adsabs/FOCAL huggingface dataset:
Among the odd Z elements, we derived the abundances of Na, Al, K, and Sc using the high-resolution spectra and the details of the lines are given in Table A1. The Na D lines were not used as the lines are too strong for deriving the abundances. So, we depended on the weaker lines 5682 Å and 5688 Å to derive the Na abundance. The NLTE corrections for these Na i lines were performed from Lind et al. (2011) whenever available and the average values are listed in Table 3. Al i lines at 6696 and 6698 are used for deriving the Al abundance and these lines have negligible contributions from NLTE effects (Baumueller & Gehren 1997; Nordlander & Lind 2017). For the case of K, whenever the feature at 7698.98 Å has contributions from telluric features, the abundance from 7664.87 Å is quoted, otherwise the average of the abundances from both the lines is quoted in Table 3. Both these lines are sensitive to NLTE effects (Takeda et al. 2002; Kobayashi et al. 2006; Andrievsky et al. 2010; Prantzos et al. 2018; Reggiani et al. 2019) and the NLTE corrections depend on the effective temperature and surface gravity of the model. Using the NLTE grid provided in Reggiani et al. (2019), we have estimated the NLTE corrections for the program stars and the NLTE corrected values are given in Table 3. The NLTE grid does not cover very low log g values, so we could not estimate the NLTE corrections for HE 1152−0355 and HE 0314−0143. We could not measure the K abundance in HD 5223 due to the contamination from telluric features. For the case of Sc abundance, we could not find any study of Sc line formation in NLTE except in Zhang, Gehren & Zhao (2008) where they have studied the NLTE effects only for the sun and identified negligible NLTE effects for Sc ii lines. So, we report only LTE results for this element in Table 3. The resolution of the NIR spectra was too low to resolve the HF feature from the nearby features, so we could not derive F abundance in these stars.
Citation: Nordlander & Lind 2017
Citation function: Uses
We use the set of wind solutions (density and velocity distribution of gas in the wind) for primordial discs (i.e. gas-rich, optically thick discs, which do not have an evacuated inner cavity) calculated by Owen et al. (2010, 2011) and EO10 for a 0.7 M⊙ star and X-ray luminosities (0.1 ≤ hν ≤ 10 keV) of LX = 2 × 1028, 2 × 1029 and 2 × 1030 erg s−1. These were obtained by means of two-dimensional hydrodynamic calculations using the zeus code (Stone & Norman 1992; Stone, Mihalas & Norman 1992; Hayes et al. 2006), modified to include the effects of X-ray irradiation with a parametrization of the gas temperature as a function of the local ionization parameter. The dust radiative transfer and photosionisation code mocassin (Ercolano et al. 2003; Ercolano, Barlow & Storey 2005; Ercolano et al. 2008a), modified according to Ercolano et al. (2008b), was used produce the temperature parametrization. The atomic data base of the mocassin code included opacity data from Verner et al. (1993) and Verner & Yakovlev (1995), energy levels, collision strengths and transition probabilities from Version 5.2 of the CHIANTI data base (Landi et al. 2006, and references therein) and hydrogen and helium free–bound continuous emission data of Ercolano & Storey (2006). The ionizing spectrum used to calculate the temperature parametrization was calculated by Ercolano et al. (2009), using the plasma code of Kashyap & Drake (2000) from an emission measure distribution based on that derived for RS CVn type binaries by Sanz-Forcada, Brickhouse & Dupree (2002), which peaks at 104 K and fits to Chandra spectra of T-Tauri stars by Maggio et al. (2007), which peaks at around 107.5 K. This spectrum has a significant EUV component (13.6 eV ≤ hν ≤ 0.1 keV), with roughly LEUV = LX. Solar abundances (Asplund, Grevesse & Sauval 2005), depleted according to Savage & Sembach (1996) were assumed, namely (number density, with respect to hydrogen): He/H = 0.1, C/H = 1.4 × 104, N/H = 8.32 × 105, O/H = 3.2 × 104, Ne/H = 1.2 × 104, Mg/H = 1.1 × 106, Si/H = 1.7 × 106, S/H = 2.8 × 105. More details about the codes and setup of the models can be found in Ercolano et al. (2008b, 2009) and Owen et al. (2010).
