adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...605A..90D (fulltext)
inhomogeneities provide a number of testable predictions, such as photometric or radial-velocity flickering of integrated starlight. Stellar brightness varies in a somewhat random fashion, as differently bright granular structures develop across the surface ( Cranmer et al. 2014 ; Ludwig 2006 ). The effects are greater for giant stars, where the small-number statistics of fewer granules across the star induces a greater variability, as observed ( Bastien et al. 2013 , 2016 ; Mathur et al. 2011 ) and as modeled ( Samadi et al. 2013a , b ). For the Sun, with 10 granules across its surface, each with a 20 intensity contrast, the amplitude of variability in integrated sunlight (assuming random variability among the granules) can be expected to be on the order of this contrast divided by the square root of the number of granules, i.e., , measurable with photometric instruments in space. Since the contrast is greater at short optical wavelengths (as expected for a given temperature contrast in the photosphere), also the amplitude of flickering is greater in the blue than in the red. In specific wavelength regions comprising numerous temperature-sensitive spectral lines, such as the -band, the photometric flickering may be expected to be greater but does not yet appear to have been either computed nor observed. An analogous variability in the disk-averaged radial velocity on a level of some m s is a concern in radial-velocity searches for low-mass exoplanets and for the confirmation of exoplanet candidates found from transits ( Cegla et al. 2013 , 2014 ; Dumusque et al. 2011a ). Again, the amplitude of flickering can be expected to increase at shorter wavelengths, where the granular contrast is greater. Further, the astrometric location of the stellar photocenter may wander and is detectable with space astrometry ( Chiavassa et al. 2011a ; Pasquato et al. 2011 ). Other effects include interpretation of stellar p-mode oscillations, where 3D models have to be applied for any more precise representation of stellar near-surface layers, detecting inconsistencies that may indicate which modeling parameters need adjustment ( Baldner Schou 2012 ; Kallinger et al. 2014 ; Piau et al. 2014 ; Schou 2015 ). Fig. 2 Top : build-up of synthetic Fe I lines at stellar disk center ( = cos.0) from a CO BOLD model of a dwarf star with approximate spectral type F7 V (250 K, log [cgs] = 4.5). Thin colored lines are spatial averages over the simulation area for each of 20 temporal snapshots; bold curves are the total averages. All five different line strengths are for 20 nm, eV. The convection patterns especially cause the weaker lines to become asymmetric and wavelength-shifted. Bottom : evolution of the continuum intensity during the simulation sequence for four different center-to-limb positions , 0.87, 0.59, 0.21. For off-center positions, slight differences exist among the four different azimuth angles (, /2, , 3/2 rad). Fig. 3 Spectral line changes across a
Paper: 2018A&A...614A.122T (fulltext)
with the predictions of Reiners et al. (2010) and Barnes et al. (2011) . Their simulations suggest that the large scatter around the std(RV)– v sin i correlation seen in the left panel of Fig. 3 could be caused by the spot characteristics of the active stars, such as their spot coverage fraction, and their spot-to-photosphere temperature contrast. We also recall that an additional source of scatter could be true orbital motions of these stars due to their as yet undiscovered planetary systems. 5.2 Correlations of RVs with spectral activity indicators After searching for correlations between std(RV) and activity-related stellar parameters of the active RV-loud sample as a whole, we moved on to investigate individual stars of the sample for correlations between their stellar activity indicators and their measured RVs. Finding a parametric function that could translate measurable spectral activity indicators into RV deviations is a great challenge, but at the same time, it holds the promise to enable the modeling of RV time series with a combined model of planetary- and activity-induced signals. Moreover, such a function would obviate simultaneous auxiliary measurements to quantify activity-induced RVs, such as multi-band photometric observations. Unfortunately, no such a function has been proposed so far for M dwarfs. Lacking a physical model to guide our spectral data exploration, we searched our active RV-loud stars for linear correlations between their varying RVs and three ofthe spectral activity indicators that we have chosen to investigate in this paper: CRX, dLW, and log L H α ∕ L bol . To best illustrate the process, we show here a few representative plots for one of the most frequently observed stars in our sample: J22468+443 (EV Lac). In order to demonstrate the CRX definition, we show in the top panel of Fig. 4 the order-by-order RVs from four representative observations of EV Lac: three observations with close-to-median H α emission but different CRX values, and one observation with extremely high H α emission. The latter observation was taken during a strong flare event ( Fuhrmeister et al. 2018 ). The slopes of the best-fit lines to these RV–order scatter plots are related to the CRX values of these four observations ( Zechmeister et al. 2018 ). The locations of activity-induced emission lines are marked at the top panel of Fig. 4 . The bottom panel of Fig. 4 shows the corresponding CARMENES spectra from these four observations, centered on the H α line. Fig. 4 CARMENES VIS measurements of J22468+443 (EV Lac). Top : Order-by-order RVs from four representative observations. Red, green, and blue show three observations with close-to-median H α emission. Black plots an observation with extremely high H α emission, taken during a strong flare event ( Fuhrmeister et al. 2018 ). The corresponding log L H α ∕ L bol values are given in the inset. Dashed lines show the best-fit straight lines to the RV–order scatter plots of each
Paper: 2019MNRAS.482L..50C (fulltext)
also that, if we assume that is roughly proportional to as suggested by observations (Thompson 2008 ; Grenier Harding 2015 ), then . A final comment is in order here before we move on to the next section. As we saw in the calculation below equation ( 5 ), near the surface of the star the separatrix electric charge fails to account for the separatrix return current by a factor ( r pc / r lc ) 2 ≪ 1. The same calculation near the Y-point yields the separatrix surface charge density σ lc near the light cylinder R → r lc , (see Uzdensky 2003 ; Timokhin 2006 , for details). Here, B lc ≡ B * ( r * / r lc ) 3 /2 is the value of the dipole magnetic field at the light cylinder. In the above estimate, ‘IN’ and ‘OUT’ at the Y-point refers to the region just outside and inside the light cylinder, respectively. Obviously, the separatrix electric charge is sufficient to support the separatrix return current. The only way to reconcile this result with the inefficiency of the separatrix electric charge to carry the same electric current near the surface of the star is the following: The separatrix must contain an extra amount of distributed corotating positive electric charge with surface charge density σ + ( R ) > 0 such that, at all distances, from the polar cap at r pc to the light cylinder. Here, σ − ( R ) ∼ − I pc /2π Rc is the negative charge density carried by the electrons that inflow from the dissipation zone. Clearly, which is in absolute terms much greater than σ pc . Consequently, σ + pc ≈ |σ − pc |. It is conceivable that these corotating positrons are the result of pair formation by the inflowing separatrix electrons. Note that the concept of one particle population of the ‘right’ type flowing through another stationary (corotating) population of the ‘wrong’ type needed to support both the electric charge and electric current distributions in the pulsar magnetosphere was first proposed in section 4 of Contopoulos ( 2016 ). 3 HIGH-ENERGY RADIATION If we know the accelerating electric field and the trajectory of particles in the equatorial dissipation region, it is straightforward to calculate the asymptotic particle Lorentz factor Γ in the curvature radiation reaction limit, Here, E acc is the accelerating electric field in the dissipation region. We have assumed here that the radius of curvature around the Y-point is equal to r lc . We can estimate E acc if we make the simplifying assumption that the magnetic flux that crosses the equatorial dissipation zone does so in a dipole-like configuration. In that case, the width Δ of that region is equal to and hence, the accelerating electric field in that region is equal to We have thus shown that the accelerating electric field is approximately equal to the value of the magnetic field near the light cylinder. One can easily check that, with this accelerating electric field, where, P is the pulsar period. This will be reached within an acceleration distance on the order of Obviously, the
Paper: 2018MNRAS.475.2891D (acknowledgments)
Acknowledgements GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel -ATLAS, GMRT, and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 179.A-2004. The G10-COSMOS redshift catalogue, photometric catalogue, and cutout tool uses data acquired as part of the Cosmic Evolution Survey (COSMOS) project and spectra from observations made with ESO Telescopes at the La Silla or Paranal Observatories under programme ID 175.A-0839. The G10-COSMOS cutout tool is hosted and maintained by funding from the International Centre for Radio Astronomy Research (ICRAR) at the University of Western Australia. Full details of the data, observation, and catalogues can be found in Davies et al. ( 2015 ) and Andrews et al. ( 2017a ), or on the G10-COSMOS website: cutout.icrar.org/G10/dataRelease.php This work is based on observations by the 3D-HST Treasury Program (GO 12177 and 12328) with the NASA/ESA HST , which is operated by the Association of Universities for Research in Astronomy, Inc. under NASA contract NAS5-26555. This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. SKA is supported by an Australian Postgraduate Award. LJD and SJM acknowledge support from European Research Council (ERC) in the form of Advanced Investigator grant COSMICISM and Consolidator grant Cosmic Dust.
