adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.463.1199H (fulltext)
gravity brightening exponent for the radiative, primary component, determined through our models, is ∼1 (von Zeipel 1924 ). For the secondary component, the posterior distribution is a slightly skewed normal distribution. By considering the peak of the skewed distribution (not the Gaussian fit to the posterior distribution), we find the gravity brightening exponent to be β = 0.59 ± 0.08. While this is not in agreement with the value of β = 0.32, suggested by Lucy ( 1967 ), we find our value in close agreement (∼2σ) with that of Claret Bloemen ( 2011 ), who computed β = 0.48 for comparable models. This value was computed using ATLAS models (Castelli Kurucz 2004 ) and the Kepler bandpass, and taking into account local gravity and convection. It is also likely that the tidal distortion affects the gravity darkening exponent, as discussed by Espinosa Lara Rieutord ( 2012 ). In our binary models, we fixed the primary and secondary rotation rates to those obtained through spectral fitting, v sin i 1 = 29(2) km s −1 and v sin i 2 = 9(2) km s −1 . Combining these values with our model-determined orbital inclination (assuming that the orbital and stellar rotational axes are aligned) and radii, for the model with pulsations, we obtained F 1 = 7.1(2) and F 2 = 4.0(1), where F is the ratio of the stellar rotational to orbital period and the subscript denotes the primary and secondary components, respectively. For both models, the one with, and the one without pulsations, we obtained the same values with slightly different uncertainties (see Table 4 ). Interestingly, under the assumption that the stellar rotational and orbital axes are aligned, the primary component is rotating slightly faster than the predicted pseudo-synchronous rotation rate (Hut 1981 ), the stellar rotation synchronous with the orbital velocity at periastron, which was calculated to be F = 5.3(2) for both components; while the secondary component is rotating slightly slower. However, if we relax the assumption that the orbital axes are aligned, it is possible to find configurations where both stars are rotating pseudo-synchronously. 5 SUMMARY AND CONCLUSIONS We have modelled the heartbeat star binary system KIC 3749404 by combining a comprehensive assortment of tools including the binary modelling software, phoebe ; emcee , a python implementation of the affine invariant mcmc techniques; and our own software to fit pulsations and Doppler boosting. With these tools, we were able to obtain two successful fits to the heartbeat star light and radial velocity curves simultaneously: one without pulsations and one with all the tidally induced pulsations greater than 20 μmag (seven pulsations). From these fits, we obtained the fundamental parameters of the binary system (see Table 3 ). The difference between the results of these two models hinges on the different inclination values: 602(1) without pulsations and 62(1)° with pulsations. This 2σ difference subsequently produced a larger
Paper: 2017ApJ...836..157H (acknowledgments)
We thank the technical staff of the University of Hawaii 2.2 m telescope, and Dan Birchall for his observing assistance. We recognize the significant cultural role of Maunakea within the indigenous Hawaiian community, and we appreciate the opportunity to conduct observations from this revered site. This work was supported in part by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We thank the Gordon Betty Moore Foundation for their continuing support. X.H. acknowledges the University of San Francisco (USF) Faculty Development Fund. Z.R. was supported in part by a USF Summer Undergraduate Research Fellowship. Support in France was provided by CNRS/IN2P3, CNRS/INSU, and PNC; L.P.N.H.E. acknowledges support from LABEX ILP, supported by French state funds managed by the ANR within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02. N.C. is grateful to the LABEX Lyon Institute of Origins (ANR-10-LABX-0066) of the Université de Lyon for its financial support within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) of the French government operated by the National Research Agency (ANR). Support in Germany was provided by the DFG through TRR33 “The Dark Universe;” and in China from Tsinghua University 985 grant and NSFC grant No 11173017. N.S. acknowledges the support from World Premier International Research Center Initiative (WPI), MEXT, Japan, and JSPS Program for Advancing Strategic International Networks to Accelerate the Circulation of Talented Researchers. This work is partially based on observations made with ESO Telescopes at the La Silla Paranal Observatory, Chile, under program 289.D-5035. Some results were obtained using resources and support from the National Energy Research Scientific Computing Center, supported by the Director, Office of Science, Office of Advanced Scientific Computing Research of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
