adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...849..134G (fulltext)
f.u., is obtained from the 16th and 84th percentiles. The lognormal parameters, μ and σ , can be calculated from the recommended (median) rate ( x med = e μ ) and the factor uncertainty ( f.u . = e σ ; for a coverage probability of 68%). The rate factor uncertainty is 1.1% for both reactions at most temperatures. Figure 6. Reaction rate probability density of d(d, n) 3 He at different temperatures. The rate samples (red histograms) are computed using the S -factor samples obtained from the Bayesian analysis. Blue curves represent lognormal approximations, where the lognormal parameters μ (“mu”) and σ (“sig”) are directly calculated from the expectation value and variance of all rate samples, ln(), at a given temperature. T9 is the temperature in GK. Figure 7. Reaction rate probability density of d(d, p) 3 H at different temperatures. The rate samples (red histograms) are computed using the S -factor samples obtained from the Bayesian analysis. Blue curves represent lognormal approximations, where the lognormal parameters μ (“mu”) and σ (“sig”) are directly calculated from the expectation value and variance of all rate samples, ln(), at a given temperature. T9 is the temperature in GK. Table 3 Present Recommended Reaction Rates a Note. a Reaction rates in units of cm 3 mol −1 s −1 , corresponding to the 50th percentile of the rate probability density function. The rate factor uncertainty, f.u., is obtained from the 16th and 84th percentiles (see the text). The parameters μ and σ of the lognormal approximation to the reaction rate are given by x med = e μ and f.u . = e σ , respectively, where x med denotes the median rate. Values for T > 2 GK, shown in italics, are adopted from Coc et al. ( 2015 ). The present rates for d(d, n) 3 He and d(d, p) 3 H agree with the results of Coc et al. ( 2015 ) within 1% at most temperatures. However, our rates are more than 15% larger than those of Coc et al. ( 2015 ) at very low temperatures (near 1 MK). This is caused by a low-energy cutoff that is too high for the numerical integration of the rates in the previous analysis. The theoretical model of Arai et al. ( 2011 ) only applies to low energies, thus we can derive Bayesian reaction rates only up to a temperature of 2 GK. The results in Table 3 for higher temperatures, shown in italics, are adopted from Coc et al. ( 2015 ). The most important temperatures for BBN are near 1 GK, corresponding to an effective kinetic energy range of 250 keV for the d+d reactions. The last step is to calculate the effect of the new reaction rates on the predicted primordial D/H ratio. The Bayesian mean value for the scale factor is larger by 0.21% for d(d, n) 3 He and larger by 0.12% for d(d, p) 3 H compared to Coc et al. ( 2015 ). The discrepancies of both reaction rates (0.21% and 0.12%, respectively), weighted by the sensitivity of the D/H abundance ratio to each reaction rate variation (−0.54 and −0.46, respectively; Coc Vangioni 2010 ), result in a 0.113% and
Paper: 2019MNRAS.483.2871D (fulltext)
with the SPT cluster cosmology analysis of Bocquet et al. ( 2015 ), when it is combined with the first release of the Planck primary CMB cosmology results (Planck Collaboration et al. 2014 ). The combination of the velocity-dispersion-based MOR normalization constraints of Bocquet et al. ( 2015 ) with CMB data leads to a shift in Ω m – σ 8 orthogonal to the cluster SPT cosmology constraints on these parameters. As a result the normalization of the mass–SZE MOR shifts accordingly, to account for the implied different cluster mass scale, leading to the difference seen in Fig. 9 . For a quantitative comparison, we follow Bocquet et al. ( 2015 ) to compute the significance of the difference of two distributions. We randomly draw points from two distributions and compute the difference δ between pairs of points. We use this to estimate the probability distribution P δ of these differences and compute the likelihood that zero is within this distribution. Assuming a normal distribution, this likelihood is then converted to a significance. The lower normalization parameter A SZ , corresponding to higher cluster masses, inferred from a joint cosmological analysis of the SPT cluster sample and Planck CMB data sets (Bocquet et al. 2015 ) disagrees with our result at the 2.6 σ level. We emphasize that the change in normalization in Bocquet et al. ( 2015 , yellow to cyan line in Fig. 9 ) when the underlying cosmology shifts is caused by the self-calibration of the scaling relations from cluster number counts. In this work, we adopt a cosmology with Ω m , σ 8 and H 0 close to the results of Bocquet et al. ( 2015 ) without the Planck data added. Because we use the cluster mass function only for Eddington bias correction and not for self-calibration of the MOR, small changes in the cosmological parameters do not have any big impact