adsabs/FOCAL huggingface dataset:
There are four possible examples of where Lyα absorption is taking place. In Figures 3(b) and (c) we see that the high-velocity component of the double profile seen in both Hβ and [C ii] is significantly suppressed compared with the low-velocity component. For Figure 2(b) in particular, the peak in the Hβ profile falls close to a strong dip in the Lyα profile, which appears as two small wings on either side of the dip. For Figure 3(c), the feature seen in both [C ii] and Hβ is largely suppressed above 6500 km s−1. Also, in Figures 2(d) and 3(d), the Lyα profile is shifted blueward of the main [C ii] and Hβ peaks, suggestive of absorbing gas centered at V
helio = 6500–6700 km s−1. Asymmetric Lyα profiles like this are often associated with radial outflows in galaxies (e.g., Heckman et al. 2011). The COS pointing (HCG92-5) samples the gas in the so-called “AGN bridge” (Cluver et al. 2010), a linear H2 filament that is apparently separate from the main collisional shock in SQ. A strong shear in the velocity field of the [C ii] emission was noted by Appleton et al. (2013) in that region. The spectrum of the CO (1–0) emission from that direction (see Figures 2(d) and 3(d)) shows significant CO emission at the high-velocity side of the profile, which would be consistent with absorption. Finally, HCG92-7 shows, in Figure 3(e), the opposite effect. In this case, again considering the high-velocity component of the double-horned profile only, we see that the Lyα is significantly redshifted with respect to the Hβ emission, with a sharp drop in emission as one approaches the peak of the Hβ (around V
helio = 6600–6700 km s−1). This may be another example of asymmetric absorption, with resonant scattering to the red side of the wings of the kinematics. In summary, we see that two regions in the main emission-line filament are almost free of absorption, whereas other regions show strong absorption. Even in those cases, at least some of the Lyα emission is able to resonantly scatter and undergo many scatterings within the gas before eventually escaping into the wings of the velocity profile, where the optical depth is much lower. In Section 5.2, we estimate the escape fraction of the Lyα photons and the number of scatterings, and we discuss the multiphase structure of the intragroup medium gas. We also remind that the differences between the Lyα and Hβ spectral profiles may not only be due to Lyα scattering, because collisional excitation could also contribute.
Citation: Heckman et al. 2011
Citation function: Similarities
GCs are the most easily noticeable objects in the haloes of galaxies. However, their surface density in the inner parts always outnumbers that in the haloes (see e.g. Hargis & Rhode 2014; Kartha et al. 2014). In fact, the radial surface density distribution can be well described by the Sérsic function (Sersic 1968) in its classical form:
(2)$$\begin{eqnarray*}
N(R) = N_{\textrm {e}}\exp \left[-b_{n}\left(\frac{R}{R_{\textrm {e}}}\right)^{(1/n)}-1\right],
\end{eqnarray*}$$where, Re is the effective radius enclosing half the population, n is the Sérsic index that controls the shape of the profile, and bn = 1.992n − 0.3271. We show the radial distribution of surface number density in Fig. 13. The radial axis is plotted in units of R25. For two of our sample galaxies (M81: Perelmuter & Racine 1995, and NGC 4258: González-Lópezlira et al. 2017), GC searches have been carried out using the ground-based data that complement the HST data in the outer parts, which have also been shown (green empty circles). The observed and completeness-corrected values are shown in solid circles of black and magenta colours, respectively, with the fits to these data shown by solid lines of corresponding colours. The numbers inside and outside the Re are corrected by the incompleteness factors corresponding to high and low surface brightness fields, respectively. For M81, the inner most number is corrected by the completeness factor obtained from simulation on the frame that included the bulge and the nucleus. The Re values before and after the incompleteness corrections are shown by dashed vertical lines of black and magenta colours, respectively. The effect of correction is to marginally shift the Re to lower values, which is noticeable only for NGC 4258 and M51 in the figure. In Table 8, we show the values for Sérsic fit. $N_{{\rm GC},R_{\rm e}}$ and $N_{{\rm GC},R_{\rm e}}^\prime$ are the numbers of GCs inside Re before and after correction for contaminants. To estimate the total number of GCs (see Section 5.6), we use the number of GCs within Re, ($N_{{\rm GC},R_{\rm e}}^\prime$).
Citation: González-Lópezlira et al. 2017
Citation function: Uses; Uses; Uses; Compare/Contrast
In order to connect with cosmic ray data, we extrapolate now from the previously studied single collision zone to a population of alike GRBs with a fixed duration of ΔT = 10 s, which implies that the emission from each GRB comes from ΔT∕tv such collisions. We perform a fit of UHECR observations from the Pierre Auger Observatory, combining the modeling of interactions in the source (computed as described in the previous sections) with the propagation of cosmic rays in the extragalactic space. For the propagation, the SimProp code (Aloisio et al. 2012) has been used with Gilmore EBL (Gilmore et al. 2012) and PSB cross-section model (as defined in Alves Batista et al. 2015). We also consider the extensive air shower in the Earth’s atmosphere and EPOS-LHC (Pierog et al. 2015) is assumed for UHECR-air interactions. We find that a good description of the data is obtained by distributing the GRBs as sources of cosmic rays following the star formation rate Yuksel et al. (2008), assuming a pure 28 Si at the injection. The primary spectrum is described in Eq. (10) and we use here k = 1.8 and P = 2. We fix the following parameters: source evolution, spectral index and cutoff shape at injection, and the nuclear species at the injection. In the present work, we keep this procedure as simple as possible in order to show the power of the method, while leaving a more detailed analysis for a future work. The fit is performed above 1018 eV (Mixed Composition Dip Model) and above 1019 eV (Mixed Composition Ankle Model) by using the combined spectrum (Valiño et al. 2015) and the shower depth (Xmax) distributions (Aab et al. 2014), which contain information about the mass of the nucleus interacting with the atmosphere, with a similar procedure as used in di Matteo et al. (2015) and Aab et al. (2017). A scan over (R, Lγ) is performed and for each pair the normalization to the experimental flux is found. For each point of the parameter space the number of expected prompt and cosmogenic neutrino events is calculated following Baerwald et al. (2015). For the prompt neutrino flux, the exposure for muon neutrinos is calculated by summing the exposure relative to the IceCube analyses of Aartsen et al. (2015,3 yr) and that of Aartsen et al. (2017, 3 yr) for a total of 1014 GRBs that occurred in the Northern Hemisphere, and that of Aartsen et al. (2017, 5 yr) for 664 GRBs that occurred in the Southern Hemisphere, and comparing the total number of bursts of the combined sample with the assumed 667 bursts per year as in Baerwald et al. (2015). For the cosmogenic neutrino flux, the exposure for muon neutrinos is taken from Aartsen et al. (2016). The exclusion regions (90% C.L.) of the prompt and cosmogenic neutrino analyses are calculated assuming both analyses as background free.
Citation: Pierog et al. 2015
Citation function: Uses; Uses
Reconnection rate in a laminar flow can be estimated, or defined, in terms of normal diffusion of the magnetic field by magnetic diffusivity η on large scales or, in other words, in terms of the (root-mean-square, henceforth rms) average distance 





