adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...833..218A (acknowledgments)
We acknowledge the partial support of the following grants: NASA NNX14AC08G, NASA/SWRI Subcontract A991132BT, NASA Subaward 37102-2, NNX14AJ53J GCR, NNX15AI65G. This paper uses data from the Heliospheric Shock Database, generated and maintained at the University of Helsinki.
Paper: 2016ApJ...823..148H (fulltext)
outflow. Left: HST image of the young stellar jet HH 34 and its bow shock (from Hartigan et al. 2011 ). Right: expanded views of the bow shock and the putative Mach stem. The structured bow shock in HH 47 (Hartigan et al. 2011 ) is another potential example where transient Mach stems may occur. This object resolves into what appear to be dozens of small knots in high-resolution images. Surprisingly, these bright points form and dissipate on timescales of a decade, which is difficult to explain if they represent discrete knots of high density that plow through the working surface of the flow. In both HH 34 and HH 47, the shock velocity is ∼100 km s −1 and the knot size ∼7.5 × 10 14 cm, and so τ F ∼ 2.4 years. The numerical models imply that we should expect any transient Mach stems to evolve on a timescale of about a decade, in agreement with the observations. The postshock regions of cooling astrophysical shocks are complex and contain various zones of ionization for each element. Hence, these regions are not characterized by a single value of γ . However, the overall dynamical effects of cooling in bow shocks have been well-established for some time (e.g., Blondin et al. 1990 ). A critical parameter is the ratio of the cooling-zone distance d COOL to the obstacle size . When this ratio is ≳1, the gas acts like an adiabatic flow with γ = 5/3 where the maximum size of a Mach stem will be comparable to . In the opposite limit, when the obstacle is very large compared with the cooling length, the gas behaves dynamically as if it is isothermal. An isothermal shock has γ = 1 where no Mach stems are possible. In this case, Mach stems only exist on scale sizes ≲ d COOL . Hence, the maximum size of a Mach stem should be the smaller of d COOL or . The cooling distance scales inversely as the preshock density and directly as a power of the shock velocity (e.g., Hartigan et al. 1987 ), and also depends on the strength of the magnetic field (Hartigan Wright 2015 ). Hence, whether or not two intersecting bow shocks create a substantial Mach stem depends on the shock velocity, density, magnetic field strength, and the sizes of the bows. Overall, Mach stems should be most common when size scales are small enough to render cooling unimportant in the dynamics. The distribution of clumps in the flow and ambient medium ultimately determines whether or not Mach stems will form and grow in any situation, with the optimal separation for Mach stem growth being several times the diameter of the obstacles in a flow with moderate Mach number. The widest range of allowable obstacle separations occurs when cooling is least important (Section 4.1.2 ). Cooling distances in the HH 34 and HH 47 bow shocks are on the order of the size of the transient bright knots in these objects (Hartigan et al. 2011 ). Hence, the effective γ for these spatial scales is ∼5/3, which is favorable for Mach stem growth. In a future paper, we will present simulations of intersecting
Paper: 2016ApJ...828...53H (acknowledgments)
Work by C. Han was supported by the Creative Research Initiative Program (2009-0081561) of National Research Foundation of Korea. The OGLE project has received funding from the National Science Centre, Poland, grant MAESTRO 2014/14/A/ST9/00121 to A.U. The OGLE Team thanks Profs. M. Kubiak and G. Pietrzyński, former members of the OGLE team, for their contribution to the collection of the OGLE photometric data over the past years. Work by A.G. was supported by JPL grant 1500811. Work by J.C.Y. was performed under contract with the California Institute of Technology (Caltech)/Jet Propulsion Laboratory (JPL) funded by NASA through the Sagan Fellowship Program executed by the NASA Exoplanet Science Institute. Work by C.B.H. and Y.S. was supported by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, administered by Universities Space Research Association through a contract with NASA. The Spitzer Team thanks Christopher S. Kochanek for graciously trading us his allocated observing time on the CTIO 1.3 m during the Spitzer campaign. We acknowledge the high-speed internet service (KREONET) provided by Korea Institute of Science and Technology Information (KISTI).