on our recovered normalization A SZ . In particular, changing the cosmological parameters to the ones obtained from SPT clusters with Planck data (Bocquet et al. 2015 ) changes our normalization by less than 1 per cent and does not bring it into better agreement with their lower A SZ value. Also used in the SPT–SZ cosmology analysis is the mass– Y X scaling relation. As for the mass– ζ relation, our marginalized posterior for the normalization is in very good agreement with the prior utilized in the cosmology analysis (de Haan et al. 2016 ). This is an important result, as the prior was based on an external calibration of the normalization of the mass– Y X scaling relation, namely the Vikhlinin et al. ( 2009 ) scaling relation updated with the weak lensing mass calibration of the Weighing the Giants (WtG) and CCCP projects (von der Linden et al. 2014 ; Applegate et al. 2014 ; Hoekstra et al. 2015 ). We are now able to confirm that these priors were appropriate for cosmology analysis based on an internal calibration . Fig. 10 shows a comparison of the marginalized and joint posterior probability distributions for the normalizations A
Paper: 2016ApJ...826...56R (acknowledgments)
We thank Alessandro Manzotti, Ariel Goobar, Mike Hudson, WeiKang Zheng, Bill Januszewski, Robert Kirshner, Licia Verde, Liz Humphreys, Dan Shafer, the PHAT collaboration, and Peter Stetson for valuable discussions and contributions, as well as Doug Welch for providing the optimal temporal spacings used in the optical Cepheid searches. We are grateful to Melissa L. Graham for her assistance with the Keck observations. This research was supported by NASA/ HST grants GO-12879, 12880, 13334, 13335 13344 from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. L.M.M.'s group at Texas AM University acknowledges additional support from the Mitchell Institute for Fundamental Physics Astronomy. D.S. acknowledges support from the Kavli Institute for Cosmological Physics at the University of Chicago through grant NSF PHY-1125897 and an endowment from the Kavli Foundation and its founder, Fred Kavli. A.V.F.'s group at UC Berkeley is also grateful for financial assistance from NSF grant AST-1211916, the TABASGO Foundation, Gary and Cynthia Bengier, and the Christopher R. Redlich Fund. P.C. is supported by NSF grant AST1516854 to the Harvard College Observatory. J.M.S. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under under award AST-1302771. R.J.F. gratefully acknowledges support from the Alfred P. Sloan Foundation. Some of the results presented here are based on data obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and NASA; the observatory was made possible by the generous financial support of the W. M. Keck Foundation. We thank the Keck staff for their expert help.
Paper: 2020MNRAS.495.1491R (fulltext)
a list of new associations in the Galaxy. Clustering depends on the choice of length-scale and groups begin to merge for larger length-scales. In this scenario, although the presence of different groups and associations is ascertained from the results of their cluster analysis using an independent method, their boundaries are not well defined. This, consequently, affects the membership assignment and identification of parent associations for those stars located at the association boundaries. Here, we investigate the properties of the well-known association Cyg OB3. We primarily focus on the distances, proper motions, and three-dimensional velocity components of the members. We study a particular case of a confirmed black hole X-ray binary Cyg X-1 (otherwise known as HD 226868; Bolton 1972 ; Webster Murdin 1972 ) located at the association boundary (Fig. 1 ). Cyg X-1 is one of the brightest X-ray binaries in the X-ray sky and is composed of a black hole of 14.8 ± 1.0 M ⊙ (Orosz et al. 2011 ) and a companion of 19.2 ± 1.9 M ⊙ (Orosz et al. 2011 ) of spectral type O9.7Iab (Walborn 1973 ). Cyg X-1 has been included in the catalogues of Humphreys ( 1978 ), Garmany Stencel ( 1992 ), Ruprecht et al. ( 1981 ), and Blaha Humphreys ( 1989 ) as a member of Cyg OB3. The cluster analysis algorithm implemented by Mel’Nik Efremov ( 1995 ) divided the association Cyg OB3 into two subgroups for a length-scale of 15 pc viz. Cyg 3A and Cyg 3B; however, Cyg X-1 was not included in either of the subgroups and remained as a field star. Figure 1. Image obtained with DSS2 in the R band shows the Cyg OB3 region. The positions of the O- and B-type stars studied in this work are encircled. Cyg X-1 is encircled in red. There are a total of 41 stars shown in the image and given in Table 1 . Mirabel Rodrigues ( 2003 ) studied the connection between Cyg X-1 and the association Cyg OB3 using the astrometry from the Hipparcos mission. The authors suggested that the parent association of Cyg X-1 is indeed Cyg OB3, and showed that the binary is located at the same distance as the other stars in Cyg OB3 and exhibits a