adsabs/FOCAL huggingface dataset:
Tol1247 was imaged with the HST (see Fig. 3) in the optical using the Wide Field Ultraviolet-Visible Channel (UVIS) of its Wide Field Camera 3 (WFC3). For UV imaging, the Advanced Camera for Surveys' Solar Blind Channel (ACS/SBC) was used. Seven filters were utilized in total, allowing to apply laxs – the Lyman alpha eXtraction software (Hayes et al. 2009) – to produce continuum-subtracted Lyα, Hα and Hβ images, corrected for underlying stellar absorption and contamination from [N ii] λ6548, 6584. The latter one is based on the spectroscopic line ratio ${[\mathrm{N}\,\small {II}]}/{\mathrm{H}\alpha }=0.0605$ published by Terlevich et al. (1993). The imaging strategy and data reduction methodology for Tol1247 is very similar to that of the Lyman Alpha Reference Sample (LARS; Hayes et al. 2014; Östlin et al. 2014; Duval et al. 2015) and the basic data reduction for this data set is done in the same way as for LARS. Flat-field-corrected frames were obtained from the Mikulski Archive for Space Telescope. The charge transfer inefficiency (CTE) correction for the ACS data was performed by the pipeline, whereas CTE losses in WFC3/UVIS (Anderson & Bedin 2010) were treated manually using the tools supplied by STScI.3 We then stacked the individual data frames and drizzled them to a pixel scale of 0.04 arcsec pixel−1 using DrizzlePac version 1.1.16 (Gonzaga et al. 2012). Further pre-processing of the data includes additional masking of cosmic rays in the drizzled frames and matching the point spread functions (PSFs) for the different filters. In order to match the PSF, we first construct PSF models for all of the filters used in the study. For the optical filters, we use TinyTim models (Krist, Hook & Stoehr 2011), re-sampled to a pixel scale of 0.04 arcsec pixel−1. However, for the FUV filters, TinyTim is not accurate enough, particularly in the wings. Therefore, the PSF models for the FUV filters are instead built from stacks of stars obtained in calibration observations; see Hayes et al. (2016) for details. All of the PSF models are then normalized by peak flux and stacked by maximum pixel value. We then proceed to calculate convolution kernels that match the PSFs for all of the filters to the maximum width model. Each kernel is built up from a set of delta functions and we find the optimum matching kernel by least-squares optimization; see also Becker et al. (2012) and Melinder et al. (in preparation). The drizzled and registered images are convolved with the kernel found for each filter, which result in a set of images matched to a common PSF.
Citation: Anderson & Bedin 2010
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Guo et al. (2015) used an automated “blob finder” to identify star-forming regions in the HST/ACS images for 3,239 log M*/M⊙  10.6 galaxies in CANDELS (GOODS-S and UDS fields) at 0.5  z  3. They defined clumps as blobs which contribute more than 8% of the total UV light of their host galaxies. In contrast to our results, they find that a much higher fraction of SFGs are clumpy (as much as ∼60% at z ∼ 2), and also that higher mass bins have lower clumpy fractions. It is worth pointing out that clumpy galaxy fractions are highly sensitive to methodology and clump definition and vary widely in the literature (e.g., Ravindranath et al. 2006; Elmegreen et al. 2007; Wuyts et al. 2012; Guo et al. 2015), so we should not expect complete agreement. Comparing their observed clumpy galaxy fractions (as a function of redshift) to fractions derived from the K15 classification scheme, Guo et al. (2015) find that their results agree best with clumpy fractions derived using both the clumpiness and patchiness flags from the K15 data release (see their Appendix A) rather than either the clumpy or patchy flags alone. This is because the blob finder does not account for the light concentration of the blobs. The inclusion of patches may help explain why their clumpy fractions are generally higher than ours. Guo et al. (2015) also exclude very small (01) and elongated (axis ratio 0.5) galaxies from their sample. Our inclusion of such galaxies could easily lead to lower clumpy fractions given that many galaxies with half-light radius 01 are Spheroids (see Figure 13), which rarely possess clumps. We also include edge-on disks whose clumps may be obscured by dust. The inclusion of galaxies with unresolved or obscured clumps may imply that we are underestimating the clumpy fractions. However, we do include the contribution from non-disky compact or irregular SFGs which would be excluded by the Guo et al. (2015) cuts. The contribution from such galaxies is not insignificant, especially at z ∼ 2, so our looser selection is not without merit. Even if we do underestimate our clumpy fractions, our consistent sample selection and methodology ensures that they should be similarly underestimated at all redshifts, preserving the general evolutionary trends.