Paper: 2021ApJ...911...84R (fulltext)
from the MIST isochrones, that is, only using the SED of the star, its parallax from Gaia DR2, and the light curves and radial velocities. The uncertainties from this analysis are 35% for the planetary mass, surface gravity, and density, and 3.3% for the radius. These are consistent with the uncertainties derived from the analysis using the MIST evolutionary tracks as constraints. The fractional uncertainties from the original paper and the analysis without constraints are shown in sky blue (with constraints) and green (without) in Figure 1 . We conclude that the inferred parameters of the system derived using purely empirical constraints are as precise (and likely more accurate) than those inferred using theoretical evolutionary tracks. Therefore, at least for systems similar to KELT-26, we see no need to invoke theoretical priors. 4.3. Kepler-93b Kepler-93b is a terrestrial exoplanet with a 4.7 day period discovered by Ballard et al. ( 2014 ). It has a mass of M p = 4.02 ± 0.68 M ⊕ and radius of R p = 1.483 ± 0.019 R ⊕ . With a radius uncertainty of only 1.2%, it is one of the most precisely characterized exoplanets to date. Ballard et al. ( 2014 ) used asteroseismology to precisely constrain the stellar density, and then used it as a prior in their MCMC analysis, leading to the remarkably precise planet radius. Their analysis did not use external constraints from stellar evolutionary models, however. Dressing et al. ( 2015 ) revisited Kepler-93 and collected HARPS-N (Mayor et al. 2003 ) spectra, which they combined with archival Keck/HIRES spectra to improve upon the planet’s mass estimate. They thus reduced the uncertainty in the mass of Kepler-93b from ∼40% (Ballard et al. 2014 ) to ∼17%. We used the photometric parameters ( T , τ , and δ ) from Ballard et al. ( 2014 ) and the semi-amplitude K ⋆ from Dressing et al. ( 2015 ) to test our analytic estimates. We compared our results to the reported uncertainties in M p , g p , and ρ p from Dressing et al. ( 2015 ), since they provide slightly more precise properties. The uncertainties in the properties of Kepler-93b are all ∼17%, and 1.2% for the radius, which are in excellent agreement with our analytic estimates, as shown in Figure 1 and Table 1 . Interestingly, this implies that the asteroseismological constraint on ρ ⋆ does not significantly improve the overall constraints on the system. 4.4. HD 21749b HD 21749b is a warm sub-Neptune on a 36 day orbit transiting a K4.5 dwarf discovered by Dragomir et al. ( 2019 ). The planet has a radius of determined from TESS data, and a mass of constrained from high-precision, RV data from the HARPS spectrograph at the La Silla Observatory in Chile. Dragomir et al. ( 2019 ) performed an SED fit combined with a parallax from Gaia DR2 to constrain the host star’s radius to R ⋆ = 0.695 ± 0.030 R ⊙ . They then used the relations of Torres et al. ( 2010 ) to derive a stellar mass of M ⋆ = 0.73 ± 0.07 M ⊙ , although they do not specify what values of T
Paper: 2021A&A...649A..50M (fulltext)
and C + 1, with If we assume m C ≪ m j , then the coagulation between any grain of bin j and a grain of bin i with a mass lower than m C , − − m j produces a grain in bin C . The fraction υ of grains transferred to bin C is therefore given by However, if m C , − − m j m i , − , then the product of coagulation is too massive for bin C and υ = 0. Conversely, if m C , − − m j > m i , + then υ = 1. The integrals of Eq. ( B.21 ) need to be calculated analytically due to the computational cost of performing a numerical integration for each pair of bins at each time step. In particular, if the distribution is steep, one end of the bin may not be captured accurately, yielding large errors. For coagulation kernels that are in the form , the analytical calculation is tedious but straightforward. Fortunately, this is the case for the kernel used in this paper (see Eqs. ( 9 ) and ( 14 )), The analytical expression of υ is a function of m β , which can lead to inaccuracies due to numerical truncation errors where the distribution is steep (| β | is large). Fortunately, for high values of | β |, some terms can be simplified. This is not the case for specific lower values (if we have to integrate m −1 into a log function instead of a power-law), however, so that the simplification cannot be generalized to all β . We also have to ensure that the simplification can be applied to both the top and bottom of the fraction. We detail here the procedure used in Ishinisan to minimize numerical inaccuracy. We define the following functions: and r ( m 1, − , m 1, + , m 2, − , m 2, + , β 1 , β 2 ), so that We therefore have for the turbulent kernel where κ k = m k , − if s k −5, and κ k = 1 else, for k = 1, 2, to match the criteria of function q ( B.23 ). When the value of υ is known, the variations rates of mass density in the four involved bins are After summing all the variation rates, we chose the time-step Δ t so that the relative variation of mass in each bin does not exceed a number ε prec , typically smaller than one, to ensure numerical accuracy, The densities are then updated and the new slopes, average size, and average mass of bins are calculated according to the calculations detailed in Appendix B.1 . B.3. Tests We tested the coagulation algorithm using two simple kernels, namely the constant and additive kernels, Menon Pego (2004) showed that these two kernels possess self-similar solutions, The mass density in each bin i is given by where erf and erfc are the error function and the complementary error function. In each case, the two formulae are mathematically equivalent but are written in this form to avoid numerical truncation errors. The fraction υ has a similar form as the turbulent kernel presented in the previous section (Eq. ( B.24 )). The function q is the same, and function r for the constant kernel reads and for the additive kernel We used Ishinisan to reproduce these self-similar solutions. We set up 30 bins between m min = 4 × 10 −6 and m max
