adsabs/FOCAL huggingface dataset:
Our knowledge of magnetic fields in molecular clouds is based mainly on light polarisation, the optical and near-infrared (NIR) observations of background stars (Goodman et al. 1995; Whittet et al. 2001; Pereyra & Magalhães 2004; Alves et al. 2008; Chapman et al. 2011; Cox et al. 2016; Neha et al. 2018; Kandori et al. 2018), and the polarised dust emission at far-infrared (FIR), sub-millimetre, and radio wavelengths (Ward-Thompson et al. 2000; Koch et al. 2014; Matthews et al. 2014; Fissel et al. 2016; Pattle et al. 2017). The Planck survey provides a large amount of data for polarisation studies at cloud scales (Planck Collaboration Int. XX 2015; Planck Collaboration Int. XIX 2015; Planck Collaboration Int. XXXIII 2016). The Planck data have been used especially to study the polarisation fraction and the correlations in the relative morphology of column density and magnetic field structures (Planck Collaboration Int. XX 2015; Planck Collaboration Int. XXXIII 2016; Malinen et al. 2016; Soler et al. 2016; Alina et al. 2019). Particularly, the drop of polarisation fraction p towards PGCC clumps has been observed with high significance in the Planck 353 GHz data (Alina et al. 2019; Ristorcelli et al, in prep.). The variations ofp are interesting because they are related to the configuration of the magnetic fields in clumps and cores during the star formation process. However, p is also affected by variations in the efficiency of the grain alignment, as predicted, for example, by the theory of radiative torque alignment (RAT; Lazarian et al. 1997; Cho & Lazarian 2005; Hoang & Lazarian 2014) and demonstrated by numerical simulations (Pelkonen et al. 2009; Brauer et al. 2016; Reissl et al. 2018). These suggest that high optical depths and more frequent gascollisions should significantly reduce the grain alignment and thus the polarised emission observable from within the clumps. ThePGCC provides a statistically significant sample to study these questions observationally, although the Planck resolution limits the investigations to structures that are typically much larger than an individual cloud core. However, polarisation of selected PGCCs has already been studied at higher resolution with the SCUBA-2 POL-2 instrument at JCMT (Liu et al. 2018b,c; Juvela et al. 2018c), and many more will be covered by ongoing surveys (Ward-Thompson et al. 2017).
Citation: Planck Collaboration Int. XIX 2015
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A large effort has been invested in understanding the chemical patterns of galaxies using analytical and numerical chemical modelling (e.g. Brook et al. 2007; Calura et al. 2012; Mollá et al. 2015). In particular, hydrodynamical simulations provide the chemical enrichment of baryons as galaxies are assembled in a cosmological framework, opening the possibility of understanding the interplay of different physical processes in the non-linear regime of evolution (e.g. Mosconi et al. 2001; Lia et al. 2002; Wiersma et al. 2009). Pilkington et al. (2012) carried out a comparison of the metallicity gradients in the ISM (traced by young SPs) obtained by different numerical and analytical models. Gibson et al. (2013) analysed different models of SN feedback schemes reporting different evolution for the gas-phase metallicity gradients. No evolution was reported when an enhanced SN model was used. Tissera et al. (2016b) studied the gas-phase metallicity gradients in discs and the specific star formation of the galaxies. For gas-phase metallicities, the simulated galaxies showed a correlation with stellar mass, which was erased when the metallicity gradients were renormalised by the effective radius, reproducing observations by Ho et al. (2015, and references there in). They also found indications of a correlation between the abundance slopes and the specific star formation rate (sSFR) in agreement with observational findings (Stott et al. 2014). As a function of redshift, the metallicity gradients of the gas-phase disc components were found to be more negative at higher redshift, principally for lower stellar-mass galaxies. The fraction of galaxies with positive metallicity gradients increased with increasing redshift and were found to be associated to mergers and interactions. The trends found for the gas-phase metallicity gradients by Tissera et al. (2016b) are relevant to the discussion of this paper because we are using the same set of simulated galaxies.
Citation: Pilkington et al. (2012)
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Many theoretical models have been proposed to explain the generation of QPPs. The most elaborated model of QPPs considers MHD oscillations, which affect almost all aspects of the flare emission generation. Indeed, QPPs are involved in triggering the magnetic reconnection, modulating the reconnection rate, accelerating and transporting non-thermal electrons, and changing the physical conditions in emitters (Nakariakov & Melnikov 2009). Other models are based on a sandpile system with self-organized critical states (Lu & Hamilton 1991; Baiesi et al. 2008), the quasi-stabilized system of non-linear plasmas governed by an oscillatory phase of wave-wave or wave-particle interactions (Aschwanden 1987). Different MHD oscillation modes have been identified to be responsible for QPPs in a single flaring loop (Nakariakov et al. 2003; Melnikov et al. 2005; Warmuth et al. 2005; Inglis et al. 2008; Kupriyanova et al. 2010, 2013; Kim et al. 2012). Kolotkov et al. (2015) studied the QPPs of the microwave emission generated in a X3.2-class solar flare. They found three well-defined intrinsic modes with mean periods of 15, 45, and 100s. These authors proposed that the 100 s and 15s modes are likely to be associated with fundamental kink and sausage modes of the flaring loop, respectively. The 100 s oscillations could also be caused by the fundamental longitudinal mode. The 45 s mode, on the other hand, could be the second standing harmonic of the kink mode. Inglis & Nakariakov (2009) reported a multi-periodic oscillatory event with three distinct periods, namely 28s, 18s, and 12s. They argued that the cause of this multi-periodic event is likely to be a kink mode that periodically triggers magnetic reconnection. Similar QPPs could be generated by different mechanisms. To discover, understand, and distinguish these mechanisms correctly, detailed and multi-wavelength observations are required. Within the framework of the EU FP7-project SOLSPANET1, we are developing a solar and space-weather knowledge base that will allow such extensive and detailed studies.
