adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...874...76H (fulltext)
has most of its high energy distribution below 10 keV, above which the cross section limits the charge-exchange rate. More specifically, from Figure 1 we can see that the multicomponent model has significantly more particles with 8–10 times the thermal speed compared to the kappa-distribution, and close to the TS where the total proton temperature is just over 1 MK this corresponds to protons in the 6–10 keV range. As expected, Case IV has significantly stronger source terms in the heliosheath, compared to the other three cases. As explained in Section 3.2 , in this case the electron temperature close to the TS is low, allowing for a PUI pressure that is enhanced compared to the other three cases in which the electron and proton pressures are assumed equal. Figure 2 (C) shows that case IV is about twice as efficient at removing energy from the plasma as case I (note the larger range on the color scale than in Figures 2 (A) and (B)). This has a dramatic effect on the size of the heliosphere, so that the heliopause for case IV is approximately the inner edge of the saturated blue region (see also Figure 5 ) where the source term ratio is in fact negative because the energy sources change sign at the heliopause. Figure 3 shows the ratio of the momentum source term magnitude to the magnitude of the momentum source term for a single Maxwellian. While the MHD code works with the momentum source term vector, the magnitude used in the Figure 3 can be thought of as indicating where the plasma is slowed down by charge-exchange. For the single kappa-distribution (Figure 3 (A)), the source term ratio is lower close to the TS, indicating less momentum transfer away from the plasma. The reasoning is similar to that discussed above for the energy transfer, and is a consequence of the energy-dependent cross section reducing the charge-exchange rate with the high energy wings of the distribution. Interestingly, as the plasma flows toward the heliotail, it eventually cools sufficiently for the wings of the distribution to participate in the charge-exchange process. At this point we can see an abrupt increase in the momentum source term that corresponds to a significant slowing of the plasma in the tail (this slowing of the heliotail flow is discussed in more detail in Section 3.4 ). In the more distant heliotail the source terms are similar (see also Figure 4 ), indicating that the flow speeds are similar in both the Maxwellian and kappa-distribution cases. Figure 4. Plasma (solid lines) and neutral bulk (dashed lines) properties along the line through the Sun and parallel to the direction the LISM flows from. Figure 3 (B) shows that momentum source for the multicomponent model (case III) is slightly lower than the Maxwellian case close to the TS, but similar elsewhere. Figure 3 (C) shows that in the region where the energy source for case IV greatly exceeds the corresponding source in the Maxwellian case (Figure 2 (C)), the momentum sources for case I and
Paper: 2020MNRAS.tmp..265H (acknowledgments)
ACKNOWLEDGEMENTS The authors are grateful to Rien van de Weygaert, Shy Genel, Marta Volonteri, and Christophe Pichon for useful discussions. This work has made use of the Rusty Cluster at the Center for Computational Astrophysics (CCA) of the Flatiron Institute. The Flatiron Institute is supported by the Simons Foundation. AP is supported by NASA grant 15-WFIRST15-0008 to the WFIRST Science Investigation Team ‘Cosmology with the High Latitude Survey’. RSS is grateful for the generous support of the Downsbrough family.
Paper: 2018A&A...619A...6O (acknowledgments)
This work has been supported by the Academy of Finland Centre of Excellence ReSoLVE (NO, MJK, JP), Finnish Cultural Foundation grant no. 00170789 (NO) and Estonian Research Council (Grant IUT40-1; JP). We thank Aki Vehtari for useful discussion and comments on GP related questions. MJK and JL gratefully acknowledge discussions with Petri Käpylä, Andreas Lagg, and Jörn Warnecke on the theoretical interpretation of the observational data. We also thank Katalin Olah for sharing insight into some questions related to MW dataset, as well as Matthias Rheinhardt and Axel Brandenburg for comments on the manuscript. The HK_Project_v1995_NSO data derive from the Mount Wilson Observatory HK Project, which was supported by both public and private funds through the Carnegie Observatories, the Mount Wilson Institute, and the Harvard-Smithsonian Center for Astrophysics starting in 1966 and continuing for over 36 yr. These data are the result of the dedicated work of O. Wilson, A. Vaughan, G. Preston, D. Duncan, S. Baliunas, and many others.