Paper: 2016A&A...585A.150N (fulltext)
These values are compatible with our results based on the cleaned open cluster sample, whereas the use of the complete data set always yield somewhat steeper slopes. However, the most apparent difference in the RMD of Cepheids and open clusters is that Cepheids show a continuous decline of metallicity out to 18 kpc. Fig. 8 RMD of the open clusters and Cepheids compared to Galactic chemical models. Upper panel : the running average and the spread in iron of the Cepheid sample is shown as a grey line and a light grey area. The red line is the chemical model for the gas by Minchev et al. (2013) , the black dashed line shows the gradient derived for the youngest cluster age group (Table 3 ). Lower panel : in grey we show the running average of intermediate-age clusters (0.53 Gyr), the red dashed line is the chemodynamical model by Minchev et al. (2013) for 2 Gyr, the black solid line is the mean value of outer disc clusters, and the black dashed line is the gradient that we derived for all clusters in the outer disc. We used the Cepheid data to derive a running average with the same criteria that we adopted for the open clusters in Sect. 4.1 . We did not detect any significant difference by using a better sampling. The upper panel of Fig. 8 compares the results for open clusters with the Cepheids. Obviously, the gradient of the youngest cluster group is somewhat steeper than the one based on Cepheids. The slope of the cluster groups does not show a clear dependence on age, thus the steeper gradient might be attributed to the smaller sample and the narrower distance range that is covered by the young clusters. The sample of the Cepheids represents a very young population with a mean age of about 60 Myr. Thus, they have not moved much from their birthplace, and a comparison with a Galactic chemical model for the gas is justified. Figure 8 shows that the model by Minchev et al. (2013) agrees very well with the observations. We shifted the model by about 0.05 dex towards lower metallicities to match the Cepheids and young open clusters at the solar position. Genovali et al. (2014) identified some candidate Cepheid groups that show an enhanced metallicity by about 0.05 dex (the groups VI to X in their paper) and are located close to the Perseus spiral arm. These can also be identified in Fig. 8 at 910 kpc. The Cepheids also show an enhancement of the metallicity in the innermost Galactic region with a much steeper gradient than the chemical model predicts. These objects could be related to the CarinaSagittarius arm. The spiral arms are the star-forming machines of the disc, thus the effect of the spiral arms cannot be ignored ( Lépine et al. 2011 ). However, a simple extrapolation of the metallicity towards the Galactic centre is not justified because inner disc and Galactic bulge and bar show different trends (see discussion by Genovali et al. 2013 , and references therein). Minchev et al. (2013) also presented a model that includes the dynamical
Paper: 2016ApJ...822...10K (fulltext)
of sight is chosen to be along the x- axis, it is useful to express the x positions of each surface with a specific r rc asDefining a new functionyields denotes the two line-of-sight ( x ) positions for a surface defined by a particular value of r rc at sky position ( y , z ). Functions a , b ( y , z ), and c ( y , z ) are defined by the geometry of the object in question. Equations ( 6 )–( 8 ) describe the appropriate functions representing a spheroid with axial ratio α and inclination i. E ( y , z ) is a function that is entirely dependent on the object's shape; the following section discusses its conceptual meaning further. 3.1. Discrete Derivation It is possible to define an object as a discrete series of shells, each of which is defined as the region between an inner () and an outer () surface with an average volume density () within the shell. The depth along the x- axis of each such shell at different ( y , z ) positions will vary according towhere is the total depth along the line of sight at position ( y , z ) for the shell made up of two surfaces defined by and . Substituting Equation ( 11 ) into Equation ( 12 ) yieldsEquation ( 13 ) describes the depth of each shell; however, this representation is of limited use since r rc is not an observable quantity. Similarly, E ( y , z ) is a function that is directly dependent on the object's unknown shape. Equation ( 13 ) must be put in terms of observable quantities: the observed column density, and the area within each column density contour. Each surface as described by Equation ( 11 ), when projected onto the line of sight, produces a closed boundary composed of those ( y , z ) positions where . In view of Equation ( 11 ) the projected boundaries of each shell are defined bySolving for E ( y , z ) using the spheroid model above yields a familiar relation,which is a simple ellipse that results from projecting a three-dimensional spheroid surface onto a two-dimensional plane. Equation ( 14 ) makes clear that each contour of equal E ( y , z ) corresponds to the boundary of a particular volume density surface, with a specific value of