adsabs/FOCAL huggingface dataset:
To calculate the dissipation we must study the properties of the wave launching region near the radiative/convective interface in these stars. In stars with convective cores, we focus on the interface of the convective envelope rather than the core, since this is found to give the maximum dissipation in the mass range we consider. In the vicinity of a radiative/convective interface at r = rc in a stellar model, we fit the buoyancy frequency profile with the linear fit3(40)$$\begin{eqnarray}
N^2(r) = \left(\frac{r}{r_c}-1\right) \left.\frac{\mathrm{d}N^2}{\mathrm{d}\ln r}\right|_{r=r_c}.
\end{eqnarray}$$The dynamical tide in the vicinity of r ∼ rc can then be shown to satisfy Airy’s differential equation (Goodman & Dickson 1998), and the energy flux in gravity waves can be calculated. We will omit the details of this calculation, since they have been reported elsewhere (e.g. Goodman & Dickson 1998; Barker 2011), and just quote the resulting tidal quality factor due to gravity waves, which is determined from
(41)$$\begin{eqnarray}
\frac{1}{Q^{\prime }_{\mathrm{IGW}}} = \frac{2\left[\Gamma \left(\frac{1}{3}\right)\right]^2}{3^{\frac{1}{3}}(2l+1) (l(l+1))^{\frac{4}{3}}}\frac{R}{GM^2} \mathcal {G} |\omega |^{\frac{8}{3}}.
\end{eqnarray}$$The quantities that depend on the radiative/convective interface region in a particular stellar model are encapsulated in the quantity
(42)$$\begin{eqnarray}
\mathcal {G} = \sigma _c^2 \rho _c r_c^5 \left|\frac{\mathrm{d}N^2}{\mathrm{d}\ln r}\right|_{r=r_c}^{-\frac{1}{3}},
\end{eqnarray}$$which takes the value $\mathcal {G}_\odot \approx 2\times 10^{47}\,\mathrm{kg}\,\mathrm{m}^2\,\mathrm{s}^{2/3}$ for the current Sun (in which σc = −1.18). In this expression, ρc = ρ(rc), and the parameter
(43)$$\begin{eqnarray}
\sigma _c=\frac{\omega _{\mathrm{ dyn}}^2}{A}\left.\frac{\partial \xi _{d,r}}{\partial r}\right|_{r=r_c},
\end{eqnarray}$$where the derivative of the dynamical tide radial displacement ξd, r is determined by integrating the linear differential equation given in equation (3) in Goodman & Dickson (1998). Note that σc (and $Q^{\prime }_\mathrm{IGW}$, for that matter) is defined such that it depends on the properties of the star but not on A.
Citation: Barker 2011
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The selected YSOs (Class I and Class II ) are mostly concentrated in the whole ring around H ii region Sh2-104. The high density of the YSOs located in the ring show that these YSOs are physically associated with Sh2-104. The dynamical age of Sh2-104 can be used to decide whether these YSOs are triggered by the H ii region. Using a simple model described by Dyson & Williams (1980) and assuming a H ii region expanding in a homogeneous medium, we estimate the dynamical age of the H ii region as
12





where 





 is the radius of the Strömgren sphere given by 





 = 





, where 





 is the ionizing luminosity, 





 is the initial number density of the ambient medium around the H ii region, and 





 cm3 s−1 is the hydrogen recombination coefficient to all levels above the ground level. For the ionized medium we adopt a sound velocity of 





 km s−1. Moreover, we adopt the radius (37) of the ring as that of H ii region Sh2-104, which is obtained from Figure 11. Taking the distance of ∼4 kpc to Sh2-104 (Deharveng et al. 2003), the H ii region radius is 4.4 pc. As a rough estimate, 





 can be determined by distributing the total molecular mass over a sphere (e.g., Zavagno et al. 2007; Cichowolski et al. 2009; Paron et al. 2009; Anderson et al. 2015; Duronea et al. 2015). The mass of the ring is 





. Using this mass, we only consider the number of atoms that can be ionized, so this would imply that 





 cm−3. H ii region Sh2-104 is excited by an O6V center star (Crampton et al. 1978; Lahulla 1985). For an O6V star, the ionizing luminosity (





) is 





 s−1 (Martins et al. 2005). Inoue (2001) suggested that only half of the Lyman continuum photons from the central source in a Galactic H ii region ionize neutral hydrogen; the remainder are absorbed by dust grains within the ionized region. Finally, we obtain the ionizing luminosity of 





 s−1. Hence, we derived a dynamical age of 





 yr for the H ii region Sh2-104. Additionally, from the 





 color–magnitude diagram for the selected YSOs, we derive that the majority of the YSOs have masses in the range 1.8–6.0 





