adsabs/FOCAL huggingface dataset:
For decades, the metallicity and the above chemical abundance ratios have been estimated in samples of star-forming galaxies (SFGs), by using their emission line ratios to calculate the abundances of the different elements that originate them. Many techniques were developed for such studies, such as the use of the Te-method, photoionization models, or optical calibrations based on strong emission lines (see the review by Maiolino & Mannucci 2019 for a more detailed discussion). By using these techniques, some studies show a correlation between both 12 + log (O/H) and log (N/O) (Vila-Costas & Edmunds 1993; Masegosa, Moles & Campos-Aguilar 1994; Andrews & Martini 2013). On the other hand, it is also important how the different properties of the host galaxies correlate with their chemical abundances. For instance, preliminary studies of the Local Group of galaxies (McClure & van den Bergh 1968; Lequeux et al. 1979) revealed the existence of a relation between the luminosity of an SFG and its metallicity, the so-called luminosity–metallicity relation (the most luminous galaxies have the higher chemical abundances), which has been later studied in further detail (Garnett & Shields 1987; Skillman, Kennicutt & Hodge 1989; Brodie & Huchra 1991; Vilchez 1995; Mateo 1998). Later on it was probed that the luminosity–metallicity relation was in fact the result of a more fundamental mass–metallicity relation (Garnett 2002; Pérez-González et al. 2003; Pilyugin, Vílchez & Contini 2004). This relation has been probed in large sample of SFG both at low-redshift (Contini et al. 2002; Melbourne & Salzer 2002; Lamareille et al. 2004; Tremonti et al. 2004) and at high-redshift (Erb et al. 2006a; Pérez-Montero et al. 2009, 2013; Izotov et al. 2015; Gao et al. 2018; Torrey et al. 2019). In addition, it has been reported that the star-formation rate is also related to gas-phase Z in SFG (Lara-López et al. 2010; Mannucci et al. 2010; Yates, Kauffmann & Guo 2012). Morphological type may also be related with the chemical abundances, as early-type galaxies, despite some of them can present episodes of on-going star-formation (Zhu, Blanton & Moustakas 2010), tend to have on average older stellar populations than late-type galaxies (Kennicutt 1998; Trager et al. 2000; Gebhardt et al. 2003; Schiavon 2007), being thus more chemically evolved.
Citation: Vila-Costas & Edmunds 1993
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Adaptive optics (AO, Roddier 1999) is a game changer in the quest for high-angular resolution, especially for ground-based astronomical observations that face the presence of wavefront aberrations introduced by the atmosphere (Roddier 1981). Thanks to AO, the point spread function (PSF) delivered by an optical instrument is much narrower, by a factor up to 50 on the full width at half maximum (FWHM), than the seeing-limited scenario (Roddier 1981). Still, some correction residuals persist and render the AO PSF shape complex to model. Consequently, standard parametric models that reliably reproduce the seeing-limited PSFs, such as a Moffat function (Trujillo et al. 2001; Moffat 1969), become inefficient at describing the AO PSF. Moreover, contrary to seeing-limited observations, AO-corrected images suffer from the anisoplanatism effect (Fried 1982) that strengthens the spatial variations of the PSF on top of instrument defects. Determining the AO PSF is necessary for two major reasons. Firstly, understanding and accurately modeling the PSF morphology is key to diagnosing AO performance. From the PSF, we can identify the major contributors to the AO residual error (Beltramo-Martin et al. 2019; Ferreira et al. 2018; Martin et al. 2017). Secondly, the image delivered by an optical instrument depends on both the science object we want to characterize and the PSF. In order to estimate the interesting astrophysical quantities, one can either use a deconvolution technique (Fétick et al. 2020, 2019a; Benfenati et al. 2016; Flicker & Rigaut 2005; Mugnier et al. 2004; Fusco et al. 2003; Drummond 1998) or include the PSF as part of a model, as is performed in PSF-fitting astrometry/photometry retrieval techniques (Beltramo-Martin et al. 2019; Witzel et al. 2016; Schreiber et al. 2012; Diolaiti et al. 2000; Bertin & Arnouts 1996; Stetson 1987) and galaxy kinematics estimation (Puech et al. 2018; Bouché et al. 2015; Epinat et al. 2010) for instance.
Citation: Trujillo et al. 2001
