adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...879...62M (acknowledgments)
L.M. and M.J. contributed equally to this manuscript. Fruitful discussions with Maria Lugaro, Carolyn Doherty, and Emese Plachy are gratefully acknowledged. L.M. was supported by the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. The research leading to these results has received funding from the LP2014-17 and LP2018-7 Lendület grants of the Hungarian Academy of Sciences and from the NKFIH K-115709 grant of the National Research, Development and Innovation Office of Hungary. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France, and NASA’s Astrophysics Data System Bibliographic Services. M.J. was supported by Martin Asplund, the Research School of Astronomy and Astrophysics at Australian National University, and funding from Australian Research Council grant No. DP150100250. M.J. wishes to acknowledge John Bourke for his assistance in revising this manuscript and Maria Lugaro for financial support of her scientific residences at Konkoly Observatory. Facility: AAVSO. Software: MESA (Paxton et al. 2018 ), GYRE (Townsend Teitler 2013 ), astropy (Astropy Collaboration et al. 2013 ; Price-Whelan et al. 2018 ), matplotlib (Hunter 2007 ), numpy (van der Walt et al. 2011 ), scipy (Jones et al. 2001 ), Period04 (Lenz Breger 2005 ), gnuplot.
Paper: 2019MNRAS.483.1496S (acknowledgments)
ACKNOWLEDGEMENTS Software for this project was developed under NASA’s Applied Information Systems and Astrophysics Data Programs. This work is based in part on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Support for this work was provided by NASA through an award issued by JPL/Caltech. Other aspects of this work were supported in part by NASA Astrophysics Data Analysis Program grant NNX11AF89G and NSF grant AST 0908370. As usual, this research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Paper: 2017ApJ...837..145C (acknowledgments)
Much of this work was in progress at the time of Steve Murray’s untimely death in 2015. This work would not have been possible without his generous GTO contribution to the XBoötes data set and his strong support of Boötes multiwavelength surveys. He will be greatly missed by all of us. We thank the referee for a careful reading and constructive suggestions that have improved the manuscript. C.-T.J.C. was supported by the William H. Neukom 1964 Institute for Computational Science. R.C.H. acknowledges support from the National Science Foundation through AST award no. 1515364 and from an Alfred P. Sloan Research Fellowship. R.J.A. was supported by FONDECYT grant no. 1151408. 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. This publication also makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III Web site is http://www.sdss3.org/ .
Paper: 2019MNRAS.482..184A (acknowledgments)
ACKNOWLEDGEMENTS The authors acknowledge financial support from the following funding agencies: Centre National de la Recherche Scientifique (CNRS), Commissariat à l’énergie atomique et aux énergies alternatives (CEA), Commission Européenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), IdEx program and UnivEarthS Labex program at Sorbonne Paris Cité (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02), Labex OCEVU (ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02), Région Île-de-France (DIM-ACAV), Région Alsace (contrat CPER), Région Provence-Alpes-Côte d’Azur, Département du Var and Ville de La Seyne-sur-Mer, France; Bundesministerium für Bildung und Forschung (BMBF), Germany; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Stichting voor Fundamenteel Onderzoek der Materie (FOM), Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; Council of the President of the Russian Federation for young scientists and leading scientific schools supporting grants, Russia; National Authority for Scientific Research (ANCS), Romania; Ministerio de Economía y Competitividad (MINECO): Plan Estatal de Investigación (refs. FPA2015-65150-C3-1-P, -2-P and -3-P, (MINECO/FEDER)), Severo Ochoa Centre of Excellence and MultiDark Consolider (MINECO), and Prometeo and Grisolía programs (Generalitat Valenciana), Spain; Ministry of Higher Education, Scientific Research and Professional Training, Morocco. We also acknowledge technical support from Ifremer, AIM and Foselev Marine for the sea operation and CC-IN2P3 for the computing facilities.
Paper: 2016ApJ...831...93S (acknowledgments)
Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center. We thank Tom Quinn and James Wadsley for use of the proprietary code Gasoline , and Charlotte Christensen for help in creating the simulations. The pynbody package (Pontzen et al. 2013 ) was used in portions of this analysis.
Paper: 2016ApJ...827...46K (acknowledgments)
Support for this work was provided by the National Aeronautics and Space Administration through Chandra grant number GO-213103A, issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. This work was also supported by the National Aeronautics and Space Administration through HST grant number GO-12762.007 issued by the Space Telescope Science Institute. We thank Derek Hammer for producing the HST images used here. Finally, we must thank the referee for pointing out a number of places where we needed to make the commentary more pointed. Facilities: Chandra (ACIS) , HST (ACS).