, and an age range of 0.15–1.0 Myr. Comparing the dynamical age of H ii region Sh2-104 with that of the YSOs shown on the ring, we conclude that these YSOs are likely to be triggered by H ii region Sh2-104. This also means that the CC or RDI processes induce the formation of these YSOs. Deharveng et al. (2003) suggested that H ii region Sh2-104 is a typical candidate for triggering star formation by the CC process.
Citation: Cichowolski et al. 2009
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We carried out a detailed 2D modeling of the galaxy using GALFIT (Peng et al. 2002). In that, we model the central bright component using a Sérsic profile (Sersic 1968) while the extended faint stellar envelope was modeled with an exponential profile. The best-fit models and residuals are displayed in Figure 6. The details of our GALFIT modeling is presented in Appendix C Based on the two-component modeling of the SDSS i-band image of the galaxy, we arrive at an insight revealing the nature of the galaxy’s light distribution. The best-fit model of the extended host galaxy reveals a faint, lopsided, LSB exponential profile that is presumably due to an old stellar host. The presence of an exponential profile as found in many BCDs (e.g., Papaderos et al. 2002; Lian et al. 2015), as well as in SHOC 579, is not convincing enough for a thin stellar disk; possibilities of a triaxial stellar system cannot be ignored. The central surface brightness of the disk is μ
0 = 22 mag arcsec−2 (e.g., Brown et al. 2001; Schombert et al. 2001; Adami et al. 2006; Pahwa & Saha 2018) with a scale length of R

d
 = 1.54 kpc. Note that the disk is relatively smaller in size and the scale length is similar to that of dwarf LSB galaxies (Schombert et al. 1995; Papaderos et al. 1996b; Schombert et al. 2001; Gil de Paz & Madore 2005). The Sérsic profile for the inner component has a Sérsic index of n = 1.48 and an effective radius of 217 pc. Because our GALFIT modeling is performed using the SDSS i-band image, it hence does not contain strong nebular emission lines (e.g., Hα line) for the given redshift of the galaxy. Nevertheless, the contributions from the nebular continuum cannot be ruled out. In fact, the contribution from the nebular continuum has especially been found to be significant in the SDSS i-band image (e.g., Izotov et al. 2011). Therefore, an exponential light distribution indicating a stellar-disk-like structure around the blueberry source may be a generic property of an extended nebular halo, similar to that seen in several luminous BCDs (e.g., Papaderos et al. 2002).
Citation: Schombert et al. 2001
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Citation: Schombert et al. 2001
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In Figure 4, the peak values of LZ for the subsamples in different cuts of 





, obtained from Sample B, are larger (∼2000 kpc km s−1) than those expected for a distribution dominated by TD stars (∼1600 kpc kms−1, assuming the Sun’s position at R = 8.5 kpc). Sample B is selected in a range of metallicity and α-elements abundance (−0.9  [Fe/H]  − 0.6 and +0.1  [α/Fe]  +0.2) that excludes the majority of thin-disk stars (see, for example, Figure 4 in Hayden et al. (2015) and Figure 2 in Cheng et al. (2012)). In this range of abundances, however, the outer thin-disk stars have been identified, −0.7  [Fe/H]  −0.2, and 0.0  [α/Fe]  +0.2 (Lee et al. 2011b; Bovy et al. 2012b). The total number of stars in Sample B is 415, while the number of likely outer thin-disk stars, selected according to the above criteria, is 77. This means that Sample B has a contamination from “likely” outer thin-disk stars on the order of ∼18%. This would explain the large values of LZ obtained for the TD. To investigate further, we explored the trend of the mean rotational velocity, 





, for Sample B (−0.9  [Fe/H]  −0.6; +0.1  [α/Fe]  +0.2) as a function of metallicity. It is known that thin-disk stars show a negative rotational velocity gradient (





) as the metallicity increases, while TD stars exhibit a positive or zero velocity gradient (Lee et al. 2011b). Figure 7 shows the mean Galactocentric rotational velocity, as a function of the metallicity, for Sample B, in two different intervals of vertical distance, 