Citation: Nakariakov et al. 2003
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Throughout the initial collapse the halo structure is well approximated by ellipsoidal collapse models. We therefore explore radial profiles of various physical quantities to extract information about the galactic environment. The density is illustrated in Fig. 2, which is reasonably approximated by a broken power law over the range r ∈ (10−3, 103) pc. The break radius is due to the choice of the density resolution threshold, or equivalently, the limited resolution implies the pre-formation profile corresponds to roughly a dynamical time, as evaluated at the maximum resolved density, of ∼10 kyr prior to the formation of the protostar. If the evolution proceeds under self-similar, isothermal collapse then the break in the profile will shift to smaller scales, eventually reaching the radius of the protostar (Abel, Bryan & Norman 2002; Becerra et al. 2015). We note that secondary infall and accretion results in a density distribution that is steeper than the ρ ∝ r−2 profile produced by violent relaxation. We find a power-law scaling of ρ ≈ r− 7/3 in good agreement with Bertschinger (1985) where ρ ∝ r−9/4 and Wise, Turk & Abel (2008) where ρ ∝ r−12/5, except in the core where the slope flattens off to ρ ∝ r−0.4 around r ≈ 0.3 pc. During the initial collapse and formation, the gas remains neutral with only a small abundance of free protons. Specifically, the ionization fraction, $x_{\rm {H \small {II}}} \equiv n_{\rm {H \small {II}}} / n_{\rm H}$, is typically of order 10−5 to 10−3 depending on the density, such that $n_{\rm {H \small {II}}} \sim 0.1\,{\rm cm}^{-3}\,(r/10\,{\rm pc})^{-5/3}$. As expected for direct collapse, the central region has access to at least 105 M⊙ of gas within a radius of ≈1.3 pc. More broadly, the enclosed baryonic mass is $M_{<r} \approx 4 \pi \int _0^r \rho (r) r^2 {\rm d}r$ (for 1D profiles), calculated explicitly as Mr ≡ ∑rρiVi where the sum is over all Voronoi cells within a radius r, and the subscript denotes cell quantities for density ρi and volume Vi. For convenience and completeness, in Table 1 we provide radial scaling relations for several relevant quantities, calculated as mass-weighted averages within shell volumes, i.e.
(1)
\begin{eqnarray}
\langle q \rangle _{\rm shell} \equiv \frac{\int q \rho \, {\rm d}V}{\int \rho \, {\rm d}V} \approx \frac{\sum q_i \rho _i V_i}{\sum \rho _i V_i} \,,
\end{eqnarray}
where the discretized version sums over all cells within each shell. Similarly, we obtain mass-weighted line of sight averages with
(2)
\begin{eqnarray}
\langle q \rangle _{\rm LOS} \equiv \frac{\int q \rho \, {\rm d}\ell }{\int \rho \, {\rm d}\ell } \approx \frac{\sum q_i \rho _i \Delta \ell _i}{\sum \rho _i \Delta \ell _i} \,,
\end{eqnarray}
where the integral is along radial rays within each shell and the summation is the discretized version calculated by ray tracing.
Citation: Becerra et al. 2015
Citation function: Uses
Many of these objects have been confirmed as DNe through an identification spectrum or a measurement of the superhump period, but we find that 27 sources were not confirmed through any method. Quiescent multiband photometry of these remaining candidates can provide insights into the distance and ultimately constrain the luminosity of the transient. In the Galactic plane, a candidate that is relatively blue is likely close by and therefore will have a lower luminosity at peak brightness, suggesting a DN outburst. Conversely, a candidate that is brighter in redder filters is likely reddened by dust, implying a higher luminosity and a CN outburst. This strategy—identifying highly reddened quiescent counterparts—is only possible for candidates within a few degrees of the Galactic plane and that can be securely matched to multiband optical catalogs. As described in Section 2.4, we find quiescent counterparts in Pan-STARRS and also add in coverage of southerly declinations by using the DeCAPS catalog (Schlafly et al. 2018; cross-matching for both catalogs is as explained in Appendix A.3). We use the griz photometry to estimate reddening by fitting the observed spectral energy distributions of the CVs in question to dereddened SDSS CV colors from Kato et al. (2012) by varying the amount of reddening according to extinction laws (Cardelli et al. 1989; Mathis 1990). For each of our candidates, this results in a distribution of extinction values that are plugged into the three-dimensional all-sky extinction map stitched together in Bovy et al. (2016) to find a range of plausible distances. This strategy only worked for 19 of the candidates: those that we were able to cross-match and in fields close to the plane, where a large amount of dust can constrain the distance. We find that all 19 are consistent with the peak luminosity of a dwarf nova, and none are consistent with the peak luminosity of a classical nova. This analysis will also be useful to shed light on the nature of CV candidates that are discovered in the future, so it is made publicly available as an iPython notebook at https://github.com/amkawash/CV_colors_luminosity.
Citation: Schlafly et al. 2018
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Scaling relations for the core radii rc, core densities δc, and core masses Mc as functions of the total halo mass M200 were fitted to the simulated halo populations, which largely agree with hydrostatic considerations of the halo cores where rc is nearly constant, as well as velocity dispersion tracing in the halo envelope, 




