adsabs/FOCAL huggingface dataset:
We made use of this catalogue, extracting three main physical properties. The first is the luminosity-weighted age (𝒜⋆,L) of the underlying stellar population, which corresponds to the first moment of the age distribution function (ADF) in light, directly provided by the stellar decomposition performed by PIPE3D. This parameter is directly connected with how sharp the SFH is in galaxies, with older stellar populations having SFHs that peak at earlier epochs (e.g. García-Benito et al. 2017). The second is the luminosity-weighted metallicity normalized to the solar value ([Z/H] or 𝒵⋆,L). This is the first moment of the metallicity distribution function (MDF) in light at the same band as the ADF (e.g. Mejía-Narváez et al. 2020). Due to the nature of the adopted SSPs, this parameter is a direct proxy of the luminosity-weighted iron abundance, [Fe/H], for a solar abundance of 12 + log(Fe/H) = 7.50 (Asplund et al. 2009). It is worth noting that we derive [Z/H] using SSP models with solar composition (i.e. [α/Fe] = 0). This may create a bias in [Fe/H] towards higher values, which can be as large as ∼30%. However, this bias affects neither our qualitative results nor the reported trends, although it may produce relative offsets in the absolute reported abundances. Hereafter we refer to this parameter as [Fe/H]. Finally, the third is the gas-phase oxygen abundance, 12 + log(O/H), derived using the calibrator presented by Ho (2019). This calibrator uses a set of emission line ratios and associates them with oxygen abundances measured using the direct method, applying a neural-network procedure. We should note that the use of this or another of the calibrators applied to derive the oxygen abundance for the considered catalogue does not qualitatively alter the results presented here (Espinosa-Ponce et al., in prep.). We transformed this value to [O/H] by adopting a solar oxygen abundance of 12 + log(O/H)⊙ = 8.69 (Asplund et al. 2009).
Citation: Asplund et al. 2009
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Citation: Asplund et al. 2009
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In more detail, the goal of this paper is to test whether preferential alignment of the CNM structures with BPOS together with fluctuations in the GMF orientation along the LOS can account fully for a number of observed statistical properties of the dust polarization over the southern low column density sky. These statistical properties include the scatter of the polarization fraction p, the dispersion of the polarization angle ψ around the large-scale GMF, and the observed dust power spectra \hbox{$\dlee$}𝒟ℓEE, \hbox{${{\cal D}_{\ell}^{BB}}$}𝒟ℓBB, and \hbox{${{\cal D}_{\ell}^{TE}}$}𝒟ℓTE at 353GHz. In doing so we extend the analysis of Planck Collaboration Int. XXXVIII (2016) and Planck Collaboration Int. XLIV (2016) to sky areas in which the filaments have very little contrast with respect to the diffuse background emission. Within the southern Galactic cap (defined here to be b ≤ −30°), in which dust and gas are well correlated (Boulanger et al. 1996; Planck Collaboration XI 2014) and the GASS HI emission is a good tracer of the dust intensity structures (Planck Collaboration Int. XVII 2014),we focus our study on the low column density portion. We choose to model the dust polarization sky at 1° resolution, which corresponds to retaining multipoles ℓ ≤ 160 where the Galactic dust dominates over the CMB B-mode signal. Also, most of the ground-based CMB experiments are focusing at a degree scale for the detection of the recombination bump of the CMB B-mode signal. Our dust polarization model should be a useful product for the CMB community. It covers 34% of the southern Galactic cap, a 3500 deg2 region containing the cleanest sky accessible to ongoing ground and balloon-based CMB experiments: ABS (Essinger-Hileman et al. 2010), Advanced ACTPol (Henderson et al. 2016), BICEP/ Keck (Ogburn et al. 2012), CLASS (Essinger-Hileman et al. 2014), EBEX (Reichborn-Kjennerud et al. 2010), Polarbear (Kermish et al. 2012), Simons array (Suzuki et al. 2016), SPIDER (Crill et al. 2008), and SPT3G (Benson et al. 2014).