Paper: 2018ApJ...866...84A (fulltext)
s) the high ionization region. Figure 5. Same as Figure 4 , but for and . 3.4. Parameter Study In Figure 6 , we show the dependence of the Balmer- α line energy flux on (a) the preshock velocity v 0 and (b) the total number density of hydrogen nuclei n H,0 , respectively. In the Appendix , we also show the dependences regarding other lines such as Balmer- β , Paschen- α , and Paschen- β . 5 5 Those profiles would be similar even at infinity except for Ly α , because hydrogen is in the form of H 2 and excited H rarely exists above the shock front. As for Ly α , absorption in the interstellar medium modifies the profiles. As seen in panel (a), for v 0 30 km s −1 , the Balmer- α line flux is quite low. This is because almost all the energy of shock heating is consumed for dissociation of hydrogen molecules. For v 0 ≥ 30 km s −1 , the energy flux is found to be nearly proportional to in panel (a) and to n H,0 in panel (b). Intuitively, however, the energy flux is proportional to , because the kinetic energy that the flowing gas has before shock is . This holds true for Ly α , but for other lines, we have to take into account the effect of absorption of radiation propagated from downstream, as described below. Figure 6. Energy flux of the Balmer- α line (H α ) radiation at the shock front (i.e., the surface of the circumplanetary disk) against the preshock velocity v 0 (panels (a) and (c)) and the total number density of atomic hydrogen (including all the hydrogen nuclei) n H,0 (panels (b) and (d)). The upper two panels are the same as the lower two, respectively, but the latter shows the energy flux relative to the total incoming energy flux at the shock front (i.e., where is the total number of particles and μ is the mean molecular weight). In the left two panels, each color shows each choice of 10 15 m −3 (black), 10 16 m −3 (red), 10 17 m −3 (blue), 10 18 m −3 (orange), 10 19 m −3 (purple), and 10 20 m −3 (brown). Also in the right two panels, each color shows each choice of v 0 ; (black), (red), (blue), (orange), (purple), (brown), and (green). Figure 7 shows the profiles of the spectral energy density of the Ly α and Balmer- α lines at the shock front. In the upper row of panels, those for different choices of n H,0 are presented for v 0 = 40 km s −1 , while in the lower panels, those for different choices of v 0 are presented for . The Gauss profile caused by the Doppler broadening is seen near the line center, while the Lorenz profile mainly caused by the natural broadening is seen far from the line center. The borders between them are at ∼121.48 nm and ∼121.53 nm for Ly α (in panel (a)) and ∼656.00 nm and ∼656.26 nm for Balmer- α (in panel (b)), respectively. In panel (b), for to , the energy density is found to be nearly proportional to n H,0 as a whole. Figure 7. Spectral energy density profile of hydrogen Ly α line (left panels) and Balmer- α line (right panels) emitted upward at the shock front. In panels (a) and (b), the preshock
Paper: 2020MNRAS.497..263K (fulltext)