v
c
2

≈
v

200
2



$ v^2_{\rm c} \approx v^2_{200} $



. However, these trends do not agree with those obtained by fitting the Burkert profile to nearby galaxies in the SPARC dataset and the classical Milky Way dSphs. This poses an issue for SIBEC-DM with Rc ≳ 1 kpc and a largely CDM-like matter power spectrum at late times (Harko 2011; Harko & Mocanu 2012; Velten & Wamba 2012; Freitas & Gonçalves 2013; Bettoni et al. 2014; de Freitas & Velten 2015; Hartman et al. 2022), as was used in our simulations, although these scenarios are not well-motivated. For SIBEC-DM given by the field Lagrangian in Eq. (1), the self-interaction is constrained to Rc  1 kpc, otherwise an early radiation-like period and a large comoving Jeans’ length washes out too much structure to be consistent with observations (Shapiro et al. 2021; Hartman et al. 2022). In fact, Shapiro et al. (2021) found by using constraints on FDM as a proxy for SIBEC-DM, and matching their transfer function cut-offs and HMFs, that the SIBEC-DM self-interaction should be as low as Rc ∼ 10 pc to not be in conflict with observations. We were unable to probe SIBEC-DM with initial conditions and parameters consistent with the Lagrangian in Eq. (1), since the large gap between the halo cores and the cut-off scale requires both a large simulation box and very high spatial resolution. It should be noted that our SIBEC-DM-only simulations do provide a better agreement with the slopes in observed scaling relations than FDM. In particular, FDM simulations generally find 




