adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.496.1295W (fulltext)
to calculate N H values for all targets studied. For 1ES 0229+200, the calculated value of N is (13.00 ± 0.37) × 10 20 cm −2 . This value is larger than the one from the LAB survey (8.06 × 10 20 cm −2 ) and consistent with the one provided by Willingale et al. ( 2013 ,10.80 × 10 20 cm −2 ). The value is also higher than free N H = (10.6–10.8) × 10 20 cm −2 , calculated by Kaufmann et al. ( 2011 ). For PKS 0548−322, the calculated value of N is (3.10 ± 0.10) × 10 20 cm −2 . This value is larger than the one from the LAB survey (2.58 × 10 20 cm −2 ) and consistent with the one provided by Willingale et al. ( 2013 , 2.87 × 10 20 cm −2 ). However, Sambruna Mushotzky ( 1998 ) have found a significantly higher value of N H of 1.03 × 10 21 cm −2 , which is needed to explain X-ray properties of PKS 0548−322. For 1ES 1741+196, the calculated value of N is (10.47 ± 0.41) × 10 20 cm −2 . This value is larger than the one from the LAB survey (7.32 × 10 20 cm −2 ) and consistent with the one provided by Willingale et al. ( 2013 , 9.58 × 10 20 cm −2 ). For 1ES 2344+514, the calculated value of N is (17.34 ± 0.15) × 10 20 cm −2 . This value is larger than the one from the LAB survey (15.10 × 10 20 cm −2 ); however, it is smaller than the one provided by Willingale et al. ( 2013 , 24.60 × 10 20 cm −2 ). We note here that the reddening E ( B − V ) and the atomic hydrogen column density ( N H ) are related. According to recent work, Liszt ( 2014 ) has described this dependence by the following formula N H = 8.3 × 10 21 E ( B − V ). The formula gives N H values of 9.7 × 10 20 , 2.5 × 10 20 , 0.6 × 10 20 , 6.3 × 10 20 , and 15 × 10 20 for 1ES 0229+200, PKS 0548−322, RX J 1136+6737, 1ES 1741+196, and 1ES 2344+514, respectively. The quoted values are consistent with the ones from the LAB survey and do not indicate a need for an additional absorption. All values of N H values, including the catalogue ones, are collected in Table 5 . Table 5. Summary of Galactic column densities values. Notes . (1) Source name; (2)–(4) N H as provided by Kalberla et al. ( 2005 ), Dickey Lockman ( 1990 ), and Willingale et al. ( 2013 ), respectively; (6) N H value calculated as a result of this work. All values are given in 10 20 cm −2 . 4.2 Constrains on the model parameters The model used to describe the IR to X-ray emission of the blazars is characterized by the host galaxy, curvature seen in the X-ray spectrum, total absorption (which includes both Galactic component and possibly additional ones), and UV excess. The host galaxy, understood as the entire component described with the template by Silva ( 1999 ), is well constrained with the model used. Still, the remaining three parameters cannot be disentangled only using information from the X-ray spectral fitting. The limitations on three entangled parameters are as follows: β : For all of the blazars studied, the convex curvature of the X-ray spectrum is assumed. This implies that negative values of β are not accepted.