Paper: 2017MNRAS.468.3917F (acknowledgments)
Acknowledgments This work was funded with grants from Consejo Nacional de Investigaciones Cientificas y Tecnicas de la Republica Argentina ( CONICET ). Thanks are also due to Dr E. Irene Vega for her continuous support during the redaction of this work.
Paper: 2019A&A...624L..12W (fulltext)
probably also affects the density distribution along the loop and is the reason that we scaled our model emission nonlinearly with a power of 0.4 in Fig. 2 (while the observations are scaled closer to linear with a power of 0.7). The loops in our model corona look more like those in a potential field than like the loops in the observations. In our model we only prescribed the vertical component of the magnetic field, but not the horizontal component (which is not available at 45 s cadence and for this large field of view with HMI). We would therefore miss any helical component of the magnetic field, which would lead to a more twisted appearance of the magnetic field. Moreover, this would increase the energy input into the system and thus help alleviate the problem described above, at least to some extent. Finally, we assume periodic boundary conditions in our model (as do most other 3D MHD models of the solar atmosphere). This implies that the field lines of the fan loops on the western (right) side of the active region leave the box on that side and enter the box again on the other side. In the model, the fan loops therefore connect to an active region (different from the first). On the real Sun, such a connected active region would be far away, at a distance that is far larger than the size of an active region. We tried to account for this in the model by surrounding the active region by enough quiet Sun, so that the appearance of the fan loops is hopefully realistic. None of the above limitations are expected to alter the main conclusion of our study: our data-driven model can account for the structures seen on the real Sun. Future models will have to address these remaining problems step by step, however. 6. Conclusions We showed that we can reproduce many aspects of the corona above an active region using a data-driven 3D MHD model. The emission that we synthesized from the model qualitatively reproduces the observed coronal features of this active region: long loops, fan-like loops, and small transient loops in the active region core. We also showed that these model features are embedded in a diffuse background of coronal emission, as is the case on the real Sun, where a typical loop has a low contrast of only 10% to 30% to the background. Our data-driven model shows that the structure of the observed photospheric magnetic field and its temporal evolution fully govern the appearance of the corona in an active region. Driving the magnetic field at the surface induces currents in the corona at just the correct places: here the plasma is heated and forms EUV loops in the model at exactly the same place where they also appear in the real observations. The energy input in our model is solely based on the driving of the magnetic field and is thus very similar to the scenarios of field-line braiding ( Parker 1988 ) and flux-tube tectonics scenarios ( Priest et al. 2002 ). The success of our model therefore supports these heating scenarios. The
Paper: 2016MNRAS.462.3660H (fulltext)
universes can measure very different power spectra of events, just because of cosmic variance. This effect dominates any dependence of predicted anisotropies on the class of EGMF models studied here, limiting the possibility of inferring the origin of EGMF based on the observed properties of UHECRs. It also stresses the importance of a realistic characterization of the local environment of the observer to interpret observations on Earth. This can be studied further making use of ‘Constrained Simulations’ of the local Universe (e.g. Jasche Wandelt 2013 ; Sorce et al. 2016 ). 3.3 Separation angle Abreu et al. ( 2010 ) investigated the anisotropy of the UHECR arrival signal by considering the angular distance of events from Cen A. Due to its similarity, all the graphics presented in this section, if not stated otherwise, are shown for observer ID61 whose closest source is at ∼4Mpc distance. The separation angle of pairs of events in Fig. 13 was computed via the scalar product of their arrival direction, i.e. the normalized negative momentum vector of the UHECRs at time of observation. The theoretical prediction for isotropy for the distribution of events over a sphere is given by the cumulative sine function and is in good agreement with the simulated isotropic cases. We can also predict the influence of the observer effect (cf. Section 2.3 ) for the signal of the closest source. To do so, we produced a test simulation without magnetic fields with a single source of UHECRs at a distance comparable to that of the closest source for observer ID61. The separation angle function of this test gives our prediction for the finite observer effect for pairs of events that both originated from the closest source. Figure 13. The fraction of pairs of observed events, E > 58, 100 EeV (left, right), separated by less than the given angle. The coloured lines indicate the magnetic field model, the error bars show the Poissonian noise. The thick black line shows the isotropic plus the geometric prediction. The left-hand panel of Fig. 13 can be compared with fig. 8 in Abreu et al. ( 2010 ). For events above E > 58 EeV there is no significant deviation from isotropy, but for E > 100 EeV there is a clear excess. This is expected because, due to lower average travel distances at higher energies, there is a higher number of events that originated from the closest source. The influence of the geometric effect changes the form of this plot by flattening the slope in the low separation part. We emphasize that this actually affects all events from close-by sources, while our geometric prediction only considers those from the closest source. None the less, there is a significant difference between the magnetic field models: the magnetic field free case ( b0 , black) and the simulation with the lowest magnetization ( agnl , purple, cf. Fig. 1 ) show the strongest deviation from isotropy. However, for other observers the case is different and the presence of nearby sources
Paper: 2015A&A...573A.122W (acknowledgments)
H.R.W. acknowledges support from the UK Science Technology Facilities Council (STFC). The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement No. 336792. D.K.S. acknowledges support from STFC consolidated grant ST/J0016/1. The authors thank the anonymous referee for their useful feedback and comments. We would also like to thank D.S. Amundsen, B. Drummond, J. Barstow, and K. Heng for useful discussions.