M
c

∼

M

200

γ



$ M_{\mathrm{c}}\sim M_{200}^{\gamma} $


 with 1/3  γ  0.6, while we find γ ≈ 0.75, which is closer to the observed γ ≈ 1.1. Additionally, FDM halos have core radii that generally decrease with the halo mass, while we find a slightly increasing trend due to larger halos experiencing more thermal heating, although not as steep as in the SPARC dataset and the Milky Way dSphs.
Citation: Hartman et al. 2022
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Citation: Hartman et al. 2022
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Bond & Ciardullo (2018) identify a periodicity of 2.06 d in their data, with a similar period clearly evident in the newly acquired data. However, no single period and time of first minima could be found that fits all the data (perhaps not unexpected given that the variability is clearly not constant, see Section 2.3 for further discussion). In order to better constrain the period, as well as the changes between observing epochs, we focus on the periodicitiy of all the data taken between 2011 and 2015 (shown in the lower left panel of Fig. 1). Given the nature of the variability, we measure the period via the reduced χ2 of a sinusoidal fit as a function of frequency (ideally suited to sinusoidal variability, e.g. Horne, Wade & Szkody 1986; Munday et al. 2020). The resulting periodogram is shown in Fig. 2. Two strong minima in the reduced χ2 are found, the strongest at 2.061 ± 0.005 d (consistent with the period of Bond & Ciardullo 2018) and a second slightly shallower at 1.938 ± 0.005 d. The data from 2010, combining that of Bond & Ciardullo (2018) and our own from SAAO, phases well on the first period but not the second, lower period, likely indicating that this is an alias. As such, we conclude that the rotation period is indeed ≈2.06 d and that there is no strong evidence for a change in period before and after the phase of negligible variability in early 2011. There is, however, a clear shift in phase between the 2010 data and the later data used to derive the period (see Fig. 2). There is no statistically significant evidence for a shift between the 2011–2015 data and the later data from 2022, although the lack of data in the intervening seven years as well as the uncertainty on the period mean that we cannot exclude a phase shift between these two data sets. The 2022 data does, however, appear to have a larger photometric amplitude, more consistent with the 2010 data. None the less, if the variability did remain constant throughout this time, we can refine the period to P = 2.060561 ± 0.000002 d.
Citation: Munday et al. 2020
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We use the final version of the MaNGA data (Abdurro’uf et al. 2022). MaNGA is the survey project in SDSS-IV (Sloan Digital Sky Survey) using IFU observations of galaxies to produce spatially resolved spectroscopic data (Gunn et al. 2006; Bundy et al. 2015; Drory et al. 2015; Blanton et al. 2017; Aguado et al. 2019; Abdurro’uf et al. 2022). MaNGA observed approximately 10 000 galaxies, selected as an unbiased sample in terms of stellar mass (${\it M}_{\star } \gt 10^{9}\, {\rm M}_{\odot }$) and environments (Law et al. 2015; Yan et al. 2016a; Wake et al. 2017). The MaNGA targets were selected as two main subgroups and two minor subgroups. Among the main subgroups, the primary sample of about 5000 galaxies was selected to observe the central part out to $\rm 1.5{\it R}_{eff}$. The secondary sample of the main subgroups includes about 3300 galaxies, observed by the IFU bundle out to $\rm 2.5{\it R}_{eff}$. These samples were selected without bias and covers a wide range in galaxy masses. The first of the two minor subgroups is a ‘colour-enhanced sample’ of about 1700 galaxies. It is selected to include blue massive galaxies and green valley galaxies that are important to study the quenching process. These targets are observed with an IFU coverage of $\rm 1.5{\it R}_{eff}$. A second minor sample includes about 1000 ancillary targets, selected for various observations using the unique capability of the MaNGA instrument (Yan et al. 2016a). The IFU bundles of the MaNGA consist of 19–127 fibres with hexagonal shape covering 12–$\rm 32~arcsec$ in diameter on sky. The spatial resolution of MaNGA is $\rm 2.5~arcsec$, which corresponds to 1.3–$\rm 4.5~kpc$ for the primary sample and 2.2–$\rm 5.1~kpc$ for the secondary sample. The BOSS spectrographs used by MaNGA provide spectra from 3600 to 10 300 Å  with a spectral resolution of about 2100–6000 Å  (Smee et al. 2013; Yan et al. 2016b). The observations were conducted to reach an signal-to-noise ratio (S/N) in the stellar continuum at $\rm 1{\it R}_{eff}$ of $\rm 14$–$\rm 35$ per spatial sample, which required about 3 h net integration for each target.
Citation: Law et al. 2015