Citation: Wuyts et al. 2012
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The major difference between the axisymmetric case and the asymmetric approach is that we need to derive the 3D, asymmetric gravitational potential of luminous baryons beforehand. We thus computed the potential in Cartesian coordinates (x,y,z), which enables us to derive both radial and azimuthal forces at any desired z. This can be derived independently for each stellar or gaseous contribution. The gravitationtal potential Φ of the mass distribution is in principle deduced from the convolution of the volume mass density by the Green function, namely (11)\begin{equation} \Phi = -G \iiint\frac{{\rm d}\rho'}{|\vec{r}-\vec{r}'|}, \label{eq:pot} \end{equation}Φ=−G$dρ′|r−r′|,where G is the gravitational constant. However, the Green function written by 1/ | r−r′ |  =  [(x−x′)2 + (y−y′)2 + (z−z′)2] − 1/2, is well known to diverge at each point where x = x′, y = y′ and z = z′. This function renders any direct estimate of Φ inaccurate and generally encourages modelers to incorporate a softening length to bypass the divergence. Here, we use the new formalism presented in Huré (2013) who showed that the Newtonian potential is exactly reproduced by using an intermediate scalar function ℋ, namely \hbox{$\Phi = \partial_{xy}^2 {\cal H}$}Φ=∂xy2ℋ. In 3D, this hyperpotential is written as (12)\begin{equation} {\cal H}(x,y,z) = -G \iiint_{\Omega'}{\rho(x',y',z')} \kappa^{xy}(X,Y,Z){\rm d}x'{\rm d}y'{\rm d}z' , \end{equation}ℋ(x,y,z)=−G$Ω′ρ(x′,y′,z′)κxy(X,Y,Z)dx′dy′dz′,with X = x−x′, Y = y−y′ and Z = z−z′. The κ function is a hyperkernel defined by (13)\begin{equation} \kappa^{xy}(X,Y,Z) = -Z \arctan \frac{XY}{Z |\vec{r}-\vec{r}'|}+Y \ln \frac{X+|\vec{r}-\vec{r}'|}{\sqrt{Y^2+Z^2}}\cdot \label{eq:k} \end{equation}κxy(X,Y,Z)=−ZarctanXYZ|r−r′|+YlnX+|r−r′|Y2+Z2·This approach is particularly simple and efficient for 2D or 3D distributions since ℋ is, in contrast to Φ, the convolution of the surface or volume density with a regular, finite amplitude kernel. The methodology thus does not make use of a softening length in the derivation of the potential. In practice, this convolution is performed using the second-order trapezoidal rule and the mixed derivatives are estimated at the same order from centered finite differences. Furthermore, the volume density of the tracer are deduced from a surface density map, considering that the vertical density follows a sech-squared or exponential law of constant scaleheight with radius. The precision of these schemes is sufficient for the present purpose.
Citation: Huré (2013)
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We have developed the simplest spherical void lens model based on the recently developed embedded lens theory. We have assumed a uniform mass profile for the void, compensated by a thin bounding shell. The infinitesimally thin bounding shell was chosen for convenience (Maeda & Sato 1983a, 1983b). To investigate other void profiles such as a non-uniform void interior or a finite-thin bounding ridge (Colberg et al. 2005; Lavaux & Wandelt 2012; Pan et al. 2012; Sutter et al. 2012; Hamaus et al. 2014; Kantowski et al. 2015) is straightforward; one has only to evaluate the Fermat potential of Equation (1) or equivalently the potential part of the time delay of Equation (4). It is also possible to build embedded void lens models with non-spherically symmetric density profiles given that the lowest-order embedded lens theory is applicable to any distributed lens (Kantowski et al. 2013). It is well accepted by the lensing community that small overdensities attract light, whereas small underdensities repel light. This fact can be rigorously proved using general relativistic perturbation theory (Sachs & Wolfe 1967) assuming 





