adsabs/FOCAL huggingface dataset:
The studies of emission lines, however, which for wide-field cameras require specific and expensive narrow-band (NB) filters of large physical size, have thus far been limited to pointed observations. Very deep Hα observations of a few galaxies in nearby clusters, including our recent observations with MegaCam, have led to several intriguing discoveries. They have shown that the ionised phase appears to be an ideal tracer of stripped gas in dense regions: approximately 50% of late-type galaxies show extended (~50 kpc) tails of ionised gas with surface brightness Σ(Hα) of approximately a few 10−18 erg s−1 cm−2 arcsec−2 (Boselli & Gavazzi 2014), while only a handful of galaxies have extended cold or hot gaseous tails (Chung et al. 2007; Sun et al. 2006, 2007, 2010; Scott et al. 2012; Sivanandam et al. 2014; Jáchym et al. 2014). In some objects, the cometary shape of the tails indicates that the gas has been stripped by the interaction with the hot ICM (Gavazzi et al. 2001; Yoshida et al. 2002; Yagi et al. 2010; Fossati et al. 2012, 2016, 2018 – paper II; Zhang et al. 2013; Boselli et al. 2016a); in other systems, bridges of ionised gas linking different nearby galaxies are associated with tidal tails in the stellar component, suggesting gravitational perturbations with nearby companions or within infalling groups (i.e. pre-processing; Kenney et al. 2008; Sakai et al. 2002; Gavazzi et al. 2003a; Cortese et al. 2006). They have also shown that within the tails of stripped gas, star formation in compact HII regions occurs in some but not in all objects (Gavazzi et al. 2001; Yoshida et al. 2008; Hester et al. 2010; Fumagalli et al. 2011a; Fossati et al. 2012; Boissier et al. 2012; Yagi et al. 2013; Kenney et al. 2014; Boselli et al. 2016a, 2018 – paper III). The removal of the gas affects the activity of star formation of galaxies on different timescales that depend on the perturbing mechanism (Larson et al. 1980; Boselli et al. 2006, 2016b; Bekki 2009, 2014; McGee et al. 2009; Cen 2014; Fillingham et al. 2015; Rafieferantsoa et al. 2015). The distribution and the morphology of the star-forming regions within galaxies is also tightly connected to the perturbing mechanisms (increases in the nuclear star formation activity and asymmetric distributions of star-forming regions are typical in gravitational interactions, radially truncated star-forming discs in interactions with the ICM, and fainter star formingdiscs in starvation, Kennicutt & Keel 1984; Barton et al. 2000; Boselli et al. 2006; Ellison et al. 2008; Woods et al. 2010; Scudder et al. 2012; Patton et al. 2011, 2013). All these pieces of evidence underline the power of NB Hα imaging data in identifying the dominant perturbing mechanism in dense environments.
