adsabs/FOCAL huggingface dataset:
The inversion of the Fourier transform from limited data is a well-known problem in several imaging domains like, for instance, medical imaging (McGibney et al. 1993; Bronstein et al. 2002; Sutton et al. 2003; Fessler 2007; Gallagher et al. 2008; Lustig et al. 2008), crystallography (Eisebitt et al. 2004; Marchesini et al. 2008; Brady et al. 2009), and geophysics (Brossier et al. 2009; Jin 2010). This image reconstruction problem inspired several computational approaches like nonuniform Fast Fourier Transform (FFT) (Bronstein et al. 2002; Fessler & Sutton 2003; Greengard & Lee 2004; Lee & Greengard 2005), compressed sensing (Donoho 2006; Lustig et al. 2008; Bigot et al. 2016), and machine learning (Wang et al. 2018; Ravishankar et al. 2019). In the case of astronomical imaging, the use of Fourier methods is mainly related to radio and optical interferometry (Le Besnerais et al. 2008; Thiébaut & Giovannelli 2009; Wiaux et al. 2009; Felli & Spencer 2012; Thompson et al. 2017; Ye et al. 2020), although a similar methodology also involves snapshot imaging spectroscopy (Culhane et al. 2007; Harra et al. 2017; Courrier & Kankelborg 2018; Winebarger et al. 2019). However, in the last three decades, this approach has been utilized also in the case of solar hard X-ray telescopes that have been conceived in order to provide spatial Fourier components of the photon flux emitted via either the bremsstrahlung or thermal processes during solar flares (Lin et al. 2002; Krucker et al. 2020). These Fourier components, named visibilities, are sampled by the hard X-ray instrument in the two-dimensional Fourier space, named the (u, v) plane, in a sparse way, according to a geometry dependent on the instrument design. For instance, NASA’s Reuven Ramaty High Energy Spectroscopic Imager (RHESSI) relies on the use of a set of nine Rotating Modulation Collimators (RMCs) whose FWHM is logarithmically spaced between 23 and 183″ (Hurford et al. 2002). Each RMC measures visibilities on a circle of points in the (u, v) space with a spatial frequency that corresponds to its angular resolution and a position angle that varies according to the spacecraft rotation (see Figure 1, left panel). On the other hand, the Spectrometer/Telescope for Imaging X-rays (STIX) on board ESA’s Solar Orbiter is based on the Moiré pattern technology (Giordano et al. 2015; Massa et al. 2019), and its 30 collimators sample the (u, v) plane over a set of six spirals for an FWHM resolution coarser than 7″ (see Figure 1, right panel).
Citation: Ravishankar et al. 2019
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Having eliminated calibration or observational effects as the cause of the spectral changes, it must be that either (1) the configuration of the sources themselves has changed or (2) the interstellar scintillation has caused time-variable focusing and defocusing of the polarized substructure in the radio jets (e.g. Rickett 2001; de Bruyn & Macquart 2015), which can generate spectropolarimetric variability (e.g. Kedziora-Chudczer 2006). Interstellar scintillation (ISS) is difficult to distinguish from intrinsic variability without fully time-resolved data (see e.g. Bignall et al. 2015 and references therein), and can coexist with it (Koay et al. 2018). Never the less, ISS does not easily explain our results: Sources located more than ∼25 degrees from the Galactic plane (3/4 of our polarized sources) and with line-of-sight H α intensities less than ∼ a few Rayleighs (7/8 of our polarized sources; see column 9 of Table 1) are typically not strongly scattered (Pushkarev & Kovalev 2015), and experience associated flux density modulations of typically less than a few per cent (Heeschen 1984; Quirrenbach et al. 1992; Rickett, Lazio & Ghigo 2006; Lovell et al. 2008). This is smaller than the changes observed in our integrated fractional polarization spectra, which should undergo less modulation than the Stokes I in any case. ISS modulation also has a distinct frequency and time dependence. At the mid-galactic latitudes inhabited by most of our sources (see column 8 of Table 1), its magnitude peaks between 4 and 6 GHz (λ2 between 0.0024 and 0.0056 m2), and drops to less than 50 per cent of this value at 1.4 and 10 GHz (i.e. λ2 of 0.0008 and 0.046 m2). Thus, changes due to ISS should ‘spike’ in this narrow λ2 window, which we see little evidence of (though see perhaps PKS B0517–726 and PKS B1903–802; see Figs 3 and 9). The characteristic time-scale of ISS is ∼hours to days (Rickett et al. 2006; Gabányi et al. 2007, and references therein), and over multiyear time-scales, intrinsic effects tend to dominate the observed variability (e.g. Lazio et al. 2001; Rickett et al. 2006; Mooley et al. 2016). We therefore claim that the spectral variability most likely reflects intrinsic changes in the sources themselves.
Citation: Bignall et al. 2015