. However, the repulsive nature of lensing by a large and deep underdense region (i.e., cosmic voids) as described by the rigorously derived but simply implemented embedded lens formalism did not appear until Kantowski et al. (2013). In the case of large density contrasts, i.e., 





 approaching its lower bound −1 for cosmic voids, the repulsive lens equation follows naturally from the embedded lensing theory. This theory is based on Swiss cheese models (Einstein & Straus 1945), which are exact solutions of Einstein’s field equations containing inhomogeneities with large density contrasts (Chen et al. 2010, 2011, 2015; Kantowski et al. 2010, 2012, 2013). The void-lensing community takes void repulsive lensing as granted (e.g., Amendola et al. 1999; Das & Spergel 2009), whereas the galaxy/cluster strong-lensing community has ignored embedding effects, i.e., the repulsive lensing caused by the large underdense regions surrounding the central overdense lens. Besides correctly predicting repulsive lensing by cosmic voids, our Fermat potential formulation can be used to compute the void-lensing time delay effects, including the ISW effect caused by voids; see Equation (5).
Citation: Kantowski et al. 2013
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Citation: Kantowski et al. (2013)
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One has to be sure to rule out cases where inorganic chemistry can mimic the presence of life (“false positives”). Potential abiotic ozone production on Venus- and Mars-like planets has been discussed by Schindler & Kasting (2000, and references therein). While this is based on photolysis of e.g., CO2 and H2O and is thus limited in extent, a sustainable production of abiotic O3 which could build up to a detectable level has been suggested by Domagal-Goldman & Meadows (2010) for a planet within the habitable zone of AD Leonis with a specific atmospheric composition. Indeed, other studies confirm that abiotic buildup of ozone is possible (e.g., Hu et al. 2012; Tian et al. 2014); however, detectable levels are unlikely if liquid water is abundant, as e.g. rainout of oxidized species would keep atmospheric O2 and O3 low (Segura et al. 2007), unless the CO2 concentration is high and both H2 and CH4 emissions are low (Hu et al. 2012). False-positive detection of molecules such as CH4 and O3 is discussed by von Paris et al. (2011). Seager et al. (2013) present a biosignature gas classification. Since abiotic processes cannot be ruled out for individual molecules (e.g. for O3), searches for biosignature molecules should search for multiple biosignature species simultaneously. It has been suggested that the simultaneous presence of O2 and CH4 can be used as an indication for life (Sagan et al. 1993, and references therein). Similarly, Selsis et al. (2002) suggest a so-called “triple signature”, where the combined detection of O3, CO2 and H2O would indicate biological activity. Domagal-Goldman & Meadows (2010) suggest to simultaneously search for the signature of O2, CH4, and C2H6. Of course, care has to be taken to avoid combinations of biosignature molecules which can be generated abiotically together (see e.g. Tian et al. 2014). The detectability of biosignature molecules is discussed, e.g. by von Paris et al. (2011) and Hedelt et al. (2013). In particular, the simulation of the instrumental response to simulated spectra for currently planned or proposed exoplanet characterization missions has shown that the amount of information the retrieval process can provide on the atmospheric composition may not be sufficient (von Paris et al. 2013).
Citation: Tian et al. 2014
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Citation: Tian et al. 2014
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Citation: Segura et al. 2007
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To perform a statistical analysis of the average quiescent fraction of satellites around our sample of massive galaxies, we use a statistical background subtraction technique (e.g., Kauffmann et al. 2010; Tal et al. 2012; Wang & White 2012; Kawinwanichakij et al. 2014). We detect objects within fixed apertures centered on our central galaxies and satisfying Equation (2). These apertures include physically associated galaxies as well as chance alignments of foreground and background galaxies. We estimate and correct for the contamination due to chance alignments by placing random apertures across the field. We adapt this procedure by restricting the placement of the random apertures to regions near the centrals, as demonstrated by Chen et al. (2006). This accounts for the bias due to contaminating galaxies that are physically associated with the centrals but are not satellites (i.e., the 2-halo term of the correlation function; see Chen et al. 2006).15