Citation: Planck Collaboration Int. XLIV (2016)
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The sky distribution of gamma-ray bursts (GRBs; Piran 2004; Mészáros 2006; Vedrenne & Atteia 2009; Gomboc 2012; Kouveliotou, Wijers & Woosley 2012; Kumar & Zhang 2015; Dai, Daigne & Mészáros 2017; Willingale & Mészáros 2017) has been tested intensively and early works claimed that GRBs were distributed isotropically (Meegan et al. 1992; Briggs et al. 1996; Tegmark et al. 1996). As the amount of observational data increased and various methods were applied, it was claimed that the group of short GRBs (T90  2 s; Balázs et al. 2003), which originate in mergers of compact objects such as neutron stars (Paczynski 1986; Eichler et al. 1989; Berger 2014; Abbott et al. 2017a,b,c), was distributed anisotropically (Balázs, Mészáros & Horváth 1998; Balázs et al. 1999; Magliocchetti, Ghirlanda & Celotti 2003; Vavrek et al. 2008; Tarnopolski 2017). Here, T90 is the duration during which 90  per cent of the detected counts from a GRB are accumulated (Kouveliotou et al. 1993). However, several other works claimed different results (Mészáros et al. 2000a; Mészáros, Bagoly & Vavrek 2000b; Litvin et al. 2001; Bernui, Ferreira & Wuensche 2008; Veres et al. 2010; Ukwatta & Woźniak 2016). The works that analysed GRBs with a duration of 2 ≲ T90 ≲ 10 s also found that these bursts were distributed anisotropically (Mészáros et al. 2000a,b; Litvin et al. 2001; Vavrek et al. 2008; Veres et al. 2010). However, Mészáros & Štoček (2003) and Ukwatta & Woźniak (2016) came to different conclusions. Anisotropical distribution on the sky was also proclaimed for very short GRBs (T90 ≤ 100 ms; Cline et al. 2005; Ukwatta & Woźniak 2016). In contrast, the group of long GRBs (T90 > 2 s), which are associated with the collapses of massive stars (Fruchter et al. 2006; Woosley & Bloom 2006), were found to be distributed isotropically (Balázs et al. 1998, 1999; Mészáros et al. 2000a,b; Magliocchetti et al. 2003; Vavrek et al. 2008; Ukwatta & Woźniak 2016; Tarnopolski 2017), although Mészáros & Štoček (2003) again came to a different conclusion. Mészáros et al. (2009a,b) and Mészáros (2017, 2018) have summarized these efforts.
Citation: Mészáros et al. 2000a
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Citation: Mészáros et al. 2000a
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Citation: Mészáros et al. 2000a
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Citation: Eichler et al. 1989
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It may be possible for the accretion disks to persist in systems that have ended active mass transfer. Occultations induced by persistent disks have been detected in a handful of long-period binaries. Photometric occultations by disks are often distinct from other events, as they exhibit long-duration, deep eclipse-like signals that can be identified in photometric surveys.  Aurigae is the classical example of such a system, with a post-AGB F0 supergiant with a B-star companion embedded in a dusty disk with an orbital period of 27 yr (e.g., Kuiper et al. 1937; Huang 1965; Kopal 1971). The occultation is observed in photometry (e.g., Gyldenkerne 1970; Kemp et al. 1986; Carroll et al. 1991), interferometry (Kloppenborg et al. 2010, 2015), and spectroscopy (e.g., Lambert & Sawyer 1986; Chadima et al. 2011; Leadbeater et al. 2012; Griffin & Stencel 2013; Muthumariappan et al. 2014; Strassmeier et al. 2014), and the system’s orbit and masses are constrained by the radial velocities (Stefanik et al. 2010). The 2 yr long eclipse is inferred to be from a ∼4 au diameter disk consisting of both dust and gas (e.g., Lissauer et al. 1996). The dusty component is required to explain the strong IR excess in the system (Hoard et al. 2010), while the gaseous component in the vertically extended disk shell induces a series of spectroscopic features that map out the Keplerian disk during occultation (e.g., Chadima et al. 2011; Strassmeier et al. 2014). A central brightening is seen during eclipse, leading to a flared disk geometry interpretation (e.g., Budaj 2011). Similar long-period, long-duration disk occultations in other potential mass-transfer systems have also been identified: the 96-day-period V383 Sco (Zola et al. 1994), the 5.6 yr period EE Cep (Mikolajewski & Graczyk 1999), the 468-day periodiocally recurring eclipses of OGLE-LMC-ECL-11893 (Dong et al. 2014; Scott et al. 2014), and the 1277-day periodic eclipses of OGLE-BLG182.1.162852 (Rattenbury et al. 2015). The recent advent of wide-field photometric surveys is now also enabling numerous new discoveries, including the 69 yr eclipsing disk system TYC 2505-672-1 (Rodriguez et al. 2016).