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Investigation of solar wind outflow velocities is generally performed by the analysis of UV spectrometric observations. The UVCS/SOHO instrument has provided H I Lyα spectral line data over a longer time than a whole solar activity cycle (1996–2012), giving the possibility of studying coronal dynamics in different activity phases. The analysis of UVCS daily Lyα synoptic data, in combination with electron densities derived from the white-light RS observations, has allowed to derive H I outflow speed maps (see e.g. Dolei et al. 2018, 2019). One of the methods based on the synergy between UV and WL observations is the Doppler dimming technique (Hyder & Lites 1970; Noci et al. 1987; Withbroe et al. 1982). It exploits the progressive UV intensity reduction of the coronal resonantly scattered component of the coronal H I Lyα line emission with increasing outflow velocities. The line emission depends on the physical quantities involved in the Lyα resonant scattering process, such as, for instance, the coronal electron density and temperature, and the chromospheric Lyα radiation that excites the coronal H I atoms. The Lyα intensity is also sensitive to the speed of the outflowing plasma from about 50–500 km s−1, that are the typical velocities for neutral hydrogen atoms in the inner corona. Following the approach of Withbroe et al. (1982) and Noci et al. (1987), the intensity of the resonantly scattered Lyα line can be also numerically computed by iteratively tuning the plasma speed value in order to reproduce the observed UV line intensity. The best match between computed and observed Lyα intensity provides an estimate of the solar wind H I outflow velocity. Figure 9 shows an example of 2D outflow velocity map, in the range of heliocentric distances between 1.5 and 4.0 R⊙, obtained via Doppler dimming technique (Dolei et al. 2018). The speed values radially increase with altitude up to about 150–200 km s−1 in the equatorial regions and 400 km s−1 in the polar regions. These values are in agreement with the expected latitudinal distribution of slow and fast solar wind components, corresponding to equatorial regions, and mid-latitude and polar regions, respectively. The methodology put in place throughout this project will be applied later to the data acquired by the Solar Orbiter’s Metis instrument, giving an unprecedented daily picture of the coronal dynamics, see Antonucci et al. (2020) for a detailed description of diagnostic techniques for Metis data.
Citation: Hyder & Lites 1970
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We apply our analysis to five open clusters of ages between ∼10 and 100 Myr that were observed within GES (25 Ori, Gamma Vel, NGC 2547, NGC 2451 B, and NGC 2516). These clusters were selected because they cover the age interval in which the effect of radius inflation could be significant, allowing us to investigate how it evolves with age. The 25 Ori cluster is a group of PMS stars that was discovered by Briceño et al. (2005) in the Orion OB1a association, with an estimated age of 6−13 Myr (Downes et al. 2014; Briceño et al. 2019; Kos et al. 2019; Zari et al. 2019); a dispersed, kinematically distinct population was also found in the region using data from the Gaia Second Data Release (DR2; e.g. Zari et al. 2019). Because only a few stars of the secondary population were observed by GES, only the main cluster is considered here. Gamma Vel (age ∼10−20 Myr, Jeffries et al. 2014, 2017) and NGC 2547 (35 ± 3 Myr, Jeffries & Oliveira 2005) are both located in the Vela OB2 association at a relative separation of ∼2°. Both clusters host two kinematically distinct populations (Jeffries et al. 2014; Sacco et al. 2015); the two Gamma Vel populations (Gamma Vel A and B) are also separated by ∼38 pc along the line of sight (Franciosini et al. 2018). NGC 2451 is a double cluster composed of two open clusters of similar age (30−40 Myr, Randich et al. 2018) located at different distances along the same line of sight (Röser & Bastian 1994; Platais et al. 1996). The GES observations cover the background cluster NGC 2451 B and only a few selected regions of the closer and more dispersed NGC 2451 A. For this reason, we considered only NGC 2451 B here. Finally, NGC 2516 is the oldest cluster in our sample, with an age of ∼100 − 140 Myr (e.g. Lyra et al. 2006; Randich et al. 2018), so that most of its members are already close to or at their main-sequence position. All clusters have solar or slightly subsolar metallicities (Biazzo et al. 2011; Jacobson et al. 2016; Spina et al. 2017).
Citation: Jeffries et al. 2014
