adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...597L...5B (fulltext)
data. We assumed radial expansion, one of the simplest laws for the velocity. Radial expansion is a simple definition and the expected case if launching takes place along the force lines of a (locally) radial magnetic field. A linear variation of the absolute value is in general expected if most accelerations take place at the beginning of the process; in our case, variation depending on the latitude and – (as for the density, see Appendix A.1) leads to somewhat better predictions than dependence on the distance to the center. We can exclude some other simple cases, such as constant velocity modulus, expansion in the axial direction, and expansion parallel to the equator, for which the predictions do not match the observations. However, other velocity laws, such as that found for the outflowing gas in Red Rectangle by Bujarrabal et al. (2016), which is more complex, would lead to results compatible with the data (although involving a higher number of parameters). We stress that the launching mechanism itself is obviously very uncertain and its possible nature is a weak argument to support any velocity field; a theoretical discussion on such processes is beyond the scope of this letter. The total size, general shape, and overall velocity of the outflowing gas are however relatively well constrained, since they are given by the observed structure at several km s from the central velocity and the total velocity extent of the emission. As mentioned in Sect. 3.1, the assumption of LTE level population simplifies a lot our analysis. Nevertheless, we must keep in mind that the rotational temperatures we use in our calculations may only represent -averaged excitation temperatures and not true kinetic temperatures. Since high- levels can obviously be less populated in reality than for LTE, these rotational temperatures are probably a lower limit to the actual ones in the outflowing, relatively diffuse gas. Another consequence of the assumption of thermalized level populations is that it is not possible to distinguish the effects of the density and the relative CO abundances, (CO, CO). Fortunately, seems relatively well constrained in the best studied objects, particularly (CO), which is the basic parameter to determine the total density and mass because of the lower opacity of CO lines. Our previous works always yielded (CO) ranging between 10 and , both for post-AGB disks and for young PNe in general. We will adopt (CO) to ease the comparison with previous results on this object (Bujarrabal et al. 2013a ). In order to match the relatively low CO/CO intensity ratio, we need a relatively low abundance ratio. We adopt (CO) 10; similar low ratios are often found in similar objects. CO is weaker than CO , confirming that the CO emission is not very opaque. Our model yields typical values of the optical depth (CO ) 3 in the disk (for the representative velocities), while we typically find (CO ) 0.2 in the outflow emission. For CO , we find optical
Paper: 2017A&A...597A..38D (fulltext)
prior odds ( Jeffreys 1998 ): Here, and denote the hypothesis of validity of the direct and non-direct abundance proxy, respectively, and the probability represents how well some data are explained under the assumption of a hypothesis . Instead of the Bayes factor integral we use the maximum likelihood estimate. We obtain -values around , except for Kepler-93b and Kepler-10b with -values of . This means that only for Kepler-93b and Kepler-10b, there is a strong evidence that the direct abundance proxy better explains the planetary mass and radius. Generally, it will be very difficult to study the validity of a direct correlation of relative bulk abundances between star and hosted exoplanet given usual data uncertainties. Further studies on solar system objects are needed to improve our understanding of such abundance correlations and their dependence on planet formation history. 