 (black), and 





 (red). Visual inspection of this figure reveals that the mean rotational velocity has a zero or slightly positive gradient as the metallicity increases. Therefore, it is unlikely that Sample B has significant contamination from outer thin-disk stars. We conclude that in the range of abundances where Sample B is selected, the majority of the stars belong to the TD, while other works claim that in such an interval of metallicity and α-abundance, the outer thin-disk stars are still present (Lee et al. 2011b; Bovy et al. 2012b). It remains unclear the reason why these TD stars possess such large rotational velocities, requiring further investigation.
Citation: Lee et al. 2011b
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In the previous section, we found no clusters and set the upper limit on the number of observed clusters. In this section, we consider that the clusters are originated from supernovae, and evaluate the detectable range. We use the number of neutrinos emitted from core-collapse supernovae (ccSNe) and failed ccSNe, which result in black hole formations, derived from the Nakazato model (Nakazato et al. 2013). This model provides 20 s supernova neutrino data and is parameterized by an initial mass M
init, metallicity Z, and shock revival time t
revive. The electron antineutrino flux arriving at Earth, 



Fe¯Earth(r)

, is written as (Dighe & Smirnov 2000)
1



Fe¯Earth(r,E,t)=14πr2p¯d2Ne¯0dEdt+1−p¯d2Nx0dEdt,

where r is the distance from the Earth to a supernova, 



d2Ne¯0/dEdt

 is the number of emitted electron antineutrinos per MeV per second, d
2
N

x0/dEdt is that of the antineutrinos 



νμ¯

, 



ντ¯

 and 



p¯

 is the survival probability, which is 0.665 (normal mass ordering) or 0.0216 (inverted mass ordering) with 



sin2θ12=0.320

 and 



sin2θ13=0.0216

 (de Salas et al. 2018). We multiply 



Fe¯Earth(r,E,t)

 by the cross section of the IBD σ
IBD(E) (Strumia & Vissani 2003), selection efficiency ϵ
eff(E), livetime ratio η
 livetime, and number of target protons in KamLAND 



target

, then integrate with the time of 20 s and the neutrino energy of 1.8–111 MeV. The expected number of observed supernova events N
KL(r) at KamLAND as a function of r is estimated from
2



NKL(r)=ηlivetimetarget×∫dtdEFe¯Earth(r,E,t)σIBD(E)ϵeff(E).

Actually, observed events in KamLAND follow a Poisson distribution with mean N
KL. To reproduce the time distribution of supernova neutrinos in KamLAND, we make the probability density function (PDF) of them. We carry out MC simulation based on the above Poisson distribution and PDF, then search for a neutrino cluster that requires two DC events within a 10 s window. N
KL yields 10–30 events for a ccSN within 10 s in case of r = 25 kpc. Here, the number of accidentally contaminated DC events in the supernova neutrino burst is negligibly small (10−5 events) within the distance we expect N
KL > 1. These calculations provide detection probabilities as a function of distance as shown in Figure 3. We use all available parameter combinations in this estimation, thus blue and red bands include model and neutrino mass ordering uncertainties. KamLAND has a 95% probability to the supernova neutrino burst detection with the distance for r ≤ 40–59 kpc and r ≤ 65–81 kpc for the ccSN and failed ccSN, respectively. In either case, our Galaxy (r ≲ 25 kpc) is covered with a ≥99% detection probability. Consequently, this result gives an upper limit on the supernova rate within our Galaxy, which includes ccSN rate and failed ccSN rate, 



RSNgal0.15yr−1

 (90% CL) assuming that the SN rate on the Large Magellanic Cloud and Small Magellanic Cloud are much smaller than the Galactic SN rate (Tammann et al. 1994).
Citation: Strumia & Vissani 2003
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In the standard ΛCDM paradigm, most of the mass in the universe resides in structures known as dark matter halos. These provide the gravitational well within which cold gas collapses, forms stars, and, at a larger scale, forms progenitors of the galaxies we observe today (e.g., White & Rees 1978; Fall & Efstathiou 1980; Blumenthal et al. 1984; Frenk & White 2012; Wechsler & Tinker 2018). The dark matter halo itself forms from the gravitational collapse of initial perturbations in the density field at the very early universe (e.g., van Albada 1960, 1961; Peebles 1970; White 1976). The emergence of the hierarchical model of structure formation (Press & Schechter 1974; Gott et al. 1975; White & Rees 1978) supported by cosmological hydrodynamical simulations and semianalytical models suggests a bottom-up scenario, in which massive halos formed from a sequence of mergers (so-called merger trees) and mass accretion, as opposed to initial rapid collapse models (e.g., White & Frenk 1991; Navarro & Benz 1991; Katz et al. 1992; Kauffmann et al. 1993; Lacey & Cole 1993; Somerville & Primack 1999). Discovery of very massive galaxies at high redshifts (





