adsabs/FOCAL huggingface dataset:
In our study, we consider that the accretion disc is threaded by the poloidal magnetic field lines. The initial poloidal field lines are prescribed by implementing a vector potential Aϕ following Zanni et al. (2007) and Vourellis et al. (2019). The functional form of the vector potential is given by
(6)$$\begin{eqnarray}
A_\phi \propto \left(r \sin \theta \right)^{3/4} \frac{m^{5/4}}{\left(m^2 + \tan ^{-2}(\theta -\pi /2)\right)^{5/8}}.
\end{eqnarray}$$The parameter m(= 0.1) is related to the initial inclination of the field lines and it also determines the magnetic flux of the system. The parameter m play crucial role in the launching of Blandford–Payne type wind from the accretion disc (Blandford & Payne 1982; Dihingia et al. 2021). The initial strength of the poloidal magnetic field is determined by the choice of the plasma-β parameter at the truncation radius rtr on the equatorial plane as, $\beta _{\rm tr} = p_{\rm gas}^{\rm tr}/p_{\rm mag}^{\rm tr}$. Here, superscript ‘tr’ denotes quantities calculated at the r = rtr. Following our motivation, we carry out eight axisymmetric simulation models, by choosing different truncation radius (rtr), initial plasma-β (βtr), effective resolution (with AMR), and magnetic resistivity. For axisymmetric models, we consider the highest effective resolution to be 2048 × 1024 (with three refinement levels). The list of input parameters, effective resolution, and the final simulation time (tfinal) for all the simulation models are shown in the Table 1. Out of all these models, we consider 2D40AH to be our reference model for the sake of explanation and comparison. In Fig. 1, we show the initial density distribution (log (ρ/ρtr)) and the initial gas pressure distribution $(\log (p_{\rm gas}/p_{\rm gas}^{\rm tr}))$ for the reference model at panels (a) and (b), respectively. In panel Fig. 1(a), we also show the initial magnetic field lines in terms of grey lines. In Fig. 1(b), the white line represent the boundary of plasma-β = 1. In the figure, the density distribution follows equation (2) outside the truncation radius (rtr = 40). Near the equatorial plane, the matter distribution is gas pressure dominated, and far from the equatorial plane and inside the truncation radius, the matter distribution is magnetic pressure dominated.
Citation: Dihingia et al. 2021
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As a first step to analysis of NU Ori’s magnetic field, least-squares deconvolution (LSD) profiles were extracted using a line mask developed from an extract stellar request from the Vienna Atomic Line Database 3 (VALD3; Piskunov et al. 1995; Ryabchikova et al. 1997, 2015; Kupka et al. 1999, 2000) using the stellar parameters determined for NU Ori Aa (Teff = 30.5 ± 0.5 kK, log g = 4.2 ± 0.1) by Simón-Díaz et al. (2011). These parameters were selected since Petit et al. (2008) identified the Aa component as the magnetic star, given that the Stokes V signature is much wider than the $v$sin i of the secondary component. The line mask was cleaned of all H lines, as well as lines strongly blended with H line wings, lines in spectral regions strongly affected by telluric contamination, lines blended with nebular or interstellar features, and lines in spectral regions affected by ripples. While He lines are often removed due to substantial differences between magnetometry results obtained from He versus metallic lines (e.g. Shultz et al. 2015, 2018b; Yakunin et al. 2015), in this case He lines were left in the mask since the majority of the Stokes V line flux comes from these lines, and the Stokes V profiles extracted using a line mask with He lines excluded did not result in detectable Zeeman signatures. Because of the very high $v$sin i of the Aa component, LSD profiles were extracted using a velocity range of ±600 km s−1 (in order to include enough continuum for normalization) and a velocity pixel size of 7.2 km s−1, or four times the average ESPaDOnS velocity pixel (thus raising the per pixel S/N by about a factor of 2). The significance of the signal in Stokes V was evaluated using false alarm probabilities (FAPs), with observations classified as definite detections (DDs), marginal detections (MDs), or non-detections (NDs) according to the criteria described by Donati, Semel & Rees (1992) and Donati et al. (1997). Since FAPs essentially evaluate the statistical significance of the Stokes V signal inside the stellar line by comparing it to the noise level, they are primarily sensitive to the amplitude of Stokes V, which unlike 〈B$z$〉 is not strongly dependent on rotational phase. FAPs are thus a complementary means of checking for the presence of a polarization signal, the principal advantage being that they can detect a magnetic field even at magnetic nulls, i.e. 〈B$z$〉 = 0.