Paper: 2017MNRAS.467.2690E (acknowledgments)
Acknowledgments We would like to thank our referee, Roberto Soria, for useful comments on this paper. We gratefully acknowledge support from the Science and Technology Facilities Council (HE through studentship grant ST/K501979/1 and TR as part of consolidated grant ST/L00075X/1). The scientific results reported in this paper are based on data obtained from the Chandra Data Archive, and on archival observations obtained with XMMNewton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA.
Paper: 2016ApJ...827...82M (acknowledgments)
We thank M. J. Irwin for allowing us to use his observational data. We are also grateful to an anonymous referee for providing useful information and comments. The computations were performed on the FIRST cluster at the Center for Computational Sciences, University of Tsukuba. This work was supported in part by JSPS Grants-in-Aid for Scientific Research: (A) (21244013), (C) (25400222), and (S) (20224002), and Grants-in-Aid for Specially Promoted Research by MEXT (16002003).
Paper: 2017MNRAS.470.3672D (acknowledgments)
Acknowledgements This research has made use of the SIMBAD data base operated at CDS, Strasbourg, France and NASAs Astrophysics Data System (ADS) Bibliographic Services. ADU gratefully acknowledges the support of the Fonds qubcois de la recherche sur la nature et les technologies . GAW is supported by an Natural Science and Engineering Research Council of Canada Discovery Grant. JOS acknowledges funding from the European Unions Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement no. 656725. ADU also warmly thanks Alex Fullerton and Vronique Petit for their helpful comments and kind guidance. We also wish to thank the anonymous reviewer who provided very useful observations and suggestions which contributed to improve this paper and its impact.
Paper: 2021ApJ...915..129K (fulltext)
which the sample was selected, i.e., all objects with significant C iv BLR size measurements. For five of the six Seyfert AGNs in Table 6 there are BH mass estimates from H β RM. In Table 7 we list these BH mass estimates as given in the database of Bentz Katz ( 2015 ). 14 14 http://www.astro.gsu.edu/AGNmass/ From a comparison of these mass estimates to our estimates from the C iv line in Table 6 , we find that for NGC 7469 and 3C 390.3, the BH mass estimates from the two lines are within the 1 σ uncertainties (i.e., the 1 σ uncertainties overlap). For NGC 3783 and NGC 4151, the BH mass estimates are within the 1.5 σ uncertainties (if we add the σ from both measurements), and for NGC 5548, they are within 2 σ . This resemblance in the mass estimates for the two emission lines increases the confidence in these results and in the ability of the RM method to give consistent mass estimates. Table 7 Black Hole Mass Estimates from H β Reverberation Mapping Note. Measurements from the database of Bentz Katz ( 2015 ) in units of . Note that the front page of the database lists the averaged mass measurements from all lines, and here we list the mass measurements from the H β line alone. 5. Summary We have reported the final results from a 13 yr spectrophotometric RM campaign on high-luminosity quasars. The spectrophotometric observations were supplemented by 18 yr of photometric monitoring, which improved the cadence and the accuracy of the spectrophotometric continuum light curves by adding to them the photometric light curves. All 11 objects with photometric light curves display continuum variability of 10%–70% over about six years in the rest frame. This value is similar to that reported in Paper I with data over a period that was about one-third of the current one. Six of the 11 objects were monitored spectroscopically. We identified C iv emission-line time lags for 3 objects, indicating C iv emission-line region sizes of about 100 to 250 light days. Together with data from previous studies, we have constructed a C iv BLR size–UV luminosity relation over eight orders of magnitude in UV luminosity; the slope of 0.45 ± 0.04 is consistent with previous studies and with photoionization theory. Although all our monitored AGNs show significant continuum variation, only in about half of them did we measure C iv time lag (the same fraction was also was found by Lira et al. 2018 ). This may indicate that the ionizing continuum behaves differently from the 1350 Å continuum we measured in this study. Although C iv is not a preferred line for the central BH mass determination, we derive the mass for the 19 objects with proper C iv RM data. We find that the mass scales approximately as the square root of the UV luminosity, but with a large scatter. The scatter probably results at least partly from the drawbacks of the C iv line for BH mass measurement, but probably also includes an intrinsic scatter in that relation for AGNs, e.g., different velocities for