data. Results from this joint analysis are listed in Tables 1 , 2 , and 6 . The QSO + H ( z ) + BAO 1D likelihoods and 2D confidence contours for all free parameters are shown in blue in Figs 4 – 15 . The updated QSO data don’t significantly tighten the BAO + H ( z ) data contours except in the cases of the non-flat XCDM parametrization and the non-flat φCDM model (Figs 10 , 11 , 14 , and 15 ). Another noticeable result is that adding the QSO data to the BAO + H ( z ) data results in the shifting of 1D likelihood distribution of the matter density parameter towards higher values in most cosmological models studied here. Table 5. Marginalized 1D best-fitting parameters with 1σ confidence intervals for all models using BAO and H ( z ) data (from Khadka Ratra 2020 ). a . From joint analyses of the QSO + H ( z ) + BAO data, from Table 6 , the matter density parameter lies in the range Ω m 0 = 0.30 ± 0.02 to 0.31 ± 0.01 (Ω m 0 = 0.30 ± 0.01 to 0.33 ± 0.02) for flat (non-flat) models and the H 0 = 68 ± 2.8 prior and lies in the range Ω m 0 = to 0.31 ± 0.01 (Ω m 0 = 0.31 ± 0.01 to 0.33 ± 0.02) for flat (non-flat) models and the H 0 = 73.24 ± 1.74 prior. In a few cases these results slightly differ from the BAO + H ( z ) data results in Table 5 , being shifted to slightly larger values. These results are consistent with those results determined from other cosmological data. Table 6. Marginalized 1D best-fitting parameters with 1σ confidence intervals for all models using QSO + H ( z ) + BAO data. a . The Hubble constant lies in the range H 0 = to ( H 0 = to ) for flat (non-flat) models and the H 0 = 68 ± 2.8 prior and lies in the range H 0 = to ( H 0 = to ) for flat (non-flat) models and the H 0 = 73.24 ± 1.74 prior. Not unexpectedly, for the H 0 = 73.24 ± 1.74 prior case the measured value of H 0 is reduced below the prior value because the BAO and H ( z ) observations prefer a lower H 0 . In most cases the H 0 error bars have increased in comparison to those derived using the 2015 QSO + BAO + H ( z ) data in Khadka Ratra ( 2020 ). In all models, except for non-flat ΛCDM with the H 0 = 68 ± 2.8 prior, closed spatial hypersurfaces are favoured at about 2σ. For the non-flat ΛCDM model the curvature energy density parameter is Ω k 0 = and −0.09 ± 0.05 for the H 0 = 68 ± 2.8 and 73.24 ± 1.74 priors, respectively. Values of curvature energy density parameter obtained for non-flat dynamical dark energy cosmological models are significantly higher than those obtained in the non-flat ΛCDM model. The curvature energy density parameter is Ω k 0 = −0.34 ± 0.18 and −0.32 ± 0.16 for the non-flat XCDM and non-flat φCDM models for the H 0 = 68 ± 2.8 prior and Ω k 0 = and −0.35 ± 0.15 for the non-flat XCDM and non-flat φCDM models for the H 0 = 73.24 ± 1.74 prior. This preference for closed spatial geometries is largely driven by the BAO + H ( z ) data (Park Ratra 2018c ; Ryan et al. 2019 ). From Table 6 , for the flat (non-flat) ΛCDM model the dark energy density
Paper: 2017ApJ...845...86E (acknowledgments)
The authors thank the anonymous referee for very valuable comments. R.S. acknowledges the support from grant AYA2014-54485-P (AEI/FEDER, UE) and from the Ministerio de Economía, Industria y Competitividad, and the Conselleria d’Innovació Recerca i Turisme del Govern Balear (Pla de ciència, tecnologia, innovació i emprenedoria 2013-2017) for the Ramón y Cajal grant RYC-2014-14970.