r rc , and possesses a unique projected area. In general, the projected area of a surface of a particular r rc can be expressed aswhere is a geometry-dependent unknown constant(). All positions with equal correspond to the projected boundary of a surface with with corresponding area A c . The additional subscript c denotes a specific contour. Therefore, Equation ( 13 ) can be reformed in terms of areas aswhere represents the depth along the line of sight of the shell between surfaces defined by and at all positions defined by the contour formed by the projected boundary of the surface defined by . The observed column density can then be defined as N c represents the column density at all positions ( y , z ) defined by the projected boundary of the surface. represents the mean volume density within the shell whose surfaces are defined by and . The column density
Paper: 2015A&A...575A..17H (acknowledgments)
We thank the referee for useful comments and suggestions which significantly improved the quality of this paper. T.M.H. gratefully acknowledges the financial support from the Belgian Science Policy Office (BELSPO) in the frame of the PRODEX project C90370 ( Herschel PACS guaranteed time and open time programs: science exploitation). I.D.L. is a postdoctoral fellow of the Flemish Fund for Scientific Research (FWO-Vlaanderen). M.B. also acknowledges the financial support of the same Flemish Fund for Scientific Research. PACS has been developed by a consortium of institutes led by MPE (Germany) and including UVIE (Austria); KU Leuven, CSL, IMEC (Belgium); CEA, LAM (France); MPIA (Germany); INAF-IFSI/OAA/OAP/OAT, LENS, SISSA (Italy); IAC (Spain). This development has been supported by the funding agencies BMVIT (Austria), ESA-PRODEX (Belgium), CEA/CNES (France), DLR (Germany), ASI/INAF (Italy), and CICYT/MCYT (Spain). SPIRE has been developed by a consortium of institutes led by Cardiff University (UK) and including Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); and Caltech, JPL, NHSC, Univ. Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China); CEA, CNES, CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Sweden); STFC, UKSA (UK); and NASA (USA). This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the NASA (USA). This research made use of APLpy, an open-source plotting package for Python hosted at http://aplpy.github.com , and Montage, which is funded by the NASA (USA) Earth Science Technology Office, Computation Technologies Project, under Cooperative Agreement Number NCC5-626 between NASA and Caltech, and maintained by the NASA/IPAC Infrared Science Archive.
Paper: 2018ApJ...864....9B (fulltext)
a radionuclide abundance in the comet based on ordinary chondritic values, for all our calculations. The possibility of inhomogeneities of short-lived radionuclides produced in the protosolar disk has been raised (Lee et al. 1998 ), which could lead to a comet enriched or impoverished in radionuclides. However, the alternate scenario of supernova injection, an efficient mechanism to enrich the PSN in radionuclides, involves extensive mixing of radionuclides into the disk (Ouellette et al. 2009 ) and would not lead to significant inhomogeneities. The radiolytic production per unit of volume can be influenced by the size of the body in which it occurs, only if its dimensions are shorter than the typical radiation range. This could have been the case for the building blocks of the comet shortly after CAI, when short-lived radionuclides were most active. Most of their radiation energy would become available for radiolytic production once the boulders become meter-sized. The accretion scenario of Davidsson et al. ( 2016 ), even though its timescale is considerably longer compared to the original scenario of Weidenschilling ( 1997 ), yields meter-sized boulders within 0.56 Myr after CAI, at most. This delay is insufficient to fully negate the production by short-lived radionuclides (see Figure 4 ). Overall, endogenic radiolysis is unlikely to have produced more than a fraction of the O 2 observed in 67P/C-G. Better experimental constraints on the radiolytic production of O 2 , as well as its subsequent emission, are required for a more definitive assessment of this fraction. Additionally, we have investigated the potential production of H 2 O 2 , where our model yields a much higher final abundance (about 0.1%) than that observed by Rosetta (upper limit of 0.0023%). A better understanding of H 2 O 2 production yields and of activation barriers in its dismutation is required to understand how the thermal and/or chemical history parameters led to the comet’s present-day composition. Appendix Appendix Information A.1. Attenuation of <inline-formula> <tex-math> </tex-math> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjaad22fieqn32.gif"></inline-graphic> </inline-formula> Particles Exiting a Grain A radionuclide type s inside a dust grain emits, in the form of emission j , a power dP s,j / dV per unit of volume. “Emission j ” designates a given emission in the decay chain (e.g., the 5.013 MeV α particle emission from the 231 Pa to 227 Ac transition in the 235 U decay chain). dP s,j / dV is given by:where n s is the density of radionuclides in the grain (atoms per unit of volume, deduced from the mass fractions in chondrites shown in Table 1 , the grain density and the molar masses of the radionuclides), τ s is the average decay time of radionuclide s (deduced from the half-life values in Table 1 ), E 0, j is the energy of emission j , and I j is the absolute intensity of emission j (i.e., the probability of emission j