Citation: Shultz et al. 2015
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Most GCs are now found to host multiple populations through photometry and spectroscopy (e.g., Carretta et al. 2010b; Mészáros et al. 2015; Milone et al. 2015; Piotto et al. 2015; Tang et al. 2017, 2018). Chemical abundances from spectroscopic data suggest that GCs have a group of so-called “second generation” (SG) stars with enhanced N and Na (sometimes He and Al), but depleted C and O (sometimes Mg). These kinds of stars presumably are only formed in the dense environments of GCs. Therefore, identifying field stars with an SG-like chemical pattern is a feasible way to find a link between field stars and GC ejection/dissolution. Thanks to large spectroscopic surveys, the search for these chemically peculiar stars is becoming more efficient. Using high spectral resolution surveys, multiple elements, like C, N, O, Na, Mg, and Al, can be measured, depending on the wavelength range and signal-to-noise ratio. Toward this, the Apache Point Observatory Galactic Evolution Experiment (APOGEE; Majewski et al. 2017) and Gaia-ESO survey have led to the discovery of a large group of N-rich field stars (Lind et al. 2015; Fernández-Trincado et al. 2016, 2017, 2019a; Martell et al. 2016; Schiavon et al. 2017). While high-resolution spectra give more elements for a detailed investigation of their chemical history, low-resolution spectra can supposedly extend the search for N-rich field stars toward fainter and more numerous samples (Martell & Grebel 2010; Martell et al. 2011; Koch et al. 2019). Simultaneously observing 4000 stars with fibers makes LAMOST an unprecedented machine in collecting low-resolution stellar spectra. Using the CN–CH band features around 4000 Å, we have identified ∼40 N-rich field stars8

8
Also called CN-strong, CH-normal stars in Paper I.
 in LAMOST DR3 (Tang et al. 2019, hereafter Paper I). The derived N abundances of these stars are clearly higher than those of the metal-poor field stars, indicating that (1) our sample is a bona fide sample of N-rich field stars and (2) the classical extra-mixing theory may not work for these stars. Moreover, a substantial fraction of retrograding N-rich field stars suggest that some N-rich field stars may be accreted. In this work, we expand our sample to ∼100 N-rich field stars in LAMOST DR5 (Section 2), making it more robust for drawing statistical conclusions, especially for the GC origin of these field stars. We put forward a detailed analysis of high-resolution chemical abundances and kinematics (Sections 3 and 4) to discuss the origins of these N-rich field stars (Section 5). As the second paper of this series, we will also call this present work Paper II.
Citation: Lind et al. 2015
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The distribution function of the electrons inside the precursor can be formally determined through a Lorentz transform of the corresponding distribution function in the shock frame, and the latter can be obtained by solving a stationary transport equation with a loss term corresponding to the finite residence time spent in the precursor. Here, we approximate this distribution function in the precursor frame as follows: (3)\begin{equation} \frac{{\rm d}n_{\rm e\vert\rm p}}{{\rm  d}\gamma_{\rm e}}\,\simeq\,\frac{R^2}{r^2}\, \frac{\vert  s-1\vert}{\gamma_{\rm m\vert p}}\left(\frac{\gamma_{\rm e}}{\gamma_{\rm  m\vert p}}\right)^{-s}\, 2\Gamma_{\rm sh}^2n_{\rm  u}\,\Theta\left[r_{\rm p}(\gamma_{\rm e})-r\right].  \label{eq:dndg1} \end{equation}dne|pdγe ≃ R2r2 |s−1|γm|pγeγm|p−s 2Γsh2nu Θ[rp(γe)−r].The Heaviside function models the finite length scale up to which particles of a given Lorentz factor can travel. This precursor length scale is related to the (upstream frame) residence time tres through \hbox{$r_{\rm p}\,=\,R+c t_{\rm res}\left(1-\beta_{\rm sh}\right)\,\simeq\,R+ct_{\rm res}/(2\Gamma_{\rm sh}^2)$}rp = R+ctres1−βsh ≃ R+ctres/(2Γsh2). The residence time can be calculated as the time it takes to deflect the accelerated electron by an angle ~1/Γsh (Achterberg et al. 2001; Milosavljević & Nakar 2006; Pelletier et al. 2009). If the background magnetic field controls the transport of the accelerated electrons, this residence time is \hbox{$t_{\rm res}^{(1)}\,\simeq\,\Gamma_{\rm sh}^{-1}\gamma_{\rm e} m_{\rm e} c/(e B_{\rm u})$}tres(1) ≃ Γsh-1γemec/(eBu). In contrast, if pitch angle scattering in a micro-turbulence of length scale λδB governs the return of particles to the shock, \hbox{$t_{\rm res}^{(2)}\,\simeq\,\Gamma_{\rm sh}^{-2}\gamma_{\rm e}^2m_{\rm e}^2c^3/(\lambda_{\delta B}e^2\delta B_{\rm p}^2)$}tres(2) ≃ Γsh-2γe2me2c3/(λδBe2δBp2). Whether one or the other occurs depends on γe and the hierarchy between Bu and δBp. As the scattering frequency in a micro-turbulence scales as \hbox{$\gamma_{\rm e}^{-2}$}γe-2, while the gyrofrequency scales with \hbox{$\gamma_{\rm e}^{-1}$}γe-1, we expect the background field to control the residence time at higher energies. In order to bracket this realistic scenario, we consider in the following the above two extremes: (1) where regular deflection in a background field dominates at all energies and (2) where stochastic deflection in a small scale micro-turbulence of uniform energy density dominates at all energies. Correspondingly, we write \hbox{$r_{\rm p}(\gamma_{\rm e})=R+\Delta_i\gamma_{\rm e}^i$}rp(γe)=R+Δiγei, with i = 1,2 for models (1) or (2); \hbox{$\Delta_i=ct_{\rm res}^{(i)}/(2\Gamma_{\rm sh}^2)$}Δi=ctres(i)/(2Γsh2).