Paper: 2021ApJ...909..207R (fulltext)
between 10 10 and 10 11 M ⊙ . McCarthy et al. ( 2020 ) also use an analytic model, but paired with SDSS observations to explore the host galaxy mass function along with other host observables. For the host galaxy mass function, our results are consistent with theirs. However, our larger range of DTDs presented in Figure 2 reveals the large spread between different assumed DTDs and the stable peak near 10 10.5 M ⊙ . Adhikari et al. ( 2020 ) also find that other host observables paired with stellar mass are necessary to obtain a better understanding where BNSs merge. Overall, we find that future explorations of this topic will need to consider a wide range of DTDs and the full range of the observable in question. While the work we present here continues our understanding of what we can learn from observations of BNS host galaxies, further investigations are necessary to fully understand how BNSs form and evolve. One example of such an investigation is to expand the set of DTDs examined using the methods in this paper. The set of DTDs we examine is broad, covering those most commonly referenced (e.g., Eldridge Stanway 2016 ; Safarzadeh Berger 2019 ), but we do not exhaustively search the full range of DTDs proposed (e.g., Dominik et al. 2012 ; Simonetti et al. 2019 ). Also, our convolution of IllustrisTNG’s SFRH with our DTDs does not include any form of natal kicks. If these kicks are strong enough to dislodge the binary from smaller galaxies, it is possible that their addition would weight the host mass functions toward higher-mass galaxies. With a greater range of DTDs examined and a more detailed convolution, we will gain a clearer picture of where BNS mergers are located, which delay times can be distinguished using the host galaxy mass function, and the most likely places they will be observed. Another way to incorporate a more complete set of DTDs would be to use a varied set of population synthesis models that cover a wide range of binary separations, kick velocities, initial mass functions, etc. Including other star formation histories could also provide a more detailed look at the spread in possible host galaxy mass functions. While IllustrisTNG is broadly consistent with the cosmic SFR density and redshift-dependent galaxy stellar mass functions (Pillepich et al. 2018a ), its accuracy should not be overinterpreted, and different simulations will surely produce somewhat varied star formation histories that could impact our results. However, we can say that up to their mass cutoff our results align with those of Artale et al. ( 2019 ), who found no significant difference when comparing results from Illustris and Eagle. The lack of variation in Artale et al. ( 2019 ) most likely indicates that—while there is some variation—the SFRHs in Illustris and EAGLE are sufficiently similar to not significantly impact the results. Thus, by adopting the SFRHs from galaxy formation simulations and assumptions about the functional form of the DTD,
Paper: 2020MNRAS.491.6065Z (acknowledgments)
ACKNOWLEDGEMENTS Our research was supported by the Joint Research Fund in Astronomy (No.11963002; U1631236 and U1431114) under a cooperative agreement between NSFC and CAS. We also want to thank Dr Frank Levinson for his generous financial support that enabled Butler University to join the SARA telescope consortium and upgrade its Holcomb Observatory telescope. This work was also supported by the Natural Science Foundation of Guizhou Province Office of Education (Grant No. 2014298). We also thank Professor Prabhakar Misra for revising the language of our paper. We acknowledge the support of the staff of the Xinglong 60-cm telescope and Cultivation Project for LAMOST Scientific Payoff and Research Achievement of CAMS–CAS.
Paper: 2021ApJ...911...54B (fulltext)
by magenta, green, and orange lines, respectively, with solid (dashed) lines indicating results for the northern (southern) hemisphere. The SSN is shown for each hemisphere. 6. Discussion Taken at face value, the results as illustrated by Figures 10 and 11 are unexpected in that the shallow return flow present in the northern hemisphere appears to be inconsistent with most prior determinations of the meridional circulation that employ multiyear observations and attempt to remove the effects of center-to-limb related artifacts. Specifically, most analyses involving modeling time–distance travel-time perturbations show a poleward flow in both hemispheres that persists from the surface down to approximately 0.9 R ⊙ (i.e., about 70 Mm below the surface). This includes the analyses of Zhao et al. ( 2013 ), Jackiewicz et al. ( 2015 ), Rajaguru Antia ( 2015 ), and Chen Zhao ( 2017 ). A notable exception is the recent analysis of Gizon et al. ( 2020 ), which is discussed below. In general, uncertainties in the inference of the flow increase with the depth of those inferences. This makes the discrepancy with other helioseismic analyses at a relatively shallow depth all the more puzzling. If we assume that the results for at least the northern hemisphere are spurious, then the challenge is to understand the cause of what appears to be an uncorrected artifact. Of course, real hemispheric differences in the meridional circulation may very well exist, but it is worth considering the possible nature of pseudoflows, which are themselves asymmetric. The comparison of the time variation of the frequency shifts with the sunspot numbers for both hemispheres (Figure 12 ) indicates that the observed hemispheric differences in the 88 month interval persist regardless of the level of solar activity or its preference for one hemisphere over the other. While this does not rule out the presence of real solar-cycle variations in flows (e.g., Chou Dai 2001 ; Zhao Kosovichev 2004 ; González Hernández et al. 2008 ; Hathaway Rightmire 2010 ) or potential pseudoflow artifacts related to magnetic regions (e.g., Liang Chou 2015 ), there is no direct correlation between hemispheric differences in solar activity and the differences in the frequency shifts. Errors in the control measurements due to uncertainties of the leakage discussed in Appendix B appear to be too small to account for the hemispheric differences observed in Figure 9 . In particular, we note that likely errors in the leakage factor amounting to 20% of the selected value of 0.84 (see Appendix B ) cause uncertainties in the scaled frequency shifts of the deepest modes of around 1 m s −1 . These are smaller than the standard errors of the observations shown in Figure 9 and substantially smaller than the 4 m s −1 discrepancy observed at the highest phase speeds. It is plausible that the results may be compromised by incorrect assumptions regarding the azimuthal invariance of the center-to-limb correction. At
Paper: 2015MNRAS.446.2540S (acknowledgments)
SLS thanks STFC for her studentship and the Australian National University for hospitality. CAT thanks Churchill College for his fellowship, the Australian National University for a visiting fellowship and the Monash University for support as a Kelvin Watford distinguished visitor. LF and DTW thank the Institute of Astronomy for supporting visits.