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Figure 2 shows the geometry used within our model. The center of the stellar sphere is located at the origin of the three-dimensional spherical coordinate system. PyTranSpot does not take into account a fractional area correction, as used within the WD code (Wilson & Devinney 1971; Wilson 1979, 1990, 2008, 2012). We argue that this effect is negligible, as the resulting loss of accuracy is much smaller than the noise currently present in observations. However, to obtain precise photometric transit and spot parameters, we recommend the use of a planetary pixel radius between 15 and 50 pixels (Tregloan-Reed et al. 2015). On the stellar sphere, every point is described by the two angles (longitude θ, co-latitude φ) and the distance to the stellar center (stellar radius rs in pixels). The longitude θ varies between − 90° and 90°, with the center of the stellar disk corresponding to a value of 0°. The co-latitude φ ranges from 0° to 180°, with the stellar equator set at 90°. Figure 3 illustrates the projection of a spot on to the stellar sphere, as seen from a two-dimensional perspective. The observer is assumed to lie far along the z-axis. To determine the pixels on the stellar sphere, which correspond to the starspot, we implement the following boundary condition: If the angle Δσ between the pixel on the sphere and the spotcenter is greater than the angular radius of the spot α, then this pixel does not belong to the spot. The values for Δσ are derived by using the spherical law of cosines: (2)\begin{equation} \cos(\Delta\sigma) = \cos(\phi_{\rm{spot}}) \cdot \cos(\phi) + \sin(\phi_{\rm{spot}}) \cdot \sin(\phi) \cdot \cos(\Delta\theta),  \label{eq:slaw} \end{equation}cos(Δσ)=cos(φspot)·cos(φ)+sin(φspot)·sin(φ)·cos(Δθ),where φspot and φ are the co-latitudes of the spotcenter and the surrounding pixels, respectively. The value Δθ represents the absolute difference in longitude between the spotcenter and the pixel center.
Citation: Tregloan-Reed et al. 2015
Citation function: Uses
We run forced photometry at these SN locations using a pipeline, hereafter known as the zuds pipeline1 (Dhawan et al. 2021), which performs aperture photometry using the astropy affiliated package PhotUtils (Bradley et al. 2019), using a 6-pixel diameter aperture on the difference images. The reference images for the difference images are constructed by co-adding exposures from epochs at least 30 d or more before the initial estimate of the time of maximum from the alert photometry, using the software swarp (Bertin 2010). In order to build the co-add, we only take epochs with seeing between 1.7 and 3 arcsec and a magnitude limit deeper than 19.2 mag. For consistency, we use the same reference image for both SNe. In the zuds pipeline, difference images are obtained using hotpants (Becker 2015), an implementation of the image subtraction algorithm (Alard & Lupton 1998). The zero points for each epoch are computed by the Infrared Processing and Analysis Center (IPAC), corrected for a 6-pixel diameter aperture. For the i band, we use the images corrected for an observed fringing pattern, using the fringez software (Medford et al. 2021). From the IPAC forced-photometry service (Masci et al. 2019) at the same locations, we obtain the metadata for each observation, including the magnitude limit mlim of the observation, the seeing of the observation, and the standard deviation σpix on the background at the pixel on which the SN is located. We combine this information with the zuds pipeline results for data quality assessment. Specifically, we only use those observations that satisfy the following conditions: $1.0 \lt \mathrm{seeing} \lt 4.0\,\mathrm{ arcsec}, \ m_{\mathrm{lim}} \lt 19.2 \mathrm{\, mag}, \ \mathrm{ and} \ \sigma _{\mathrm{pix}} \lt 14.0$, where σpix is the robust σ per pixel in the science image and is used as a metric to remove non-photometric data. We then use a maximum likelihood method to fit the salt2 model to each of these two SN light curves. The low seeing values are removed to protect against undersampling during image subtraction. We then remove the epochs that have 5σ flux outliers relative to the best-fitting salt2 model (discussed later and summarized in Table 2) and use the remaining selected points as the light curves of the individual SNe. The final photometry data sets used are included as Tables C1 and C2. The resulting light curves are shown in Fig. 3. As stated earlier, this light curve clearly shows the presence of two transients with no detections for a period of over 100 d in between. For the unclassified transient AT 2019lcj, we notice that AT 2019lcj has a shoulder in the redder bands (r- and i band) as seen in Fig. 3 strongly indicating that the SN is of Type Ia. We will verify this shortly after discussing its redshifts and host properties.
Citation: Masci et al. 2019
Citation function: Uses; Uses