Citation: Ercolano et al. 2008a
Citation function: Uses; Uses
(3) What is the likelihood of obtaining Mercury and Mars analogs in systems with Venus–Earth pair analogs? Recent terrestrial planet formation studies managed to statistically produce low-mass Mars analogs. In particular, there are currently five main competing models. (1) Grand Tack: the protoplanetary disk is truncated at ∼1 au by perturbations of an inward-then-outward gas-driven migrating Jupiter within the first 1–3 Myr of the solar system history, leaving a disk with mass concentrated within that distance after the disk gas dispersal (Walsh et al. 2011; Jacobson & Morbidelli 2014; Walsh & Levison 2016; Brasser et al. 2016a); (2) Empty Asteroid Belt: embryos and planetesimals in the protoplanetary disk formed concentrated within a narrow belt at ∼0.7–1 au (Hansen 2009; Drazkowska et al. 2016; Raymond & Izidoro 2017; Ogihara et al. 2018); (3) Early Instability: the protoplanetary disk is perturbed by the giant planets’ instability that occurred within ∼10 Myr after the disk gas dispersal, strongly depleting the disk mass beyond ∼1.3 au (Clement et al. 2018, 2019b); (4) Pebble Accretion: embryos and planetesimals form preferentially in the inner and outer regions of the protoplanetary disk, respectively, and the disk mass is concentrated within ∼1.5 au (Levison et al. 2015; Chambers 2016); (5) Sweeping Secular Resonance: the growth of embryos and planetesimals is inhibited beyond ∼1–1.5 au by perturbations of secular resonances that swept the disk during the disk gas dispersal (Bromley & Kenyon 2017). In all these models, Mars analogs may form as a result of the disk mass depletion or absence of mass beyond ∼1–1.5 au. Additionally, from the standpoint of the initial conditions of the disk after gas dispersal, models 1 and 2 are very similar, while models 3–5 probably also share similar properties. However, in the majority of those studies, it remains unclear whether these Mars analogs were obtained in systems that also contained Venus and Earth analogs. Moreover, even if they were, it is also unclear what fraction these three-planet analog systems would represent compared to all the systems obtained in those studies, because their results are often presented with all planets mixed (e.g., in plots of distance vs. mass). Furthermore, Mercury and Mars analogs in those studies are often defined as planets from the mixed population that satisfy an arbitrary distance range: e.g., 0.5 au and 1.2–2.0 au, respectively. However, this approach can lead to incomplete classifications in a given system (e.g., by failing to identify more massive Mars analogs at 1.2 au or failing to properly identify planet analogs). Indeed, it is difficult to discriminate Venus from Earth analogs, unambiguously identify Mercury/Mars analogs, and avoid misclassifications in past studies. If the five models described above are successful in reproducing a low-mass Mars, how can the best among them be discriminated without a proper system classification?
Citation: Hansen 2009
Citation function: Background; Background
The effects of the wet air, the variable water vapour cells, are the main cause of the refraction at submillimetre/millimetre wavelengths. The dipole moment of water makes water vapour, the wet component in the troposphere, a strong absorber at submillimetre/millimetre wavelengths and significantly increases the refractive index of the air. Because the water vapour is not well mixed there are localised pockets of air with different refractive indices. In what is called the “frozen-screen” hypothesis (Taylor 1938), these pockets, or turbulent eddies, are assumed to be fixed in the atmospheric layer that advects over an interferometric array (Thompson et al. 2017). Thus, these cause various delays in the path length (variable in time and position) along the line of sight to each antenna. Interferometers are sensitive to the variations in path length, the interferometric phase difference, between pairs of antennas that form a baseline. For a given baseline (distance and orientation) the line-of-sight path to each component of an astronomical source has an intrinsic phase that relates the measured intensities to their location in an image. Thus any additional variable atmospheric delays that cause anomalous phase changes on many baselines making up an array have the effect of blurring the interferometric image; this is analogous to the effect of seeing at optical and infrared wavelengths. The introduced delays scale linearly with the difference in precipitable water vapour (ΔPWV) between an antenna pair (excluding dispersive effects) and linearly with frequency. The correlated signals between pairs of antennas (the visibilities V = V0eiφ) become partly decorrelated as a result of the phase noise. The reduced coherence for the visibilities is given by (1)\begin{equation} \label{eqn0} \langle V \rangle = V_0 \times \langle {\rm e}^{\rm i\phi} \rangle = V_0 \times {\rm e}^{-\phi^2_{\rm rms}/2} , \end{equation}⟨V⟩=V0×⟨eiφ⟩=V0×e−φrms2/2,