Citation: Milosavljević & Nakar 2006
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Many factors can affect the prevalence of AGN activity. One important question is how gas is brought down to the galaxy center to fuel supermassive black holes (SMBHs). In the literature, two kinds of mechanisms are proposed. One is the internal secular evolution process. The torque induced by non-axisymmetric galactic structures can drive slow and significant inflow (Kormendy & Kennicutt 2004; Hopkins & Quataert 2011; Sellwood 2014; Fanali et al. 2015). The galactic bar is one of the most prominent non-axisymmetric structures and it exists in about 40% of spiral galaxies (Oh et al. 2012). In addition, there is evidence demonstrating that bars can enhance star formation in the central regions of galaxies (e.g. Oh et al. 2012; Chown et al. 2019). However, the question of whether galactic bars can significantly affect AGN activity is still under debate (Arsenault 1989; Mulchaey & Regan 1997; Oh et al. 2012; Galloway et al. 2015; Goulding et al. 2017; Alonso et al. 2018). Other mechanisms, such as galaxy merger and interaction, are also expected to displace the angular momentum of the gas and transport the gas inward (e.g. Hopkins et al. 2006; Di Matteo et al. 2008; Bhowmick et al. 2020). Similarly to studies of secular evolution, observational evidence for this scenario is also mixed. Some studies have found significant environmental dependence of AGN activity (e.g. Koulouridis et al. 2006; Koss et al. 2010; Ellison et al. 2011; Sabater et al. 2013; Khabiboulline et al. 2014; Lackner et al. 2014; Satyapal et al. 2014; Hong et al. 2015; Kocevski et al. 2015; Goulding et al. 2018; Gao et al. 2020), while others have found no or only weak environmental effects (e.g. Grogin et al. 2005; Li et al. 2006a, 2008; Pierce et al. 2007; Ellison et al. 2008; Gabor et al. 2009; Darg et al. 2010; Wang & Li 2019; Man et al. 2019). The contradictory results may be caused by the difference in AGN selection criterion, observational bias, control sample, and environmental indicator used. As we show below, understanding the environmental effects on AGNs also requires knowledge about the evolutionary status of their host galaxies, as it can help us to better understand how to construct control samples and to adopt appropriate environmental indicators.