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The transport of angular momentum (AM) and chemical elements in stars strongly affects their evolution, from pre-main sequence (PMS) to evolved stages. These processes are particularly crucial in the stellar radiative zones, but their modelling remains an open question. In standard evolution models, these stably stratified zones are assumed to be motionless despite early works pointing out the lack of a static solution in uniformly rotating stars (Von Zeipel 1924) and a related large-scale meridional circulation driven by the centrifugal acceleration (Eddington 1925; Sweet 1950). A more complete formalism was then introduced by Zahn (1992) including a self-consistent meridional flow and models of the turbulent transport driven by hydrodynamical instabilities. The importance of additional processes such as internal waves (e.g., Talon & Charbonnel 2005) and magnetic fields (Spruit 2002) has also been investigated. Zahn’s formalism successfully explained a number of observed stellar properties, such as the nitrogen abundances at the surface of red supergiants or the observed blue-to-red supergiant ratio in the Small Magellanic Cloud (Maeder & Meynet 2001). The dynamics of internal gravity waves could possibly explain the flat rotation profile of the solar radiative zone (Talon et al. 2002 and Charbonnel & Talon 2005) inferred through helioseismology (Schou et al. 1998), as well as the lithium dip in solar-like stars (Charbonnel & Talon 2005). Despite these early encouraging results many stellar observations remain unexplained, especially the internal rotation rates revealed by asteroseismology in evolved stars and in intermediate-mass main sequence stars (see Aerts et al. 2019 for a review). The current theoretical understanding of the structure of the differential rotation in stellar radiative zones, crucial for the development of instabilities and thus for the related turbulent transport, is still largely incomplete. In particular, the assumption that the differential rotation is mostly radial, rather than radial and latitudinal, is at the base of Zahn’s formalism, but the validity of this assumption has never been thoroughly tested. In this paper we are particularly interested in the differential rotation and the large-scale meridional flows generated in periods of the stellar life when contraction, expansion, or both processes occur. This is the case for example for PMS stars which are gravitationally contracting before starting their core nuclear reactions, or for subgiant and giant stars which undergo contraction of their core and expansion of their envelope. Within these stars a strong redistribution of AM is expected to happen, producing differential rotation and thus potentially unstable shear flows.
Citation: Talon & Charbonnel 2005
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A lognormal mass function:
(7)$$\begin{eqnarray}
\psi (M;\mu ,\sigma)=\frac{1}{M\sqrt{2\pi \sigma ^2}}\exp {\left(- \frac{\ln ^2(M/\mu)}{2\sigma ^2}\right)},
\end{eqnarray}$$where μ > 0 and σ > 0. The mean PBH mass in the assumption of a lognormal mass function is $\bar{M}=\mu \exp \left(-\sigma ^2/2\right)$. Such mass function is a good approximation for a large class of PBH formation scenarios, e.g. axion-curvaton, running-mass, and single field double inflation (Green 2016; Kannike et al. 2017). The (μ, σ) pairs allowed in the window relevant for the operational gravitational wave detectors [$\mathcal {O}(1\operatorname{-}100)\, {\rm M}_\odot {}$; Abbott et al. 2019] are mainly constrained by (i) the results from searches for microlensing events on stars in our Galactic neighbourhood and (ii) the effect PBH gas accretion would have on the CMB temperature and ionization history (Carr et al. 2017; Sasaki et al. 2018). Together, the allowed points roughly constitute a sub-plane described as (see fig. 3 in Carr et al. 2017):
(8)$$\begin{eqnarray}
(\mu ,\sigma)\in [25\, {\rm M}_\odot ,100\, {\rm M}_\odot ]\times [0.0,1.0].
\end{eqnarray}$$It is worth mentioning that these points are ruled out completely if one further takes the following constraints into account (Carr et al. 2017): (iii) the survival of the stellar cluster in Eridanus II and of the entire stellar populations in UFDGs (Brandt 2016; Green 2016; Koushiappas & Loeb 2017; Zhu et al. 2018) and (iv) the survival of wide binaries in the Milky Way (Monroy-Rodríguez & Allen 2014). These constraints are somewhat less restrictive, as they rely on further astrophysical assumptions (Carr et al. 2017). For instance, the stellar cluster in Eridanus II could have only recently spiralled down into the centre of the galaxy, where dynamical heating by PBHs becomes effective (Brandt 2016). Additionally, wide binaries are in principle hard to detect, yielding some uncertainty in identifying them and consequently in drawing any conclusion on the PBH abundance (Sasaki et al. 2018). Moreover, we emphasize that, most recently, even previous microlensing constraints were called into question as spatial PBH clustering (García-Bellido & Clesse 2018) and updated galactic rotation curves (Hawkins 2015) tend to relax them.
Citation: Sasaki et al. 2018
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Citation: Sasaki et al. 2018
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