15
The contaminating galaxies that are physically associated with the central galaxies in our sample are expected to have marginally different properties than truly random field galaxies due to the fact that they exist in biased regions of the universe. There may be an additional effect due to large-scale 2-halo conformity. If 2-halo conformity exists, our procedure effectively corrects for it.
 We therefore place the random apertures within annuli with inner and outer radii equal to 1 and 3 cMpc from each central galaxy for the UDS and UltraVISTA. Parenthetically, our tests showed that the restriction on the location of the background apertures has only a small effect on the conformity signal. Relative to apertures that are placed randomly through the field, this correction increases the quiescent fractions of background galaxies by 0.4%–10%. For the smaller ZFOURGE fields, placing the random apertures within annuli is too restrictive, and for this survey we randomly place the apertures across the fields. We do note that the ZFOURGE fields are small enough that even these randomly placed apertures trace the same large-scale environment as the centrals. Additionally, we find that when we restrict the background apertures to be 





 cMpc from the centrals, it changes the measured quenching efficiencies (see Section 4 below) by 10%, and none of our conclusions would be changed.
Citation: Chen et al. 2006
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Given the quite strong surface magnetic field of the M7, thermal radiation is expected to be polarized, either if emission is from a bare surface or from an atmosphere (see Turolla et al. 2004; Potekhin 2014). The polarization properties are quite different in the two cases, although there are still uncertainties, especially at optical/ultraviolet (UV) wavelengths. One of the first predictions of quantum electrodynamics (QED), even before it was properly formulated, was vacuum birefringence, and, in particular, that a strong magnetic field affects the propagation of light through it (Heisenberg & Euler 1936; Weisskopf 1936). In thermally emitting INSs, radiation comes from a region comparable with the entire star surface, over which the magnetic field direction changes substantially. In the absence of QED vacuum polarization effects, this would produce a drastic depolarization of the radiation collected at infinity (Heyl, Shaviv & Lloyd 2003, see also Taverna et al. 2015; González Caniulef et al. 2016 and references therein). Vacuum birefringence dramatically increases the linear polarization of the observed radiation, from a level of a few  per cent up to even ∼100 per cent, depending on the viewing geometry and the surface emission mechanism (Heyl & Shaviv 2000, 2002; Heyl et al. 2003; Taverna et al. 2015; González Caniulef et al. 2016). Detecting polarization in the thermal emission from the surface of an INS will be therefore extremely valuable. First, and independently on the physical conditions of the emitting region, the detection of a large degree of linear polarization in the signal would constitute the observational evidence of QED vacuum polarization effects in the strong-field regime. Secondly, the polarization observables can be compared with emission models and help to uncover the physical conditions of INS surfaces and atmospheres, ideally complementing spectral observations (Taverna et al. 2014; González Caniulef et al. 2016).
Citation: Taverna et al. 2015
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Citation: Taverna et al. 2015
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Accreting stellar-mass black holes in binary systems regularly display quasi-periodic oscillations (QPOs) in their X-ray flux with frequencies drifting from ∼0.1–10 Hz (e.g. Van der Klis 1989). Three main components can be identified in the spectrum of these sources: disc blackbody emission, power-law emission from the inner accretion flow, and a reflection spectrum from photons reflected off the disc (Done, Gierlinski & Kubota 2007). So-called Type-C low-frequency QPOs (Casella, Belloni & Stella 2005) are believed to originate from the inner accretion flow/corona that is associated with the Comptonized power-law component of the X-ray spectrum, as this component shows a much larger variability amplitude than the blackbody disc component (Sobolewska & Zycki 2006; Axelsson, Hjalmarsdotter & C. 2013). Since currently no consensus on the origin of QPOs exists, we can generally divide QPO models into two broad categories: geometric and intrinsic models. In the former, the X-ray emission is constant but an oscillating accretion geometry quasi-periodically alters the observed flux. A possible origin for these geometric oscillations could be Lense–Thirring precession of the Comptonizing medium, due to misalignment of the black hole spin and the binary orbit (Stella & Vietri 1997; Stella, Vietri & Morsink 1999; Ingram, Done & Fragile 2009). Alternatively, in intrinsic models the emitted luminosity itself varies, for example due to changes in mass accretion rate (Tagger & Pellat 1999; Cabanac et al. 2010) or due to a standing shock in the accretion flow (Chakrabarti & Molteni 1993). Recently, Heil, Uttley & Klein-Wolt (2015) and Motta et al. (2015) confirmed that the QPO amplitude depends on the inclination of the binary orbit, strongly suggesting a geometric origin (Schnittman, Homan & Miller 2006). Ingram & Van der Klis (2015) found that the iron line equivalent width changes over a QPO cycle in GRS 1915+105, also strongly pointing towards a geometric origin.
Citation: Casella, Belloni & Stella 2005
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Current sheet drift is implemented following the approach proposed by Burger (2012) to calculate the drift velocity Vd in Equation (7). The heliospheric current sheet angle is modeled as by Kóta & Jokipii (1983), but now with a time-dependent tilt angle from Equation (8) such that
12