Citation: Oh et al. 2012
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Citation: Oh et al. 2012
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Nearly all of the cm-wavelength radio emission from star-forming galaxies, such as SMGs, is non-thermal synchrotron radiation from relativistic electrons accelerated in supernova (SN) remnants produced by the short-lived, high-mass OB-type stars (M ≳ 8 M⊙; main-sequence lifetime τMS ≲ 30 Myr). Because SNe trace the recent/on-going star formation, the radio synchrotron emission has the potential to trace the spatial scales on which star formation is occurring. This connection between radio emission and star formation is strongly supported by the close infrared (IR)-radio correlation observed in galaxies (e.g. Helou et al. 1985; Beck & Golla 1988; Xu et al. 1992; Condon 1992; Yun et al. 2001; Bell 2003; Tabatabaei et al. 2007; Murphy et al. 2008; Sargent et al. 2010; Morić et al. 2010; Dumas et al. 2011). On the basis of this correlation, the IR-emitting region of a star-forming galaxy is expected to be comparable in size to that of radio continuum emission. However, the most recent studies of the sizes of IR-emitting regions of SMGs based on continuum imaging observations with the Atacama Large Millimetre/submillimetre Array (ALMA) show that these are significantly smaller than SMG radio sizes presented in the literature (Simpson et al. 2015a; Ikarashi et al. 2015). A possible explanation for this discrepancy, as suggested by Simpson et al. (2015a), is cosmic ray (CR) diffusion in the galactic magnetic field away from their acceleration site, which would render larger radio sizes. To test this further here we present a study of radio sizes of SMGs from a well selected sample of SMGs in the Cosmic Evolution Survey (COSMOS; Scoville et al. 2007) deep field using radio data from the Karl G. Jansky Very Large Array (VLA)-COSMOS 3 GHz Large Project (1σ noise of 2.3 μJy beam-1, angular resolution \hbox{$0\farcs75$}0 .̋ 75; Smolčić et al., in prep.). We describe the SMG sample and the employed VLA data in detail in Sect. 2. The 3 GHz images are presented in Sect. 3, and the analysis (size measurements and radio spectral indices) are presented in Sect. 4. We compare our results with literature studies in Sect. 5, discuss the results in Sect. 6, and summarise the main results of the paper in Sect. 7.
Citation: Sargent et al. 2010
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As in Flowers et al. (2019), to compare our model transmission spectra directly against the Ehrenreich et al. (2020) results, we must calculate the transmission spectra as a function of orbital phase throughout the duration of transit. To account for orbital phase dependencies, we apply the following procedure:1.Account for phase-dependent backlighting of the planet (i.e., stellar limb-darkening effects). At different points of its transit, the planet will occult regions of its host star of varying brightness. Furthermore, at a fixed orbital phase, different regions of the planet’s limb will be backlit by varying intensities of stellar light. Similar to Flowers et al. (2019), we calculate the normalized stellar intensity at the center of each cell of the 2D projected planetary grid produced by our GCM at each modeled orbital phase of the planet. We use the quadratic limb-darkening coefficients reported by Ehrenreich et al. (2020) to establish the stellar center-to-limb intensity profile, and we take into account the 89.°623 orbital inclination of WASP-76b (Ehrenreich et al. 2020) to determine where the planet resides on the stellar disk as a function of its orbital phase. We make the assumption of constant impact parameter b over the course of transit.
9


9
In reality, a planet on an inclined orbit will not have a constant b over the entire duration of transit; rather, the planet’s distance from the stellar equator will be decreased at ingress and egress, reaching its maximum at center of transit. Our tests reveal that, for WASP-76b, the relative error induced by the constant b assumption is on the order of 4% in distance, which results in a change on the order of 1 m s−1 at the blueshift level (see Section 3.1). Hence, our b treatment is justified. This procedure allows us to calculate a backlighting factor f, which ranges from 0 to 1, effectively replacing the constant I

λ,0 from Equation (3) with a variable 



Iλ,0×f(θ′,z,φ)

, for a given orbital phase φ and 2D projected polar angle 



θ′

.2.Account for the decreasing of the continuum by interpolating a light curve produced by the batman code (Kreidberg 2015). Step 1 ensures that less light is transmitted through the planet’s atmosphere than a uniform stellar disk would emit. Step 2 further enforces that the inner, optically thick core of the planet is simulated crossing a limb-darkened star, as opposed to a star of uniform brightness.3.Account for the planet’s rotation over the course of transit. Because the planet is continually rotating as it travels across the face of its host star, we must transform the GCM coordinate system so that the correct observer-facing hemisphere is modeled at each instance during transit. For simplicity, we assume zero obliquity, which allows us to calculate the coordinate transform simply by assigning a linear offset to each planetary longitude; i.e., ϕ
rotated = ϕ + φ.
