adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...911...10W (fulltext)
The overall null results are just due to cancellation globally. Figure 4. Additive bias c 1 for different boundary sizes ( l x , l y ). All c 1 values are units of 10 −3 . The variances are about 4 × 10 −5 . The parameter 2 cs defined in Equation ( 2 ) has a best-fit value of 408 ± 12 in the current simulation. To further check the validity of the relation in the equation, one can quantitatively check the change of the bias c 1 with the ratio of the axis sizes l x / l y for a fixed image area l x l y , or the other way around. The results are shown in Figure 5 , which are quite consistent with Equation ( 2 ). Figure 5. Change of the additive bias c 1 as a function of l x . The upper panel is under the condition of fixed axis ratio ( l x / l y ), and the lower one is for fixed total area (). The solid curves are fittings from Equation ( 2 ) using the data shown in Figure 4 . The variances of the data points are about 4 × 10 −5 . Another prediction of the model is that the boundary effect should be more significant for galaxies of larger average sizes. To observe this phenomenon, we generate another set of simulations with RW galaxies that are larger by 50% than those in the previous set of simulations. The results are shown in Figure 6 , from which we can clearly see that c 1 is larger than those reported in Figure 4 under the same condition of ( l x , l y ). Figure 6. Similar to Figure 4 . The radii of the RW galaxies used here are larger than those used in Figure 4 by 50%. All c 1 values are in units of 10 −3 . The variances are about 4 × 10 −5 . We also expect the boundary effect to be more significant for more elliptical galaxies. To observe this, we repeat the simulations twice with the Galsim galaxies. Instead of using the ellipticity probability distribution function, this time, we set the galaxy ellipticities at two specific values: e = 0.2 and e = 0.8. The results are shown in Figures 7 and 8 accordingly. One can see that the c 1 of the more elliptical cases are 10 times larger than their counterparts with smaller ellipticities. Figure 7. Similar to Figure 4 , but with Galsim galaxies. The ellipticity of these galaxies are set at 0.2. All c 1 values are in units of 10 −3 . The variances are about 1 × 10 −5 . Figure 8. Same as Figure 7 , but with more elliptical Galsim galaxies. The ellipticity of the galaxies is set to 0.8. All c 1 values are in units of 10 −3 . The variances are about 1 × 10 −5 . Overall, we can draw the conclusion that the additive bias due to the boundary is also influenced by the galaxy morphologies, such as their radii, ellipticities, etc. The results in this section only represent the bias levels for several special cases. 2.3. The Solution and Its Application to Real Data As we have shown, source selection is a statistically anisotropic process near the image boundaries. The resulting shear bias cannot be treated as a universal additive bias, as it is significant only in the neighborhood of the boundaries. To
Paper: 2015ApJ...808..131H (acknowledgments)
J.M. acknowledges support by Polish National Science Centre grants No. 2011/03/B/ST9/03281 and 2013/10/M/ST9/00729.
Paper: 2018MNRAS.474.4447C (acknowledgments)
Acknowledgements We thank Yusuke Zemba for his great contribution to the laboratory experiments.
Paper: 2017MNRAS.466.4201W (acknowledgments)
Acknowledgments We thank the anonymous referee for the useful feedback. The authors would like to acknowledge Matthew J. Payne for help in obtaining this data set. JAW and ACB acknowledge support from an NSERC Discovery Grant, the Canadian Foundation for Innovation, The University of British Columbia and the European Research Council (agreement number 320620). EBF acknowledges support from the Center for Exoplanets and Habitable Worlds. The Center for Exoplanets and Habitable Worlds is supported by the Pennsylvania State University, the Eberly College of Science and the Pennsylvania Space Grant Consortium. This paper makes use of the following ALMA data: ADS/JAO.ALMA[2013.1.00486.S]. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan) and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ.
Paper: 2017MNRAS.465L.109F (acknowledgments)
We thank Fan Liu for discussions about future tests of these trends. All authors acknowledge the grant ‘The New Milky Way’ from the Knut and Alice Wallenberg Foundation. PJM acknowledges support from the Swedish National Space Board .