Paper: 2018ApJ...855L..23A (fulltext)
Observatory Ultraviolet Optical Telescope. A large uncertainty in the bolometric luminosity in the first epoch is due to the fact that ultraviolet images were obtained four hours after the first optical and infrared ones—a delay comparable to the predicted evolution timescale of the emission at that time. Therefore the bolometric luminosity at the epoch is calculated in two different ways: 1. By fitting a blackbody only to the simultaneous optical and infrared data (blue fit in Figure 2 ). 2. By fitting a blackbody to the optical, infrared, and (four-hour later) ultraviolet data (red fit in Figure 2 ). Figure 2. Blackbody fits to the optical and infrared SED at discovery (black points; blue fit) and to the ultraviolet, optical, and infrared (the ultraviolet data are from four hours after discovery; gray points; red fit). Including the ultraviolet data reduces the inferred blackbody temperature by ∼1500 K, resulting in a lower bolometric luminosity at discovery (Figure 5 ). Optical and infrared magnitudes interpolated to the ultraviolet epoch are shown in purple empty circles for comparison and demonstrate the rapid evolution of the SED at these times. The insets show the corner plots for each MCMC fit, with contour lines denoting 50% and 90% bounds, red and blue solid lines (overplotted on each histogram) denoting the mean and median of each parameter distribution (on top of each other in most cases), and dashed lines denoting 68% confidence bounds. The bandpasses of the different filters used are shown at the bottom of the main plot. Both fits for the discovery epoch are presented in Figure 2 . They each match the data well and converge cleanly on a set of blackbody parameters. Including the ultraviolet data from four hours after discovery in the discovery epoch lowers the best-fit temperature by about 1500 K, compared to not having any ultraviolet constraints. A total of 21 additional epochs are fit. All fits converge onto single-peaked radius and temperature distributions except in five epochs where a small number of walkers converged on disjointed local likelihood maxima, leading to a >15% relative error in the radius, temperature, or deduced bolometric luminosity. These epochs were removed. The resulting bolometric light curve is presented and compared to other published light curves in Figure 3 . Figure 3. Bolometric light curve of the GW170817 kilonova derived here compared to those from Cowperthwaite et al. ( 2017 ), Drout et al. ( 2017 ), and Waxman et al. ( 2017 , SED integration method). After the first day, the bolometric light curve declines at an initial rate of ∼ t −1 followed by a steeper ∼ t −1.3 decline at later times, consistent with the expected radioactive heating rate. It is possible that the true SED of the kilonova cannot be described by a single blackbody in all epochs. However, the good fits in most epochs suggest that the blackbody approximation is reasonable. In addition, the bolometric values derived here are
Paper: 2016A&A...586A.103W (fulltext)
abundances in Paper III ( Rab et al. 2015 ). We intend to offer our modelling tools and collected data sets to the community at the end of the FP7 DIANA project 12 . Appendix A: Stellar parameters and irradiation Appendix A.1: Stellar parameters The photospheric component of the stellar emission is characterised by the effective temperature , the surface gravity , and the stellar (photospheric) luminosity . Since these properties are fairly well-known for most of our target objects in the literature, we have decided not to put too much efforts into this issue. Assuming solar abundances for the star, we use standardPHOENIX stellar atmosphere models ( Brott Hauschildt 2005 ) to fit , and the interstellar extinction , to our photometric and spectroscopic data (see Fig. A.1 ). A thorough determination of requires to fit high-resolution optical spectra. For most target objects, this has been done already in the literature, so we can pick from the literature and only fit and for the assumed distance , using at first an estimate of the surface gravity . Once and are determined, we involve pre-main sequence stellar evolutionary models ( Siess et al. 2000 ) to find the stellar mass and the age. The stellar radius and surface gravity are then given by and . We use the resulting value for to redo the fitting above. This procedure is found to converge very quickly, and – thanks to having fixed from the literature – gives quite unambiguous results, see examples in Table A.1 . Fig. A.1 Fitting stellar parameters, and compilation of the stellar irradiation for two