adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.489..771N (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank Federico Lelli, Harley Katz, Cameron Lemon, and the anonymous reviewer for helpful comments and discussions. APN thanks the Science and Technology Facilities Council (STFC) for their PhD studentship. EP acknowledges support by the Kavli foundation. ACD acknowledges partial support from STFC under grants ST/L000385 and ST/L000636. DS acknowledges ERC starting grant 638707 and support from the STFC. HD is supported by St John’s College, Oxford and acknowledges financial support from ERC grant no. 693024 and the Beecroft Trust. This work used the DiRAC ( www.dirac.ac.uk ) system: Data Analytic at the University of Cambridge [funded by BIS National E-infrastructure capital grant (ST/K001590/1), STFC capital grants ST/H008861/1 and ST/H00887X/1, and STFC DiRAC Operations grant ST/K00333X/1]. DiRAC is part of the National E-Infrastructure.
Paper: 2016MNRAS.457.1089M (fulltext)
that the 3:2 MMR of the orbital periods affect periodic variations of the Keplerian orbital parameters. She has obtained the expansion of disturbing function and identified only two first-order 3:2 resonance term. But we identify ( r + 1): r MMR terms in the expression of disturbing function and obtained the perturbations from the truncated disturbing function. In this manuscript, we have taken three exoplanetary systems namely Kepler-62, HD 200964 and Kepler-11 and as per our knowledge they contain planets in 2:1, 4:3 and 5:4 near MMRs, respectively. We apply theory discussed in Sections 3 and 4 individually in our three exoplanetary systems that we have chosen. This paper is organized as follows. In Section 2, we introduce the general three-body problem and the stability of exoplanetary system. The expansions of the disturbing function and perturbation equations of orbital elements are introduced in Section 3 . In Section 4, we present our secular resonance dynamics of exoplanetary system. Applications of the model for the case of 2:1 resonance are shown in Section 5 for Kepler-62 system. The case of the 4:3 resonance is discussed in Section 6 for HD 200964 system. In Section 7 , the case of 5:4 is discussed for Kepler-11 system. Finally, Section 8 is devoted to conclusions. 2 GENERAL THREE-BODY PROBLEM AND DYNAMICAL STABILITY Suppose the barycentric position vectors of star and two planets be , and , respectively. Let two planets of masses M 1 and M 2 orbiting a star of mass M ⋆ . Let the Jacobi coordinates and be the position of M 1 relative to M ⋆ and position of M 2 relative to the centre of mass M ⋆ and M 1 . We notice that this system is different from the restricted problems in which one of the bodies must have negligible mass. Also we ignore the effects of oblateness of star and the planets. We assume that the planets in all system are coplanar. This approximation of coplanarity makes sense because for exoplanets, their mutual inclination is not known with any precision, and is taken equal to zero (Beaugé, Ferraz-Mello Michtchenko 2003 ). Let a i , e i , λ i and ω i be the semimajor axis, eccentricity, mean longitude and the longitude of the pericentre of the i th planet for i = 1, 2. In the three-body problem, one of the interesting topic is the stability of the Solar system or exoplanetary system. Although, in celestial mechanics it is the unsolved problem till now, many scientist studied this because of its importance in many areas like space science, astronomy and astrophysics. In the context of planets and exoplanets formulation, Graziani Black ( 1981 ) studied conditions under which model planetary systems which consisting of a star and two planets with coplanar and initially circular orbits. Based on their results, they obtained a necessary condition for orbital stability where M 1 and M 2 are masses of planetary companions and M ⋆ is the mass of the star. The parameter Δ is the ratio of the separation distance between the
Paper: 2015A&A...575A..56B (acknowledgments)
We are grateful to Desika Narayanan, Mark Sargent, and Anita Zanella for stimulating discussions, to Damien Chapon for help with the implementation of some post-processing modules, and to the anonymous referee for a very careful reading and useful comments. The simulations presented in this work were performed at the Très Grand Centre de Calcul of CEA under GENCI allocations 2013-GEN 2192 and 2014-GEN2192 and on SuperMuc at the LRZ under a PRACE allocation. We acknowledge financial support from the EC through an ERC grant StG-257720 (F.B., F.R.).