Paper: 2016ApJ...827...95W (acknowledgments)
This work was supported by the European Research Council under ERC grant agreement no. 337705 (CHASM) and by a Consolidated Grant from the UK STFC (ST/J001627/1). Some of the calculations for this paper were performed on the DiRAC Complexity machine, jointly funded by STFC and the Large Facilities Capital Fund of BIS, and the University of Exeter supercomputer, a DiRAC Facility jointly funded by STFC, the Large Facilities Capital Fund of BIS, and the University of Exeter. We thank Isabelle Baraffe for providing the 1D stellar structure model used in this paper and for offering comments on an earlier version of this manuscript. We also thank the referee for a thorough reading of our paper and providing many helpful comments, improving the clarity of our paper.
Paper: 2017MNRAS.472.3854S (acknowledgments)
Acknowledgements We thank the referee for useful suggestions; Sebastian Hönig for many illuminating discussions; Claudio Ricci for careful reading and comments on the manuscript. MS acknowledges support by the Ministry of Education, Science and Technological Development of the Republic of Serbia through the projects Astrophysical Spectroscopy of Extragalactic Objects (176001) and Gravitation and the Large Scale Structure of the Universe (176003); by FONDECYT through grant no. 3140518; by the Center of Excellence in Astrophysics and Associated Technologies. This research made use of Astropy, a community-developed core python package for Astronomy (Astropy Collaboration et al. 2013 ). Based on European Southern Observatory (ESO) observing programmes 60.A-9629, 076.B-0599, 078.B-0255, 087.B-0746, 088.B-0159, 089.B-0070, 385.B-0896 and 386.B-0026.
Paper: 2018A&A...611A..62B (acknowledgments)
Acknowledgements We thank David Bühler and Jayant Joshi for acquiring the quiet-Sun data. The Swedish 1-m Solar Telescope is operated on the island of La Palma by the Institute for Solar Physics of Stockholm University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the High Performance Computing Center North at Umeå University and the PDC Centre for High Performance Computing (PDC-HPC) at the Royal Institute of Technology in Stockholm. JdlCR is supported by grants from the Swedish Research Council (2015-03994), the Swedish National Space Board (128/15) and the Swedish Civil Contingencies Agency (MSB). This research was supported by the CHROMOBS and CHROMATIC grants of the Knut och Alice Wallenberg foundation and by the Research Council of Norway through the grant “Solar Atmospheric Modelling” and through grants of computing time from the Programme for Supercomputing.
Paper: 2019MNRAS.489.2298C (acknowledgments)
ACKNOWLEDGEMENTS SJC would like to acknowledge the support of the Lau Endowment as part of the Undergraduate Research Scholars Program of the UCLA Undergraduate Research Center – Sciences, the Undergraduate Research Fellows Program, and the Maggie Gilbert Research Award through UCLA College Honors. SN thanks Howard and Astrid Preston for their generous support. We would like to thank Professor Stassun, Professor Gómez Maqueo Chew, Professor Heller, and Dr Hamers for their thoughtful comments and suggestions.