5. Discussion of the assumptions The success of an inference method is subject to the limitations and assumptions of the forward function . Firstly, we consider pure iron cores, which may lead to a systematic overestimation of core density and thus an underestimation of core size, if volatile compounds in the core are significant. The trade-off between predicted and independently inferred core sizes can be on the order of few to tens of percents, as shown for the terrestrial solar system planets in Dorn et al. (2015) . Secondly, our approach assumes sub-solidus conditions for core and mantle as well as a perfectly known EoS parameters for all considered compositions. For the six exoplanets considered here, their pressures and temperatures exceed the ranges for which these parameters can be measured in the laboratory. Clearly, extrapolations introduce uncertainty. Thirdly, we have assumed pure water composition of the ice layer, but other compounds such as CO, CO, CH, NH, etc. are also possible since the additional elements are relatively abundant. We have used water as a proxy for these volatiles in general because (1) oxygen is more abundant than carbon and nitrogen in the universe, and (2) water condenses at higher temperatures than ammonia and methane. Fourthly, our atmospheric model uses prescribed opacities of Jin et al. (2014) , which is suitable for solar abundances. In this aspect, the prescription is therefore not self-consistent with the non-solar metallicity cases we present in this work. We have checked that using different infrared and/or visible opacities can introduce errors in radius of 5%. Models that compute line-by-line opacities with their corresponding atmospheric abundances should be performed in the future to compute planetary radii in a self-consistent way. Another aspect is our assumption of ideal gas behavior. We have checked that for atmospheric mass fractions (/) of up to 1%, the difference in the radius between using the Saumon et al. (1995) EOS and ideal gas (for H-He atmospheres) can reach 10%. For the vast majority of our results,
Paper: 2017A&A...601A..98S (acknowledgments)
We thank Hendrik Hildebrandt, Douglas Applegate, and Reiko Nakajima for useful discussions. We also thank Violeta Gonzalez-Perez and the anonymous referee for fruitful comments. Hananeh Saghiha gratefully acknowledges financial support of the Deutsche Forschungsgemeinschaft through the project SI 1769/1-1. Stefan Hilbert acknowledges support by the DFG cluster of excellence “Origin and Structure of the Universe” ( http://www.universe-cluster.de ).
Paper: 2019ApJ...873...44C (acknowledgments)
A.C.C. acknowledges the support of the UK Science and Technology Facilities Council, as well as the Scottish Universities Physics Alliance. J.L. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1701487. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalog is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programs, including GALEX MIS, VST KiDS, VISTA VIKING, WISE , Herschel -ATLAS, GMRT, and ASKAP, providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . Based on observations made with ESO telescopes at the La Silla Paranal Observatory under program IDs 179.A-2004 and 177.A-3016.
Paper: 2019ApJ...879..117K (fulltext)
There are no noteworthy objects adjacent to IMS J2204+0112, and we found no spectral features with respect to the velocity as one can expect from its redshift. 3 3 At z = 5.926, the prominent [C ii ] 158 μ m line is located at the band gap between bands 6 and 7. In the defined spectral windows, there could be a highly excited CO( J = 21–20) line and several H 2 O lines, but they are expected to be weak and/or rare at z ∼ 6 (Narayanan et al. 2008 ; Bañados et al. 2015 ). Using the IMFIT task of the CASA package, we fitted the source on each continuum map with a simple 2D Gaussian model, resulting in the integrated flux densities at 250 and 343.5 GHz being f 250 GHz = 1.474 ± 0.023 and mJy, respectively. Note that the peak flux densities are 1.289 ± 0.020 and 2.966 ± 0.027 mJy beam −1 , respectively. These flux densities are higher than the value expected from the relation between and of other high-redshift quasars (Equation (2) in Venemans et al. 2016 ; see details in Section 4.1 ) by a factor of six, although there has been a recent suggestion that there is no correlation between and (Venemans et al. 2018 ). Assuming that the FIR flux is dominated by the host galaxy, no features in the residual maps after the point source model subtraction (right panels of Figure 1 ) is consistent with its host galaxy being as compact as (or about 4 kpc in physical scale at z ∼ 6), like those of other high-redshift quasars (Wang et al. 2013 ; Willott et al. 2015 , 2017 ; Venemans et al. 2016 , 2017a ; Mazzucchelli et al. 2017 ; Decarli et al. 2018 ). The central positions of the ALMA detection are offset by only about 02 from the z -band position (see crosses in Figure 1 ). These small offsets between the optical and submillimeter detections are in agreement with the previously reported uncertainties of ALMA astrometry (Capak et al. 2015 ; Willott et al. 2015 ; Pentericci