Paper: 2016A&A...593A...5D (acknowledgments)
X. Dumusque is extremely grateful to S. Aigrain, G. Anglada-Escudé, G. Boué, A. Correia, R. Díaz, D. A. Fischer, E. B. Ford, P. C. Gregory, A. Hatzes, J. S. Jenkins, A. Santerne, S. H. Saar, G. Scandariato, and M. Tuomi for interesting discussions about the design of the data set of the RV fitting challenge and the best way to organize it. X. Dumusque also acknowledges the Society in Science The Branco Weiss Fellowship for its financial support.
Paper: 2016MNRAS.455..178N (fulltext)
velocities are conveniently expressed in terms of spherical Bessel functions, j l , and spherical Harmonics, Y lm . To do that, we imagine a very large spherical volume or radius R max which entirely encompasses the data and is also sufficiently large that the effects boundary conditions are small. Expressing the density contrast as where the wavenumbers k n are fixed by the boundary conditions at R max (Fisher et al. 1995 ). We advocate the boundary conditions which yield gravity potential decaying as r − l for r > R max . Since j l Y lm are eigenfunctions of the Laplacian, these boundary conditions are equivalent to finding k n which satisfy . The numbers C ln are fixed entirely by the boundary conditions and are given in table A1 of Fisher et al. ( 1995 ). The density coefficients are Using the linear velocity–density relation one obtains (Regos Szalay 1989 ; Scharf Lahav 1993 ; Fisher et al. 1995 ), where . For velocities measured with respect to the Local Group frame the l = 1 term should be modified by subtracting 1/3 from . Using this representation for u ( r ) yields where P l is Legendre polynomial of order l and we have used . From , the cross-correlation can easily be computed . Using the expression ( 7 ) which is appropriate for definition ( 2 ) we get 2 7 DISCUSSION An aim of this contribution is to show that different approaches to the determination of are tightly related. All methods yield something which mimics the behaviour of the theoretical bulk flow. The non-trivial challenge is to contrast any estimate with predictions of cosmological model. Systematic errors related to observables of galaxy properties, contaminate various methods in different ways. More distant galaxies are more prone to these systematics, therefore, consistency checks must be performed by applying the methods subsamples of the data. Furthermore, spatial coverage of the data depends on galaxy-type and unavoidable cuts on the observables. Thus, methods should be tested on mock galaxy catalogues that mimic the observations in as much as possible, including the observed large-scale structure. The constrained simulations (Sorce et al. 2013 ) designed to match the low-redshift large-scale structure, can potentially be highly beneficial if combined with galaxy formation models. We have also emphasized the differences between the methods. Hoffman et al. ( 2015 ) provide an estimate for the bulk flow as defined in equation ( 1 ), while Feldman et al. ( 2010 ) aim at given by equation ( 2 ). These two definitions of do not coincide (Nusser 2014 ) and should not produce identical results. None the less, both methods can easily be adapted to any of the two definitions. In Section 3 , we derive a new estimator which naturally arises from the likelihood . This is a generalization of the standard ML of Kaiser ( 1988 ), but incorporates velocity modes beyond the (constant) bulk flow of the entire data catalogue. Essentially, this estimator is equivalent to
Paper: 2016MNRAS.462..565C (fulltext)
of the total stress-energy tensor , namely which yields Here, γ αβ = g αβ + U α U β is the projection tensor, and α is the lapse function. Latin indices take values 1, 2, 3 and Greek ones 0, 1, 2, 3. Vectors and tensors with tilde are purely spatial. For an ideal fluid with density ρ, 3-velocity , and pressure p we have We will also assume an equation of state p = p (ρ) from which we deduce the ‘speed of sound’, Finally, we will also assume ideal MHD conditions, namely In order to investigate the Rayleigh–Taylor instability in an astrophysical context, we will now consider the special case of a Kerr space–time. 2.1 Kerr space–time In Boyer–Lindquist (BL) coordinates the Kerr metric reads where M is the mass of the black hole, a is the angular momentum per unit mass (0 ≤ a ≤ M ), and (Cowling 1941 ). Notice that we work in geometrical units in which c = G = 1. For our further study we need the components of the 4-velocity of fiducial observers, now identified as zero angular momentum observers (ZAMOs), namely where In the Kerr space–time ( 11 ) with 4-velocity U μ given by equation ( 13 ), the expansion θ vanishes, the shear has two non-zero components e.g. the σ 13 and σ 23 but σ αβ γ αβ = 0 (see Thorne Macdonald 1982 , paper I, equation 2.5). The acceleration a μ is given by γ ij is the spatial metric on the space-like hypersurface x 0 ≡ t = const., with normal vector n α : where β i = γ ij g 0 j . 