Paper: 2018A&A...619A..77K (fulltext)
( Schneider et al. 2017 ; Mapelli et al. 2017 ). Interestingly, Mapelli et al. (2017) predict that the distribution of metallicity of BH–BH merger progenitors peaks at around ∼ 0.1 Z ⊙ and declines towards lower Z , which conflicts with our Fig. 8 . The most likely explanation of this discrepancy is that we are overestimating the contribution from very low metallicities at close redshifts of star formation. The majority of the low-metallicity content of the recent Universe is locked in dwarf galaxies with little star formation, for which our simplification that the mean metallicity of the Universe ≈ mean metallicity of star formation is likely less accurate. This interpretation also agrees with the fact that Mapelli et al. (2017) did not find the clearly bimodal distribution of massive BH–BH merger birth redshifts as shown in the case of our simulations in Fig. 2 of B16, but rather only the single maximum around the peak of cosmic star formation at z ≈ 2. However, even if we were overpredicting the star formation in low metallicities Z 0.1 Z ⊙ at redshifts z 2 to the point at which we would have to discard all our mergers formed in these conditions, this would still not affect our predictions for the merger rates by more than a factor of 2. Although making predictions for CBM based on cosmological simulations as carried out by Schneider et al. (2017) or Mapelli et al. (2017 ; or, in a simplified way, also by Belczynski et al. 2018) may be burdened with additional biases, this ultimately seems to be the superior approach that hopefully will allow us to confront models with various galactic properties in the case of mergers with identified hosts (such as GW170817, Abbott et al. 2017d ). Finally, we wish to highlight that the small impact of IMF variations on merger rates (Sect. 6.2 ) is a general result that should also appear in the predictions based on cosmological simulations. Even though such simulations often provide SFR values as their output, changing the assumptions on the massive-star IMF without renormalizing the SFR make their results inconsistent with star formation tracers such as UV observations. 6.4. Comparison with LIGO/Virgo detection rates and other studies We show the merger (Gpc −3 yr −1 ) and detection rates (yr −1 ) from our simulations in Table A.1 . While our models perform relatively well in retrieving the BH–BH merger rate as constrained by the LIGO/Virgo observations R BHBH = 12–213 Gpc −3 yr −1 ( Abbott et al. 2017a ), they underpredict the rate of NS–NS mergers that was inferred after the recent detection of GW170817: ( Abbott et al. 2017d ) 6 . Recently, Chruslinska et al. (2018) analysed NS–NS merger rates within a large suite of S TAR T RACK models, varying multiple binary evolution assumptions, and found that it is, in fact, very difficult to obtain R NSNS consistent with the LIGO/Virgo constraints. The only three models marginally consistent with the reported NS–NS merger rates, which have R NSNS values of
Paper: 2017MNRAS.469.4063W (fulltext)
the photometry in all bands has been convolved to match the WFC3/ F 160 W resolution (Guo et al. 2013 ), any influence caused by different PSF sizes of HST filters has been eliminated. All galaxies receive the same statistical weight in a stack. The maximum radial extent to measure colour gradients is set to two times the SMA, where reliable photometry is still available. 4.2 Colour gradients uncorrected for beam smearing and stacked by radius scaled to SMA This subsection shows raw stacked colour gradients without any beam-smearing correction. First, we scale the radial colour profiles of each individual galaxy by its SMA (effective radius along the galaxy major axis), by interpolating between the apertures as described in Section 2 . Then these scaled colour profiles are stacked together to produce the average profiles as shown in Fig. 3 . Figure 3. UVI colour gradients for star-forming galaxies in mass–redshift bins, as indicated at the top and right margins. The radial colour profiles are scaled by effective radius before stacking, without correction for PSF smearing. For each panel, the stacked radial trajectories in U − V and V − I are plotted as the black curves. Dashed lines denote the Calzetti reddening vector. Mean integrated A V and the number of sample galaxies are indicated at upper left in each panel, and the typical standard deviation of the mean of stacked gradients is shown at lower right. The gradient trajectories follow the reddening vector, which indicates that most of the gradients are caused by dust variations and that sSFR is nearly constant with radius. The grid of panels in Fig. 3 shows the shapes of the stacked colour trajectories in UVI space as a function of mass and redshift. Each panel is a UVI diagram, and the black lines are the paths taken by the colours in going from the innermost radius (upper right) to the outermost radius (lower left). The three symbols indicate local colours at 0.2 SMA (star), 1 SMA (open circle) and 2 SMA (dot). The direction of dust reddening by the Calzetti law is shown as the dashed lines. The main feature of this figure is that gradient trajectories approximately follow the reddening vector, which indicates that most of the gradients are caused by dust variations and that sSFR is nearly constant with radius. This conclusion is unchanged under beam-smearing correction and stacking by radius rather than scaled radius, as shown in the next section. 