Citation: Thompson et al. 2017
Citation function: Background; Background
The global volume-weighted neutral fraction of hydrogen QH I in the high-resolution volume is presented as the thick blue line in Fig. 10. The reionization process starts when the first stars are born, and by z50 ≃ 7.63, half of the volume is reionized. We identify the redshifts at which 1%, 10%, 50%, 90% and 99% of the volume is ionized as z01 = 11.13, z10 = 8.68, z50 = 7.63, z90 = 6.58, and z99 = 5.92 respectively; corresponding to a reionization duration Δz = z99 − z10 = 2.8 (Δt ≃ 385 Myr), broadly consistent with the estimates of Robertson et al. (2015) The dark and light shaded areas in Fig. 10 correspond to the 1σ and 2σ constraints on the redshift of reionization from the cosmic microwave background measurements of the Planck mission (Planck Collaboration VI 2020), with a reionization midpoint zre = 7.67 ± 0.73. We also compare the OBELISK reionization history to a selection of observational constraints: Black hexagons correspond to the measurements of the Lyman-α forest transmission (Lyα forest, Fan et al. 2006b), the green circles show constraints on the IGM opacity from the fraction of Lyman-α emitters in Lyman-break galaxy samples (Schenker et al. 2014; Ono et al. 2012; Pentericci et al. 2014; Robertson et al. 2013; Tilvi et al. 2014), the purple diamonds show measurements from quasar damping wings by Mortlock et al. (2011), Schroeder et al. (2013), Bañados et al. (2018), Ďurovčíková et al. (2020), the red diamonds show similar measurements on gamma-ray bursts (GRB, Totani et al. 2006, 2016), and the black squares from Ouchi et al. (2010), Ota et al. (2008) represent constraints derived from the evolution of the Lyman-α luminosity function. Some of these data points come from the compilations of Bouwens et al. (2015). Overall, we find that the simulation agrees with most observations in terms of reionization history, despite the fact that we focus on an overdense region. Interestingly, the simulation manages to capture residual neutral fraction after reionization is complete at z  6 similar to what is observed. We discuss this point further below.
Citation: Planck Collaboration VI 2020
Citation function: Uses; Uses
More recently, García-Bernete et al. (2022) found that the PAH molecules responsible for the 11.3 μm PAH emission band are more resilient in the hard environments often present in AGN. In particular, the authors found larger 11.3/7.7 μm and 11.3/6.2 μm PAH ratios in AGN-dominated systems compared to SF galaxies, indicating a larger fraction of neutral PAH molecules (as noted by Smith et al. 2007 using a sample of relatively weak AGN). However, these studies were limited by the spatial resolution (∼4″) and the low spectral resolution (R ∼ 60–130) of Spitzer/InfraRed Spectrograph (IRS). Previous sub-arcsecond angular resolution N-band (∼8–13 μm) ground-based spectroscopic studies investigated the 11.3 μm PAH feature in the nuclear and circumnuclear regions of AGN (e.g., Hönig et al. 2010; González-Martín et al. 2013; Alonso-Herrero et al. 2014, 2016; Ramos et al. 2014; Esquej et al. 2014; García-Bernete et al. 2015; Jensen et al. 2017; Esparza-Arredondo et al. 2018). However, these works were unable to provide definitive details regarding the effect of the AGN on the PAH molecules due to limited wavelength coverage and sensitivity. The changes in the PAH properties due to the presence of the AGN might be more prominent in their innermost regions of galaxies. Therefore, the unprecedented combination of high angular and spectral resolution (R ∼ 1500 − 3500) in the entire mid-IR range (4.9–28.1 μm) afforded by the James Webb Space Telescope (JWST)/Mid-Infrared Instrument (MIRI; Rieke et al. 2015; Wells et al. 2015; Wright et al. 2015) is key to investigating PAH properties. In this Letter we report on the first investigation of PAH emission in the nuclear regions of three luminous Seyfert (Sy) galaxies and compare them with emission from SF regions using JWST/MIRI Medium Resolution Spectrograph (MRS) data. This enables us, for the first time, to characterise the PAH properties of local luminous Sy galaxies (log (Lbol)> 44.46 erg s−1)1 at sub-arcsecond scales (∼0.45″, ∼142–245 pc).