), which constitute most of the luminous baryonic component inside the dark matter halos, however, suggests a rapid buildup of the bulk of their stellar mass at z > 2, with intense star formation activity at early times. Submillimeter observations further confirm the starburst populations with star formation rates (SFRs) exceeding hundreds of solar masses per year (e.g., Blain et al. 2002; Capak et al. 2008; Marchesini et al. 2010; Smolčić et al. 2015). There have been many recent spectroscopic confirmations of such sources at high-redshift galaxies experiencing suppressed star formation activity (e.g., Belli et al. 2014, 2017b, 2017a, 2019; Whitaker et al. 2014; Newman et al. 2015; Glazebrook et al. 2017; Newman et al. 2018; Schreiber et al. 2018; Forrest et al. 2020; Tanaka et al. 2019; Valentino et al. 2020). To use these systems to constrain galaxy formation and evolution scenarios and study feedback and quenching mechanisms at early times requires a robust photometric selection of these objects followed by deep spectroscopic observations.
Citation: White & Frenk 1991
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Citation: Blain et al. 2002
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Citation: Whitaker et al. 2014
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We calculate the expected radio fluxes following the prescriptions of Piran, Nakar & Rosswog (2013). We consider an expanding spherical, non-relativistic ejecta that collects mass from the uniform circum-merger medium with particle density n as it moves outwards with velocity v ≡ βc, where c is the speed of light. The velocity of the ejecta decreases as it collects more mass. The relation between ejecta velocity and radius (R) can be calculated by assuming that kinetic energy is conserved (see equation 14 of Piran et al. 2013):
(2)
\begin{eqnarray*}
M(R)(\beta c)^2 \approx E(\ge \beta),
\end{eqnarray*}
where M is the collected mass at radius R from the merger, and E(≥β) is the kinetic energy of the part of the ejecta that was launched with velocities faster than βc. In our case, this means that initially only the component with 0.3c velocity affects the evolution of the ejecta front and hence the radio flux until it slows down to 0.1c where the slower component can catch up. With R and β known functions of time, we can calculate the emitted radio flux as (see equations 4 and 6 and table 2 in Piran et al. 2013)
(3)
\begin{eqnarray*}
F(t)\approx 4\, \mbox{mJy}\, R_{17}^3 n_{-1}^{15/8} \beta ^{19/4} d_{200}^{-2}\left(\frac{\nu }{6\, \mbox{GHz}}\right)^{-3/4},
\end{eqnarray*}
where $R_{17}\equiv (R/10^{17}\, \mbox{cm})$, $n_{-1}\equiv (n/0.1\, \mbox{cm}^{-3})$, and $d_{200}\equiv (d/200\, \mbox{Mpc})$. We assume that the observational frequency νobs = 6 GHz is greater than both the typical synchrotron frequency of electrons in the forward shock driven by the ejecta (νm), and the synchrotron self-absorption frequency (νa; see e.g. Piran et al. 2013). We further assume that electrons and the magnetic field carry $\epsilon _{\rm e}\approx \epsilon _{\rm m}\sim 10{{\ \rm per\ cent}}$ of the total internal energy of the shocked gas, and that the power-law index of the distribution of the accelerated electrons’ Lorentz factors is p = 2.5.
Citation: Piran et al. 2013
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The equation of state is
14





where 





, 





, 





, and 





 are the gas pressure from nucleons, the radiation pressure of photons, the degeneracy pressure of electrons, and the radiation pressure of neutrinos, respectively. k, T, 





, a, and h are the Boltzmann constant, temperature of the disk, proton mass, radiation constant, and Planck constant, respectively. The equation can be applied anywhere on the disk to describe the pressure, but we also need an equation to completely give the vertical distributions of the density, temperature, and pressure. For simplicity, we assume the polytropic relation, i.e., 





, in the vertical direction, with K as a constant (e.g., Lee et al. 2005; Liu et al. 2008, 2010a, 2012a, 2013). Here we also assume the electron fraction 





 is 0.5 (e.g., Di Matteo et al. 2002; Gu et al. 2006; Liu et al. 2014), because its influences are very limited on the structure and neutrino luminosity of the disk, unless we care about the types of neutrinos emitted from the disk (e.g., Liu et al. 2013). 





 is the mass fraction of free nucleons, and its expression is obtained by taking 





 as (e.g., Kohri et al. 2005)
15





where 





 and 





. 





 is the energy density of neutrinos, which can be expressed as (e.g., Lee et al. 2005; Liu et al. 2012a)
16





where 





 is the total optical depth for neutrinos, 





 and 





 are the absorption and scattering optical depth for neutrinos, and the subscript i  runs for the three species of neutrinos 





, 





, and 





, which can be defined as (Di Matteo et al. 2002; Lee et al. 2005; Liu et al. 2012a)
17