Paper: 2016ApJ...819...33L (fulltext)
or less blended and we saw only one to three components. Next, we used the least-squares deconvolution (LSD; Donati et al. 1997 ) technique and calculated LSD profiles with the LSD code by Tkachenko et al. ( 2013 ), based on the same line mask as we used for our previously mentioned synthetic spectrum. Figure 1 shows the LSD profiles vertically arranged according to the JD of observation. The resolution is distinctly better than that of the CCFs; four components are now seen in the majority of the LSD profiles. The measured RVs are marked, and the calculated orbital curves plotted in Figure 1 show that we could assign all components in all LSD profiles to four different stars located in two different binary systems, S 1,2 and S 3,4 . Figure 1. LSD profiles calculated from the 29 spectra, vertically arranged according to the JD (2,457,000+) of observation. Measured RVs are marked by open circles and crosses, in the left panel for components C 1 and C 2 and in the right panel for C 3 and C 4 , respectively. The red and green curves illustrate the corresponding, calculated orbital solutions. The RVs of the four components, C 1 to C 4 , were determined by fitting the LSD profiles with multiple Gaussians. The mean (internal) errors of the fit were 0.25, 0.36, 0.28, and 0.37 km s −1 for the RVs of C 1 to C 4 , respectively. We used the method of differential corrections (Schlesinger 1910 ) to determine the orbits from the RVs. Because of the short time span of the observations, this could be done for the two systems separately, without accounting for any interaction between them or for light-travel-time effects. In the case of S 1,2 , we fixed the orbital period to the value known from the Kepler light-curve analysis. The RVs of the four components of KIC 7177553 determined from the multi-Gaussian fit to the LSD profiles are listed in Table 5 in Appendix B . Figure 2 shows the RVs and orbital curves folded with the orbital periods. Table 1 lists the derived orbital parameters. The times of observation of all spectra are based on UTC. To be consistent with the Kepler DCT time scale and the results listed in Table 3 , we added 68 s to the calculated times of periastron passage T . Figure 2. RVs and calculated orbital curves folded with the orbital periods. Left: C 1 (open circles) and C 2 (open squares). Right: C 3 (open circles) and C 4 (open squares). Phase zero corresponds to the time of periastron passage. Table 1 Orbital Solutions Obtained from Multi-Gaussian Fits of the LSD Profiles and with KOREL Note. The table lists period P and orbital elements (eccentricity e , argument of periastron ω , time of periastron passage T , and mass ratio q ) of the two systems, and the RV semiamplitudes K and individual systemic velocities γ . Errors are given in units of the last two digits in parentheses. The last two rows list the rms of the residuals after subtracting the orbital solution from the RVs and the projected masses calculated from the spectroscopic
Paper: 2016ApJ...833...63B (acknowledgments)
The work of R. B. is supported by the Deutsche Forschungsgemeinschaft (DFG) under grant BU 3115/2-1. We thank R. J. MacDowall for providing the Wind/WAVES data and acknowledge the use of the Wind/3DP data. Work at JPL was supported by NASA. Work at APL ( ACE /ULEIS and STEREO /SIT) was supported by NASA grant NNX13AR20G/115828 and NASA subcontract SA4889-26309 from the University of California Berkeley.
Paper: 2018ApJ...856..121G (acknowledgments)
We thank I. Smail for his detailed comments and suggestions that help improve this manuscript; J. M. Simpson for providing the SCUBA2 data; C. Y. Peng and G. Barro for their advice on GALFIT ; S. Zibetti for his support with ADAPTSMOOTH ; C. M. Casey for providing the DSFG comparison data plotted in Figure 4 ; and D. Watson, J. Hjorth, I. Davidzon, H. Rhodin, K. K. Knudsen, P. Laursen, D. B. Sanders, M. P. Haynes, R. Pavesi, T. K. D. Leung, and S. Martín-Álvarez for helpful comments and suggestions. We are grateful to the anonymous referee, whose comments have been very useful to improving our work. C.G.G. and S.T. acknowledge support from the European Research Council (ERC) Consolidator Grant funding scheme (project ConTExt, grant number 648179). A.K., E.J.A., and F.B. acknowledge support by the Collaborative Research Centre 956, subproject A1, funded by the Deutsche Forschungsgemeinschaft (DFG). Support for B.M. was provided by the DFG priority program 1573, “The physics of the interstellar medium.” D.R. acknowledges support from the National Science Foundation under grant number AST-1614213. V.S. acknowledges support from the European Union’s Seventh Framework program under grant agreement 337595 (ERC Starting Grant, “CoSMass”). M.A. acknowledges partial support from FONDECYT through grant 1140099. ERD also acknowledge support by the Collaborative Research Centre 956, subproject C4, funded by the DFG. M.J.M. acknowledges the support of the National Science Centre, Poland, through POLONEZ grant 2015/19/P/ST9/04010; this project has received funding from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement No. 665778. Based on observations made with the NASA/ESA Hubble Space Telescope , obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program #13294. Support for program #13294 was provided by NASA through a grant from the Space Telescope Science Institute. This research made use of the following ALMA data: ADS/JAO.ALMA#2012.1.00978.S. ALMA is a partnership of the ESO (representing its member states), NSF (USA), and NINS (Japan), together with the NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by the ESO, AUI/NRAO, and NAOJ. This paper employed Astropy , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ); APLpy , an open-source plotting package for Python (Robitaille Bressert 2012 ); CASA (McMullin et al. 2007 ); Matplotlib (Hunter 2007 ); Numpy ; Photutils (Bradley et al. 2016 ); PyBDSF ; and R, a language and environment for statistical computing.