Citation: Ramos et al. 2014
Citation function: Background; Motivation
Citation: Esquej et al. 2014
Citation function: Background; Motivation
Citation: Esparza-Arredondo et al. 2018
Citation function: Background; Motivation
In this study we are focusing on rest-frame UV emission red-wards of the strongest UV emission line, Lyα, partially motivated by the challenges of observing this line at high redshift (z ≳ 6) where the significantly neutral CGM and IGM absorbs the Lyα photons escaping the galaxy along the line of sight (e.g., Dijkstra et al. 2011; Laursen et al. 2011, 2019; Dijkstra 2017). Nevertheless, the Lyα line itself has improved our understanding of star-forming galaxies in the (early) Universe. In particular, the asymmetric Lyα line profile has enabled redshift confirmations of large samples of sources at both 2  z  6 (e.g., Steidel et al. 2014; Le Fevre et al. 2015; Herenz et al. 2017; Inami et al. 2017; Urrutia et al. 2019) and high redshift at z > 6 (e.g., Finkelstein et al. 2013; Oesch et al. 2015; Schmidt et al. 2016; Tilvi et al. 2016; Huang et al. 2016; Pentericci et al. 2018; Fuller et al. 2020). The resonant scattering of the photons and the resulting (occasional) multipeaked emission has been shown to relate closely to the column density and dynamics of the neutral hydrogen in the ISM and the CGM (Verhamme et al. 2015; Gazagnes et al. 2018, 2020). The fraction of galaxies with confirmed Lyα emission has been used to probe the evolution (or lack thereof) of the fraction of LAEs among Lyman-break galaxies from low redshift to the EoR (e.g., Treu et al. 2013; Pentericci et al. 2014; Tilvi et al. 2014; de Barros et al. 2017; Caruana et al. 2018; Kusakabe et al. 2020). Together with the observed velocity offset of the Lyα line resulting from resonant scattering (Schenker et al. 2013; Erb et al. 2014; Hashimoto et al. 2015; Stark et al. 2017; Verhamme et al. 2018), this has probed the amount of neutral gas in the IGM and has constrained the neutral fraction of the Universe during the EoR (Ouchi et al. 2010; Greig et al. 2017; Mason et al. 2018a,b; Banados et al. 2018; Hoag et al. 2019). Furthermore, comparisons between Lyα and Hα or UV emission line strengths have been used to study the production efficiency and escape of ionizing photons from LAEs (Nakajima et al. 2016; Matthee et al. 2017; Harikane et al. 2018; Lam et al. 2019; Maseda et al. 2020). It is therefore of interest to relate and compare the measured rest-frame UV emission lines red-wards of Lyα studied here, with the characteristics of the Lyα line itself and the properties of the LAEs in our sample.
Citation: Urrutia et al. 2019
Citation function: Background
In the context of solar wind turbulence, one crucial question concerns turbulence evolution and heating (Parashar et al. 2015; Chen 2016; Viall & Borovsky 2020). Numerous correlations exist between solar wind parameters and magnetic fluctuations, which can provide important indications and constraints on the physics of turbulence evolution and heating processes. Early observations showed that faster solar wind usually corresponds to higher proton temperature (e.g., Marsch et al. 1982; Elliott et al. 2012). The faster solar wind tends to be more imbalanced with greater wave energy flux antisunward than sunward (Tu & Marsch 1995), and can be characterized by larger inertial-scale magnetic energy density and steeper proton-scale magnetic spectrum (Bruno et al. 2014). A study by Wind observations revealed that higher proton temperature is associated with a steeper proton-scale magnetic spectrum (Leamon et al. 1998b). Statistical studies by Wind and Advanced Composition Explorer measurements later revealed that higher proton temperature is related to larger inertial-scale magnetic energy density (Smith et al. 2006; Vech et al. 2018). Recent statistical studies by Wind and Parker Solar Probe (PSP) observations further revealed that higher proton temperature is linked to larger proton-scale magnetic energy density (Zhao et al. 2020, 2022). On the other hand, cross and magnetic helicities at inertial and kinetic scales, respectively, are often employed to describe the imbalance and handedness of solar wind turbulence. A correlation between cross and magnetic helicities was reported, which was interpreted as the signature of cyclotron-resonant dissipation in solar wind turbulence (Leamon et al. 1998a). A strong correlation (with a correlation coefficient (CC) up to 0.8 occasionally) between the proton-scale spectral index and magnetic helicity was found (Pine et al. 2020; Zhao et al. 2021, 2022). Via PSP observations, a mild correlation (with a CC of 0.36) between the spectral index and cross helicity was displayed in the most recent literature (Huang et al. 2021). However, less attention has been paid to the relations between these correlations despite the large body of research.