4.3 Colour gradients stacked based on angular distance and beam-smearing correction Next, we turn to another stacking method of colour profiles and then evaluate and correct the beam-smearing effects caused by the PSF. To start with, galaxy colour profiles are stacked together based on the angular distance in arcsec to their centres, instead of the normalization used in Section 4.2 . The resultant colour gradients are shown as red curves in Fig. 4 . This stacking method ensures that the stacked profiles have a well-defined angular
Paper: 2016MNRAS.462.3646B (acknowledgments)
We thank the anonymous referee for a critical reading of the paper and valuable suggestions that helped improve the quality and clarity of this work. We also thank to Dany Page, John Beacom and W. H. Lee for useful discussions. CB acknowledges to CAPES–Brazil by the postdoctoral fellowship received through the Science Without Borders programme. This work was supported by the projects IG100414 and Conacyt 101958. The software used in this work was in part developed by the DOE NNSA-ASC OASCR FLASH Center at the University of Chicago.
Paper: 2021MNRAS.503..354G (fulltext)
Y , X – Z , and Y – Z planes. The inner Sagittarius, the closest Local (Orion), and the outer Perseus spiral arm segments can be recognized in the X – Y projection (a top-down view of the Galactic disc) (cf. Hou Han 2014 , figs 5–7 therein; Nakanishi Sofue 2016 , fig. 7 therein; Xu et al. 2018 , fig. 1 therein; Reid et al. 2019 , fig. 1 therein; Cantat-Gaudin et al. 2020 , fig. 8 therein; Xu et al. 2021 , fig. 1 therein). Stars are indeed located within the extremely thin disc. The Sun is situated within the Orion segment, at the inner edge of this arm (cf. Simonson van Someren Greve 1976 ; Basharina, Pavlovskaya Filippova 1980 ; Cohen et al. 1980 ). Figure 2. Space locations of all 1972 (top panels) and selected 964 (bottom panels) OB stars. The Cartesian ( X , Y , Z ) coordinates are shown, and a dashed line indicates the solar circle and the Galactic mid-plane. The Sun is at (0, 8.2, 0.02) kpc. Fig. 3 gives a plot of los for all and selected stars as a function of the longitude l. A well-defined effect of the Galactic differential rotation is clearly seen. For these two samples of stars – all 1972 and selected 964 ones – the space and velocity distributions have approximately the same properties, as is apparent from Figs 2 and 3 . Figure 3. The heliocentric line-of-sight velocities of OB stars as a function of the Galactic longitude l. Top panel: the velocities of all 1972 stars. Bottom panel: the velocities of selected 964 stars. 3 RESULTS OF CALCULATION To compute the 11 mutually independent unknowns , , , , u 0 , 0 , 0 , , , φ 0 , and λ rad for a given m , we define the weighted sum, S , of squared residuals of measured and modeled (equation 3 ) velocities: where N is the total number of objects, q is the number of parameters to be derived, and is the uncertainty of . The best-fitting values of unknowns are found by fitting that minimizes S . The model depends linearly on the set of nine parameters , , , , u 0 , 0 , 0 , , and and non-linearly on two parameters φ 0 and λ rad . For each model m was fixed and minimization of S by the least-squares fitting was performed in φ 0 and λ rad (that is to say, in the phase of the wave φ 0 and in the pitch angle p at the solar position r 0 for a given number of spiral arms m ). The quantities and are periodic with respect to φ 0 with period 2, while the quantity S is periodic with respect to φ 0 with period . As usual, for a trailing pattern, we chose for the phase φ 0 the values that satisfy the condition (Mishurov et al. 1997 ; Griv Wang 2014 ; Griv et al. 2017 , 2020 ). The surface of S ( φ 0 , λ rad ) and cuts through the S space versus φ 0 and λ rad constructed over selected OB stars for the most interesting m = 1, 2, 3, 4 models are shown in Fig. 4 . A global and several local minima of S with respect to φ 0 and λ rad are seen from our simulation for all models as λ rad increases from 0 to 3 kpc (as | p | increases from 0° to 15°, respectively). Mishurov et al. ( 1979 ) and Pavlovskaya Suchkov
Paper: 2017ApJ...846..170T (acknowledgments)
We thank the organizers of and all participants in the MIAPP 2015 workshop on “The Many Faces of Neutron Stars” for many stimulating discussions. T.M.T. and M.U.K. acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) Grant No. TA 964/1-1. At Garching, the work was supported by the DFG through the Excellence Cluster “Universe” EXC 153 and by the European Research Council through grant ERC-AdG No. 341157-COCO2CASA.