examples, TW Hya ( left ) and AB Aur ( right ). The upper plots show measured photometric fluxes (coloured symbols) as function of wavelength , in comparison to our best fitting, reddened Phoenix stellar atmosphere model spectrum (black), and the averaged observed FUV data (orange). The lower plots show the de-reddened surface intensities (black), and the averaged, de-reddened FUV data ( nm, red). The blue lines show fits to de-absorbed X-ray data, using a two-component X-ray gas emission model (energy keV). The dashed grey line marks the EUV regime in between, which is assumed to be absorbed by neutral hydrogen between the star and the disk and hence disregarded in the disk model. The dashed magenta lines show quick parametric fits to the UV and X-ray data, see Eqs. ( A.1 ) and ( A.2 ) and text, not used for the TW Hya and AB Aur models. Appendix A.2: Stellar UV irradiation The UV irradiation by the central star is much more difficult to determine, and hampered by the lack of high-quality UV data, especially in the hard FUV region (130 nm), and for Ly. We have systematically scanned and collected UV data from IUE, FUSE, HST/STIS, HST/COS, and HST/ACS. These data have been re-binned and collated, using the inverse square of the flux uncertainties as weighting factors, following the idea used by Valenti et al. (2000 , 2003) . The results of this data collection will be described elsewhere
Paper: 2016A&A...586A.132P (fulltext)
avoid the Galactic disk where a fraction of the dust is farther than 4.5 , and therefore, traced by dust in emission, but not present in the SGF absorption map. Fig. 10 Comparison between the renormalized DL estimates (), and those derived from optical stellar observations (). The top row shows the maps derived from optical stellar observations. The lines have been added for reference. The bottom row left panel compares the renormalized estimates with those derived from optical stellar observations in the sky. The agreement of these independent estimates is a successful test of our empirical renormalization. The bottom row right panel show the histogram of the difference of the two estimates, also in the sky. Figure 10 shows the comparison between the renormalized DL estimates () with the stellar observations based estimates in the sky. The agreement between both estimates is remarkable. The difference between the estimates () has a mean of 0.02 mag and scatter (variance) of 0.12 mag. This comparison validates our renormalized DL estimates as a good tracer of the dust optical extinction in the sky. Our optical extinction estimate () was tailored to match the QSOs estimates. QSOs were observed in diffuse diffuse Galactic lines of sight (most of the QSOs have ). The agreement of the and estimates extends the validity of the DL renormalized estimate () to greater column densities. The scatter of – provides an estimate of the uncertainties in the different estimates. 7. FIR SEDs per unit of optical extinction The DL model parameter and the QSO data are used to compress the FIR IRAS and sub-mm Planck observations of the diffuse ISM to a set of 20 SEDs normalized per , which we present and discuss. The parameter is mainly determined by the wavelength where the SED peaks; as a corollary, SEDs for different values of differ significantly. The values obtained from the QSO analysis,, allow us to normalize the observed SEDs (per unit of optical extinction) and generate a one-parameter family of . This family is indexed by the parameter; the QSO lines of sight are grouped according to the fitted Galactic value. We divide the sample of good QSOs in 20 bins, containing QSOs each 15 . To obtain the values we proceed as follows. For each band and , we would like to perform a linear regression of the values as a function of. The large scatter and non-Gaussian distribution of and the scatter on make it challenging to determine such a slope robustly. Therefore, we smooth the maps to a Gaussian PSF with FWHM to reduce the scatter on , redo the dust modelling (to obtain a coherent estimate), and perform the regression on the smoothed (less noisy) maps. The non-Gaussian distribution of do not introduce any bias in the slope found 16 . Figure F11 presents the set of SEDs. The left panel shows the SEDs for the different values. The right panel shows each SED divided by the mean to highlight the differences between the individual SEDs. The complex statistics of the
Paper: 2015MNRAS.446...65B (acknowledgments)
DB and AG acknowledge ICRANet for partial support. All the authors thank Professor L. Stella for useful discussions at the beginning of the present project. AP acknowledges support from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 267251 ‘Astronomy Fellowships in Italy (AstroFIt)’.