Paper: 2020ApJ...905..143P (fulltext)
this case, the inpainting performances with DP and GAN can be affected by noise, and the reconstruction area can be visually distinguished in the square patch. Figure 12. Thumbnail images of Planck temperature maps at (a) 353 and (b) 857 GHz. The radius of the reconstructed area (black circle) is 15′. Two locations are chosen to highlight different inpainting performances with high and low SNRs of Planck 353 map, respectively, at high Galactic latitude, i.e., and at low Galactic latitude, i.e., . For a more quantitative assessment, we thus estimate the summary statistics shown in Section 4.1 and we show the results in Figure 13 . Figure 13. Summary statistics estimated on Planck dust T maps at (a) 353 and (b) 857 GHz. A clear indication that the thermal dust emission data is contaminated by instrumental noise can be inferred by comparing the shapes of Minkowski functionals estimated for the maps at 353 GHz, with the ones from signal-only simulations (Figure 4 ) or from signal-dominated maps (Figure 13 (b)). The morphology of the Minkowski functionals of the former largely resembles the functionals estimated from a Gaussian signal. Two possible candidates for the Gaussian component are (i) Planck instrumental noise, which can be approximated as white within patches, and (ii) CMB residual emission. DP inpaintings are the most affected ones in the presence of the noise at , e.g., notice how the pixel distribution (top left panel) of Figure 13 (a) noticeably departs from the ground-truth one. However, the KS test p -value performed on the DP and ground-truth samples is , i.e., not significantly low enough to reject the null hypothesis that the two samples are different. On the other hand, inpainting with GAN and NN does not show any dependence with SNR, as the performances with these methodologies are essentially similar to the ones observed with the signal-only simulated maps (e.g., Figures 7 , 8 , and 10 ). Finally, we would like to point out that for the case of inpainting with GAN, we use the weights derived from the training set composed of signal-only dust TQU simulated maps. Looking at Figure 13 we notice that GAN is able to statistically reproduce at 353 GHz the features composed by signal and noise, indicating that the network has correctly learned the features related to the intrinsic signal in the presence of noise, and injects signal plus noise features statistically coherent with the ones outside the masked area. However, when the noise is highly dominating in the patch, we can clearly distinguish smooth artifacts in some cases inpainted with GAN (see Figure 12 (a, bottom)). This is somewhat expected because GAN is trained on signal-only simulated images. Further investigations on training GAN with noisy data are needed, and we will address it in a future work. 4.3. Inpainting Maps with Point Sources In this section, we aim at showing real world applications of the three reconstruction methodologies by inpainting areas in real maps
Paper: 2017ApJ...851...54W (acknowledgments)
We thank the anonymous referee for constructive suggestions. We are grateful to Maryam Modjaz and Yuqian Liu for sending us the photospheric velocity data before the publication of their paper. This work is supported by the National Program on Key Research and Development Project of China (Grant No. 2016YFA0400801), the National Basic Research Program of China (“973” Program, Grant No. 2014CB845800), and the National Natural Science Foundation of China (grant Nos. U1331202, 11533033, U1331101, 11673006, 11573014, 11422325, and 11373022). D.X. acknowledges the support of the One-Hundred-Talent Program from the National Astronomical Observatories, Chinese Academy of Sciences), and by the Strategic Priority Research Program “Multi-wavelength Gravitational Wave Universe” of the CAS (No. XDB23040100).
