adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.456.3245M (fulltext)
field, and the interaction between them in a conservative way using Godunov, finite-difference methods (Rezzolla Zanotti 2013 ). The radiation is evolved adopting the M1 closure scheme (Levermore 1984 ). The simulations assume axisymmetry and run in 2.5 dimensions, that is, allowing for non-zero azimuthal vector components. We assume a black hole mass of 10 M ⊙ and evolve the models for roughly 1.2 s, corresponding to ∼165 orbital periods at the pressure maximum of the initial model. The numerical domain extends from r min = 5 to r max = 14 along the radial direction, and from θ = π/2 − 0.3 to θ = π/2 + 0.3 along the θ direction, and it is covered by a uniform (in radius and polar angle) grid composed of 448 × 448 grid-points. We note that the radial resolution is therefore Δ r = 0.02. Standard outflow/no-inflow boundary conditions are adopted at all the boundaries. 2 For simplicity, in this work we adopt only the scattering opacity κ s = 0.34 cm 2 g −1 , and we account for the Comptonization using the ‘blackbody’ method (see Sadowski Narayan 2015 ). Since we do not assume any external heating source, the tori are expected to cool down by emitting the radiation that supports them. 3 RESULTS 3.1 Torus dynamics All the models have been evolved up to t = 25 000, corresponding to ∼165 orbital periods computed at the maximum density radius r c = 8.35. Two purely hydrodynamic models, namely Hydro43 and Hydro53, have been also studied, in order to provide a comparison with respect to the radiative models, which is especially useful in the spectral analysis of Section 3.2 . Because of the radial perturbation introduced, the tori start oscillating in the potential well. However, since the initial models are significantly below the marginally stable configuration, no appreciable mass accretion is induced by the oscillations and the periodic behaviour can last for a long time. In Fig. 1 , a full oscillation sequence at t ∼ 0.1 s is monitored through six snapshots for the model Rad30. The left- and the right-hand panels report the distribution of the rest-mass density and of the radiation energy density, respectively. The arrows in the radiative panels reflect the free-streaming radiation leaving the torus surface. As expected, during the evolution the energy density of the radiation remains concentrated towards the centre of the torus, where the optical depth is the largest. Radiation diffuses out following the gradient of radiative energy density and ultimately leaves the surface of the torus. The distribution of the out coming radiation is determined by the shape of the photosphere. In the case of the tori studied here, most of the radiation is emitted roughly in the direction perpendicular to the equatorial plane. Figure 1. Illustration of a typical oscillation sequence for the model Rad30 through six snapshots (from top to bottom). Left-hand panels: distribution of the rest-mass density with the velocity represented by orange arrows. Right-hand
Paper: 2018MNRAS.480.3907V (acknowledgments)
ACKNOWLEDGEMENTS The cosmological simulations described in this work were performed using the enzo code ( http://enzo-project.org ) that is the product of a collaborative effort of scientists at many universities and national laboratories. We gratefully acknowledge the ENZO development group for providing extremely helpful and well-maintained on-line documentation and tutorials. Most of the analysis done in this work was performed with the julia code ( https://julialang.org ). F.V. and D.W. acknowledge financial support from the ERC Starting Grant ‘MAGCOW’, no.714196. We acknowledge the usage of computational resources on the Piz-Daint supercluster at CSCS-ETHZ (Lugano, Switzerland) under project s701 and s805. We thank F. Zandanel, J. L. Han, and T. Akahori for useful scientific feedback, and our anonymous referee for valuable suggestions.