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Selected as part of ESA’s Cosmic Vision programme, Athena will study the evolution of large scale structure through the detection of WHIM filaments to trace the missing baryons in the local universe. Athena aims to measure the local cosmological baryon density in the WHIM to better than 10% and to constrain structure formation models in the low-density regime by measuring the redshift distribution and the physical parameters of 200 filaments against bright background sources. To achieve this, Athena will detect 200 filaments in the WHIM through absorption, 100 towards active galactic nuclei (AGN), and 100 towards bright gamma-ray burst (GRB) afterglows, up to redshifts of z = 1 (Kaastra et al. 2013). The WHIM can be detected by observing the absorption of highly ionised elements such as C, N, O, Ne, and possibly Fe (e.g. O VII, O VIII, Ne IX, Fe XVII). The strongest lines expected correspond to the H-like and He-like oxygen ions of the O VII 1s−2p X-ray resonance line (574 eV) and the unresolved O VIII 1s−2p X-ray doublet (653.5 eV, 653.7 eV). A bright X-ray background source such as an AGN (e.g. Padovani et al. 2017) or a GRB (e.g. Willingale & Mészáros 2017) can be used to detect the WHIM by producing absorption features in its energy spectra (Perna & Loeb 1998; Hellsten et al. 1998; Fiore et al. 2000). These sources provide a high probability of detection for the WHIM because they are sufficiently bright and distant to obtain a large statistical sample of lines. Bright AGN are common but are typically nearby, having an average redshift of z ≈ 0.8 for flat-spectrum radio quasars (FRSQ) and z ≈ 0.3 for BL-Lacs (Ackermann et al. 2011) and so, they probe relatively short lines of sight. Gamma-ray bursts occur at an average redshift of z ≈ 2 (Evans et al. 2009) and have been detected out to redshifts of 9.4 (Cucchiara et al. 2011). This allows for the probing of long lines of sight and can potentially provide multi-filament detections in a single observation. In addition, GRBs occur at an approximate rate of 1 GRB per day, with Fermi-GBM detecting ≈250 per year with a ≈70% sky coverage (Bhat et al. 2016). Both sources provide similar fluences in the 0.3−10 keV energy range, but GRBs can provide this fluence in much shorter integration times. The challenge associated with the use of GRBs as background sources is their transient nature, emitting a considerable percentage of the soft X-ray photons within the first hour of their afterglow phase. Therefore, an instrument capable of having a high efficiency to react on target of opportunity (ToO) events is required to probe the WHIM with GRBs. In spite of this, GRBs should allow Athena to perform its science mission of tracing the missing baryons in GRB afterglow spectra throughout its four-year mission lifetime.
Citation: Willingale & Mészáros 2017
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The multiwavelength spectral data of 13 SNRs with hard γ-ray spectra. The γ-ray spectra are fitted with a hadronic model with the normalization of the individual spectrum as free parameters. The model assumes that protons have a single power-law energy distribution with an exponential high-energy cutoff. Note that the TeV spectra of G78.2+2.1 (HAWC) and N132D (HESS) cut off at relatively lower energies, and the soft spectral component of GeV of HESS 1912+101 may be from other contributors and are not considered in SED fitting. The best-fit model parameters are indicated in the figure. References for the observational data are as follows: RX J0852.0−4622: radio (Duncan & Green 2000), GeV (Tanaka et al. 2011), X-ray (Aharonian et al. 2007), TeV (H.E.S.S. Collaboration et al. 2018c); RX J1713.7−3946: radio (Lazendic et al. 2004), X-ray (Tanaka et al. 2008), GeV and TeV (H.E.S.S. Collaboration et al. 2018a); HESS J1731−347: radio (Tian et al. 2008), GeV (Condon et al. 2017; Guo et al. 2018), X-ray (Doroshenko et al. 2017), TeV (H.E.S.S. Collaboration et al. 2011); RCW 86: radio (Clark et al. 1975; Lemoine-Goumard et al. 2012), X-ray (Lemoine-Goumard et al. 2012), GeV (Ajello et al. 2016), TeV (H.E.S.S. Collaboration et al. 2018d); SN 1006: radio Dyer et al. 2009, X-ray (Bamba et al. 2008), GeV (Condon et al. 2017), TeV (Acero et al. 2010); G150.3+4.5: radio (Gerbrandt et al. 2014), X-ray and GeV (Devin et al. 2020); G296.5 + 10.0: radio (Milne & Haynes 1994), GeV (this work), HESS J1534−571: radio (Maxted et al. 2018), GeV (Araya 2017), X-ray and TeV (H.E.S.S. Collaboration et al. 2018b); RCW 103: radio (Dickel et al. 1996), GeV (Xing et al. 2014); G78.2+2.1: radio (Wendker et al. 1991; Zhang et al. 1997; Kothes et al. 2006; Gao et al. 2011), X-ray (Leahy et al. 2013), GeV (Abeysekara et al. 2018) and TeV (Fleischhack 2019); G279.0+1.1: radio (Woermann & Jonas 1988; Duncan et al. 1995), GeV Araya (2020); N132D: radio (Dickel & Milne 1995), X-ray (Hughes et al. 1998; Bamba et al. 2018), GeV (Y. L. Xin et al. 2020, in preparation), and TeV (H.E.S.S. Collaboration et al. 2015).
Citation: Condon et al. 2017
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Citation: Condon et al. 2017
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The metallicity calculated for HD 212869 is higher than that estimated prior to using high-resolution spectra. The iron abundance is found to be [Fe/H] = 