2.2 The perturbed MHD equations We consider only small perturbations of physical quantities as and keep only linear terms of the perturbations. In this case and In the Cowling approximation of a fixed Kerr space–time: The first-order perturbed MHD equations now become 3 The static equatorial disc We will now restrict our analysis to the investigation of a static equatorial distribution of matter of thickness h ≲ r . By ‘static’ we mean that the disc fluid is initially at rest with respect to ZAMOs, i.e. that . Our disc configuration only vaguely mimics astrophysical accretion discs. We acknowledge that neglecting the Keplerian disc rotation is an important simplification that we apply only to make some progress with the complex general relativistic formalism. However, our results may be physically relevant in MADs in which rotation plays a secondary role (see Discussion section). The problem we have in mind is a distribution of matter consisting of two regions inside and outside some radius r 0 . We will thus only consider perturbations in the immediate neighbourhood of r 0 of the form in the equatorial plane θ = π/2, where m takes integer values 1, 2, 3...For simplicity, we will henceforth ignore the index ‘0’ from the zeroth-order terms. In this case, We will also assume dipolar symmetry in the magnetic field, namely B r (π − θ) = − B r (θ), B θ (π − θ) = B θ (θ), and B φ (π − θ) = − B φ (θ). On the equatorial plane in particular, and B r , θ ∼ h 1 B θ / r , B φ, θ ∼ h 2 B θ / r , with h 1 , h 2 ≈ const. In what follows, we will set for
Paper: 2020MNRAS.496L..85S (fulltext)
lines) are shown. The colours represent different values of the orbital periastron argument (ω) in Fig. 1 . Lower panel: Relationship between the bright time ( t bright ) and the eccentricity ( e ) given by equation ( 5 ). In the lower panel of Fig. 5 , we analyse how ω affects the relationship between t bright and e (equation 5 ). As expected, t bright is minimum for the toy model (ω = 0°) and reaches a maximum value when the summer solstice occurs at quadrature (ω = 90°, −90°). Since an optimal orbital configuration was assumed in the toy model, equation ( 8 ) and the observed t bright will underestimate the true eccentricity of the orbit (see the dashed curve in the lower panel of Fig. 5 ). However, even in a more realistic situation, equation ( 8 ) is still valid as a formula for constraining e : Also, an unknown orbital inclination contributes to either increasing or reducing F dark and F max with respect to their corresponding values in the toy model (equations 9 and 11 , respectively). In the case of F dark , ω tends to increase its true value, while i may either reduce it or increase it, thereby compensating the effect of ω. On the other hand, the constraint given by equation ( 10 ) will not be substantially modified (at least within a first-order estimation of A planet ). By comparison with the toy model, both ω and i lower the measured value of F max . This implies that equation ( 13 ) would remain valid, although the minimum A ring will probably be underestimated. To find fiducial parameters of real systems, a more general and complex model for fitting stellar light curves needs to be developed. To that end, we should include second-order effects such as the shadow/shine of the planet’s surface and atmosphere, forward scattering, the opacity of the rings, the polarization and reflection of light by dusty/icy ring particles, etc. For this optical complexity , we propose (and intend) to create a scattering model centred on the ring (most models are centred on the star), where the properties of the scattered light are computed in the sky above the planetary rings after describing the astronomical position of the star, the planet, and the observer. However, such a model is the subject of an ongoing research that will be introduced in a forthcoming paper. 