 the field spreads relative to a fixed point. The related random walk of magnetic field lines was discussed by Jokipii (1966; see also Snodin et al. 2013, 2016; Servidio et al. 2014). This is of course the Taylor or normal diffusion (of particles or magnetic field) in which the rms distance between the diffusing material and a fixed point increases as 





 with time; see, e.g., Eyink et al. (2013) and Jafari et al. (2019). For a diffusing magnetic field, 





. In the absence of turbulence, in a reconnection zone of width δ and length Δ (parallel to the local magnetic field), using the Alfvén timescale 





, and using mass conservation 





, we recover the reconnection speed
1





This is, of course, the well-known Sweet–Parker reconnection rate (Parker 1957; Sweet 1958). Reconnection, and/or other instabilities such as tearing modes (Furth et al. 1963), will in general generate turbulence (Eastwood et al. 2009; Jafari & Vishniac 2018a), with the implication that the laminar Sweet–Parker model is far from realistic in turbulent systems such as most astrophysical fluids. In the turbulence inertial range, i.e., at scales larger than dissipative scale but smaller than the larger scales where Taylor (normal) diffusion of the magnetic field occurs, diffusing particles will undergo super-linear Richardson diffusion; 





 which is a two-particle diffusion, i.e., d is the rms separation between any pair of particles undergoing diffusion in the inertial range. If we consider magnetic diffusion in the turbulence inertial range, we have to consider Richardson diffusion of the field, in terms of the rms distance the field spreads during the time t; see Figure 1. The eddy turnover time, in the inertial range, is of order 





 with d being the length scale perpendicular to the mean magnetic field. Here, 





 denotes the energy transfer rate, with turbulent velocity VT and parallel energy injection length scale 





. This corresponds to the Richardson diffusion; 





. The super-linear nature of Richardson diffusion broadens the reconnection zone and thereby enhances the reconnection rate. To see this, using mass conservation 