Paper: 2019A&A...630A.144B (acknowledgments)
We thank the anonymous referee for the careful and constructive reading of the paper and for his/her useful suggestions. We would like to thank Marta Volonteri and Lorenzo Lovisari for helpful feedback during the draft of the paper and Volker Springel for making the GADGET-3 code available to us. This project has received funding from: ExaNeSt and Euro Exa projects, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 671553 and No 754337, the agreement ASI-INAF n.2017-14-H.0, the Agencia Nacional de Promoción Científica y Tecnológica de la República Argentina under the PICT2417-2013 grant; the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina (CONICET); the Secretaría de Ciencia y Técnica de la Universidad Nacional de Córdoba – Argentina (SeCyT); the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 734374, PRIN-MIUR 2015W7KAWC, the INFN INDARK grant, NASA Chandra GO7-18121X and GO8-19104X. Simulations have been carried out using MENDIETA Cluster from CCAD-UNC, which is part of SNCAD-MinCyT (Argentina) and MARCONI at CINECA (Italy), with CPU time assigned through grants ISCRA C, and through INAF-CINECA and University of Trieste – CINECA agreements. The post-processing has been performed using the PICO HPC cluster at CINECA through our expression of interest.
Paper: 2019ApJ...874...43L (fulltext)
indicated by the green cross-hairs. The dotted line connects the feature with the core feature and is plotted with the mean position angle. The position at the image epoch is shown by a filled blue circle while other epochs are plotted with unfilled blue circles. The red solid line indicates the vector fit (or accelerating fit, if there is significant acceleration) to the feature positions. The gray dashed circles / ellipses indicate the fitted FWHM sizes of the feature at the measured epochs. Figure 2.1057. 1345+125 Component 01 Positions are relative to the core position. The left-hand panel shows a 15 GHz VLBA total intensity contour image of the jet at the epoch closest to the middle reference epoch. The green box delimits the zoomed region that is displayed in the middle panel. The feature's position at the image epoch is indicated by the green cross-hairs. The dotted line connects the feature with the core feature and is plotted with the mean position angle. The position at the image epoch is shown by a filled blue circle while other epochs are plotted with unfilled blue circles. The red solid line indicates the vector fit (or accelerating fit, if there is significant acceleration) to the feature positions. The gray dashed circles / ellipses indicate the fitted FWHM sizes of the feature at the measured epochs. Figure 2.1058. 1345+125 Component 02 Positions are relative to the core position. The left-hand panel shows a 15 GHz VLBA total intensity contour image of the jet at the epoch closest to the middle reference epoch. The green box delimits the zoomed region that is displayed in the middle panel. The feature's position at the image epoch is indicated by the green cross-hairs. The dotted line connects the feature with the core feature and is plotted with the mean position angle. The position at the image epoch is shown by a filled blue circle while other epochs are plotted with unfilled blue circles. The red solid line indicates the vector fit (or accelerating fit, if there is significant acceleration) to the feature positions. The gray dashed circles / ellipses indicate the fitted FWHM sizes of the feature at the measured epochs. Figure 2.1059. 1345+125 Component 03 Positions are relative to the core position. The left-hand panel shows a 15 GHz VLBA total intensity contour image of the jet at the epoch closest to the middle reference epoch. The green box delimits the zoomed region that is displayed in the middle panel. The feature's position at the image epoch is indicated by the green cross-hairs. The dotted line connects the feature with the core feature and is plotted with the mean position angle. The position at the image epoch is shown by a filled blue circle while other epochs are plotted with unfilled blue circles. The red solid line indicates the vector fit (or accelerating fit, if there is significant acceleration) to the feature positions. The gray dashed circles / ellipses indicate the fitted FWHM sizes of the feature at the
Paper: 2016ApJ...833..217B (acknowledgments)
W.T.B. was provided travel support to the Coronal Loops Workshop VII held in Cambridge, UK, at which a preliminary version of this work was presented, by NSF award number 1536094. This work was supported in part by the Big-Data Private-Cloud Research Cyberinfrastructure MRI-award funded by NSF under grant CNS-1338099 and by Rice University. Software: astroML (VanderPlas et al. 2012 ), IPython/Jupyter (Pérez Granger 2007 ), matplotlib (Hunter 2007 ), NumPy/scipy (van der Walt et al. 2011 ), seaborn (Waskom et al. 2016 ).