Paper: 2018ApJ...865...58P (fulltext)
of the material. At the surface of silicates, either hydrophilic silanol groups (Si–OH) or hydrophobic Si–O–Si groups are present. The ratio of these groups depends on the production process, the thermal treatment of the silicates, and on a possible processing of silicate surfaces. In silicates produced by laser ablation, the amount of Si–OH groups is small. However, reactions between water molecules and grains have probably increased the number of silanol groups on the surface of the grains. Consequently, the number of hydrophilic surface sites was increased. Thus, with our results, we demonstrate a principal difference between the surface properties of two classes of interstellar grains, namely, the finding of differently bound water ice molecules to silicate grains in contrast to fullerene-like carbon grains. However, this is not a general statement for carbon because carbon surfaces can be very different. For example, oxygenation of C-atoms can produce hydrophilic surface sites. 4.3. Astrophysical Implication The knowledge of the desorption properties of water ice mixed with dust is very important for the understanding of astronomical observations of water vapor in planet-forming regions and for the revealing of the architecture and evolution of planet-forming disks. The desorption of water ice triggering the release of other volatiles trapped in the ice influences the chemistry of the gas phase of disks by feeding it with molecules. In addition, the formation efficiency of larger bodies depends on the ice coverage of dust grains. Knowing the amount of ice relative to dust in a planet-forming disk and the kinetics of desorption of the ice, the structure and morphology of grains and larger bodies can be modeled. It will help to trace back the thermal history of the disk. Conversely, knowing the thermal history of a cosmic body seeded with molecular ice and the kinetics of desorption of ice, the abundance of molecules stayed in the ice and released into the gas phase can be estimated. It will improve our understanding of astrochemical processes in the gas phase and on surfaces. However, more systematic studies on the thermal desorption of molecular ices mixed with different dust analogs are needed. This work represents the first small step in this challenging direction. 5. Summary We measured the TPD of water ice, which had been premixed with amorphous carbon or silicate dust grains, and demonstrate the dependence of the kinetics of desorption of water ice on the dust/ice ratio. The desorption order increases with an increase of the dust/ice mass ratio and reaches 1, indicating the desorption of ice molecules from the large surface of dust grains. This result shows an important link between the structure and morphology of icy dust grains in planet-forming disks and the amount and the desorption properties of seeded ice. If more information on the thermal history of dust grains was known, more information about the structure and
Paper: 2018A&A...617A..21S (fulltext)
the pre-event frame acquired at 16:04 UT from the LASCO-C2 frame containing the CME at 19:30 UT. LASCO images were also used to measure the plane-of-the-sky (POS) velocity of the prominence, ν POS ≃ 300 km s −1 , as well as its projected thickness, D ≃ 56 000 km, which was taken as an approximation of the prominence average LOS thickness, under the simple assumption of cylindrical geometry. Fig. 1. LASCO-C2 mass image acquired at 19:30 UT on August 2, 2000, and intensity distribution of the Ly α line along the UVCS slit (measured at the same time and represented with color gradient). The two points considered in this analysis (P1, located at a helio-latitude of ~57° N, and P2, located at ~37° N) are also indicated. UVCS recorded spectra of several UV coronal lines (see Table 1 of Paper I for a complete list) as the prominence crossed its field of view (FOV), a 40 arcmin long slit placed perpendicularly to the solar radius at a latitude of 40°NE and heliocentric distance of 2.3 R ⊙ (see Fig. 1 ). In particular, spectral