Citation: Chung et al. 2007
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To estimate the total integrated intensity of a shock-excited molecular line coming from a GMC, the shock models are scaled up so that the total energy dissipated in shocks is equal to the expected turbulent energy dissipation rate of the molecular cloud, as done in Basu & Murali (2001) and Pon et al. (2012). The dissipation rate of the turbulent energy of a molecular cloud, Lturb, is
2





where ρ is the gas density, σ is the one-dimensional velocity dispersion, R is the radius of the (spherical) cloud, and κ is the ratio of the dissipation timescale to the flow crossing timescale of the cloud. For this paper, κ is taken to be equal to 1, in agreement with numerical simulations of decaying turbulence (Gammie & Ostriker 1996; Mac Low et al. 1998; Stone et al. 1998; Mac Low 1999; Padoan & Nordlund 1999; Ostriker et al. 2001). This value of κ, however, is relatively uncertain, such that the predicted integrated intensities should only be considered to be accurate to a factor of a few. The integrated intensity of any shock-powered line, assuming a mean mass per hydrogen nuclei of 2.77 amu, is thus
3





where  is the fraction of the shock energy being emitted in the line. While the total turbulent energy of the cloud depends on the cube of the radius, the conversion of a luminosity to an intensity introduces an r−2 dependence, and setting the dissipation timescale to be equal to the turbulent crossing time introduces a further r−1 dependence, such that this predicted integrated intensity is independent of the size of the cloud. Pon et al. (2012) show that if the velocity distribution of gas particles in a molecular cloud is Gaussian and isotropic, then the shock velocity at which the peak energy dissipation occurs is approximately 3.2 times larger than the one-dimensional velocity dispersion of the gas, since the energy dissipation rate of a shock scales with the third power of the shock speed, but the probability of gas particles having a particular velocity difference decreases with increasing velocity. We assume that all of the turbulent energy in a cloud is dissipated at a shock speed of 3 km s−1, which would be the peak energy dissipation velocity for a velocity dispersion of roughly 1 km s−1 that would lead to observed FWHMs of 2.3 km s−1. While this velocity dispersion is on the lower end for what is usually associated with IRDCs (e.g., Paper II), slightly larger velocity dispersions should create larger peak temperatures and larger energy dissipation rates, such that our shock models can be considered to be conservative estimates for the shock emission. See Pon et al. (2012) for a more detailed discussion about this method of scaling the shock models.
Citation: Padoan & Nordlund 1999
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In the coupled ADAF-jet model, the accretion flow ADAF and the jet are connected by a defined parameter, $\eta \equiv \dot{M}_{\rm jet}/\dot{M}$, and $\dot{M}_{\rm jet}$ is input by assuming a value of, η, which is free parameter in the present model. The half-opening angle ϕ of the jet in the low/hard state of NS-LMXBs is uncertain. In this paper, we fix ϕ = 0.1 as assumed by several other authors for modelling the SED of the BH-LMXBs (e.g. Yuan et al. 2005; Zhang et al. 2010). Observationally, the bulk Lorentz factor of the jet in the low/hard state of X-ray binaries can be restricted in a relatively narrow range and the velocity of the jet is mildly relativistic, i.e. Γjet ≲ 2. More strictly, the bulk Lorentz factor is restricted to be as Γjet ≲ 1.67 (Gallo et al. 2003), and Γjet ≲ 1.2 (Fender 2006). In the internal shock model, the energy density of the internal shock increases with increasing Γjet, which finally will result in an increase of both the radio emission and X-ray emission (Yuan et al. 2005). However, since Γjet is restricted in a very narrow range by observations, we expect that a slight change of Γjet will result in a slight change of the jet emission. In this paper, we fix Γjet = 1.2 corresponding the bulk velocity of the jet 0.55c (Fender 2006). The value of ϵe and ϵB describing the fraction of the internal energy of the internal shock stored in the accelerated electrons and the magnetic field, and the index, pjet, describing the power-law distribution of the electrons in the jet after the acceleration by the shock are uncertain. Qiao & Liu (2015) tested the effect of ϵe and ϵB on the emergent spectrum of the jet in an observationally inferred range of 0.01  ϵe  0.1 and 0.01  ϵB  0.1. It was found that a change of ϵB in the range of 0.01–0.1, the emergent spectrum of the jet nearly does not change (see the right-hand panel of fig. 3 of Qiao & Liu 2015). A change of ϵe in the range of 0.01–0.1, the radio spectrum nearly does not change. However, the X-ray luminosity changes obviously by changing the value of ϵe from 0.01 to 0.1 (see the left-hand panel of fig. 3 of Qiao & Liu 2015). As shown in the left-hand panel of fig. 2 of Qiao & Liu (2015), the X-ray emission is completely dominated by the accretion flow (corona) in the luminous X-ray state, which is also true in this paper, i.e. the X-ray emission from the ADAF completely dominates the X-ray emission from the jet. In this paper, we fix ϵe = 0.04 and ϵB = 0.02, respectively, throughout the paper as (Qiao & Liu 2015). The value of the power-law index pjet of the electron distribution in the jet predicted by the shock acceleration is 2  pjet  3. By modelling the SEDs of three BH-LMXBs, the value of the power-law index pjet of the electron distribution is constrained to be 2.1 (Zhang et al. 2010). Meanwhile, a change of pjet in the range of 2  pjet  3 has very minor effect on the X-ray spectrum. In this paper, we fix the power-law index pjet = 2.1 throughout the paper.