The second data release (DR2) of the ESA Gaia mission has provided the largest available 6D phase-space (positions and velocities) dataset for 7.2 million stars brighter than GRVS = 12 mag (Gaia Collaboration et al. 2018b), making possible precise studies of the Milky Way structure and kinematics on large scales. Gaia data have already revealed signatures of non-equilibrium and ongoing vertical phase mixing in the Milky Way disc (Antoja et al. 2018), likely induced by a previous pericentric passage of the Sagittarius dwarf galaxy (Laporte et al. 2019; Bland-Hawthorn et al. 2019; Haines et al. 2019) or by internally driven bending waves (Khoperskov et al. 2019; Darling & Widrow 2019). A large number of kinematic arches with various morphologies not known prior to Gaia were found (Ramos et al. 2018; Kushniruk & Bensby 2019; Monari et al. 2019) and large-scale wiggles were discovered in the Vϕ − R plane (Kawata et al. 2018; Antoja et al. 2018). These features can be interpreted as the signature of the impact of an external perturbation (D’Onghia et al. 2016; Laporte et al. 2019) or as evidence of the spiral structure and the bar of the Milky Way (Hunt et al. 2018; Fragkoudi et al. 2019). At the same time, in the solar vicinity the Galactic spiral structure is believed to generate various patterns in the phase-space (Siebert et al. 2012; Williams et al. 2013), but uncertainties on the location and strength of the spiral arms have so far prevented us from explaining the observed patterns. In this work, we use the high-quality Gaia DR2 sample of stars with radial velocities (hereafter GRV2), together with a new method to highlight the stellar density structures, to explore the Milky Way spiral arms without relying on any specific stellar tracer. The outline of the Letter is as follows: in Sect. 2 we describe the data and the method we used; in Sect. 3 we examine the phase-space structure of the Milky Way and compare its various features with Milky Way-type galaxy simulations; and in Sect. 4 we summarize the conclusions that can be drawn from our study.
Citation: Ramos et al. 2018
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More recently, Long et al. (2018) performed an analysis on 32 discs in the Taurus star-forming region using ALMA. From this sample 12 discs containing axisymmetric structure were identified. This structure takes the form of dark band bright ring pairs, emission bumps and cavities. Overall 19 gap ring pairs were identified, indicating that a number of these systems contain multiple gaps. These gaps range in location from R = 10–120 au with no preferred distance. The majority of these gaps are narrow, but the weak correlation between gap location and gap width potentially implies formation via planet–disc interactions. In addition a significant number of these gaps cannot be explained by condensation fronts. Long et al. (2018) perform an analysis on the width of these gaps in order to determine possible masses of planets that could form them. Assuming the width of the gaps corresponds to 4RHill (Dodson-Robinson & Salyk 2011) they estimate the masses of the planets to be in the 0.1–0.5MJ (q = 1.0 × 10−4–5.0 × 10−4) range, however they stress that these masses have large uncertainties and that the gap widths may be as large as 7–10RHill (Pinilla, Benisty & Birnstiel 2012). Alternatively they compare the distance between the minimum of the gaps and the maximum of the rings to the results of hydrodynamic simulations (Rosotti et al. 2016) with an α = 10−4 (ν = 2.5 × 10−7), resulting in a predicted planet mass of 0.05MJ (q = 5.0 × 10−5) and an α = 10−2 (ν = 2.5 × 10−5), resulting in a predicted planet mass of 0.3MJ (q = 3.0 × 10−4). Using equation (7) and an estimated disc lifetime of 2 Myr [based on stellar age estimates for the spectral type of the target stars (Baraffe et al. 2015; Feiden 2016)] we can see that in the higher viscosity case planets of 0.3MJ (q = 3.0 × 10−4) can form at any of these radii without creating vortices. The lower viscosity case is more difficult to predict as it is significantly lower than the viscosities we have explored, however for this planet mass it seems unlikely from our results that the predicted planet could form without creating vortices. Instead, we expect the maximum mass planets that could be found here to be roughly half the mass they predict. In addition, from our results planets of mass 0.5MJ (q = 5.0 × 10−4) would require the disc viscosity to be ν ⪆ 3.0–5.0 × 10−6 to form without creating vortices, depending on the radius at which they are located. Conversely we find planets as small as 0.1MJ (q = 1.0 × 10−4) can even form at ν = 2.0 × 10−6 without creating vortices. This can be seen in Fig. 8. Hence our results show that these gaps could be opened by planets of the masses predicted by Long et al. (2018) without forming vortices.
Citation: Dodson-Robinson & Salyk 2011
Citation function: Uses
We used the 