 0.2 dex on the basis of ionized lines that are almost free from NLTE effects. The carbon abundance was found to be high in the atmosphere of HD 112869, 





 = 8.3 ±  0.1 dex. The nitrogen abundance is 





 = 6.55 ± 0.2 dex. With the obtained abundances [C/Fe] = +2.2 dex and [C/N] = +1.15, HD 112869 occupies the region of CEMP-s stars on the plots [C/Fe] versus [Fe/H] and [C/N] versus [Fe/H] (see Figures 5 and 6 in Campbell & Lattanzio 2008). However, the s-process elements Sr, Y, and Ba are not enhanced significantly, thus confirming by definition (Beers & Christlieb 2005) the CEMP-no status of HD 112869. However, the Nd abundance seems to be enhanced relative to iron, and a similar overabundance was recognized for lanthanum and samarium. From inspection of the Eu ii line at 6645.127 Å, the upper limit was set for the r-process element europium, [Eu/Fe] ≲ +0.8 dex. According to calculations carried out by Bisterzo et al. (2011), the abundances of three s-peaks are strongly dependent on the choice of the 





-pocket, as well as on the initial mass and the metallicity. [ls/Fe], [hs/Fe] and [Pb/Fe] do not follow a linear behavior with decreasing metallicity and can cover a large range of values. For the low-mass models the enhancement of the first s-process peak is low or absent. A spectrum of very high resolution is needed to estimate the abundances for a large number of the second s-process peaks and to recognize an overabundance of the third s-process peak. With the adopted oxgen abundance, [O/Fe] = +0.8 dex, the carbon-to-oxygen ratio was found to be very high for HD 112869, C/O 





 12.6. The isotopic lines of C2 and CN are too week to be detected in the crowded spectrum, and the lower limit of isotopic ratio was found to be extremely high, 





 1500. A large isotopic ratio is not typical of CEMP stars. However, for low-mass AGB stars the CN processing is not expected after the second dredge-up and a total amount of 





 dredged-up during the AGB phase leads to a high [C/N] ratio and a high 





 ratio observed for HD 112869. On the contrary, intermediate-mass AGB stars with a hot bottom burning should have low both [C/N] and 





 ratios.
Citation: Campbell & Lattanzio 2008
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Critical luminosity (Lcrit) is the luminosity above which a state transition from subcritical to supercritical takes place. The subcritical state (LX Lcrit) is known to be the low luminosity state whereas the supercritical state is high luminosity state (LX > Lcrit) (Becker et al. 2012). The critical luminosity is crucial to determine whether the radiation pressure of the emitting plasma is capable of decelerating the accretion flow (Basko & Sunyaev 1976; Becker et al. 2012). The luminosity during the 2020 giant outburst reached a record high, which was significantly higher than the critical luminosity (Reig & Nespoli 2013). The source entered a supercritical regime from a subcritical regime during the outburst. In the supercritical regime, radiation pressure is high enough to stop the accreting matter at a distance above the neutron star, forming a radiation-dominated shock (Basko & Sunyaev 1976; Becker et al. 2012). For the subcritical regime, accreted material reaches the neutron star surface through nuclear collisions with atmospheric protons or through Coulomb collision with thermal electrons (Harding 1994). These accretion regimes can also be probed by noting changes in the cyclotron line energies, pulse profiles, and changes in the spectral shape (Parmar, White, & Stella 1989; Reig & Nespoli 2013). During the transition from the subcritical to the supercritical regime, sources show two different branches in their hardness–intensity diagram (HID) which are known as horizontal branch (HB) and diagonal branch (DB) (Reig & Nespoli 2013). The HB implies the low-luminosity state of the source, which is represented by spectral changes and high X-ray variability. The DB corresponds to the high-luminosity state that appears when the X-ray luminosity is above the critical limit. The classification HB and DB depends on HID patterns that the source follows. The HB pattern is generally observed in the subcritical regime and the DB pattern is observed in the supercritical regime (Reig & Nespoli 2013).
Citation: Reig & Nespoli 2013
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Citation: Reig & Nespoli 2013
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Citation: Reig & Nespoli 2013
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The physical conditions within giant molecular clouds (GMCs) establish the initial conditions for star formation, thus understanding the factors that determine molecular cloud properties is of major interest. A correlation between the size and line width, of the form σv ∝ Rα with α ≈ 0.5, has long been noted in samples of nearby molecular clouds (Larson 1981; Solomon et al. 1987, hereafter S87). This correlation, hereafter referred to as the R–σv relation, is usually interpreted as the result of turbulent motions in the interstellar medium on all scales (Mac Low & Klessen 2004; Falgarone et al. 2009). It closely resembles the turbulent cascade with a power-law slope falling between the Kolmogorov (1941) and Burgers (1939) values for incompressible and highly supersonic turbulence, respectively (see also Falgarone et al. 1994; Brunt & Heyer 2002; Federrath 2013; Kritsuk et al. 2013). At the same time, a study of 13CO emission in the Boston University–FCRAO Galactic Ring Survey (GRS) by Heyer et al. (2009) showed that the normalization of the relation, 