Paper: 2018MNRAS.481.2981S (fulltext)
and reported that the amplitude of cycle 25 will be weaker than that of the current cycle 24. Macario-Rojas, Smith E ( 2018 ) also applied a similar model-based method and predicted the cycle 25 will be 14.4 per cent ( per cent) weaker than the cycle 24. Kakad, Kakad Ramesh ( 2017 ) used the estimation of Shannon entropy to predict a very weak solar cycle (ISSN peaks at 63), similar to th Dalton minimum. Predictions of a very weak solar cycle were also reported by Kirov et al. ( 2017 ), who used a precursor method and the original version of the ISSN. They found that the smoothed monthly mean sunspot number at the maximum of cycle 25 will be within 50–55 range. It should also be noted that the original version of the ISSN is statistically 45 per cent lower than the new version 2. Hathaway Upton ( 2016 ) and Gopalswamy et al. ( 2018 ) used a precursor method and predicted moderate amplitudes of cycle 25, similar to the current cycle 24, while Cameron, Jiang Schüssler ( 2016 ) reported that cycle 25 may slightly stronger than the current one. Pesnell Schatten ( 2018 ) also applied a precursor method by using SODA Index, which is determined by polar magnetic fields and the spectral index. They estimated the next cycle’s smoothed peak activity will be with a possibility of exceeding this early estimation due to the increasing trend of the SODA Index. Jiang Cao ( 2017 ) used a surface flux transport model-based method and concluded that cycle 25 might be stronger than cycle 24. Predictions of similar or stronger cycle 25 as compared to the current cycle 24 are in agreement with our predictions presented in this study. Precursor methods use some measure of geomagnetic activity or polar magnetic fields at a particular time to predict amplitude of the next cycle. In general, this particular time is chosen at the minimum phase of a cycle it may produce misleading results if the selection is made before the ISSN reached the minimum. To the contrary, the approach presented here can be applied at any time during the descending phase, but the starting point should be chosen carefully. There is additional advantage that this method allows us to also predict fluctuations of sunspot numbers during a solar cycle such as double or multiple maxima as can be seen in Figs 2 and 3 . These plots clearly show the accurate predictions of maximum times and amplitudes as well as main fluctuations occurred during solar cycles. Further analysis showed that apart from the embedding dimension and delay-times, performance of this non-linear method is highly dependent on the starting point of the prediction. This is mostly because the algorithm searches for nearest neighbours to the prediction vector, which is formed from the last (( m *τ))) +1 points before the starting point. In this case, the consecutive starting points may yield different prediction vectors and eventually different outcomes. Another explanation for the starting point dependence can be made by analysing
Paper: 2017A&A...600A..24M (fulltext)
results in Tables 3 , A.1 , A.2 ). The addition of this component also significantly improves all the fits. We consider “rdb-rfxconv-pl”, which includes the hard power-law component, rfxconv, as a reflection component smeared by the rbdblur component, to be our best fit model (see Table 3 ). The fit of the XMM-Newton spectra with the same model gave values of the parameters that agree overall with those obtained fitting the NuSTAR spectra. In this case, we found values of the ionization parameter log() ranging between 2.58 and 2.71 (slightly higher, i.e., approx. 3, for the XMM-Newton spectra) and reflection amplitudes between 0.2 and 0.3, indicating a relatively low superposition between the source of the primary Comptonization continuum and the disk (a value of 0.3 would be compatible with a spherical geometry of a compact corona inside an outer accretion disk). For the smearing parameters of the reflection component, we find values of the emissivity index of the disk ranging from 2.8 to 2.48, an inner radius of the disk from 10.6 to , and an inclination angle of the system with respect to the line of sight of . In our results, the inclination angle is higher than that found by ( Miller et al. 2013 ; who report an inclination angle less than ), but is very similar to that estimated from Chandra spectra (, see Chiang et al. 2016a ). Moreover, the inner disk radius we find is not compatible with the ISCO. Assuming a for the neutron star, the inner radius of the disk is located at km from the neutron star center. Note that this value is compatible to the estimated radius of the emission region of the soft blackbody component, which is in the range km. We interpret this component as the intrinsic emission from the inner disk since this is the coldest part of the system and because the temperature of the blackbody component appears to be too low to represent a boundary layer. 5. Conclusions The main aim of this paper is to test the robustness of disk parameters inferred from the reflection component in the case of neutron star LMXBs. To this aim, we used broad-band, moderately-high-resolution spectra of Serpens X-1, a neutron star LMXB of the atoll type with a very clear reflection spectrum that has been studied with several instruments. In particular, we carried out a broad-band spectral analysis of this source using data collected by NuSTAR and XMM-Newton satellites, which have the best sensitivity at the iron-line energy. These data have already been analyzed in literature. In particular, Miller et al. (2013) have analyzed the NuSTAR spectra and obtained a low inclination angle of approximately 8, an inner disk radius compatible with the ISCO, an ionization parameter between 2.3 and 2.6, along with an iron abundance of approximately 3. We performed the fitting using slightly different continuum and reflection models with respect to those used by other authors to fit the X-ray spectrum of this source. Our best fit of the NuSTAR spectrum of Ser
Paper: 2016ApJ...833...87C (acknowledgments)
Eoin Carley is supported by ELEVATE: Irish Research Council International Career Development Fellowship—co-funded by Marie Curie Actions. We would like to thank Sophie Masson and Etienne Pariat for some very useful discussions of this event. We would also like to thank the referee for very useful and constructive comments. We are grateful to the SDO, SOHO , SWAP, RHESSI , FERMI, and GOES teams for open access to their data. The NRH is funded by the French Ministry of Education and the Région Center. Orfées is part of the FEDOME project, partly funded by the French Ministry of Defense. The authors acknowledge the Nançay Radio Observatory / Unité Scientifique de Nançay of the Observatoire de Paris (USR 704-CNRS, supported by Université d'Orléans, OSUC, and RÃ©gion Centre in France) for providing access to NDA observations accessible online at http://www.obs-nancay.fr .