Paper: 2019ApJ...874...66G (fulltext)
at radial gradients in stellar populations have worked within R e (e.g., Spinrad Taylor 1971 ; Mehlert et al. 2003 ; Annibali et al. 2007 ; Spolaor et al. 2010 ; Jimmy et al. 2013 ; McDermid et al. 2015 ; Goddard et al. 2017 ). In recent years, integral-field spectroscopy (IFS) has enabled stellar population measurements at a large radius (e.g., Greene et al. 2013 , 2015 ; Scott et al. 2013 ; McDermid et al. 2015 ; Boardman et al. 2017 ; Goddard et al. 2017 ; Barone et al. 2018 ; van de Sande et al. 2018 ). Here, we focus on the most massive galaxies in the present-day universe using the MASSIVE survey (Ma et al. 2014 ). We have gathered integral-field data for 90 MASSIVE galaxies and measured their stellar kinematics, including higher-order moments (Veale et al. 2017a , 2017b ), stellar velocity dispersion profiles (Veale et al. 2018 ), and kinemetry (Ene et al. 2018 ). In addition, we have Hubble Space Telescope ( HST )/Wide Field Camera 3 (WFC3) imaging for 30 of the galaxies (Goullaud et al. 2018 ) and uniform Canada–France–Hawai’i Telescope (CFHT) K -band imaging for nearly all objects. 2. Galaxy Sample MASSIVE is a volume-limited survey of 116 galaxies (see details in Veale et al. 2017b ) in the northern hemisphere within D 108 Mpc (i.e., to the distance of the Coma cluster), with K -band magnitudes of M K −25.3 mag (roughly > 10 11.5 M ⊙ ). Details of the target selection are described in Ma et al. ( 2014 ). Briefly, our targets are drawn from the Two Micron All-sky Survey (2MASS; Skrutskie et al. 2006 ) Extended Source Catalog (XSC; Jarrett et al. 2003 ) combined with distances from the high-density contrast (HDC) group catalog from Crook et al. ( 2007 ), supplemented by surface brightness fluctuations when available (Blakeslee et al. 2009 , 2010 ; Blakeslee 2013 ) and with the flow model of Mould et al. ( 2000 ) when needed. We have completed IFS observations for 90 galaxies, including complete coverage of the 75 galaxies that have M K −25.5 mag. We use the Mitchell IFS at the McDonald Observatory (Hill et al. 2008 ), which has a 107 × 107 arcsec 2 field of view, one-third of which is filled by 246 fibers of a 4′′ diameter. Each galaxy is observed at three dither positions with 20-minute exposures, interspersed with 10-minute sky observations. The spectra span 3650–5850 Å, with a spectral resolution of 4.5 Å FWHM. The data are reduced using the custom software Vaccine (Adams et al. 2011 ; Murphy et al. 2011 ). For more details, see Greene et al. ( 2015 ). A primary goal of the MASSIVE survey is to measure spatially resolved stellar kinematics for dynamical modeling (e.g., Thomas et al. 2016 ). To achieve a mean signal-to-noise ratio (S/N) of at least 20 pixel −1 , we group individual fibers into spatial bins and co-add the spectra from fibers in a given bin into a single spectrum. The binning scheme is described in detail in Veale et al. ( 2017a ), but we summarize the procedure here briefly for completeness. Central fibers with S/Ns >
Paper: 2018ApJ...862...30D (acknowledgments)
We thank Gary Ferland for helpful suggestions that led to improvement of this paper. We are grateful to Kanti Aggarwal, Guiyun Liang, Brendan McLaughlin, Swaraj Tayal, and Oleg Zatsarinny for providing inelastic cross-section data. This work made use of the references provided in the CHIANTI atomic database (Dere et al. 1997 ; Del Zanna et al. 2015 ). This research was supported in part by NSF grant AST-1408723, and in part by NSF Research Fellowship DGE 1656466 to C.D.K.
Paper: 2015MNRAS.450.1042R (acknowledgments)
CDF acknowledges support from the National Science Foundation collaborative grant ‘Collaborative Research: Accurate cosmology with strong gravitational lens time delays’ (AST-1312329). LVEK is supported in part through an NWO-VICI career grant (project number 639.043.308).