Paper: 2019A&A...627A..57J (fulltext)
is able to remove binary stars, and (b) that the removed fraction of stars in the range where we expect unresolved binary or triple stars is higher, in line with the previous point (a). We note that the filtering removes a substantial fraction of multiple systems. In particular, Gaia cannot find astrometric solutions typically for binaries. The exact fraction of binaries filtered out by which of the criteria is however a non-trivial problem and cannot be quantified here as this would depend on detailed modeling of the space craft and the stellar population at the distance of the ONC, see for example Michalik et al. (2014) . 4.2. PMS isochrones Stellar models have been computed using the most recent version of the Pisa stellar evolutionary code (see e.g., Tognelli et al. 2011 ) described in detail in previous papers (see Randich et al. 2018 ; Tognelli et al. 2018 , and references therein). We simply recall the main aspects relevant for the present analysis. The reference set of models adopts a solar calibrated 4 mixing length parameter, namely α ML = 2.0. However, for the comparison we also used a much lower value, namely α ML = 1.00 (low convection efficiency models) which produces cooler models with inflated radii (see e.g., Tognelli et al. 2012 , 2018 ). Evolutionary tracks of mass ≥1.20 M ⊙ have been computed adopting a core convective overshooting parameter β ov = 0.15, following the recent calibration of the Pisa models by means of the TZ Fornacis eclipsing binary ( Valle et al. 2017 ). From the models (in the mass range 0.1−20 M ⊙ ) we obtained isochrones in the age range [0.1, 100] Myr with a variable spacing in age (down to δ t = 0.1 Myr), allowing for a very good age resolution. For the reference set of models we used [Fe/H] = +0.0, corresponding to an initial helium abundance Y = 0.274 and metallicity Z = 0.013 given the Asplund et al. (2009) solar heavy-element mixture. We have converted the theoretical results into the OmegaCAM r and i absolute A B magnitudes using bolometric corrections we computed using a formalism similar to that described in Girardi et al. (2002) , employing the MARCS 2008 synthetic spectra library ( Gustafsson et al. 2008 ), which is available for T eff ∈ [2500, 8000] K, log g ∈ [ − 0.5, 5.5], and [Fe/H] ∈[−2.0, +0.5] completed with the Castelli Kurucz (2003) models for T eff > 8000 K. Next we compared the evolutionary models with the data in order to identify the best fitting isochrones for the observed PMS populations. This was performed by using the same Bayesian maximum likelihood technique described in Randich et al. (2018) . Briefly, the method computes the distance of each star in the observational plane from a given isochrone extracted from our database. The square of the distance is used to define the likelihood of a single star. Then, the total likelihood is defined as the product of all the single star likelihoods. The parameters to be recovered in this work are the age t and the reddening E (
Paper: 2019A&A...623L...4D (fulltext)
shifts of the energy-release region along the separator. Nevertheless, some hints concerning this issue can be drawn from the behavior of the emission spots as function of the radii of the energy-release spheres R , which are plotted by various colors in Figs. 3 and 5 . We also checked the patterns of emission obtained under the similar assumptions in a few other topological models of solar magnetic fields (i.e., involving the bifurcations of null points). In these models, however, we did not find the behavior of the ribbons as rich as in the GKSS model. This is most probably because only the latter model exhibits the genuine topological instability, i.e., when a small rearrangement of the magnetic sources results in the dramatic reconstruction of the magnetic field lines in the entire space. In summary, the four-ribbon configurations exist only in a quite small semilunar region of topological instability (where the bifurcation takes place); at all other locations of the fourth magnetic source, a three- (or even two-)ribbon emission appears. Hence, we should expect that fraction of the four-ribbon flares will be sufficiently small because of the relatively small area of the region of topological instability. This prediction agrees very well with the observations. Most of the anemone flares detected by Hinode satellite possess three ribbons; the four-ribbon flares also exist but they are formed seldomly. This is why we believe that our