Citation: Kloppenborg et al. 2010
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Citation: Kuiper et al. 1937
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Citation: Lissauer et al. 1996; Strassmeier et al. 2014
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Citation: Hoard et al. 2010
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Citation: Budaj 2011
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Citation: Zola et al. 1994
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Citation: Rodriguez et al. 2016
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High-precision photometric measurements have shown that the present treatment of limb darkening is not sufficiently accurate and leads to systematic errors in the derived parameters of the exoplanets (e.g., Espinoza & Jordán 2016; Morello et al. 2017a; Maxted 2018). Typically, limb darkening is represented by rather simple laws, such as a linear law (Schwarzschild 1906), a quadratic law (Kopal 1950), a square-root law (Diaz-Cordoves & Gimenez 1992), a power-2 law (Hestroffer 1997), or a four-coefficients law (Claret 2000), so that when modeling the transit light curves, limb darkening is parameterized by some set of coefficients. Ideally, these coefficients should be constrained by the stellar modeling. Consequently, many libraries of limb-darkening coefficients covering a wide range of effective temperatures (Teff), surface gravity (log g), and metal-licities (M/H) have been produced (e.g., Claret 2000; Sing 2010; Claret & Bloemen 2011; Magic et al. 2015) using various radiative transfer codes, such as ATLAS (Kurucz 1993), NextGen (Hauschildt et al. 1999), PHOENIX (Husser et al. 2013), MARCS (Gustafsson et al. 2008), and STAGGER (Magic et al. 2013). However, the limb-darkening parameters diverge between different libraries and often lead to an inadequate quality of fits to the observed transit profiles (Csizmadia et al. 2013). First, this can be due to errors in limb-darkening coefficients introduced by interpolation from the grid of stellar parameters used in these libraries to the actual stellar fundamental parameters. Second, available stellar calculations may not treat mechanisms that affect limb darkening with sufficient accuracy, for instance, convection (Pereira et al. 2013; Chiavassa et al. 2017) or magnetic activity (Csizmadia et al. 2013). Therefore, limb-darkening coefficients are often left as free parameters in a least-squares fit to observed light curves (e.g., Southworth 2008; Claret 2009; Cabrera et al. 2010; Gillon et al. 2010; Csizmadia et al. 2013; Maxted 2018). While this method usually leads to a good quality fit to observed transit profiles, it introduces additional free parameters, resulting in possible biases and degeneracies in the returned planetary radii (Espinoza & Jordán 2015; Morello et al. 2017b). The way to reduce these biases and reliably determine planetary radii is to improve theoretical computations of stellar limb darkening.
Citation: Morello et al. 2017a
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Citation: Csizmadia et al. 2013
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Citation: Csizmadia et al. 2013
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Citation: Csizmadia et al. 2013
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Citation: Morello et al. 2017b
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Not only are electron populations with two different temperatures and densities frequently observed in experimental plasmas (Morales & Lee 1974; Jones et al. 1975), they are also an essential feature of space plasmas (Delory, Ergun & McFadden 1998; Ergun et al. 1998; Pottelette, Berthomier & Malingre 1998). Observations of electron velocity distribution functions (VDFs) in the solar wind and magnetosphere show a clear picture of a non-Maxwellian distribution function, that is, of a dense thermal population ‘core’ superimposed on a hot superthermal population ‘halo’ (Lin 1998; Makimovic et al. 2001; Pierrard, Maksimovic & Lemaire 2001; Pagel et al. 2005; Marsch 2006; Gaelzer et al. 2008). Two-component electron populations are a common feature of interplanetary shocks in the solar wind with relative drifts parallel to the magnetic field (Feldman et al. 1975, 1983b). They are also found around the Earth's bow shock, with a comparatively dense hot component and a tenuous cold beam (Feldman et al. 1982, 1983a). Reiff & Reasoner (1975) presented a comprehensive picture of electron VDFs from in the magnetosheath at lunar distances using Apollo 14. They found two distinct populations of electron VDFs, with the hot population density higher near the bow shock, and the colder one mid-way of the magnetosheath. They found that the two components are distinct but are not completely independent, and that the high- and low-energy components grow at the expense of each other. They argued that the high-energy portion of electron VDFs originates and is accelerated at the bow shock owing to the potential difference across this shock. Marsch (2006) also showed the interdependence of the core and halo components of electron VDFs in the solar wind. Such types of electron VDFs cannot be modelled by a single electron Maxwellian population or by core and halo both modelled by a bi-Maxwellian distribution. However, solar wind electron velocity distributions comprising a bulk core and a tenuous halo population are well fitted by employing a model distribution containing a bulk Maxwellian superimposed on a kappa distribution (Nieves-Chinchilla & Vinas 2008). Schippers et al. (2008) fitted the two-population electron VDFs observed in Saturn's magnetosphere with a combination of the bi-kappa distribution function that was used by Balaku et al. (2011) to study the damping characteristics of electron acoustic waves in Saturn's magnetosphere. Saeed et al. (2017, 2017a) studied the characteristics of electron heat flux and electromagnetic electron cyclotron instabilities by considering the relative drift between the core and halo electron components in typical solar wind conditions.