Citation: Ehrenreich et al. (2020)
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Galactic winds: Galactic winds driven by CRs have often been simulated in two limits: a diffusion-dominated regime, due possibly to ‘extrinsic confinement’, where CRs are scattered by extrinsic turbulence, and/or due to various wave damping mechanisms (e.g. ion neutral damping) and streaming-dominated ‘self confinement’, where CRs are confined by Alfven waves they produce via the gyroresonant streaming instability. In the diffusive ‘extrinsic confinement’ case, CRs do not heat the gas.19 In the streaming dominated ‘self confinement’ case, CR transport heats gas at a rate vA · ∇Pc. The diffusive case fits γ ray observations better, because CRs can propagate out of the galaxy faster (Chan et al. 2019). It is also much better at driving winds, because the CRs do not suffer strong energy losses via Alfven wave heating (Wiener et al. 2017b; Hopkins et al. 2020). However, we expect self-confinement to be very strong at the ∼GeV energies where CR energy peaks (Kulsrud & Pearce 1969; Farmer & Goldreich 2004; Wiener et al. 2013), while extrinsic compressible turbulence is strongly damped at small scales, and unlikely to efficiently scatter ∼GeV CRs (Yan & Lazarian 2002). Thus, CR winds should be streaming dominated and relatively inefficient. The CR staircase changes these dichotomies by changing the structure of the wind. We have seen how CR pressure can build up in streaming dominated simulations, due to trapping at bottlenecks. This increases mass outflow rates, similar to the effect of increased opacity in radiative outflows. In CR streaming simulations of isothermal winds where the CR acoustic instability arose, Quataert et al. (2022a) found an increase in wind mass loss rates by an order of magnitude, compared to analytic models without a CR staircase, illustrating the potential impact of CR staircases. High-resolution cosmological zoom simulations of CR staircases are actually well within reach. As seen in Appendix Section B, all that is required is that the diffusion length $l_{\rm diff} \sim \kappa /c_{\rm s} \sim 2 \, {\rm kpc} \, \left(\frac{\kappa }{10^{29} {\rm cm^2 s^{-1}}} \right)\left(\frac{c_{\rm s}}{150 \, {\rm km \, s^{-1}}} \right)^{-1}$ is resolved. However, to date only the FIRE collaboration has implemented the two moment method (capable of dealing with CR streaming) in such simulations, and – in contrast to, for instance, van de Voort et al. (2021) – the plasma β in their winds is too high for the acoustic instability to develop (Hopkins et al. 2020). But alternate setups where CR staircases appear are certainly numerically feasible.
Citation: Hopkins et al. 2020
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Citation: Hopkins et al. 2020
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An increasing number of recent works focus on the study of high-redshift Lyman-α emitters (LAEs), objects showing prominent rest-frame Lyα emission within a spectrum (usually) devoid of other line features (e.g. Cassata et al. 2011; Nakajima et al. 2018). The spectral properties of LAEs are usually interpreted as coming from young (≲50 Myr) and low-mass (M*​   ​1010 M⊙) galaxies (e.g. Wilkins et al. 2011; Amorín et al. 2017; Hao et al. 2018; Santos et al. 2020) with small rest-frame UV half-light radii (R​ ≲ ​1 − 2 Kpc, as in e.g. Møller & Warren 1998; Lai et al. 2008; Bond et al. 2012; Guaita et al. 2015; Kobayashi et al. 2016; Ribeiro et al. 2016; Bouwens et al. 2017a; Paulino-Afonso et al. 2018) which are actively star forming (SFR​ ∼ ​1 − 100 M⊙ yr−1) and dust poor (dust attenuation AV​   ​0.2, see e.g. Gawiser et al. 2006, 2007; Guaita et al. 2011; Nilsson et al. 2011; Bouwens et al. 2017b; Arrabal Haro et al. 2020). When observed at high redshift, isolated and grouped LAEs appear to represent the progenitors of present-day galaxies and clusters, respectively, providing extremely valuable insights into structure formation (e.g. Matsuda et al. 2004, 2005; Venemans et al. 2005; Gawiser et al. 2007; Overzier et al. 2008; Guaita et al. 2010; Mei et al. 2015; Bouwens et al. 2017b; Khostovan et al. 2019). A basic statistical tool to study the population of high-z LAEs is the description of their number density at a given redshift as a function of line luminosity (LLyα), namely the Lyα luminosity function (LF; see e.g. Gronke et al. 2015, for a theoretical approach). Several recent works have focused on the construction of the Lyα LF at z​ ≥ ​2 (Gronwall et al. 2007; Ouchi et al. 2008; Blanc et al. 2011; Clément et al. 2012; Konno et al. 2016; Sobral et al. 2017, 2018a) by making use of deep observations of narrow sky regions (up to few squared degrees, as in e.g. Matthee et al. 2014, 2017b; Cassata et al. 2015; Ono et al. 2018). Their findings describe a Lyα LF that follows a Schechter function (Schechter 1976) at relatively faint line luminosity (i.e. LLyα ≲ 1042.5, see e.g. Ouchi et al. 2008; Konno et al. 2016; Sobral et al. 2016; Matthee et al. 2017a), a regime mostly occupied by low-mass star-forming galaxies (e.g. Hu et al. 1998; Kudritzki et al. 2000; Stiavelli et al. 2001; Santos et al. 2004; van Breukelen et al. 2005; Gawiser et al. 2007; Rauch et al. 2008; Guaita et al. 2011).
Citation: Nilsson et al. 2011
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