, and substituting the Alfvén time 





, one arrives at the fast reconnection speed (Lazarian & Vishniac 1999; Jafari et al. 2018; Lazarian et al. 2019, 2020);
2
Citation: Eastwood et al. 2009
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The radial distribution of S/H ratios and the corresponding gradient are shown in panel (b) of Fig. 12. We obtain a slope of −0.035 ± 0.006 dex kpc−1 (very similar to the one we obtain with the ICF of ADIS20, as can be seen in Table 8), which is consistent with the one of −0.041 ± 0.014 dex kpc−1 estimated by Rudolph et al. (2006) using FIR lines and also very similar to the slope of our O/H gradient. We report a dispersion around the S/H gradient of 0.10 dex, somewhat larger than the individual observational uncertainties. Recently, Fernández-Martín et al. (2017) reported a slope of −0.108 ± 0.006 dex kpc−1 using optical spectra for H  ii regions located at RG between 5 and 17 kpc. That value of the slope is considerably much steeper than our determination and other previous estimates from the literature (e.g. Shaver et al. 1983; Simpson et al. 1995; Afflerbach, Churchwell & Werner 1997; Rudolph et al. 2006). Esteban & García-Rojas (2018) observed some H ii regions with a very low ionization degree – O2 +/O  0.03 – but measurable [S iii] 6312 or 9069 Å lines. For such nebulae is possible to assume that S/H ≈ S+/H+ + S2 +/H+, since the contribution of S3 +/H+ in those objects is expected to be negligible. The objects with such properties are IC 5146, Sh 2-235, Sh 2-257, and Sh 2-271, whose total abundances of S/H range from 6.70 to 7.01 but, unfortunately, cover a rather narrow range of RG – between 9.3 and 11.7 kpc – and no confident gradient can be estimated with so small baseline. Inspecting Tables 3 and 4 we can see that there is a sizable group of low-ionization degree H ii regions that lack of determination of their S2 +/H+ ratios. This is because the rather faint auroral [S iii] 6312 Å line could not be detected in those objects. We plan to obtain additional optical spectra covering the bright nebular [S iii] 9069, 9532 Å lines of that group of nebulae for trying to increase the number of objects with S/H ratios determined without ICF and estimate a more precise gradient for this element.
Citation: Rudolph et al. (2006)
Citation function: Similarities; Similarities
Various surveys of SNRs in our Galaxy and nearby galaxies have been carried out at radio, X-ray, Infrared (IR), and optical wavelengths. The first extragalactic SNR candidates were identified in the LMC by Mathewson & Healey (1964) and later confirmed with a combination of radio and optical techniques by Westerlund & Mathewson (1966). To date, a total of 60 SNRs have been confirmed in the LMC with an additional 14 suggested candidates (Maggi et al. 2016; Bozzetto et al. 2017; Maitra et al. 2019). However, sensitivity and resolution limitations severely reduce the effectiveness of the past and present generations of radio and X-ray searches for SNRs in galaxies beyond the Small and Large Magellanic Clouds (MCs) (Goss et al. 1980; Long et al. 1981; Cowan & Branch 1985; Matonick et al. 1997; Matonick & Fesen 1997; Millar, White & Filipovic 2012; Galvin & Filipovic 2014; Sasaki et al. 2018; Lin et al. 2020; Sasaki 2020). As a result, optical studies have produced the largest number (∼1200) of new extra-Magellanic SNR candidates. Optical extragalactic searches for SNRs are mainly done by using an emission line ratio criterion of the form [S ii]/H α > 0.4–0.5 (Mathewson & Clarke 1973; Dodorico, Dopita & Benvenuti 1980; Fesen 1984; Blair & Long 1997; Matonick & Fesen 1997; Dopita et al. 2010b; Lee & Lee 2014; Vučetić et al. 2019b, a, 2018; Lin et al. 2020). This criterion separates shock-ionization from photoionization in SNRs from H ii regions and Planetary Nebulae (PNe) (Frew & Parker 2010). SNR radiative shocks collisionally excite sulphur ions in the extended recombination region resulting in S+, hence the larger contribution of [S ii] accounting for an increase of the [S ii] to H α ratio. In typical H ii regions, sulphur exists predominantly in the form of S++, yielding low [S ii] to H α emission ratios. Ratios from narrow-band imaging are usually verified spectroscopically, since [N ii] lines at 6548 and 6584 Å can contaminate the H α images at an unknown and variable level. Spectroscopic observations of such emission nebulae also can provide other evidence of shock heating, such as strong [O i] λ6300 emission, elevated [N ii] to H α with respect to H ii regions, or high [O iii] electron temperatures, verifying the candidate as being an SNR (Blair, Kirshner & Chevalier 1981, 1982; Long et al. 1990; Smith et al. 1993; Blair & Long 1997). Although somewhat biased as an isolated criterion, this method is proven and a good way of identifying ordinary radiatively cooling SNRs in nearby galaxies. We note that young, Balmer-dominated SNRs (Chevalier, Kirshner & Raymond 1980) would be missed by this criterion.
Citation: Matonick & Fesen 1997
Citation function: Background
These two cases correspond to the upper and lower boundaries of the tilt term. They are usually written as