et al. 2016 ), disfavoring the possibility that the submillimeter flux comes from a neighboring or foreground galaxy. Figure 2 shows the spectral energy distribution (SED) of IMS J2204+0112 in the observed frame. The black filled circles represent the flux densities of IMS J2204+0112 from Kim et al. ( 2015b , 2018 ) and the values derived from the archival data (see Section 2.2 ), while the red filled circles are from our ALMA observation. Also plotted are the composite quasar spectrum (gray line; Selsing et al. 2016 ), the intrinsic SED of a type 1 quasar (green line; Lyu Rieke 2017 ), and the empirical SED of ULIRGs hosting AGN at z ∼ 2 (purple line; AGN4 of Kirkpatrick et al. 2015 ). The ULIRG AGN template is consistent with the submillimeter data, which suggests that the host of IMS J2204+0112 is ULIRG-like, similar to the hosts of other high-redshift quasars (Wang et al. 2013 ; Willott et al. 2013 , 2015 , 2017 ; Venemans et al. 2016 ; Decarli et al. 2018 ; Izumi et al. 2018 ). Note that the templates were redshifted to the observed frame using z = 5.926 (Kim et al. 2018 ), including
Paper: 2016MNRAS.459.2948L (fulltext)
They used an automated search, applying a ‘friends-of- friends’ algorithm to find groups in the magnitude-limited Center for Astrophysics 2 redshift survey (CfA2; de Lapparent, Geller Huchra 1986 ) and Southern Sky Redshift Survey (SSRS2; da Costa et al. 1991 ) catalogues. This algorithm used projected distances between galaxies (ΔD ≤ 50 kpc), and their line-of-sight velocity differences (Δv ≤ 1000 km s −1 ) to determine whether two galaxies belong to a group. This methodology was adopted in order to eliminate biases as a function of distance which are introduced by visually selecting groups, as Hickson ( 1982 ) had done. Selecting CGs based on their redshift reduces the possibility of chance projections, however the isolation criterion was not rigorously satisfied and therefore many RSCGs are actually components of larger structures. Because of this, RSCGs 21, 67 and 68 are excluded from our analysis as they are part of clusters, thus reducing our sample to 46 CGs (Walker et al. 2012 ). The RSCGs in our sample were therefore selected to have properties similar to HCGs. In addition, Walker et al. ( 2010 ) chose to include only CGs that are at a sufficiently low redshift ( z 0.035) that the polycyclic aromatic hydrocarbon features do not shift out of their rest-frame IRAC bands to maintain their rest-frame positions in mid-IR colour space. The 46 remaining CGs in our sample satisfy this requirement. The coordinates and group velocities of each CG are given in Table 1 , as well as the breakdown of group properties such as group H i content. Table 1. HCG and RSCG sample. Notes . a Position data taken from Hickson ( 1982 ) for HCG sample. b Position data taken from Barton et al. ( 1996 ) for RSCG sample. c Velocities corrected to CMB taken from http://ned.ipac.caltech.edu/ for all CGs in our sample. d CMB corrected values from http://ned.ipac.caltech.edu/ with a cosmology of H 0 = 70 kms −1 Mpc −1 , Ω M = 0.3, Ω Λ = 0.7. e Values taken from http://ned.ipac.caltech.edu/ with a cosmology of H 0 = 70 kms −1 Mpc −1 , Ω M = 0.3, Ω Λ = 0.7. f SDSS Optical r -band Data from Walker et al. ( 2013 ). g Group types as defined by J07 . h Walker et al. (submitted). i These CGs are part of clusters. Their photometry is presented in Table 5 , but they are not included in the subsequent analysis. 2.2 UV data Swift is a multiwavelength observatory (Gehrels et al. 2004 ) whose primary goal is to study gamma-ray bursts. When Swift is not following gamma-ray burst targets, there is a fill-in program of other observations. The UVOT (Roming et al. 2005 ) instrument is a 30 cm telescope with a 17 × 17 arcmin 2 field of view that provides UV and optical coverage (1600–8000 Å) with a spatial resolution of ∼ 2.5 arcsec. It uses a micro-channel-plate, intensified photon- counting CCD detector. It has 256 × 256 active pixels, each of which is subdivided into 8 × 8 pixels using a centroiding algorithm (Breeveld et al. 2010 ). The data used in this study originated from
Paper: 2015A&A...579A.123H (acknowledgments)
I would like to thank J. Bovy, A. Büdenbender, and L. Zhang for kind access to their data. I particularly appreciate detailed comments by C. Moni Bidin and the anonymous referee for their helpful comments that led to considerable improvements in this manuscript. This work used the HI galaxy images available at http://www.mpia-hd.mpg.de/THINGS/Overview.html courtesy of the THINGS collaboration.