5 SUMMARY AND CONCLUSIONS The first-order semi-analytical model presented here demonstrates that rings may produce detectable and distinguishable photometric signatures on the light curve of non-transiting exoplanets. We have quantified the phase-dependent amount of light that a tilted ringed planet can reflect towards a distant observer, and the model is described through four main morphological features of the so-called bright curve that help us derive some analytical constraints to orbital and physical parameters of the system. This model can be useful to detect (ringed) planets with large ring surfaces lying in face-on orbits, whose survival time-scale is ∼1 Myr (Schlichting Chang
Paper: 2021MNRAS.504.4428C (fulltext)
sum except for T = 500 K, which is obtained with the classical approximation. For more details, see the text. From Table 2 , it can be observed that the rotational contributions to the internal partition functions of the four isomers have similar values at the lowest temperatures, e.g. down to T = 10 K. For higher temperatures, above T = 225 K, the difference between the rotational partition function of fulminic acid and the values of the other three isomers (isocyanic acid, cyanic acid, and isofulminic acid), with more similar values, becomes larger. This could be explained because of the structure of fulminic acid is linear and of the other have a near-prolate asymmetric top structure. Nevertheless, the internal partition functions of isocyanic acid, cyanic acid, and isofulminic acid become completely different at higher temperatures despite the fact that these three isomers have a bent structure. This stems from the differences among the vibrational partition functions of the bent isomers. In Table 2 , the weight of the vibrational contribution to the internal partition function can also be assessed for the set of the four CHNO isomers at different temperatures. The differences between the internal partition function and the rotational partition function become noteworthy (more than 1 per cent) at temperatures higher than 225 K for HNCO, 150 K for HOCN, and T = 75 K for HCNO and HONC. Even when the vibrational partition function has been calculated with the harmonic approximation and, in some cases, using fundamentals, the values of this approximation, although underestimated, are in good agreement with the direct sum values (Carvajal et al. 2019 ). As supplementary material, the rotational, vibrational, and rovibrational partition function calculated in the present work from 1 to 500 K (in intervals of 1 K) is also provided for the following isomers: cyanic acid (HOCN); fulminic acid (HCNO); and isofulminic acid (HONC). For isocyanic acid (HNCO), same material can be found elsewhere (Carvajal et al. 2019 ). In Appendix A , we will give some details about the calculation of the partition function for the four CHNO isomers. 4.4 Chemical modelling with <sc>Nautilus</sc> In order to better understand the chemical pathways involved in the production and destruction of HNCO, we used the Nautilus code (Ruaud et al. 2016 ) to carry out a time-dependent chemical model. Nautilus allows to simulate and study grain- and gas-phase chemical processes for instance in hot and cold cores (Semenov et al. 2010 ; Reboussin et al. 2014 ; Ruaud et al. 2015 , 2016 ). The predictions of the model include the time evolution of the chemical abundances for a given set of physical and chemical parameters. For the solid-state chemistry, it considers mantle and surface as chemically active, following the formalism of Hasegawa Herbst ( 1993 ) and the experimental results of Ghesquière et al. ( 2015 ). In this sense, the code can perform a three-phase (gas plus grain
Paper: 2019A&A...621A..44H (acknowledgments)
Acknowledgements D.H. acknowledges the Spanish Ministry of Economy and Competitiveness (MINECO) for the financial support under the FPI programme BES-2015-075200, R.A. under the Ramón y Cajal program RYC-2010-06519, and the three authors under the programme RETOS ESP2014-57495-C2-R. We are grateful for the hospitality of J. Cabrera and the group of H. Rauer at the DLR Institute of Planetology, where part of this work was performed.