Paper: 2016A&A...592A.156D (fulltext)
higher rotational velocities than expected for single stars, we conclude that stellar multiplicity is an important enhancer of rotation in Sun-like stars. Blue stragglers are expected to have a strong enhancement on rotation owing to injection of angular momentum from the donor companion. 5. The rotational braking law We removed from this analysis all the spectroscopic binaries in order to correctly constrain the rotational braking. The non-SB HIP 29525 displays a that is much higher than expected ( km s), but it is likely that this is the result of an overestimated isochronal age ( Gyr). We decided to not include HIP 29525 in the rotational braking determination because it is a clear outlier in our results. Maldonado et al. (2010) found X-ray and chromospheric ages of 0.55 and 0.17 Gyr, respectively, for HIP 29525. We then divided the remaining 65 stars and the Sun in bins of 2 Gyr, and removed all the stars that were below the 70th percentile of in each bin from this sample. Such a procedure can be justified because we verified that 30% of the stars should have above 0.9 by doing a simple simulation with angles drawn from a flat distribution between 0 and . This allowed us to select the stars that had the highest chance of having above . In total, 21 solar twins and the Sun compose what we hereafter reference as the selected sample . Albeit this subsample is smaller, it has the advantage of mostly removing uncertainties on the inclination angle of the stellar rotation axes 7 . We stress that the only reason we can select the most probable edge-on rotating stars () is because we have a large sample of solar twins in the first place. We then proceeded to fit a general curve to the selected sample (see Fig. 5 ) using the method of orthogonal distance regression (ODR, Boggs Rogers 1990 ), which takes the uncertainties on both and ages into account. This curve is a power law plus constant of the form (the same chromospheric activity and vs. age relation used by Pace Pasquini 2004 ; Guinan Engle 2009 ), with (rotational velocity) and (asymptotic velocity) in km s and (age) in Gyr. We find that the best-fit parameters are , , and (see right panel of Fig. 5 ). These large uncertainties are likely due to i) the strong correlation between and ; and ii) the relatively limited number of data points between 1 and 4 Gyr, where the parameters are most effective in changing the values of . This limitation is also present in past studies (e.g., van Saders et al. 2016 ; Barnes 2003 ; Pace Pasquini 2004 ; Mamajek Hillenbrand 2008 ; García et al. 2014 ; Amard et al. 2016 ). On the other hand, our sample is the largest comprising solar twins and, therefore, should produce more reliable results. With more data points, we could be able to use 1 Gyr bins instead of 2 Gyr in order to select the fastest rotating stars, which would result in a better subsample for constraining the rotational evolution for young stars. The relation we obtain is in contrast with some
Paper: 2018A&A...611A..57M (acknowledgments)
Acknowledgements Financial support was provided by the German Federal Ministry of Education and Research (BMBF in the framework of the Verbundforschung, D-LOFAR 05A11BAA). The authors would like to thank Mark DeRosa’s help for extending the magnetic field calculations beyond the default distance, NASA’s SDO and ESA’s PROBA2 teams as well as the DAM and ARTEMIS observatories for providing open access to their data, and J. Anderson, A. Warmuth, J. Hessel, and G. Heald for their careful reading of the manuscript. LOFAR, designed and constructed by ASTRON, has facilities in several countries that are owned by various parties (each with their own funding sources) and are collectively operated by the International LOFAR Telescope (ILT) foundation under a joint scientific policy. C. F. acknowledges financial support from the “Agence Nationale de la Recherche” through grant ANR-09-JCJC-0001-01.