Paper: 2017ApJ...849...69Y (acknowledgments)
This paper used data from the JCMT Survey M16AL001 awarded by the East Asian Observatory. We thank the JCMT staff for carrying out the observations and contributions to the data reduction. We greatly thank K. Hodapp for help with the ephemeris for EC 53. We also thank S. Lee for help with the SED fitting. The authors appreciate valuable comments by A. Pon, A. Scholz, H. Kirk, Y. Aikawa, V. Chen, and C. W. Lee. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (grant No. NRF-2015R1A2A2A01004769) and the Korea Astronomy and Space Science Institute under the RD program (Project No. 2015-1-320-18) supervised by Ministry of Science and ICT. G.J.H. is supported by general grant 11473005 awarded by the National Science Foundation of China. D.J. is supported by the National Research Council of Canada and by an NSERC Discovery Grant. S.M. is partially supported by an NSERC graduate scholarship. M.K. was supported by by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (No. NRF-2015R1C1A1A01052160). The JCMT is operated by the East Asian Observatory on behalf of The National Astronomical Observatory of Japan, Academia Sinica Institute of Astronomy and Astrophysics, the Korea Astronomy and Space Science Institute, and the National Astronomical Observatories of China and the Chinese Academy of Sciences (Grant No. XDB09000000), with additional funding support from the Science and Technology Facilities Council of the United Kingdom and participating universities in the United Kingdom and Canada. This research has made use of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. This research used the services of the Canadian Advanced Network for Astronomy Research (CANFAR, specifically VOSpace), which in turn is supported by CANARIE, Compute Canada, University of Victoria, the National Research Council of Canada, and the Canadian Space Agency. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. Facility: JCMT (SCUBA-2) (Holland et al. 2013 ). Software: Starlink (Currie et al. 2014 ), Astropy (Astropy Collaboration et al. 2013 ), Python version 2.7, APLpy (Robitaille Bressert 2012 ).
Paper: 2016MNRAS.458...56W (acknowledgments)
AW acknowledges support by Polish Ministry of Science and Higher Education in Mobility Plus Program and Polish National Science Centre for supporting this work through grant DEC-2011/03/N/ST9/01867. Support from the German Ministry for Education and Research (BMBF) through the Verbundforschung Astroteilchenphysik grant 05A11VH2 is gratefully acknowledged. This research was supported in part by PLGrid Infrastructure.
Paper: 2018MNRAS.474.2544F (fulltext)
respect to the geometric centre, with a sinusoidal function was derived by Williams et al. ( 2016 ). Fig. 5 shows the resulting expansion velocity of the forward shock as a function of the position angle for Model B at t = T age = 450 yr; the detected velocities of the proper motion from the X-ray and the radio in Williams et al. ( 2016 ) for a distance of 2.3 kpc are also indicated. The velocities from the simulation for Model B at different position angles of the forward shock range from 3000 to 4000 km s −1 . For the shock velocities from the simulation and the ones derived from X-ray/radio observations according to the position angle, a significant deviation exists between 50° and 100°. In particular, the detected velocity at θ = 80° is about 1000 km s −1 smaller than the simulation value. Moreover, the velocities from θ = 200° to 250° are about 500 km s −1 smaller than the detected ones. Apart from this variation, at θ = 15° and 30°, the resulting velocities are consistent with those detected in the radio. From 120° to 150°, coincident with the protrusion to the south-east, the detected values of velocities and the decreasing trend are reproduced. The increasing trend, which is indicated in both the X-ray and the radio observations, is also consistent with the simulation of the velocities for the position angle from 150° to 180°. Moreover, except for 276°, the simulated velocities from 250° to 350° are about 3500 km s −1 , which is in agreement with the averaged values of the detected ones in the radio and the X-ray band. Figure 5. The distribution of the speed of the forward shock in relation to the position angle in the plane x = 0 for Model B at t = T age = 450 yr. The detected velocities at different positions on the periphery of Tycho's SNR for a distance of 2.3 kpc based on the radio and the X-ray observations are also indicated for comparison (Williams et al. 2016 ). 