18





where 





 is the summation of the cooling rates per unit volume due to the neutrino reactions for different neutrinos, especially the Urca processes and electron–positron pair annihilation (e.g., Di Matteo et al. 2002; Liu et al. 2007). The neutrino scattering processes include scattering by free protons, free neutrons, α-particles, and electrons (e.g., Di Matteo et al. 2002).
Citation: Liu et al. 2008
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AstroSat data enabled us to derive the X-ray spectrum in the 0.8–70 keV energy band as shown in Figure 7. The spectrum was fitted reasonably well using two models. The first model was defined as an absorbed Fermi–Dirac cutoff model along with a blackbody, an Fe emission line, and three Gaussian absorption lines introduced to model cyclotron scattering features and its two higher harmonics as observed in the spectrum. The CompTT model was used as the second model in combination with an Fe emission line and three Gaussian absorptions lines as defined in the first model. The CompTT model is generally used for neutron-star-based low-mass X-ray binaries such as Z-type and atoll sources with a relatively lower magnetic field (





 G) of the neutron star (Ferinelli et al. 2008). However, the model could also successfully define the spectrum of some of the pulsars in Be binaries, for example, Cep X-4 (Jaiswal & Naik 2015), 4U 1907+09 (Varun et al. 2019), and GRO J2058+42 (Molkov et al. 2019). The parameters derived from the two models are tabulated in Table 2. The first model estimated a blackbody temperature of 0.83 ± 0.04 keV and detected the presence of a cyclotron resonance scattering feature and its harmonics. The Comptonization model, on the other hand, enabled us to determine the input photon Wien temperature of 0.52 ± 0.02 keV, the plasma temperature of 8.22 ± 0.10 keV, and the plasma optical depth of 5.21 ± 0.12 for the phase-averaged spectra. The Wien temperature, per the CompTT model, originates far from the neutron star surface and closer to the inner accretion disk i.e., at the outer transition layer; hence, the Wien temperature is always found to be relatively lower than the neutron star blackbody temperature as it originates closer to the inner transition layer, i.e., near the surface of the neutron star (Ferinelli et al. 2008). For the CompTT model, bulk Comptonization occurs in the innermost part of the transition layer region, while thermal Comptonization is dominant in the outer transition layer and presumably within some extended region located above the accretion disk. Similar deviations were also observed in the case of Cep X-4 fitted with the CompTT model and a blackbody combined with the FDCUT model (Table 1 of Jaiswal & Naik 2015). However, the centroid energy of the cyclotron absorption features and its detected harmonics are found to be consistent within errors for the two models (Table 2).
Citation: Ferinelli et al. 2008
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Citation: Ferinelli et al. 2008
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A clear transition in the spectra is observed at scales 





 with a change in the spectral indices of all fields. In particular, the spectrum of the perpendicular magnetic fluctuations steepens at 





 following a power law with a spectral index 





 for another decade. The location of the break does not show any significant dependence on the number of particles, the spatial resolution, and the resistivity adopted, provided that a sufficient number of grid points allows us to cover approximatively a decade at sub-proton scales, i.e., that the scale at which resistive dissipation acts is sufficiently separated from the region of the break. The parallel component of the magnetic field, together with the density, follows a similar but slightly shallower slope with a spectral index of 





 in very good agreement with observations (Chen et al. 2012, 2013b) and other simulations (Howes et al. 2011; Passot et al. 2014). As a result, magnetic fluctuations tend to become isotropic at small scales, resulting in an increase of the magnetic compressibility, as observed in the solar wind (Podesta & TenBarge 2012; Salem et al. 2012; Kiyani et al. 2013). The spectrum of the perpendicular velocity fluctuations quickly drops above 





 without any clear power-law trend. The observation of a spectral index of 





 has been ascribed to the effect of the electron Landau damping by previous studies (Howes et al. 2011; Passot et al. 2014); however, this cannot be the case in our simulations, where the electron kinetics is not taken into account. Alternatively, the presence of coherent structures, such as current sheets, can produce a steepening of the energy spectra (e.g., Wan et al. 2012; Karimabadi et al. 2013). The increase of intermittency at small scales, observed in our simulations, seems to confirm this path toward the dissipation. We have to note, however, that a 





 power law for the magnetic energy and the density spectra (not far from the 2.8 found here) has been also interpreted as related to the dimensionality (1D or 2D) of the magnetic and the density intermittent structures, without invoking dissipation (Boldyrev & Perez 2012; Meyrand & Galtier 2013).
Citation: Chen et al. 2012
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