proper motion similar to that of the association. The authors also showed that the peculiar velocity of Cyg X-1 is very small (9 ± 2 km s −1 ) with respect to the association, and is consistent with the expected random velocities of stars in expanding associations. These results indicate that Cyg X-1 is most likely a member of Cyg OB3. This was later confirmed with precise measurement of the trigonometric parallax and the proper motion of Cyg X-1 using VLBI radio observations (Reid et al. 2011 ). In this paper, we revisit the proposed connection between Cyg X-1 and Cyg OB3 using the astrometric data from Gaia DR2, which provides trigonometric parallaxes and proper motions for over 1 billion stars in the Galaxy with the best accuracy and precision available to date (Gaia Collaboration 2018 ). We define the sample of stars and their filtering criteria in the next
Paper: 2016MNRAS.459L.109B (acknowledgments)
The development of HIPPI was funded by the Australian Research Council through Discovery Projects grant DP140100121 and by the UNSW Faculty of Science through its Faculty Research Grants program. JPM is supported by a UNSW Vice-Chancellor's Fellowship. The authors thank the Director and staff of the Australian Astronomical Observatory for their advice and support with interfacing HIPPI to the AAT and during the three observing runs on the telescope. The authors wish to thank referee Hans Martin Schmid for constructive criticism of the Letter.
Paper: 2018MNRAS.476..354C (fulltext)
we find κ = 2 − 835.6440/582.143 23 = 0.564 5388. With these values of κ for both stars and a representative range for the neutron star radius (see Section 2.1 ), we can solve for M in equation ( 1 ), including the κ corr term from equation ( 3 ), the rotational correction κ 2 as given by Fig. 2 and a differential rotation term κ 3 consistent with a realistic differential rotation with Δ f ≳ 1 Hz inside the star. In Fig. 3 , we present our results for uniformly and differentially rotating models, and compare with the fit given by equation ( 5 ) of Andersson et al. ( 2014 ). Our results for uniformly rotating stars are ≈ 15 per cent lower than the estimate given by Andersson et al. ( 2014 ), where the fully relativistic r-mode frequency for slowly rotating uniform density stars from Lockitch et al. ( 2003 ) was combined with the second-order rotational corrections for Newtonian polytropes from Lindblom et al. ( 1999 ). As we mentioned in Section 3.3 , the main factor for this difference is our improved calculation of the rotational correction κ 2 (see Fig. 2 ). Differential rotation increases our mass estimate, but the effect is rather small: Δ f = 10 Hz increases the total mass by only ≈0.02 M ⊙ . In the range 11–13 km for the stellar radius, we find from our results 1.41–1.71 M ⊙ for XTE J1751−305 and 1.53–1.87 M ⊙ for 4U 1636−536. We can use the transversal curves in Fig. 3 , which indicate M ( R ) for a choice of three realistic tabulated equations of state (APR4 Akmal, Pandharipande Ravenhall 1998 , SLy Douchin Haensel 2001 and H4 Lackey, Nayyar Owen 2006 ) to gain more insight. Each equation of state crosses our M ( r ) line only once, providing a unique prediction for M and R . However, we can see that H 4 requires R > 13 km for both stars and is therefore less favoured according to current expectations for the neutron star radius. Combining the constraints from both the r-mode frequencies and the APR4 and SLy equations of state, we find a more restricted range of values for M and R : R ≈ 11.5–12 km and M ≈ 1.48–1.56 M ⊙ for XTE J1751−305 and R ≈ 11.4–11.9 km and M ≈ 1.60–1.68 M ⊙ for 4U 1636−536. However, it must be stressed here that these tight constraints are only valid if the frequencies reported in Strohmayer Mahmoodifar ( 2014a , b ) are indeed core r-mode frequencies (see the discussion in Section 1 ). 5 CONCLUSIONS Here, we have explored the consequences for the mass and radius estimates of these two stars if the frequencies detected by Mahmoodifar and Strohmayer really correspond to their r-modes. We used a careful calculation of the r-mode frequency, taking into account relativistic, rotational, and differential rotation corrections, as well as an estimate for the error due to the Cowling approximation, to find improved constraints on the compactness M / R of XTE J1751−305 and 4U 1636−536. Our results yield, for a representative radius of 12 km, masses of 1.55 M ⊙ and 1.70 M ⊙ , respectively. For XTE J1751−305, our value is
Paper: 2015ApJ...806..158D (acknowledgments)
We thank the referee for a careful reading of the manuscript and for suggesting the inclusion of Table 2 . I.D. is supported by the Harvard College Observatory Menzel Fellowship and the Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship (NSERC PDF-421224-2012). The Smithsonian Institution supports the research of M.J.G. H.J.Z. gratefully acknowledges the generous support of the Clay Fellowship.