(73)

\begin{equation}
\frac{{\mathrm{\partial} \sigma _{Rz}^2\left( {R,0} \right)}}{{\mathrm{\partial} z}} = \lambda (R)\frac{{\sigma _{RR}^2\left( {R,0} \right) - \sigma _{zz}^2\left( {R,0} \right)}}{R},
\end{equation}

whereλ(R) ∈ [0, 1]. The factor λ(R) can be derived either analytically from orbit integration or is assumed λ = 0 for simplicity (e.g. van der Kruit & Freeman 1986; Lewis & Freeman 1989; Sackett & Sparke 1990). Numerical simulations (e.g. Carlberg & Innanen 1987; Bienaymé 2000) are performed to calculate explicitly the moments for a given gravitational potential. The result of the above studies is that, at the Sun's position λ ≃ 0.5. In our model for the Galactic kinematics, we prefer to adopt the analytical formulation of λ(R) given by Amendt & Cuddeford (1991):

(74)

\begin{equation}
\lambda (R) = {\left. {\frac{{{R^2}{\mathrm{\partial} _{R,z,z}}\Phi }}{{3{\mathrm{\partial} _R}\Phi + R{\mathrm{\partial} _{R,R}}\Phi - 4R{\mathrm{\partial} _{z,z}}\Phi }}} \right|_{\left( {R,z = 0} \right)}}.
\end{equation}

The expression (74) is null for a potential separable in cylindrical coordinates because the term $\frac{{\mathrm{\partial} {\Phi _{{\rm {tot}}}}}}{{\mathrm{\partial} R\mathrm{\partial} {z^2}}} = 0$. In spherical coordinates λ = 1, the relation (73) will be used in the following to describe the tilt of the velocity ellipsoid, obtaining for the DM halo

(75)

\begin{equation}
{\lambda _{{\rm DM}}}(R) = - \frac{{{R^2}}}{{{R^2}\left( {{q_\Phi }^2 - 2} \right) + 2{h_{r,{\rm DM}}}^2\left( {{q_\Phi }^2 - 1} \right)}},
\end{equation}

and similarly, a unitary constant value for the bulge and stellar halo components is estimated. Finally, for the important contribution of the disc we simplify equation (73) as

(76)

\begin{equation}
{\lambda _D}(R) = - \int _0^\infty {\frac{{k{R^2}{J_1}(kR)h_z^{ - 1}{\rm d}k}}{{R\left( {k - \frac{4}{{{h_z}}}} \right){J_0}(kR) + 2{J_1}(kR)}}}
\end{equation}