 band high-resolution spectra of Arcturus and μ Leo, obtained with WINERED, to estimate the microturbulence and iron abundance with a precision similar to that of previous results from spectra at different wavelengths. Our lists of Fe i lines in the 0.91–1.33 μm range will be useful for obtaining the precise metallicities of stars obscured by severe interstellar extinction compared with the optical regime, for which the extinction is stronger. For many objects in the Galactic disk found in recent infrared surveys, this new wavelength window may be ideal for detailed abundance analyses. One of the major error sources is the uncertainty in ξ in various studies, including ours, based on spectra at different wavelengths from the optical (e.g., Table 3 of Jofré et al. 2014) to the H band (e.g., Table 7 of Smith et al. 2013). Furthermore, how to determine the microturbulence and its error is not established or straightforward. The bootstrap method that we demonstrated in this paper can give quantitative estimates of the microturbulence and its error. The error in microturbulence is 0.11–0.24 





 for each combination of target and line list. The obtained microturbulences are consistent with those that were estimated or assumed in previous studies on the same targets. Note, however, that using different line lists (or different sets of lines) can result in slightly different microturbulences depending especially on the 





 values of strong lines used in the analysis. The very strong lines (X > −6) were rejected because these lines are likely to introduce problems into a chemical abundance analysis due to severe saturation, non-LTE effects, contribution of EW from the damping wing, and so on. Considering the comparison of our estimates with previous ones in addition to the scatters of 





, we adopt the measurements with the Fe i lines selected from MB99 as our best estimates: 





 and 





 for Arcturus and μ Leo, respectively.
Citation: Smith et al. 2013
Citation function: Uses; Uses
In this article, I present an analytical model of resonant chains. Analytical models have already been proposed, in particular to study the dynamics of the Laplace resonance (1:2:4 chain) between Io, Europa, and Ganymede (e.g., Henrard 1983). However, while several numerical studies have been dedicated to the capture of planets in various resonant chains (e.g., Cresswell & Nelson 2006; Papaloizou & Terquem 2010; Libert & Tsiganis 2011; Papaloizou 2016), general analytical models have not yet been proposed. Recently, Papaloizou (2015) proposed a semi-analytical model of three-planet resonances taking into account only the interactions between consecutive planets in the chain, with a particular focus on the Kepler-60 system (12:15:20 resonant chain, see also Steffen et al. 2013; Goździewski et al. 2016). This model is very similar to the studies of the Laplace resonance between the Galilean moons, but is not well suited in the general case. For instance, four-planet (or more) resonances are not considered. Moreover, for some three-planet resonances, the interactions between non-consecutive planets cannot be neglected. For instance, in a 3:4:6 resonant chain, each planet is locked in a first-order resonance with each of the other planets. In particular, the innermost and outermost planets are involved in a 2/1 MMR that strongly influences the dynamics of the system. I describe here a general model of resonant chains, with any number of planets, valid for any resonance order. I particularly focus on finding the equilibrium configurations (eccentricities, resonant arguments, etc.) around which a resonant system should librate. While a real system may be observed with significant amplitude of libration around the equilibrium, or could even have some angles circulating, the position of the equilibria still provides useful insights into the dynamics of the system. In Sect. 2, I describe this analytical model, and the method I use to find the equilibrium configurations. In Sect. 3, I apply the model to Kepler-223. I show that six equilibrium configurations exist for this resonant chain, and that the system is observed to be librating around one of them. I also show that knowing the current configuration of the system allows for interesting constraints to be put on its migration scenario, and in particular on the order in which the planets have been captured in the chain.
Citation: Goździewski et al. 2016
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The simulations presented in this work have been performed with the publicly available hydrodynamic code GIZMO (Hopkins 2015), which is a descendant of GADGET2 (Springel 2005). The code evolves the magneto-hydrodynamics equations for the gas including a constrained-gradient divergence-cleaning method (Hopkins & Raives 2016; Hopkins 2016), together with the gas self-gravity. For the purpose of this study, we equipped the code with an on-the-fly non-equilibrium chemical network, which was implemented via the public chemistry library KROME (Grassi et al. 2014), similarly to Bovino et al. (2019). In particular, in our simulations, we assumed an isothermal equation of state for the gas, with the temperature set to 10 K or 15 K. These temperatures are in line with kinetic temperatures obtained from NH3 for the same regions (Friesen et al. 2009, 2017). The initial conditions of our simulations consist of a collapsing filament, modelled as a cylinder with a typical observed (Arzoumanian et al. 2011) Plummer-like density profile 