Paper: 2018ApJ...862..116H (acknowledgments)
We thank an anonymous referee for helpful comments that improve the presentation of this paper. This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2017R1D1A1B03035359). We thank Attila Juhasz for help with radmc3dPy and J.-Y. Seok for discussion. One of the authors (Q.L.N.) was supported by the U.S. Department of Energy under Nuclear Theory grant DE-FG02-95-ER40934 and in part by the National Science Foundation under grant no. PHY-1430152 (JINA Center for the Evolution of the Elements).
Paper: 2015ApJ...811..103M (acknowledgments)
The first author is grateful to Prof. Lynne Hillenbrand (Caltech) and Ben Zuckerman (UCLA) for their advice and comments. T.J.D is supported by the National Science Foundation Graduate Research Fellowship under grant No. DGE1144469. M.B. is supported by a NASA Space Technology Research fellowship, grant No. NNX13AN42H. This publication makes use of data products from the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Facility: Very Large Telescope.
Paper: 2017MNRAS.470.4720B (fulltext)
5 per cent lower). This can be attributed to the same reason EPICal underperformed with the full sky model: EPICal only used a single pixel in the sky to form its solutions. When using the full sky model, it was under utilizing the information in the rest of the sky. But when the sky model only contains the bright point source, the single pixel used in EPICal is the location where this model is most accurate, effectively downweighting the pixels with incomplete sky model. In contrast, the visibility-based calibration method does not have any preference to a certain direction, and weights the sky equally (beyond the inherent response of the instrument). This results in a more harsh penalty for missing sources in the model. In the regime typical of HERA discussed above, EPICal's error floor was 10 per cent lower than that of visibility-based solutions. Any realistic sky model will lie somewhere between the two extremes explored here. In the case where the sky is modelled by a single source, as is often the case for an initial calibration, EPICal actually achieves smaller gain errors compared to the traditional visibility-based calibration. As the sky model improves, both visibility-based and EPICal gains improve, though the former quicker than the latter. In the limit of a perfect model, both calibration methods produce noise-like errors in their gains which scale down with more integration time. Comparing the two regimes of receiver noise levels (red versus black lines in Fig. 8 ), we see that the gains errors behave as one might expect given our above interpretations. In the case of a perfect sky model, the higher receiver noise results in larger gain errors for a given integration time, but follows the same t −1/2 noise trend. The imperfect model lines begin exhibit the same offset at low integration time because the errors are dominated by the thermal noise. But as integration time increases, they approach the same floor due to the same level of power from unmodelled sources. 5 APPLICATION TO LWA DATA We next demonstrate our calibration algorithm using an observation from the LWA station in New Mexico (Ellingson et al. 2013 ). The data are from the LWA narrow-band transient buffer, with time-domain voltage data from the 255 core antennas within a 110 m × 100 m ellipse. The central frequency is 74.03 MHz, with a total observing bandwidth of 100 kHz, analysed by a filter bank into 512 channels of bandwidth 195.3125 Hz each and with a post-channelization sampling period of 5.12 ms. For this demonstration, we limit ourselves to a single polarization. For the gridding step, we adopt a grid with extent 120 m to encompass the footprint of the array, and a grid spacing of half-wavelength (≈2 m). The result is a 64 × 64 pixel grid, making the MOFF computational complexity of N g log 2 N g on the same order as the N a 2 computations for a cross-correlator. Our gridding kernel is a square tophat function with sides of 3 m, corresponding to the size of
Paper: 2017ApJ...844..154S (acknowledgments)
The authors thank K. Bowen, P.-M. Hillenbrand, J. Liévin, D. C. Lis, O. Novotný, I. R. Sims, and A. Viggiano for stimulating discussions. D.W.S. and R.G.B. were supported in part by the NASA Astrophysics Research and Analysis (APRA) program and the NSF Division of Astronomical Sciences Astronomy and Astrophysics Grants (AAG) program. S.V. was supported in part by a Barry Goldwater Scholarship and the USRA James B. Willett Educational Memorial Scholarship Award. X.U. is a Senior Research Associate of the Fonds de la Recherche Scientifique-FNRS.