topological model, although not universal, provides a reasonable interpretation of the relevant phenomena by varying only a single parameter. Appendix A: Basic parameters of the separator in the two-dome structure In general, computing a spatial configuration of the separator is a non-trivial task: the separator is an inherently unstable structure, since any field line originating in its neighborhood quickly deviates outward. So, the straightforward numerical integration is meaningless, and a more elaborate type of algorithm should be applied. In the present work, we used the following procedure: Since footpoints of the separator, ( x f1 , y f1 ) and ( x f2 , y f2 ), are actually the null points of the magnetic field in z = 0 plane, we calculated the distribution of B 2 in this plane, sought for its minima, and finally checked that B 2 = 0 in these minima (and, therefore, B = 0). The analysis of the scalar function B 2 ( x , y ) was employed only because, from the computational point of view, it is much more simple and robust than the analysis of the vector field B ( x , y ). To find a position of the separator above the plane z = 0, we analyzed the following “global” behavior of the magnetic field lines: (a) First of all, we fixed a vertical plane intersecting the line connecting the opposite footpoints of the separator ( x f1 , y f1 , 0) and ( x f2 , y f2 , 0); to get a better accuracy, it is desirable that this plane be approximately perpendicular to the line. (b) Then, we generated a mesh of points
Paper: 2016ApJ...832..208N (fulltext)
spectral template characterized by . Figure 7 shows the photometric completeness as a function of the redshift and apparent -band magnitude. Although the derived completeness is generally higher for brighter -band magnitudes as expected, the opposite tendency is seen at . This is due to the luminosity dependence of the quasar spectrum, which makes the completeness higher for lower-luminosity quasars (see Section 2.3 ). The estimated completeness at is generally higher than that estimated by Ikeda et al. ( 2011 ), which is also due to the luminosity dependence of quasar spectra, since Ikeda et al. ( 2011 ) used a composite spectrum of bright SDSS-selected quasars (Vanden Berk et al. 2001 ) for the completeness estimate. However, at , the estimated completeness in this work is lower than that estimated by Ikeda et al. ( 2012 ). This is because the new extinction model (Inoue et al. 2014 ) makes the completeness lower especially at , whose effect has a stronger impact on the completeness estimate than the effect of the luminosity dependence of the quasar spectrum. Figure 7. Photometric completeness for COSMOS quasars at (left) and at (right). Red, green, blue, purple, and cyan lines denote the estimated completeness for quasars with , respectively. 4. DISCUSSION 4.1. The QLF at <inline-formula> <tex-math> </tex-math> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjaa4497ieqn184.gif"></inline-graphic> </inline-formula> Based on the revised completeness for the previous quasar survey through optical color selection at the COSMOS field (Ikeda et al. 2011 , 2012 ), we revisit the QLF at . First we compute the effective comoving volume of the COSMOS quasar survey aswhere (1.64 deg 2 ) is the solid angle of the survey and is the photometric completeness studied in Section 3.2 . In Ikeda et al. ( 2011 ), eight spectroscopically confirmed quasars in the COSMOS field are used to derive the QLF at . Taking into account the possibility that some of the photometric quasar candidates without spectra could also be quasars (which is estimated based on the success rate of the spectroscopic run), we statistically adopt the number of quasars in the COSMOS survey at as follows: 4.67 for and 6.06 for (Ikeda et al. 2011 ). By using these corrected numbers of quasars and the effective comoving volume, we calculate the space density of quasars and their standard deviation as where and is the corrected number of quasars. The calculated space density of quasars and their standard deviation are Mpc −3 mag −1 for and Mpc −3 mag −1 for , respectively. The corrected number and calculated space density of quasars in COSMOS are shown in Table 2 , and the obtained QLF is plotted in Figure 8 with the results of Ikeda et al. ( 2011 ) and SDSS (Richards et al. 2006 ). Although the redshift range of the SDSS data ( Richards et al. 2006 ) is slightly different from our study (), we apply the weighted least-squares fit to the space density of quasars