where, after Burger et al. (2008), we employ
13





with ϕ0 = 0°, thereby allowing for the inclusion in the model of the effects of a fully time-dependent heliospheric current sheet. Diffusion coefficients are modeled as by Moloto et al. (2018). A composite slab/2D model for transverse magnetostatic turbulence is assumed (see, e.g., Matthaeus et al. 1995), as well as slab/2D turbulence power spectra with wavenumber-independent energy-containing ranges, and Kolmogorov inertial ranges. This latter assumption does not perfectly reflect spacecraft observations of the same (see, e.g., Bieber et al. 1993; Matthaeus et al. 2007; Bruno & Carbone 2013), but leads to relatively simple, tractable expressions for the parallel and perpendicular MFPs. For more detail on diffusion coefficients derived assuming more realistic forms for the turbulence power spectra, see Shalchi et al. (2010), Engelbrecht & Burger (2015b), and Strauss et al. (2016). In brief, then, the parallel MFP expression used here is that constructed by Burger et al. (2008) from the quasilinear theory results presented by Teufel & Schlickeiser (2003), and given by
14





where s = 5/3, R = RLkm, and km = 1/λsl the wavenumber at which the slab spectrum inertial range commences, and RL is the maximal proton Larmor radius. To model the perpendicular MFP, we use the Nonlinear Guiding Center result of Shalchi et al. (2004), as modified by Burger et al. (2008) to take into account a general ratio of slab to 2D energies (see, e.g., Bieber et al. 1994, 1996). This expression is similar to that derived by Zank et al. (2004), and is given by
15





where ν = 5/6 denotes half the Kolmogorov inertial range spectral index, and we assume that α2 = 1/3 (Matthaeus et al. 2003). Drift coefficients, reduced in the presence of turbulence, are modeled following the approach of Engelbrecht et al. (2017), so that the lengthscale corresponding to the drift coefficient is given by
16





with 





 being the total magnetic variance. This expression is chosen as it provides results in reasonable agreement with numerical test particle simulations done by Minnie et al. (2007) and Tautz & Shalchi (2012) for the range of turbulence conditions expected in the supersonic solar wind, and due to the fact that its use in a CR modulation code has been shown by Moloto et al. (2018) to lead to computed CR intensities in reasonable agreement with spacecraft observations.
Citation: Bruno & Carbone 2013
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