Paper: 2019MNRAS.484..476C (acknowledgments)
ACKNOWLEDGEMENTS We thank Frank van den Bosch and the anonymous referee for constructive comments on the paper. The simulations analysed in this paper were run on the Harvard Odyssey and CfA/ITC clusters, the Ranger and Stampede supercomputers at the Texas Advanced Computing Center as part of XSEDE, the Kraken supercomputer at Oak Ridge National Laboratory as part of XSEDE, the CURIE supercomputer at CEA/France as part of PRACE project RA0844, and the SuperMUC computer at the Leibniz Computing Centre, Germany, as part of project pr85je. KTEC acknowledges support from the Singapore National Science Scholarship.
Paper: 2018MNRAS.474.3720W (fulltext)
of the H 2 ortho to para spin ratio (OPR), the correlations and anticorrelations between D and 15 N found in primitive materials (van Kooten et al. 2017 ). In this paper, we report calculations that re-evaluate those of W12 in light of the revised R15 fractionation chemistry. This study is necessary because, apart from the rate coefficients adopted, there exist significant differences in the physics and chemistry of the models of W12 and R15 . The calculations of W12 considered densities similar to pre-stellar core conditions, about 10–100 times greater than those of R15 , which were based on a standard dark cloud similar to TMC-1 ( n H = 0.2–2× 10 5 cm −3 ). In contrast to W12 , R15 neither included the depletion of gas-phase species by freeze-out on to dust grains, nor the time-dependence in the H 2 OPR – both effects which have been shown to have substantial impact on the relative fractionation in nitriles and amines (here nitrogen hydrides and chemical derivatives thereof) over time ( W12 ). Furthermore, even when reactions ( 1 )–( 3 ) are not operating, there are alternative fractionation processes that could still become important in the physical conditions of the W12 model. For example, W12 identified as playing a role in the fractionation of N 2 and NH 3 , whereas Rodgers Charnley ( 2008a ) speculated that the neutral–neutral pathway could be viable and efficient if there is a barrier to the reaction However, the reaction appears to have no formation barrier, and the recommended reaction rate from the KIDA data base (Wakelam et al. 2013 , http://kida.obs.u-bordeaux1.fr ) of k (T) = 8.8 × 10 −11 (T/300) 0.42 cm 3 molecule −1 s −1 agrees with laboratory measurements down to 56 K (Daranlot et al. 2012 ). W12 assumed a 200 K barrier for reaction ( 6 ) (see Le Teuff, Millar Markwick 2000 ) and R15 found that isotopic exchange through ( 5 ) was indeed plausible, a process not considered in W12 . We have therefore updated the W12 model with respect to the revisions to nitrogen fractionation advocated by R15 , and specific reactions in the nitrogen chemistry from the KIDA data base (Wakelam et al. 2013 ). The paper is structured as follows. Revisions to the 14 N fractionation chemistry and the updates to specific reactions in the nitrogen chemistry are described in Section 2 . The chemical model is summarized in Section 3 . Results from the model calculations are given in Section 4 where they are compared to previous work. Conclusions from this study are in Section 5 . 2 REVISED CHEMICAL NETWORK 2.1 <sup>15</sup>N fractionation Table 1 lists the major reactions potentially leading to 15 N fractionation in cold, dark clouds, both ion–molecule and neutral–neutral reactions, with ZPE differences, Δ E , as determined by R15 . The resulting equilibrium coefficient, K ( T ) = k f / k r , the ratio between the forward and reverse reaction rate (e.g. Terzieva Herbst 2000 ), is listed for each reaction at 10 K, both for the new fractionation