Paper: 2018ApJ...860..120P (fulltext)
This completes the presentation of the Galaxy model potential. Appendix C Implementation Notes In this section, we sketch a brief technical resume of the GalMod platform contents (ver. 16.2). Nevertheless, we warn the reader that the platform “GalMod” is continuously updated and the Web page (the tutorial page: www.galmod.org/gal/tutorial ) is the most up-to-date place to search for the latest refinements, bug corrections, and tested implementations that might differ from what is presented in this section. A cycle of 12 upgrades per year is planned. We organized GalMod in modules, i.e., independent sub-units of the source code that interact through interfaces (e.g., the Poisson solver modules, the CMD generator modules, the extinction integrator modules, and so forth). This approach facilitates the use of multiple language codes/libraries with different types of licenses. A rough scheme of the GalMod infrastructure is shown in Figure 17 . We presented each of these modules in dedicated papers. The technique to generate the photometry and chemistry of the stars is introduced in Pasetto et al. ( 2012b ) and references therein, the kinematics and potential solver in Pasetto et al. ( 2016b ) and references therein, and the extinction based on DART-Ray in Natale et al. ( 2017a ). The Web-interface system and cloud computing service is realized and maintained by Clover-lab TM . These references collect the results of several years of work performed by several researchers and programmers. They are not meant to be an exhaustive list. Figure 17. The superstructure of GalMod. Each square box represents a series of modules of GalMod where some computation is performed (either in serial or parallel mode). The numbered blue arrows represent the ideal flux from the input parameters to the delivery of the mock catalog (or the output) . The modules are the Poisson-solver modules that, once the density/potential parameters have been adopted, provide Φ, its gradients and Laplacian, and a series of kinematics indicators (rotation curve, total mass, etc.) relative to . are the modules computing the maximum number of stars for the galaxy considering and the input parameters . can be simply an input parameter depending on the type of simulation. M are the modules assigning the star-like particle mass in the case GalMod is used to generate N -body i.c.. N is the number of stars generated so far. are the modules distributing the stars according to the density profiles, , their number N , and the input condition . The age/metallicity/velocity dispersion modules compute the distribution of the stars or star-like particles in the age τ , metallicity Z , and velocity space. They include the mass generation of the star and the extinction modules in the case of mock stellar catalogs only, while a separate module provides the same information with a preassigned stellar particle mass in the case of i.c. generation. GalMod produces a stochastic realization (i.e., stellar
Paper: 2015A&A...584A..63S (acknowledgments)
This work was supported in part by the Collaborative Research Center TR 33 “The Dark Universe” of the Deutsche Forschungsgemeinschaft. This work is based on data collected at the ESO VLT (prog. ID 186.A-0798).
Paper: 2016A&A...586A.123S (acknowledgments)
M.Š. acknowledges the support of the Czech Science Foundation (grant 14-04338S) and of the institute research project RVO:67985815 to Astronomical Institute of Czech Academy of Sciences. A.S.B. acknowledges financial support by CNES Solar Orbiter grant, CNRS/INSU Programme National Soleil-Terre, ERC STARS2 207430 and SolarPredict 640997 grants and wishes to thank M. DeRosa for useful discussions.