profiles of the neutral hydrogen Lyman- α ( λ 12 = 1215.67 Å) and Lyman- β ( λ 13 = 1025.72 Å) lines were acquired with a spatial resolution of 21 arcsec (≈15 000 km) and an integration time of 120 s during the entire CME event. Both lines systematically appear to be the superposition of two components with different widths, the narrower one representing, in our interpretation, real prominence emission, while the broader one is more likely due to plasma in the PCTR or, alternatively, in the hot plasma shroud surrounding the prominence (see Habbal et al. 2010 ). Table 1. Observed quantities and modeling results. The two components were separated with a double Gaussian fit to the line profiles, and consistency of the fit was verified with a minimum chi-squared analysis (see Paper I). A mean pre-event spectrum, obtained by averaging over several exposures preceding the onset of the eruption, was subtracted from the line profiles in order to remove the contribution from the quiet corona surrounding the prominence from the observed emission. This correction may not be accurate because, as evidenced by the depleted (black) region around the prominence in Fig. 1 , much of the pre-event corona has been blown away by the CME during the eruption. However, prominences are generally very bright in the Lyman lines (e.g., Ciaravella et al. 2003 ), and the quiet-corona contribution can be assumed to be very small; therefore possible uncertainties related to the background subtraction can be considered negligible in this case. Figure 2 reports the total integrated intensities of the Ly α and Ly β narrow components as functions of time and position along the UVCS slit. The prominence exhibits a complex structure in both lines, with ramifications and apparently superimposed emission features. The intensity of the Ly α line is up to 250 times higher than that of the Ly β . The Ly α to Ly β intensity ratio is consistent with the value somewhat
Paper: 2020MNRAS.491.3101C (acknowledgments)
ACKNOWLEDGEMENTS MC thanks A. Mead for useful conversations in the early stages of this work. MC, JHD and CH acknowledge support from the European Research Council under grant number 647112. CH acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. Work at Argonne National Laboratory was supported under U.S. Department of Energy contract DE-AC02-06CH11357. The authors are grateful to Ue-Li Pen for his assistance with computing resources. Computations were performed on the BGQ and Niagara supercomputers at the SciNet HPC Consortium (Loken etal. 2010 ; Ponce etal. 2019 ). SciNet is funded by the Canada Foundation for Innovation; the Government of Ontario; Ontario Research Fund - Research Excellence; and the University of Toronto.
Paper: 2017ApJ...845..141T (acknowledgments)
The contribution of V.S.T., Z.M., T.T., and J.A.L. was supported by NASA's HSR, LWS, and HGI programs, NSF grants AGS-1156119 (SHINE program) and AGS-1560411, NSF (Frontiers in Earth System Dynamics program), and contracts from Lockheed-Martin and Stanford University to Predictive Science Inc. O.P. was supported by NASA Grant NNX15AB89G. Computational resources for the MHD computation were provided by NASA Advanced Supercomputing and the NSF-supported XSEDE program.
Paper: 2015MNRAS.453.1026K (acknowledgments)
Based on observations collected at the European Southern Observatory Very Large Telescope in programme 177.D-3023(C). VMK and JSV acknowledge funding from DCAL-NI and the UK Science and Technology Facilities Council. VMK thanks the anonymous referee, and Drs R. Oudmaijer and G. Ramsay for providing helpful and constructive comments.
Paper: 2019ApJ...887...70Z (acknowledgments)
This work is supported by the Fundamental Research Funds for the Central Universities and from the National Science Foundation of China (NSFC, grant No. 11421303 and grant No. 11590782). Studies of interstellar chemistry at Leiden Observatory are supported through a grant by the Netherlands Organisation for Scientific Research (NWO) as part of the Dutch Astrochemistry Network and through the Spinoza premie.