Citation: Fender 2006
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Running penumbral waves in velocity and intensity observations were first reported by Giovanelli (1972) and Zirin & Stein (1972). Later, they were found in the photosphere as well (Musman et al. 1976), but there they appear to be more intermittent and to have higher radial phase velocity (40–90 km s−1) than the waves in Hα. Whereas the velocity amplitudes are less in the photosphere than in the chromosphere, the density is very low there and most of the wave energy lies in the photosphere and subphotosphere. Larger amplitudes on the disk-side penumbra demonstrate an alignment of the oscillations along the magnetic field. Running waves are also detected in the umbra, but the waves were believed to be unrelated to those in the penumbra (Kobanov & Makarchik 2004). In the chromosphere, the frequency of travelling waves decreases as they propagate from the umbra into the outer penumbra (e.g., Lites 1988). A similar effect is also found in measurements of the propagation velocity of travelling waves (Brisken & Zirin 1997; Sigwarth & Mattig 1997; Alissandrakis et al. 1998; Kobanov & Makarchik 2004; Tziotziou et al. 2006, 2007). Generally, the waves decelerate from 40 km s−1 near the inner part of the penumbra to 10 km s−1 or less near the outer edge of the penumbra. More recently, from a multi-wavelength study including the coronal channels of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), Jess et al. (2015) revealed the presence of a wide range of frequencies, with longer periodicities preferentially occurring at increasing distance from the umbra. The phase speeds also tend to decrease with increasing periodicity as the waves propagate away from the umbral barycenter. These observations also suggest that these slow waves are driven by a regular coherent source. The physical nature of running penumbral waves has been controversial. Some researchers have regarded them as trans-sunspot waves originating from umbral oscillations since they detected waves starting from the umbra and propagating through the penumbra (e.g., Alissandrakis et al. 1992; Tsiropoula et al. 1996, 2000). However, others suggest that the trans-sunspot (i.e., outward) motion is apparent to a given line of sight, and that these oscillations actually represent the upward propagation of field-guided magnetoacoustic waves from the photosphere (e.g., Christopoulou et al. 2000, 2001; Georgakilas et al. 2000; Rouppe van der Voort et al. 2003; Bogdan & Judge 2006; Kobanov et al. 2006; Bloomfield et al. 2007; Jess et al. 2013, 2015). The gradual change in the inclination of the penumbral field lines is responsible for changes in the oscillation periods and phase speeds.