Citation: Tu & Marsch 1995
Citation function: Background
Figure 1 shows the best fits of a multiple-Gaussian model to the T
90 distributions of distinct Swift/BAT GRB samples. Interestingly, we find from Figures 1(a)–(d) that the lognormal T
90 durations of all Swift GRBs except those with good spectra in sample V are triply distributed. It is confirmed in Figure 1(e) that the Swift/BAT GRBs with a well-measured peak energy are bimodally distributed at a boundary of ∼1 s, which is in good agreement with Zhang et al. (2020). Note that the third component remains controversial. On the other hand, Hakkila et al. (2000) suggested that the third subgroup proved by statistics was only a deviation caused by complex instrumental effects, which could reduce the duration of some weak long pulses. In addition, another classification scheme uses a scatter plot of flux and duration fitted with two-dimensional Gaussian functions (Bálazs et al. 2003). Some authors pointed out that there are more than two clusters (Mukherjee et al. 1998; Horváth 1998). Unfortunately, the physical origin of the extra components cannot be reasonably interpreted. The two-Gaussian fit to data in Figure 1(e) demonstrates that the duration distribution peaks at 0.21 ± 0.38 s with a spread of 0.93 dex for short bursts and at 43.97 ± 1.05 s with a spread of 1.12 dex for long GRBs. The best fit returns a good reduced Chi-square of 



χν2

 ≈ 0.82, indicating that two classes are evidently reconfirmed and separated at T
90 ≈ 1.06 s instead of at T
90 = 2 s shown by CGRO/BATSE data. The dividing line of 1 s is consistent with some previous results of Swift/BAT GRBs (Berger et al. 2013; Gruber et al. 2014; Zhang et al. 2020). Additionally, the number of components in each T
90 distribution of Figure 1 has been accordingly tested by the traditional Bayesian information criterion (BIC) as we previously did (e.g., Zhang et al. 2016; Li et al. 2021b). Figure 1 demonstrates that the S/N level and the EE component are two important contributors to the third class of GRBs. In other words, one of them can separately confuse the classification of GRBs in terms of T
90 only.
Citation: Bálazs et al. 2003
Citation function: Compare/Contrast
Several qLMXBs have been identified in GCs and in the Galactic field (for some examples, see table 4 in Guillot et al. 2009 and references therein). While LMXBs in the field were detected following the onset of a bright accretion outburst, most qLMXBs in GCs, including all those with the highest flux at Earth, have not shown accretion activity.3 Most of these sources have only been spectrally identified based on their similarities to field LMXBs, observed during quiescence (e.g. Cen X-4 or Aql X-1). Previous works have confirmed that H-atmosphere models accurately describe the spectra of qLMXBs, with radii in the range 10–15 km, as expected for NSs, either from single sources (e.g. Heinke et al. 2006a; Webb & Barret 2007; Guillot, Rutledge & Brown 2011; Heinke et al. 2014; Bogdanov et al. 2016), or from statistical analyses of multiple qLMXBs (e.g. Guillot et al. 2013; Guillot & Rutledge 2014; Lattimer & Steiner 2014; Guillot 2016; Steiner et al. 2018). However, in some cases the accreted material may not be hydrogen, but helium (e.g. Servillat et al. 2012; Catuneanu et al. 2013; Heinke et al. 2014). One way to circumvent this is to identify the nature of the donor star, i.e. to determine the nature of the material transferred on to the NS (e.g. with the detection of an H α emission line, presumably originating in a faint accretion disc, Haggard et al. 2004). The possibility of helium (or heavier element) atmospheres is well-founded on the existence of ultracompact X-ray binaries (UCXB), with white dwarfs or helium-dominated donors4 (e.g. Zurek et al. 2009; Altamirano et al. 2010; Sanna et al. 2017; Cadelano et al. 2019). In fact, around 1/3 of the LMXBs in GCs with constraints on the companion nature, possess a white dwarf donor (Bahramian et al. 2014). Since NS He-atmosphere models have harder spectra than H-atmosphere models, using the incorrect composition for the observed thermal emission can result in biases of the inferred radii (Servillat et al. 2012; Heinke et al. 2014).
Citation: Heinke et al. 2014
Citation function: Background
Citation: Heinke et al. 2014
Citation function: Uses