4 DISCUSSION AND CONCLUSIONS A series of 3D MHD simulations have been performed both to investigate the formation mechanism of the peculiar morphology of Tycho's SNR and to derive the ambient material distribution prior to the supernova. These simulations are based on the assumption that the progenitor is imbedded in a bubble that is blown by the wind, which has a latitude-dependent density distribution. Moreover, the inclination angle between the pole axis of the wind and the north direction, and the spatial motion of the progenitor are included in order to reproduce the asymmetry of the profile of the SNR shock. With a pole velocity of v p = 100 km s − 1 , ξ = 20 and a duration of T w ∼ 10 5 yr, an inclination angle of ∼50° between the pole of the wind and the north direction and a spatial velocity of v s ∼ 0.3 v p are derived based on the simulation to reproduce the detected profile of the outer border of the remnant. Furthermore, because a wind with a velocity greater than 100 km s −1 can be produced as a result of accretion onto a WD, an SD scenario for the
Paper: 2016ApJ...823...73L (acknowledgments)
H.J. acknowledges support from the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2013R1A6A3A04064993).
Paper: 2019ApJ...885...60R (fulltext)
mass) of the ejecta are mainly associated with the observational data near the peak time (∼1 day) and the optimal pulsar luminosity mainly depends on the late observations (i.e., a few days after the peak time). If a high ejecta mass is adopted in the fittings, the contribution of the PWN emission to the late phase may be low. Then, a low pulsar luminosity may be found if a high ejecta mass is adopted in the fittings. Figure 4. Fitting result (solid lines) of AT 2017gfo, where the observational data are described with circles and the upper limits are represented with triangles. Figure 5. Posterior probability density functions for the physical parameters of AT 2017gfo from MCMC simulations. Table 1 Fitting Result from MCMC Fitting In order to test our fits result, we confront our result with HST observations (left panel), Swift observations (middle panel, UVW1 , UVW2 , and UVM2 bands), MASTER optical observations (middle panel, W band), and Spitzer Space Telescope observations (right panel) in Figure 6 . We add the observational data of HST at >100 days (Lyman et al. 2018 ; Margutti et al. 2018 ; Lamb et al. 2019 ; Piro et al. 2019 ). It is found that the model curve is lower than data points, which suggests the late time observation is dominated by the afterglow of GRB 170717A. The observational data of the Spitzer Space Telescope is from Villar et al. ( 2018 ). This figure reveals that our result is consistent with the data from HST , Swift , MASTER , and Spitzer Space Telescope , which were not used in our MCMC fits. In observations, it is found that the observed radiation spectrum of AT 2017gfo gradually deviates from the thermal radiation spectrum over time (Troja et al. 2017 ). Then, we plot the radiation spectrum from 11.5 to 19.5 days after trigger based on our fits result. The radiation spectra are shown in the left panel of Figure 7 , where the circles and triangles are the observational data laying closest to our selected time. One can find that our fits result describes the observed radiation spectra nicely. It is interesting to point out that the spectral peak of the thermal component moves from ∼10 14 Hz at t = 10.5 days to the very low frequency at t = 19.5 days. Moreover, the thermal component is comparable to the nonthermal component at t ≲ 19.5 days. This behavior suggests that the bolometric luminosity at t ≲ 19.5 days can be well described with the thermal emission from the ejecta. In the right panel of Figure 7 , we compare the thermal luminosity based on our fitting result with the observed bolometric luminosity (Waxman et al. 2018 ). One can find that our thermal luminosity describes the observed bolometric luminosity very well. Figure 6. Confronting our fitting result with HST observations (left panel), Swift observations (middle panel, UVW1 , UVW2 , and UVM2 bands), MASTER optical observations (middle panel, W band), and Spitzer Space Telescope observations (right panel). Figure 7. Comparison of radiation spectra