Citation: Brisken & Zirin 1997
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Even though the physical processes giving rise to the flaring emission of blazars remain debatable, considerable progress has been made in characterizing the statistical properties of blazar variability at different wavelengths, and in different time domains. In particular, it has been demonstrated repeatedly that the power spectral density (PSD) of blazar light curves is, in general, of a power-law form (Simonetti et al. 1985; Kataoka et al. 2001; Brinkmann et al. 2003; Papadakis et al. 2003; Aharonian et al. 2007; Ciprini et al. 2007; Chatterjee et al. 2008, 2012; Abdo et al. 2010a; Carini et al. 2011; Kastendieck et al. 2011; Edelson et al. 2013; Nakagawa & Mori 2013; Max-Moerbeck et al. 2014; Park & Trippe 2014; Revalski et al. 2014; Sobolewska et al. 2014; Aleksić et al. 2015; Isobe et al. 2015; Kasliwal et al. 2015). A physical process with such a variability power spectrum, denoted hereafter as 





, where νk is the temporal frequency (corresponding to the timescale 1/νk), A is the normalization constant, and β is the spectral slope, is called white noise when β = 0, flicker (pink) noise when β = 1, and Brownian (red) noise when β = 2 (Press 1978). The PSD integrated over some variability frequency range is then a measure of the variance of the underlying signal in the time series within the corresponding range of variability timescales. Breaks in the slope or in the normalization of a PSD may appear, signaling characteristic/critical variability timescales in the system. In the case of blazars, various segments of radio, optical, X-ray, and γ-ray power spectra within the variability time domains from years to days (and in some instances, even sub-hour timescales), are characterized by spectral slopes 1 ≤ β  3, meaning that the variability amplitude increases with increasing variability timescale. Rarely, however, have blazar PSDs been analyzed in a systematic way at different wavelengths across the electromagnetic spectrum and over a truly broad range of temporal frequencies. It is important to note that colored-noise-type power spectra are expected to flatten on longer variability timescales (to preserve the total finite variance), and to cut-off at frequencies corresponding to the shortest variability timescale in a system. The detection of such cutoffs in blazar periodograms would be of primary importance for constraining the physics of blazar jets; however, such detections may be hampered by the finite duration of available monitoring blazar data on the one hand, and statistical fluctuations resulting from the measurement errors on the other hand.
Citation: Kasliwal et al. 2015
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Under the Applegate model, the change in orbital period is directly related to the change in the companion star’s gravitational quadrupole moment Q (Applegate & Patterson 1987),
(8)$$\begin{eqnarray*}
\frac{\Delta P_{\rm orb}}{P_{\rm orb}} = -9\frac{\Delta Q}{M_{\rm c} A^2},
\end{eqnarray*}$$where A = x(1 + q)/sin i is the orbital separation. For comparison, the total quadrupole moment induced by the spin of the companion star and the tidal distortion in the pulsar’s gravitational field is (Voisin, Breton & Summers 2020a)
(9)$$\begin{eqnarray*}
\frac{Q}{M_{\rm c} A^2} = -\frac{2}{9} k_2 \left(\frac{R_{\rm c}}{A}\right)^5 \left(4 q + 1\right),
\end{eqnarray*}$$where Rc is the radius of the companion star and k2 is the apsidal motion constant, a parameter describing the deformability of the companion star (Sterne 1939). For solar-type stars k2 ∼ 0.035 (Ogilvie 2014), while if we assume that redback companions are akin to the companions in CV systems whose outer envelopes have also been stripped through accretion, then we may expect a smaller value k2 ∼ 10−3 (Cisneros-Parra 1970). For J2039, the hyperparameter $h = 3.9^{+2.2}_{-1.2}$ s corresponds to the typical fractional amplitude for the variations in orbital phase. Taking the simpler squared exponential covariance function of equation (4) corresponding to n → ∞, then the deviations in orbital period have covariance function,
(10)$$\begin{eqnarray*}
K_{\Delta P_{\rm orb}/P_{\rm orb}}(t_1,t_2) &amp;=&amp; \frac{\partial ^2 K}{\partial t_1 \partial t_2} \nonumber\\
&amp;=&amp; \frac{h^2}{l^2} \exp \left(\!-\frac{(t_1 - t_2)^2}{2\ell ^2}\!\right) \left(\!1 - \frac{(t_1 - t_2)^2}{\ell ^4}\!\right).
\end{eqnarray*}$$The typical (fractional) amplitude of the orbital period variations is therefore ΔPorb/Porb ∼ h/ℓ = (3 ± 1) × 10−7, corresponding to $\Delta Q / Q \sim 3\times 10^{-5} k_2^{-1}$. The time-varying component to the gravitational quadrupole moment is therefore required to be of order a few per cent of the total expected quadrupole moment at most to explain the observed orbital period variations. From this, it seems plausible that the observed period variations can be powered by quadrupole moment changes, without requiring that a large fraction of the star be involved in the process. The required fractional quadrupole moment changes are very similar to those recently calculated for the companion to the black widow PSR J2051–0827 by Voisin et al. (2020b), despite the large difference in their masses.