Paper: 2017MNRAS.464.4133R (fulltext)
reported 420.8 d period obtained from analysing all the MACHO data, i.e. including the orbital outburst points. Figure 1. MACHO V (a) and R (b) light curves; (c) MACHO colour variation, (d) OGLE IV light curves together with the mean MACHO V light curve (in grey) projected forward using the ephemeris of Alcock et al. ( 2001 ). The red and blue vertical lines refer to the times of the SALT and SAAO 1.9 m telescope observations, respectively. See Fig. 10 for more detail regarding the timing of the SALT spectra within the overall OGLE IV light curve. Figure 2. Lomb–Scagle periodogram of the MACHO V data showing peaks at 445.1 d, 352.7 d and 391.4 d for the first half (a), second half (b) and complete light curve (c), respectively. (d) Power spectrum of the OGLE IV data showing a peak at 423.74 d. To further explore the variation in the time-scale of the superorbital modulation, the complete MACHO data were divided into two parts. The power spectrum of the first and second half shows a peak at 446.0 ± 0.2 d and 355.4 ± 0.29 d, respectively (Fig. 2 ). The OGLE IV also shows a long-term variation but now of a much smaller amplitude, although still with a period consistent with that in the MACHO data. When all bright points related to the 16.64 d outburst were removed (as in the MACHO data) the power spectrum shows its highest peak at P sup = 423.74 ± 0.42 d and some additional peaks around 200 d (see Fig. 2 ). We have compared this behaviour to the original superorbital period by projecting the 420.8 d light curves and ephemeris by Alcock et al. ( 2001 ) into the OGLE IV data (Fig. 1 ). The optical maxima in the MACHO data are flat (i.e. no optical outbursts were seen) and last ∼200 d, meaning that we are seeing the completely disc-less B star. However in the OGLE IV phase, the optical maxima are not flat and the orbital outbursts now occur throughout the OGLE superorbital cycle, implying that during the OGLE IV optical maxima the equatorial disc was not completely dissipated. The MACHO and OGLE IV light curves folded on the superorbital period are shown in Fig. 4 . The colour variation ( V − R ) of A0538–66 shows that the source is bluer at optical maxima and becomes redder as the source fades. This behaviour is opposite to that seen in most other BeX where the source is redder when it is brighter. However, during outbursts on the orbital period associated with periastron passage of the NS, the optical brightening is accompanied by a slight increase in reddening (i.e. the source gets redder). The amplitude of the outbursts in the MACHO data clearly varies throughout the long-term 420.8 d cycle, being very strong (∼0.7 mag) at optical minima, decreasing in amplitude as the source brightens and completely disappearing at optical maxima. Similar behaviour is seen in the OGLE IV data; however, there are clearly orbital outbursts occurring even when the source is at maximum in the long-term cycle. In order to refine the orbital period, we removed the
Paper: 2017ApJ...835....4B (acknowledgments)
The authors thank an anonymous referee for constructive comments, which were useful to improve the paper. D.C. thanks the MIT-India Program for travel support.
Paper: 2016ApJ...829...86S (fulltext)
Yonsei-Yale (Spada et al. 2013 ). With the isochrones chosen, it creates synthetic populations that are then contrasted with observations. To populate the synthetic models, Talos uses a Kroupa IMF (Kroupa 2001 ) and takes into account various properties of the input data to make the models directly comparable to the observations. These properties include distance, reddening, binary fraction, photometric errors, and completeness fractions. Then, the combination of synthetic populations that best matches the observed decontaminated CMD and MDF is selected as the SFH of the object. The comparison between synthetic and observed CMDs is done using Hess diagrams, in which each point represents the number of stars at a specific color-magnitude bin. Each time a combination of synthetic populations is created, the routine calculates the differences between the star counts in the model and the observations at each color-magnitude bin, along with the difference at each metallicity bin from the MDF. With these differences, Talos calculates a Poissonian chi-squared, whose minimization gives the resulting SFH (i.e., the model that best reproduces simultaneously the observed CMD and MDF). 3.2. General Setup To run Talos and calculate the SFH, we have to determine a set of parameters associated with our data. These parameters are used to populate the synthetic models that are directly comparable to the data. From the entire CMD we used the photometry of a region that consisted of a magnitude range of 21–24.5 and a color range of −0.5–1.15, which we denominated the photometric CMD region (blue dashed box on Figure 1 ). The colors and magnitudes of the stars in the photometric CMD region are compared to the different models to fit the SFH. This region was chosen to avoid CMD locations significantly affected by completeness corrections or contamination sources, while maximizing our signal. Additionally, magnitudes brighter than g = 21 were excluded to avoid regions of late stages of stellar evolution, where the different sets of isochrones we tried (Teramo, Dartmouth, and Yonsei-Yale) differed significantly from each other. The individual bins that defined the Hess diagrams were chosen to have a size of 0.1 in magnitude and 0.025 in color. These dimensions were selected to be small enough to be able to trace the different features of the stellar populations in the CMD, and big enough to avoid significant shot noise in each bin. To construct the MDF of the data and the different models, Talos uses a region of the CMD where significant spectroscopic data exist, which we denominated the spectroscopic CMD region (green solid box on Figure 1 ). In our case, this region of the CMD had a g − r color range of 0.5–1.25 and a g- magnitude range of 18–20.5. Then, each time Talos generates a model stellar population, it constructs the associated artificial MDF with the metallicity values of all the stars from the model that lie in the spectroscopic CMD region. Analogously,