that has to be included in equation (73) with a sum over all the discs components.
Citation: Bienaymé 2000
Citation function: Uses; Uses
Since pulsation can be detected from the ULX pulsars, their Alfv$\rm \acute{e}$n radius should be larger than the neutron star radius RA ≥ R. This means that the neutron star dipole magnetic field cannot be arbitrarily small. This places a lower limit on the neutron star dipole magnetic field (as a function of mass accretion rate):
(16)
\begin{eqnarray*}
B_{\rm p} \gt B_{\rm p,min}= 2.8\times 10^8 \skew4\dot{M}_{\rm 18}^{1/2} \, \rm G.
\end{eqnarray*}
During the calculations, a typical neutron star radius of $10\, \rm km$ is assumed.8 This idea is similar to that of accreting normal neutron stars ( Bildsten et al. 1997; Zhang & Kojima 2006). Accreting neutron stars in high-mass X-ray binaries are often observed as X-ray pulsars. While, the pulsation of accreting neutron stars in low-mass X-ray binaries is often not detected. This may because neutron stars in low-mass X-ray binaries have a lower dipole magnetic field. This also means that if some accreting magnetars have a low dipole magnetic field, then they may not been observed as pulsating sources. Finding pulsations is only one way to confirm the neutron star nature of ULX sources. Dall’Osso et al. (2015)’s lower limits on the mass accretion rate is about $5\times 10^9 \, \rm G$, corresponding to an accretion rate $\skew4\dot{M} \sim 38 \times 10^{18} \, \rm g \, s^{-1}$ (inset in their fig. 2). According to equation (16), the corresponding lower limit at that accretion rate is about $1.7 \times 10^9 \, \rm G$. A factor of 3 difference is caused by the coefficient of 0.5 in the definition of the magnetospheric radius. The lower limits on magnetic field here (equation 16) is a function of the corresponding mass accretion rate. Although the accretion torque information is not required in obtaining equation (16), Dall’Osso et al. (2015) chose a specific accretion torque and combined it with equation (16). Therefore, a specific numerical value for the magnetic field lower limit can be obtained there.
Citation: Zhang & Kojima 2006
Citation function: Compare/Contrast
Left panel: observed-frame H − [3.6] color plotted vs. the observed [3.6] magnitude for our sample of BBGs, color-coded by their photometric redshift and scaled in size as a function of their stellar mass (legend shown in right panel). The CANDELS color-selected sample is plotted in gray with the size also scaled according to the masses. The 0.3σ, 1σ, and 2σ distributions of the mass-limited sample are shown by the light blue density regions. The black triangles represent the four massive galaxies at z > 4.5 found by Huang et al. (2011), black filled circles show the extremely red sources from Caputi et al. (2012), and black diamonds correspond to the H-band dropouts reported by Wang et al. (2016). Lines (dashed) of constant H values are also shown. The darker background indicates the regions of lower completeness for the H band (Guo et al. 2013). Individual error bars are not plotted for clarity, but the average values for our sample of BBGs are shown in the top right corner of each panel. The H-band upper limits (H − [3.6] lower limits) are also shown. Right panel: rest-frame U − V vs. V − J color–color plot, where BBGs are color-coded by SFR and scaled by stellar mass. The SFR lower limits are shown in dark gray. Sources from the CANDELS color-selected sample scaled by mass are also shown. The 0.3σ, 1σ, and 2σ distributions of the mass-limited sample are shown by the light blue areas. The MIPS-detected galaxies are surrounded by a circle, while the X-ray detected galaxies are highlighted with an asterisk inside the symbol. The Whitaker et al. (2011) upper boundary (black wedge) separates quiescent galaxies (top left) from SFGs (bottom). The black diagonal line denotes an additional criterion proposed in our work (perpendicular to the attenuation vector) to separate bSFGs from dSFGs. The plot also includes a 1 mag attenuation vector (which assumes a Calzetti et al. 2000 law). The H-band upper limits (U − V lower limits) are also shown.
Citation: Guo et al. 2013
Citation function: Uses
Fast radio bursts (FRBs) are cosmological radio transients with millisecond durations. Since the first FRB (FRB 010724, the Lorimer burst) was discovered in 2007 (Lorimer et al. 2007), hundreds of FRB sources have been detected, dozens of which are repeaters (e.g., the CHIME/FRB Collaboration et al. 2021). Recently, a Galactic FRB, FRB 200428, was detected to be associated with SGR J1935+2154 (Bochenek et al. 2020; CHIME/FRB Collaboration et al. 2020; Mereghetti et al. 2020; Li et al. 2021a; Ridnaia et al. 2021; Tavani et al. 2021), which suggests that at least some FRBs originate from magnetars born from the core collapse of massive stars (e.g., Popov & Postnov 2013; Katz 2016; Murase et al. 2016; Beloborodov 2017; Kumar et al. 2017; Yang & Zhang 2018, 2021; Metzger et al. 2019; Lu et al. 2020; Margalit et al. 2020; Wadiasingh et al. 2020; Wang et al. 2022; Zhang 2022). However, FRB 20200120E was found to be in a globular cluster of a nearby galaxy, M81 (Bhardwaj et al. 2021; Kirsten et al. 2022). This is in tension with the scenario that invokes active magnetars with ages ≲10 kyr formed in core-collapse supernovae (Kremer et al. 2021; Lu et al. 2022) and suggests that FRBs might originate from magnetars formed in compact binary mergers (Margalit et al. 2019; Wang et al. 2020; Zhong et al. 2020; Zhao et al. 2021). Therefore, the physical origin of FRBs is still not well constrained from the data (e.g., Cordes & Chatterjee 2019; Petroff et al. 2019; Zhang 2020; Xiao et al. 2021). The growing FRB detections start to shed light on the diversity among the phenomena. The repeaters presented in the first CHIME FRB catalog have relatively larger widths and narrower bandwidths compared with one-off FRBs (Pleunis et al. 2021). The behaviors of fluence with respect to peak flux exhibit statistically significant differences between bursts with long and short durations (Li et al. 2021c). Multiple origins for the FRB population seem increasingly likely.
Citation: Murase et al. 2016
Citation function: Background; Background; Motivation; Motivation