Citation: Feldman et al. 1982
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Citation: Schippers et al. (2008)
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To interpret precisely the finding that high-inclination Centaurs had nearly polar orbits at the end of planet formation, it is useful to recall the current understanding of early Solar system structure (Pfalzner et al. 2015). The Solar system started forming 4.6 Gyr in the past. Sometime after a few Myr, giant planets formed and radially migrated while interacting with the planetesimal disc. The migration relaxation time is thought to have been about 1 to a few 10 Myr whereas the full migration stage is thought to have lasted about a 100–500 Myr at the end of which the planets reached their final orbits. The planets’ orbital evolution in this stage is largely uncertain owing to the multitude of complex processes that operate in the first 100 Myr of planet formation (Ribeiro et al. 2020). What is certain, however, is first, that the planetesimal disc before the migration stage as well as at the end of this stage had a small inclination dispersion in order to explain the very low inclination TNOs currently observed in the Kuiper belt (known as the cold population) (Pfalzner et al. 2015). Secondly, the early planetesimal disc could not have extended beyond 30–40 au (Gomes et al. 2004) to ensure that Neptune does not end up with a much larger final semimajor axis than the current one. Thus the early scattered disc and Oort cloud were devoid of Solar system material. Our orbit determination method was run for 4.5 Gyr in the past. The fact that the planets migrated towards the end of that period is not included in our calculation because the planets’ evolution in the migration stage is largely uncertain but more importantly, because it does not affect the Centaur orbits end states in any significant way. The reason lies in the fact that the bulk of stable Centaur clones that end up on nearly polar orbits do so in the first 1 Gyr of the simulation. Therefore near 4 Gyr in the past, the polar stable clones are already located far away in the scattered disc and the inner Oort cloud. Changes in the planets’ semimajor axes over a few 10 Myr do not affect such distant polar orbits significantly, as in reality, the planets’ effect had long started to decay in the last billion years before migration even occurred.
Citation: Ribeiro et al. 2020
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Citation: Gomes et al. 2004
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From the list of major SEP events with a metric type II burst, we selected all those events for which adequate GOES data of the associated soft X-ray flare was available. We used GOES 1–8 Å  X-ray profiles and the daily NOAA Solar Event Reports (Edited Events) (http://www.swpc.noaa.gov/ftpmenu/warehouse.html) to determine the magnitude and the onset and peak times of the associated GOES X-ray flare. Flare locations are taken from NOAA reports or determined from images of the EIT (Delaboudinière et al. 1995) on board the Solar and Heliospheric Observatory (SOHO). The associated CMEs were identified using the Large Angle and Spectrometric Coronagraph (LASCO; Brueckner et al. 1995) CME catalog (http://cdaw.gsfc.nasa.gov/CME_list/index.html; Yashiro et al. 2004; Gopalswamy et al. 2009b). Our final data set consists of 59 SEP events listed in Table 1. The columns in Table 1 are: the type II date (1) and time (2), the first observation date (3) and time (4) of the associated CME, the CME space speed (5) and width (6), the location of the associated flare (7), the flare-onset date (8) and time (9), the flare peak date (10) and time (11), the magnitude of the flare (12), the calculated constant acceleration of the CME (13), and the height of the CME (14) at the type II onset calculated using the flare-onset method. The last column (15) of Table 1 gives the measured height of the CME at the type II onset obtained by 3D-fitting a spherical shock model to CME images. The onset times of the metric type II bursts listed in Table 1 are the earliest reported times compiled from the NOAA Solar Event Reports except for the onset times of the 2000 June 6 and November 8 events that are from Gopalswamy (2003). Often different observatories report different onset times for the same metric type II burst; therefore, by selecting the earliest onset times we guarantee that our estimation is the lowest CME height consistent with the radio observations. Some additional details of the entries in Table 1  are given in Sections 3 and 5 discussing data analysis methods.