$n(R) = n_0/[1+(R/R_{\textrm{flat}}){}^2]^{p/2}$


n(R)=
n
0

/

[1+

(R/
R

flat

)
2

]

p/2



, where R is the cylindrical radius, n0 is the ridge volume density and is constant along the filament axis, Rflat is the characteristic radius of the flat inner part of the density profile, and p is the characteristic exponent. Following observed estimates (Arzoumanian et al. 2011), we set p = 2 and Rflat = 0.033 pc, which gives a mean filament width of 3 × Rflat ~ 0.1 pc. The setup follows previous works (Seifried & Walch 2015; Körtgen et al. 2018). To avoid any spurious effect at the edges of the filament along its axis, we embed the cylinder in an exponentially decaying background, with the decay scale length set to the filament length Lfil = 1.6 pc. The box is large Lbox = 2.4 pc. We initialised the filament in a turbulent state, assuming a Burgers-like power spectrum that grows as ∝ k10 up to λpeak = Lbox∕6 and then decays as ∝ k−2 (see also Körtgen et al. 2018). Finally, we assumed the box is permeated by a constant magnetic field B0 = 40 μG (Seifried & Walch 2015; Körtgen et al. 2018).
Citation: Grassi et al. 2014
Citation function: Uses
In Table 3, we list the physical parameters of our model (not including the normalizations for each telescope and each eclipse), and in Tables 4 and 5 we show the resulting best-fit model and the 16th and 84th percentiles (approximately 1σ) for each parameter for each season. We are able to fit our measured light curves equally well regardless of which star we place star spots. However, if we place the star-spot signal on the secondary component, the out-of-eclipse model parameters vary significantly between observing seasons, whereas if the star-spot signal is originating on the primary component, the parameters are stable between seasons. Particularly, F, the ratio between the orbital and rotational frequency has different values for each season if the secondary component is responsible for the star-spot modulation signal. Since the rotation period of the secondary star should not change (and the effect of differential rotation for star spots at different latitudes is small for M dwarfs; Davenport et al. 2015), we conclude that the star-spot signal cannot be originating on the secondary component. Since we only detect one rotational frequency, and this frequency differs from the orbital frequency of the system, we have assumed that it is originating solely on one component of the system (the primary component); though, in reality, there is likely to be some star spots on the surface of both components and that rotation of the secondary component may also be contributing somewhat to this signal (though we do not have a significant detection). We will utilize the model for which star spots are located on the primary component for the rest of this paper. We note, however, that this choice does not significantly affect the values of the masses and radii of the components but does change the uncertainty. The light curves themselves are most directly sensitive to the sum of the component radii and their ratio. The eclipse duration measures a combination of (





)/a (where R1 and R2 are the component radii and a is the semimajor axis) and the inclination of the orbit. In the case of grazing eclipse (where we cannot break this degeneracy with a measurement of the duration of the total phase), this degeneracy is instead broken by the eclipse depth, which also depends on the limb darkening of the stars and the star spots on the stellar surfaces.
Citation: Davenport et al. 2015
Citation function: Uses; Uses