Paper: 2018ApJ...856...75T (fulltext)
for a different simulation in Schwadron et al. 2014 ). Other intended applications have been mentioned throughout the text. We encourage interested researchers to contact us if they would like to use our simulation data for complementary investigations. Appendix A The MAS Thermodynamic MHD Model The numerical code MAS employed in this article integrates the standard viscous and resistive one-fluid MHD equations in 3D spherical coordinates. For the coronal simulation described in Sections 3.1 – 3.3 and 4 , the so-called “thermodynamic MHD model” was used, in which the standard equations are extended to include parallel electron thermal conduction, radiative losses, and parameterized coronal heating. The MAS thermodynamic MHD model has been used extensively for simulating the global corona and solar wind (e.g., Mikić et al. 1999 , 2007 ; Lionello et al. 2001 , 2009 ; Downs et al. 2013 ; Linker et al. 2017 ; Titov et al. 2017 ) and eruptive phenomena such as soft X-ray jets (Lionello et al. 2016 ; Török et al. 2016 ) and CMEs (e.g., Linker et al. 2001 , 2003 ; Mikić et al. 2013b ). In this article, we use a version of the model in which the solar wind is accelerated with Alfvén waves using a Wentzel–Kramers–Brillouin (WKB) approximation (Jacques 1977 ). A more sophisticated wave-acceleration model is under development (Lionello et al. 2014 ; Downs et al. 2016 ). In the version used here, the governing equations take the following form: where A is the magnetic vector potential, is the magnetic field, is the current density, ρ is the plasma density, T is the temperature, is the plasma pressure, v is the plasma velocity, is the direction of the magnetic field, c is the speed of light, γ = 5/3 is the adiabatic index, m p is the proton mass, k is Boltzmann’s constant, κ 0 is the coefficient of the classical Spitzer thermal conductivity, is a profile that limits the radial extent within which collisional (Spitzer’s law) thermal conduction is active, is the equivalent profile for collisionless thermal conduction, Q ( T ) is the radiative loss function, H is the coronal heating term that typically consists of a sum of empirical heating functions (Lionello et al. 2009 ), is the Alfvén-wave speed, and is the gravitational force. Note that joule heating, , is not included in Equation ( 3 ). While it is implemented in MAS, we switch it off in our simulation, otherwise the insertion of the current-carrying flux rope into the background corona (Section 3.2 ) would lead to an instantaneous, unphysical temperature increase of the rope. The last expression in Equation ( 4 ) is a semi-implicit term that is added to the equations to stabilize the algorithm for time steps larger than the fast magnetosonic wave limit (Lionello et al. 1999 ; Caplan et al. 2017 ). Equations ( 5 ) and ( 6 ) are the WKB approximation for Alfvén-wave pressure advance (Mikić et al. 1999 ), where + and − are the forward and backward Alfvén-wave energy densities. The electron density
Paper: 2017MNRAS.472.3378F (fulltext)
of three stellar half-mass radii at infall on the individual orbits compared to the isolated run as a function of the central dark matter density slope at infall infall . The different initial dark matter density slope also affects the stellar mass-loss, since the dark matter acts as shield for the stars. In Fig. 16 , we plot the mass-loss as defined in Fig. 15 as function of the initial dark matter profile density slope but this time for stars and dark matter. In order to avoid a too crowded plot, we only show results for orbitIV , which is the most disruptive orbit for which all five satellites still have a well-defined centre. Figure 16. Dark matter (dots) and stellar (stars) mass enclosed in a constant sphere with a radius of three stellar half-mass radii at infall on orbitIV compared to the isolated run as a function of the central dark matter density slope at infall infall . In general stars are more resilient to tidal forces than dark matter, and this is due to their smaller spatial extent and larger stellar density; for example satIII is able to retain 40percent of its stars while it is practically totally stripped of dark matter. Nevertheless there is still a correlation between the stellar mass-loss and the initial dark matter slope. An exception to this relation seems to be given by satI , which has a very strong mass-loss both in the stellar and dark matter components despite having a similar infall as satIII . This is due to the different slope for the stellar density profiles between satI and satIII , while the first satellite has a slope of 1.80 (again evaluated between 1 and 2percent of the virial radius), the stellar slope for satIII (and all the other satellites) it is close to 3.0. This difference in stellar slope is most likely related to a major merger event that occurred for satI shortly before redshift one and strongly reshuffled the stellar particle orbits. Finally, we note that some satellites (especially satI ) are left at z 0 with practically no dark matter in their central region, where they are fully stellar dominated, resembling more an extended globular cluster than a dwarf galaxy, we plan to look more into this issue in a forthcoming paper. 4 DISCUSSION AND CONCLUSIONS This work is the second of a series of papers in which we are trying to understand the formation and evolution of the smallest galaxies in the universe. In the first paper ( Paper I ), we presented a series of 27 cosmological simulations of haloes in the mass range 510 8 to 10 10 , these simulations were run till z 1 and aimed to describe the properties of satellite galaxies before accretion. In this second paper, we used a subsample of these cosmological simulations as initial conditions for a series of binary hydrodynamical simulations (satellite host) with the goal of understanding the effects of accretion and environment on satellite galaxies. More specifically, we used a total of seven haloes (five luminous and two dark) with a virial mass
Paper: 2019MNRAS.484.3984W (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank Sarah Bridle and Nicolas Tessore for bringing this problem to our attention. We also thank them for their helpful comments which greatly contributed to this paper. We would furthermore like to express our thanks to the referee, whose comments were very constructive and helped to improve this paper.