Paper: 2015ApJ...814...54A (acknowledgments)
We thank the SOFIA ground crew, flight crew, and Mission Operations for their successful execution of the SOFIA observations. We also thank an anonymous referee for making suggestions that led to the improvement of this paper. This work is based on observations made with the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA science mission operations are conducted jointly by the Universities Space Research Association, Inc. (USRA), under NASA contract NAS2-97001, and the Deutsches SOFIA Institut (DSI) under DLR contract 50 OK 0901. Financial support for FORCAST was provided to Cornell by NASA through award 8500-98-014 issued by USRA. This work is based on observations made with the Spitzer Space Telescope , which is operated by JPL/Caltech under NASA contract 1407. This work is based in part on observations made with Herschel , a European Space Agency Cornerstone Mission with significant participation by NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech. R.D.G. acknowledges support from NASA and the United States Air Force. Facilities : Spitzer , SOFIA , Herschel .
Paper: 2018ApJ...862..162H (fulltext)
is γβ ≳ 1.3, i.e., β ≳ 0.8. So, this means that the fastest ejecta has a velocity v ≳ 0.8 c . For lower-density medium, the fastest velocity should be even higher. On the other hand, Figure 4 shows that the lower limit to the shock energy at t = 156.4 days is E sh ≳ 2 × 10 50 erg assuming B ≲ 10 −2 , implying that the total ejecta kinetic energy is E ej ≳ 2 × 10 50 erg. This constraint is insensitive to B and/or E 1 . 4. Conclusion and Discussion We have used a simple spherical shock model to explain the nonthermal afterglow emission of GW 170817. We consider only the emission from the forward shock. The shock dynamics derived is available not only to ultrarelativistic and Newtonian shocks, but also to mildly relativistic shocks. The model parameters, except for those assuming e = 10 −1 , are constrained by multiband observations, and the main results are as follows: 1. The merger ejecta profile has k ≈ 6.7 and E 1 ≳ 4 × 10 49 erg for n 10 −1 cm −3 . 2. The fastest ejecta has γ max β max ≳ 1.3, i.e., β max ≳ 0.8, for n 10 −1 cm −3 . The slowest ejecta has γ min β min ≳ 0.7, i.e., β min ≳ 0.6, for n 10 −1 cm −3 . 3. The total ejecta kinetic energy is E ej ≳ 2 × 10 50 erg, if B ≲ 10 −2 . Recently, there have been some works claiming that the rising light curve turns over around t turn ∼ 150 days (Alexander et al. 2018 ; Dobie et al. 2018 ; Nynka et al. 2018 ). This turnover implies that there is no longer any energy injection, and thus the light curve starts to decline. So, the lower panel (for t = 156 days) in Figure 4 shows us the constraint of the total kinetic energy of the ejecta. The H i observation of the host galaxy constrained n 0.04 cm −3 (Hallinan et al. 2017 ). However, since the location of GW 170817 appears to be within the effective radius of the host galaxy (Levan et al. 2017 ), the density of the medium at GW 170817's location may not be very low. So, a reasonable circumburst medium density for GW 170817 could be n ∼ 10 −2 cm −3 . Moreover, GRB afterglow modeling usually gives a postshock magnetic field with B ∼ 10 −5 –10 −3 (e.g., Lemoine et al. 2013 ; Barniol Duran 2014 ; Santana et al. 2014 ). Taking n ∼ 10 −2 cm −3 and B ∼ 10 −5 –10 −3 , we can obtain from the lower panel of Figure 4 that the total kinetic energy is E ej ∼ (0.3–5) × 10 51 erg and that the slowest ejecta has γ min β min ∼ 1–1.5, i.e., β min ∼ 0.7–0.8. For the same range of n and B values, the upper panel of Figure 4 implies that the fastest ejecta has a velocity γ max β max ≳ 1.8, i.e., γ max ≳ 2. The gamma-ray emission from GW 170817 may be produced by the fastest part of the ejecta. Its energy E fast ≈ E γ ≃6 × 10 46 erg (Abbott et al. 2017b ) is many orders of magnitude smaller, compared with the constrained kinetic energy of the ejecta. For n ∼ 10 −2 cm −3 and B ∼ 10 −5 –10 −3 , the lower panel of Figure 4 also shows that E 1 ∼ 10 50.5 –10 52.5 erg, with which we can estimate the velocity of the fastest ejecta, γ max β max = ( E 1 / E γ ) 1/ k ∼
Paper: 2017MNRAS.469..492M (fulltext)