Citation: Brueckner et al. 1995
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Citation: Gopalswamy (2003)
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The trends between the LF slope α and the aforementioned parameters, with the addition of the morphological T type, are shown in Fig. 4 together with their Spearman correlation coefficient (ρ) and their p value, indicating the probability that the two sets of data are uncorrelated. We summarize the properties for which we look for a correlation in Table 4. We define a correlation to be negligible when |ρ|=[0 − 0.2], weak when |ρ|=[0.2 − 0.4], moderate when |ρ|=[0.4 − 0.6], strong when |ρ|=[0.6 − 0.8], and very strong when |ρ|=[0.8 − 1]; using the p value to evaluate the probability that, despite showing a correlation, two variables may be uncorrelated. It should be noted that only a handful of studies so far have looked at the correlation between α and global galaxy properties: Kennicutt et al. (1989), Elmegreen & Salzer (1999), Youngblood & Hunter (1999), van Zee (2000), and Thilker et al. (2002) investigated nebular LFs as in this paper, while Liu et al. (2013) identified H II regions via Paα, and Cook et al. (2016) studied the GALEX far-ultraviolet (FUV) LFs of H II regions. While the sample of Cook et al. (2016) includes a few hundred galaxies, the other studies are based on samples ranging from 10 to 35 galaxies, similar to our study. In this section and in Sect. 6.1, we compare our results to those studies that, as in our case, applied a uniform analysis methodology on galaxy samples. It should be noted that using different tracers means probing different source ages and, as reported by Oey & Clarke (1998), older H II regions tend to have steeper LF slopes, mainly due to the short main-sequence lifetimes of the more massive stars constituting the brighter H II regions. This is the reason why, for example, FUV observations, probing H II regions with ages less than 100 Myr, are expected to deliver a steeper LF compared to Hα observations, typically probing H II regions younger than 10 Myr, and our comparison remains qualitative.
Citation: Oey & Clarke (1998)
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The plethora of cosmological observations has turned cosmology into a quantitative science. From combining several probes that observe the Universe at different epochs and have different systematics and statistics, emerged the ‘concordance model’ of cosmology, a six parameter model, most of them measured to the accuracy of a per cent. Among these probes, Type Ia supernovae (SNe) are a powerful cosmological tool to directly measure the expansion history of the Universe (see e.g. a recent review of Weinberg et al. 2013). The type Ia SNe are known to be standard candles and one can measure the luminosity distances to them accurately. Several SNe surveys have set strong constraints on cosmological models from the distance–redshift relation (e.g. Riess et al. 1998; Perlmutter et al. 1999; Riess et al. 2007; Sullivan et al. 2011; Campbell et al. 2013). However the measured apparent magnitudes, have a residual scatter arising from its intrinsic scatter and effects due to line-of-sight (LOS) structures. The intrinsic scatter (∼0.4 mag) can be significantly reduced to ∼0.1 mag by empirically calibrating the luminosity curves. The scatter due to photon deflection along the LOS is composed of many different physical effects. The dominant effects are peculiar velocities at z  0.1 and gravitational lensing at z ≳ 0.3. Within the realm of cosmological perturbation theory and the stochastic nature of the LOS structures, all effects are expressed as an integral over the power spectrum with appropriate kinematical factors (Ben-Dayan et al. 2012). The lensing effects create residuals from the best-fitting curve in the magnitude–redshift relation. The lensing magnifications of SNe can be extracted by correlating the residuals with the surface densities of nearby foreground galaxies. The lensing dispersion is roughly proportional to the SNe redshift z, (e.g. Holz & Linder 2005; Ben-Dayan et al. 2013), and specifically for the concordance model, the predicted value is ∼0.06z mag.
Citation: Weinberg et al. 2013
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