Paper: 2016ApJ...832...54K (acknowledgments)
This work was aided greatly by discussions with L. Coogan, N. Cowan, C. Goldblatt, A. Lenardic, L. Schaefer, N. Sleep, and K. Zahnle. We thank the anonymous referee for helpful comments that greatly improved the manuscript. We thank the Kavli Summer Program in Astrophysics for the setting to perform this research and the hospitality of the program members and community at the University of California, Santa Cruz. T.D.K. acknowledges support from NASA headquarters under the NASA Earth and Space Science Fellowship Program Grant PLANET14F-0038. D.S.A. acknowledges support from the NASA Astrobiology Institute Virtual Planetary Laboratory, which is supported by NASA under cooperative agreement NNH05ZDA001C.
Paper: 2018ApJ...862....4L (fulltext)
we consider, the subhalo and two-halo term contributions are quite negligible. This is also the reason why we cannot place tight constraints on the subhalo fraction and subhalo mass. The contribution of the stellar mass of the BCG is only important at very small scales. Thus, the observed ESDs in this study can mainly provide us with the constraints on the host halo properties. Figure 6. The ESD measurements (shaded area) and the best-fit model predictions (black solid line) for groups in the four different mass bins. The dashed, dotted, dotted–dashed, and blue dashed lines are the contributions of an off-centered NFW profile, subhalo, stellar mass of the galaxy under consideration, and the two-halo term, respectively. Table 3 lists the best-fit parameters for groups in different mass bins. As we have seen from the likelihood distribution of parameters in Figure 5 , we can have a fairly good constraint on the halo mass of the group samples. However, if we compare the halo masses obtained from the ESDs with those obtained from the group catalog (c.f. Table 1 ), they are roughly underestimated by 0.1 to ∼0.2 dex. We will discuss this discrepancy in the following section. The overall off-center distances for our BCGs are quite small, assuring that BCGs are indeed good tracers of halo centers. On the other hand, the concentrations of the halos seem to be somewhat larger than the theoretical predictions (e.g., Zhao et al. 2009 ). However, since the concentration c and the off-center distance R sig are quite tightly correlated, if we adopt the lower value of R sig , the concentration c will drop significantly as well. Table 3 Posteriors of the Parameters We Fitted to Our Group Samples Listed in Table 4 are the best-fit parameters for the groups that are separated into the high and low X-ray luminosity subsamples. Although due to the smaller number of lens systems the error for each data point for our X-ray subsamples is somewhat larger and thus the constraints on the five parameters are somewhat weaker, we do see the prominent feature of the halo masses of the high X-ray luminosity subsamples being higher than their low luminosity counterparts in all mass bins. The difference is to 0.2 to ∼0.3 dex, which means that the high X-ray luminosity subsamples are nearly a factor of 2 more massive than their low X-ray luminosity counterparts. Table 4 Posteriors of the Parameters We Fitted to Our High and Low X-Ray Luminosity Subsamples 4. Eddington Bias of the Halo Mass Estimation Recent studies have shown that the combination of galaxy–galaxy lensing and the clustering of galaxies can be used to constrain cosmology (e.g., Cacciato et al. 2013 ; More et al. 2013 ; van den Bosch et al. 2013 ; Leauthaud et al. 2017 ), and that the halo masses estimated for groups using galaxy–galaxy lensing signals and hence the halo mass function can also be used to constrain the cosmological parameters. However, before doing so, one needs to make a careful study of the
Paper: 2016A&A...596A..41V (acknowledgments)
The authors benefited greatly from participation in the International Team 276 funded by the International Space Science Institute (ISSI) in Bern, Switzerland. E.D. was supported by the Grant Agency of the Czech Republic under Grant No. P209/12/1652.