
NGC 4104 | 1.9 | 1.5 | 0.0 |  |  |  | 19
RXC J0352.9+1941 1 | 8.0 | 5.9 | 9.3 |  |  |  | 20
RXC J0352.9+1941 2 | 7.9 | 4.3 | 10.8 |  |  |  |
RX J0419+0225 | 1.2 | 0.9 | 1.7 |  |  |  | 20
A 2550 1 | 18.9 | 9.3 | 10.3 |  |  |  | 19
A 2550 2 | 10.7 | 5.9 | 7.8 |  |  |  | 19
A 2717 1 | 11.2 | 6.3 | 7.9 |  |  |  | 19
A 2717 2 | 13.4 | 5.8 | 8.4 |  |  |  | 19
AS1101 1 | 21.0 | 14.7 | 24.2 |  |  |  | 19
AS1101 2 | 24.1 | 15.7 | 23.6 |  |  |  | 19
ESO 351-021 | 12.2 | 8.6 | 14.8 |  |  |  | 19
RX J1159+5531 1 | 7.7 | 3.9 | 7.5 |  |  |  | 19
RX J1159+5531 2 | 6.7 | 4.3 | 9.7 |  |  |  | 19
RX J1206-0744 | 27.6 | 21.4 | 29.1 |  |  |  | 19
NGC 2300 | 1.3 | 1.0 | 1.5 |  |  |  | 19
UGC 5088 1 | 7.3 | 5.4 | 8.4 |  |  |  | 19
UGC 5088 2 | 6.5 | 3.6 | 5.4 |  |  |  | 19
NGC 777 E | 1.9 | 2.3 | 4.6 |  |  | 0.99 | 22,23
NGC 777 W | 2.1 | 2.4 | 4.0 |  |  |  | 22
NGC 4235 E | 2.4 | 4.6 | 11.8 |  |  |  | 22
NGC 4235 W | 2.1 | 2.4 | 4.0 |  |  |  | 22
NGC 1553 1 | 4.1 | 3.5 | 4.6 | 0.42${}^{+0.49}_{-0.22}$ | 13.5-10.7-33.0 | 1.72 | 23,24
NGC 1553 2 | 3.5 | 2.7 | 3.3 | 0.28${}^{+0.23}_{-0.13}$ | 9.8-7.4-25.2 | 1.72 | 23,24
NGC 1600 1 | 0.87 | 0.82 | 1.21 | 0.15${}^{+0.24}_{-0.09}$ | 2.2-1.6-4.2 | 0.12 | 23,24
NGC 1600 2 | 0.83 | 0.72 | 1.42 | 0.11${}^{+0.13}_{-0.06}$ | 2.5-2.1-4.3 | 0.12 | 23,24
NGC 3608 1 | 3.2 | 2.2 | 6.0 | 0.04${}^{+0.03}_{-0.01}$ | 18.7-15.9-26.4 | 0.008 | 23,24
NGC 3608 2 | 2.5 | 1.7 | 5.3 | 0.02${}^{+0.02}_{-0.01}$ | 16.3-14.8-21.5 | 0.008 | 23,24
NGC 7626 1 | 5.6 | 3.1 | 14.4 | 0.37${}^{+0.21}_{-0.14}$ | 33.1-36.5-39.9 | 0.12 | 23,24
NGC 7626 2 | 1.4 | 0.7 | 3.0 | 0.03${}^{+0.03}_{-0.01}$ | 6.8-7.0-8.7 | 0.12 | 23,24
NGC 7626 3 | 1.6 | 1.1 | 3.8 | 0.06${}^{+0.04}_{-0.02}$ | 8.7-8.8-11.5 | 0.12 | 23,24
NGC 7626 4 | 4.7 | 4.0 | 16.2 | 0.36${}^{+0.48}_{-0.21}$ | 37.2-42.5-43.2 | 0.12 | 23,24
A 262 E | 2.6 | 2.6 | 6.2 | 1.7${}^{+3.2}_{-1.1}$ | 11-13-20 |  | 1,25

yes

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