Citation: Cisneros-Parra 1970
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Citation: Ogilvie 2014
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We have seen that some of our runs show clear activity cycles. Therefore we expect to see a corresponding modulation of the flow. In Fig. 13, we show for Run A the temporal variation of the mean large-scale magnetic field (\hbox{$\mean B$}B) normalized by Beq, the latitudinal component of the meridional circulation uθ(r, ± 32°) at r ≈ 0.95 R⊙ and r ≈ 0.73 R⊙, the mean rotation rate \hbox{$\mean\Omega(0.95~R_\odot,\pm32^\circ)$}Ω(0.95R⊙,±32◦), \hbox{$\overline\Omega(r,0^\circ)$}Ω(r,0◦) at r = 0.73 R⊙ and r = 0.95 R⊙, as well as the latitudinal and radial differential rotation \hbox{$\Delta_\Omega^{(r)}$}ΔΩ(r) and \hbox{$\Delta_\Omega^{(\theta)}$}ΔΩ(θ), defined in Eq. (13). We see that the meridional circulation varies with the magnetic field, becoming weaker during maximum and stronger during minimum, the overall temporal variation being about 50% in this case. (The linear correlation coefficient between \hbox{$\mean B$}B and uθ(0.95 R⊙, ± 32°) ≈  −0.36, − 0.38.) This kind of weak anti-correlation between the activity cycle and the meridional flow has been found in solar observations (Chou & Dai 2001; Hathaway & Rightmire 2010) and is believed to arise at least in part from the Lorentz force of the dynamo-generated magnetic fields (see, e.g., Rempel 2006, Karak & Choudhuri 2012, Passos et al. 2012).The meridional circulation at the bottom is also weakly correlated with the activity cycle (correlation coefficients between \hbox{$\mean B$}B and uθ(0.73 R⊙, ± 32°) ≈  −0.22, − 0.47). We see that \hbox{$\mean\Omega(0.95~R_\odot,\pm32^\circ)$}Ω(0.95R⊙,±32◦) (Fig. 13c) also shows a weak anti-correlation with the magnetic variations (having correlation coefficient ≈ − 0.25). The strong magnetic fields during maxima change \hbox{$\mean\Omega$}Ω by a few per cent (≈ 6%). However \hbox{$\mean\Omega(0.95~R_\odot,0^\circ)$}Ω(0.95R⊙,0◦) (Fig. 13d) shows positive correlation (correlation coefficient ≈ 0.36) and the overall variation is larger (≈ 12%). Because of this variation of \hbox{$\mean\Omega$}Ω at the equator, the values of \hbox{$\Delta_\Omega^{(r)}$}ΔΩ(r) and \hbox{$\Delta_\Omega^{(\theta)}$}ΔΩ(θ) (Figs. 13e, f) show a positive correlation with the magnetic field (correlation coefficients 0.36, 0.21) with the overall variation being ~ 75% and 166%, respectively.