, exhibits a linear correlation with the mass surface density, Σ = M/πR2, across more than an order of magnitude in Σ. The data are consistent with a state of virial balance between gravity and turbulent motions, except that the observed normalization, v0, is about a factor of 2 too large. Subsequent work by Field et al. (2011) has suggested that the larger than expected v0 may result from external pressure confinement, although to a lesser extent than has been inferred for clouds in the outer Galaxy (Heyer et al. 2001) or near the Galactic Center (Oka et al. 2001; Shetty et al. 2012). On the other hand, Ballesteros-Paredes et al. (2011) have interpreted the Heyer et al. (2009) result in terms of gravitational collapse near freefall, which differs from the virial equilibrium prediction by a factor of 





 in v0 and is thus roughly consistent with the GRS data. A third possibility is that errors in the measured or inferred cloud properties create the appearance of excess kinetic energy when in fact clouds are close to being virialized.
Citation: Falgarone et al. 2009
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Figures 8 and 9 show the effects of solar activity on the CR parallel λ∥ (blue line), perpendicular λ⊥ (red line), and radial mean free path λrr (gray line) for a proton with rigidity 445 MV (corresponding to a 100 MeV proton) for the inwardly and outwardly directed IMF, respectively. As described in Zank et al. (1998), the parallel mean free path (mfp) based on standard QLT and assuming magnetostatic turbulence is approximated by
13





where 





, 





, and 





. RL is the particle Larmor radius, P is the particle rigidity, and B0 is the mean magnetic field strength. The analytic form of the perpendicular mfp based on NLGC theory is given by (Zank et al. 2004; Shalchi et al. 2010)
14





where a2 = 1/3 is a factor related to the gyrocenter velocity. 





 is a constant such that ν = 5/6 yields a Kolmogorov (1941) spectrum. Note that Equation (14) was derived under the assumption of a specific form of 2D wave spectrum, which is a constant at large turbulence scales. It means that the 2D turbulence spectrum is independent of wavenumber in the energy range in Equation (14). Observation of magnetic fluctuations in the SW indicates that omnidirectional power spectra approach a k−1 wavenumber dependence and that low-frequency turbulence exhibits some sunspot cycle variability (Bieber et al. 1991). Based on this, Engelbrecht & Burger (2015) derived the perpendicular mfp by specifying the energy range spectral index of 2D turbulence power spectra as −1. A more general form of the 2D power spectrum with an energy range spectral index q was proposed by Shalchi et al. (2010). They show that the spectral index has a strong influence on the perpendicular diffusion coefficient. In their model, negative values of q correspond to a decreasing spectrum in the energy range, q = 0 corresponds to the constant spectrum we use here, and positive values of q correspond to an increasing spectrum. Matthaeus et al. (2007) presented a similar spectrum in different regimes: energy range, inertial range, and intermediate regime where the spectrum is proportional to k−1 to coincide with observations (Bieber et al. 1991; Goldstein & Roberts 1999). However, Shalchi (2013) argues that a spectrum that behaves like k−1 does not provide a different perpendicular diffusion coefficient (see also Shalchi et al. 2010), since the field lines for such length scales behave superdiffusively as in the inertial range (Shalchi & Kourakis 2007). In view of this uncertainty, we do not take into account a more elaborate spectrum in the present paper. The behavior of the 2D wave spectrum in the energy range, which may also be correlated with the sunspot cycle, is an important factor in deriving the CR perpendicular mfp. A general form (e.g., Shalchi et al. 2010; Shalchi 2013) should be employed in future studies of CR diffusion.
Citation: Bieber et al. 1991
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Citation: Bieber et al. 1991
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