Paper: 2016A&A...585A..99M (acknowledgments)
We give a special thank to Sambit Roychowdhary, then at NCRA in Pune, for helping us carry out some of the GMRT observations. We are grateful for the tips we got from Jayaram Chengalur at NCRA and K. S. Dwarakanath at RRI in Bangalore regarding the reduction of the GMRT data. T.P.K.M. acknowledges financial support from the Spanish Ministry of Economy and Competitiveness (MINECO) under grant number AYA2013-41243-P. He is grateful to the Kapteyn Astronomical Institute for their hospitality as a long-term guest during the time much of this paper was being written. Part of this work was done at Leiden Observatory. M.A.W.V. and T.P.K.M. acknowledge financial support provided by NOVA, the Netherlands Research School for Astronomy, and travel support from the Leids Kerkhoven-Bosscha Fonds. M.A.W.V. acknowledges support from the FP7 Marie Curie Actions of the European Commission, via the Initial Training Network DAGAL under REA grant agreement 289313. M.A.B. acknowledges support from NSF/AST-1009471. K.B.W. acknowledges grants OISE-754437 (NSF) and 614.000.807 (NWO). We finally thank the referee for constructive comments and suggestions.
Paper: 2018MNRAS.473.3765M (fulltext)
determine whether the remnant continues to migrate in the HR diagram towards the hotter S Dor instability strip occupied by quiescent LBVs, or if increasing extinction from ongoing dust condensation in the ejecta pushes it redwards again. Figure 10. Modification of the HR diagram for LBVs and their kin from Mauerhan et al. ( 2015 , see their fig. 8). The magenta coloured square is the previous estimate for the precursor star from Mauerhan et al. ( 2015 ), uncorrected for possible circumstellar extinction. The red square represents the revised best-matching model of the precursor SED, corrected for grey circumstellar extinction (see the text). The blue square indicates the aftermath of the eruption, a hot B4–B5 supergiant, also corrected for grey circumstellar extinction. The luminosities of the remnant and the revised precursor were calculated by integrating the UV–IR SEDs of the best-matching models shown in Figs 8 and 9 . The revised parameters of the stellar precursor warrant re-analysis of the star's initial mass as well, which was previously estimated at ∼30 M ⊙ (Mauerhan et al. 2015 ). Fig. 11 shows the data, after correcting for the purported grey circumstellar extinction parameters discussed above (for the remnant, E ( B − V ) = 0.48 mag and R V = 5.4; for the precursor, E ( B − V ) = 0.16 mag and R V = 5.4), along with evolutionary tracks from the Geneva rotating stellar models for 50 and 60 M ⊙ (Ekström et al. 2012 ); the data appear to most closely match (but are slightly below) the 60 M ⊙ model, which at the locations of both the remnant and precursor is undergoing core-He burning (Ekström et al. 2012 ). This revised initial mass is approximately twice as high as the circumstellar extinction-free estimate by Mauerhan et al. ( 2015 ). Interestingly, the photometry of the remnant and precursor, which was corrected for different values of circumstellar extinction and based on the best SED matches, is consistent with no significant change in stellar luminosity between before and after the 2014 eruption. Figure 11. The precursor (open black square) and remnant (filled black square) photometry of SN Hunt 248 on the HR diagram, after correcting for our estimated circumstellar extinction parameters (see the text, Section 4.1.2 ). The evolutionary tracks are from Geneva rotating stellar models (Ekström et al. 2012 ) at solar metallicity for initial masses of 50 M ⊙ (green curve) and 60 M ⊙ (magenta curve). 4.2 The 2014 outburst, revisited 4.2.1 Massive binary merger-burst? The energy source of the 2014 eruption is uncertain. The structure of the outburst light curve, the outflow velocity and the large-amplitude pre-outburst variability detected over prior decades might provide clues. We can speculate that the cool-hypergiant precursor was a massive interacting binary, perhaps similar to HR 5171 (Chesneau et al. 2014a ), and that its large pseudophotosphere was the signature of a common envelope. If this is the case, it is plausible that SN
Paper: 2018ApJ...852...12Y (acknowledgments)
We are grateful to the anonymous referee for the constructive comments that helped us improve this paper. Prof. Neal J. Evans II and Dr. Kee-Tae Kim are also acknowledged for useful discussions. This work is supported by the National Natural Science Foundation of China through grants 11503035, 11573036, 11373009, 11433008 and 11403040, 11403041, the International ST Cooperation Program of China through the grant of 2010DFA02710. T.L. is supported by the EACOA fellowship. K.W. is supported by grant WA3628-1/1 through the DFG priority program 1573 “Physics of the Interstellar Medium.”