Citation: Hathaway & Rightmire 2010
Citation function: Similarities
The reasons for studying the dynamic properties of small-scale magnetic fields in the quiet photosphere are manifold. Firstly, magnetic fields provide an opportunity to probe some aspects inherent to turbulent convection (see, e.g. Abramenko et al. 2011; Lepreti et al. 2012; Giannattasio et al. 2013, 2014a,b; Giannattasio et al. 2019; Abramenko 2014, 2017; Del Moro et al. 2015; Caroli et al. 2015; Chian et al. 2019; Giannattasio & Consolini 2021) and energy propagation to the upper atmospheric layers (see, e.g. Viticchié et al. 2006; Jefferies et al. 2006; Tomczyk et al. 2007; De Pontieu et al. 2007; Chae & Sakurai 2008; Stangalini et al. 2014, 2015, 2017; Stangalini 2014; Rouppe van der Voort et al. 2016; Gošić et al. 2018; Bellot Rubio & Orozco Suárez 2019; Rajaguru et al. 2019; Keys et al. 2019, 2020; Guevara Gómez et al. 2021; Jess et al. 2021) that cannot be addressed otherwise from an observational point of view. Secondly, they allow us to constrain the available energy in the quiet Sun and infer some details of the processes that amplify and organise them from subgranular to supergranular scales (see, e.g. November 1980; Roudier et al. 1998; Berrilli et al. 1999, 2002, 2004; Berrilli et al. 2005, 2013, 2014; Consolini et al. 1999; Getling & Brandt 2002; Rast 2002; Del Moro 2004; Del Moro et al. 2004; Getling 2006; Nesis et al. 2006; Centeno et al. 2007; Brandt & Getling 2008; de Wijn et al. 2008; Yelles Chaouche et al. 2011; Orozco Suárez et al. 2012; Giannattasio et al. 2018, 2020; Requerey et al. 2018). Thirdly, their study provides important constraints for theoretical models and/or models implemented in simulations of the photospheric layer (see, e.g. Stein & Nordlund 1998, 2001; Cattaneo et al. 2003; Vögler et al. 2005; Rempel et al. 2009; Shelyag et al. 2011; Beeck et al. 2012; Rempel 2014; Danilovic et al. 2015; Khomenko et al. 2017). All these aspects coalesce, advancing our knowledge of the processes capable of accumulating, transporting, and eventually dissipating the enormous amount of energy available in the photosphere of the quiet Sun.
Citation: Stangalini et al. 2014
Citation function: Motivation; Motivation; Motivation
Rather than AGN feedback, it is possible that the effects we are seeing are from a different process coeval or prior to the onset of AGN accretion. Several works have pointed out that AGN activity coincides with a recent starburst, with the AGN having significant accretion events at least ∼200 Myr after the starburst has occurred (Davies et al. 2007; Wild et al. 2007; Wild, Heckman & Charlot 2010; Yesuf et al. 2014) giving the neutral material time to propagate out to the impact parameters probed by COS-AGN (Heckman et al. 2017). With a sample of QSO sightlines probing the CGM around 17 low-redshift starburst and post-starburst galaxies, Heckman et al. (2017) have observed a similar signature of enhanced EWs of Ly α, Si iii, and C iv (the latter of which is not measured in our control sample) relative to a control-matched sample (matched in stellar mass and impact parameter). In the range of impact parameters and stellar masses probed by COS-AGN, the strength of our enhanced EW signature is consistent with the values probed by Heckman et al. (2017). However, the results of Heckman et al. (2017) show strong offsets in the kinematics of the gas from the host galaxy (≈100 km s−1; see fig. 5 from Heckman et al. 2017), whereas the COS-AGN sightlines do not (bottom panel of Fig. 6). Assuming that the AGN activity was triggered by the starburst, a minimum delay time of 200 Myr could allow for any starburst-driven winds to dissipate and kinematic offsets to no longer be present at the impact parameters of the COS-AGN sample. Although this starburst picture provides a possible explanation of our observations, we caution that starbursts are not the only astrophysical event linked to AGN accretion activity. For example, mergers that trigger the AGN (Ellison et al. 2011, 2013; Satyapal et al. 2014; Silverman et al. 2014; Goulding et al. 2018) could potentially affect the surrounding CGM gas. Past and future work focusing on the CGM of galaxy mergers can further test this result (Johnson et al. 2014; Hani et al. 2017; Bordoloi et al. in preparation).
Citation: Heckman et al. 2017
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Citation: Heckman et al. (2017)
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Citation: Heckman et al. 2017
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