ratio 3.5. This process resulted in 726 and 1088 sources in the COSMOS and UDS fields, respectively. The source S2CLSJ021830-053130 with an 850m flux of 50mJy is the lensed candidate discussed by Ikarashi etal. ( 2011 ). 2.2 Radio and mid-infrared The Karl G. Jansky Very Large Array (VLA) 1.4GHz radio data were taken from Schinnerer etal. ( 2007 , 2010 ) for the COSMOS field, and from Ivison etal. ( 2005 , 2007 ) and Arumugam etal. (in preparation) for the UDS field. The catalogues include sources for which 3 detections were obtained. The mid-infrared (mid-IR) Spitzer (Fazio etal. 2004 ; Rieke etal. 2004 ; Werner etal. 2004 ) data are from the Spitzer Extended Deep Survey (SEDS; Ashby etal. 2013 ), the Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH, PI: P. Capak), the S-COSMOS project (Sanders etal. 2007 ; Le Floch etal. 2009 ) and the Spitzer Public Legacy Survey of the UKIDSS Ultra Deep Survey (SpUDS; PI: J. Dunlop) 2 described in Caputi etal. ( 2011 ). To obtain the 3.6 and 4.5m photometry, we used the de-confusion code T-PHOT 3 (Merlin etal. 2015 ). This utilizes prior information on the positions and morphologies of objects from a high-resolution image (HRI; in this case the K -band or K s -band images) to construct a model of a given low-resolution image (LRI; in this case the Spitzer imaging) while solving for the fluxes of these objects. The 3 depths of the VLA radio and Spitzer mid-IR imaging in both fields are summarized in Table 1 . Table 1. The 3 depths of the multifrequency data used in the COSMOS and UDS fields. Note. a The two alternative values correspond to the shallower and deeper strips of the UltraVISTA near-IR imaging. 2.3 Optical and near-infrared The optical data in both fields were obtained with Subaru/SuprimeCam (Miyazaki etal. 2002 ), as described in Dye etal. ( 2006 ) and Furusawa etal. ( 2008 ), and from the CanadaFranceHawaii Telescope Legacy Survey (CFHTLS), as described in Bowler etal. ( 2012 ). The deep z -band images are described in Bowler etal. ( 2012 ) and Furusawa etal. ( 2016 ). The near-infrared (near-IR) data in the COSMOS field was obtained from Data Release 2 of the UltraVISTA survey (McCracken etal. 2012 ; Bowler etal. 2014 ), while in the UDS field the near-IR data were provided by Data Release 10 of the UKIRT Infrared Deep Sky Survey (UKIDSS; Lawrence etal. 2007 ; Cirasuolo etal. 2010 ; Fontana etal. 2014 ). In both fields, the optical and near-IR fluxes were measured in 3-arcsec diameter apertures, and the resulting 3 depths of this aperture photometry are summarized in Table 1 . Finally, we used a list of spectroscopic redshifts from 3D- HST (Brammer etal. 2012 ; Skelton etal. 2014 ; Momcheva etal. 2016 ), VIMOS Ultra-Deep Survey (VUDS; Le Fvre etal. 2015 ; Tasca etal. 2017 ), zCOSMOS (Lilly etal. 2007 , 2009 ), MOSFIRE Deep Evolution Field (MOSDEF; Kriek etal. 2015 ), PRIsm MUlti-object Survey (PRIMUS; Coil etal. 2011 ) and from Trump etal. ( 2009 , 2011 ) in the COSMOS field and UDSz
Paper: 2016ApJ...818...86B (acknowledgments)
We thank an anonymous referee for several insightful comments that helped improve the content and presentation of this paper. We also thank J.-P. Macquart, R. M. Shannon, M. Bailes, and H. Knight for several useful discussions. This scientific work makes use of the Murchison Radio-astronomy Observatory operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. NDRB is supported by a Curtin Research Fellowship. Support for the operation of the MWA is provided by the Australian Government Department of Industry and Science and Department of Education (National Collaborative Research Infrastructure Strategy: NCRIS) under a contract to Curtin University administered by Astronomy Australia Limited. We acknowledge the iVEC Petabyte Data Store and the Initiative in Innovative Computing and the CUDA Center for Excellence sponsored by NVIDIA at Harvard University and support from the Centre for All-sky Astrophysics (CAASTRO) funded by grant CE110001020.
Paper: 2018MNRAS.481L.100V (acknowledgments)
ACKNOWLEDGEMENTS I thank V. Belokurov, D. Erkal, N. W. Evans, N. Hambly, S. Koposov, and the anonymous referee for valuable suggestions. This work relied exclusively on the data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium. This work was supported by the European Research council under the 7th Framework programme (Grant No. 308024).