adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...899..119L (fulltext)
how the fitted magnetic field inside MEs and the radial size of MEs vary with distance and discussed the implications of their study for the self-similar expansion of MEs. They concluded that, for individual cases, reconnection between the ME and the solar wind and/or pancaking of the ME cross section is necessary to understand the evolution of the ME size as compared to the evolution of the magnetic field. In a previous work, Leitner et al. ( 2007 ) noted that the expected difference in the decrease rate with the distance of the axial and azimuthal components of the magnetic field may create differences in the trend found for the inner and outer heliosphere. Good et al. ( 2019 ) discussed the change in orientation of the 18 CMEs measured in conjunction that they studied, finding a tendency toward lower-inclined MEs at the outer spacecraft compared to the first spacecraft. This implies a (small) difference in the way different magnetic-field components change with distance. We note that for a force-free field with self-similar expansion, the axial magnetic field is expected to vary with distance as r −1 , whereas the poloidal field should vary as r −2 . In Lugaz et al. ( 2020 ) for the 2013 July 10–13 CME, we found that a uniform decrease of the magnetic-field components as r −1.6 was a better fit to the data than a separate fit for the y (axial) or z (poloidal) components of the magnetic field. Here, we continue this analysis for the 42 CMEs measured in conjunction between two spacecraft. Because MESSENGER and VEX were planetary missions, there are significant “data gaps” in the interplanetary magnetic field measurements corresponding to the time when the spacecraft were in the planetary magnetospheres. In addition, the magnetic fields inside MEs have been reported to significantly rotate in the inner heliosphere in some cases (e.g., see Nieves-Chinchilla et al. 2012 ; Winslow et al. 2016 ) and it is unclear how this should be considered when comparing the axial or poloidal fields measured by these spacecraft with those measured near 1 au. As such, we compare the tangential and normal components of the magnetic field measured at the two spacecraft in the RTN coordinate system. We focus on the extrema of the variation of the magnetic-field components. For an ME that has a clear low (respectively high) inclination, the B T (respectively B N ) component typically keeps the same sign throughout the ME interval. In addition, the expansion of the front and back half of the ejecta may occur at different rates. For example, in event 21-2013, discussed in Lugaz et al. ( 2020 ), B T is always positive, while B N varies from positive to negative (North–West–South ME following the classification of Bothmer Schwenn 1998 ). For this event, as shown in Table 1 , the B N positive component (at the front) decreases with an index of −1.4, whereas the B N negative (at the back) decreases with an index of −1.1. We therefore calculate the average of the indices
Paper: 2016A&A...592A..44M (fulltext)
of the outer star forming rings in visually unbarred galaxies are shown in Fig. 1 . This figure presents a particular class of ring galaxies, those with accreted star forming rings in axisymmetric S0s which are especially suitable to host a dynamo process. The class is however far from being homogeneous. Figure 1 illustrates that the ring galaxies of interest can vary considerably in appearance. This is why we have to consider a variety of possible dynamo effects that might contribute to magnetic field formation in such scenarios and experiment with the dynamo numbers that estimate the strengths of the effects. A particular challenge is that NGC 4513, shown in the first panel of Fig. 1 , has a counter-rotating ring. IC 5285, UGC 5936, and UGC 9980 do not demonstrate observable radial gas velocities, thus ruling out a collisional origin for their rings ( Ilyina Sil’chenko 2011 ; Ilyina et al. 2014 ; Katkov et al. 2015 ). In fact, to discriminate between the origin scenarios for an outer ring in a particular galaxy, kinematical data are necessary, and 3D (two spatial coordinates on the plane of sky and one along the wavelength axis Courtes 1995 ); spectral data are especially useful ( Moiseev Bizyaev 2009 ). The collisional rings and OLR (Outer Lindblad Resonance) rings generate radial gas outflow while accreted gaseous rings rotate circularly, although sometimes outside the main galactic plane. In addition, the optical spectral data provide the possibility of determining the excitation mechanism for the ionized (warm) gas. The flux ratios of strong emission lines discriminate easily between the mechanisms of shock excitation and of ionization by UV radiation of young stars ( Baldwin et al. 1981 ; Veilleux Osterbrock 1987 ). To facilitate the action of a dynamo in magnetic-field generation, we need long-lived (order of Gyrs) rings; hence collisional rings, with their lifetimes of a few hundred Myr, are unlikely to be candidates; a supply of free electrons, hence an ionized state of the gas that can be provided and supported by continuous star formation in the rings; regular circular gas rotation in the main galactic planes, presumably with a flat rotation curve, to provide a differential character for the gas rotation. In some ways rings, generated by accretion may be be the most favourable for our investigation, with estimated lifetimes possibly approaching the Hubble time ( Buta Combes 1996 ). Based on the results of Ilyina Sil’chenko (2011) , Ilyina et al. (2014) and Katkov et al. (2015) we select a small sample with the properties mentioned above and present it in Fig. 1 . The typical properties of these galaxies will be used in the further theoretical consideration of the dynamo magnetic-field generation in ring galaxies. Some extension of the typical picture can be also considered, by taking into account possible cases of the outer gas counter-rotation, as in NGC 4513 ( Ilyina et al. 2014 ), or strong flaring of the gas layer in the
Paper: 2016MNRAS.462.3660H (acknowledgments)
We would like to acknowledge the developers of crpropa for making the code publicly available. The cosmological simulations presented in this work were performed using the enzo code ( http://enzo-project.org ), and were partially produced at Piz Daint (ETHZ-CSCS, http://www.cscs.ch ) in the Chronos project ID ch2 and s585, and on the JURECA supercomputer at the NIC of the Forschungszentrum Jülich, under allocations no. 7006 and 9016 (FV) and 9059 (MB). We also acknowledge the use of the Golem Cluster at Hamburger Sternwarte for analysis. FV acknowledges personal support from the grant VA 876/3-1 from the Deutsche Forschungsgemeinschaft (DFG). FV and MB also acknowledge partial support from the grant FOR1254 from DFG. This work was supported by the DFG through collaborative research centre SFB 676, by the Helmholtz Alliance for Astroparticle Physics (HAP) funded by the Initiative and Networking Fund of the Helmholtz Association and by the Bundesministerium für Bildung und Forschung (BMBF). We thank R. Clay, R. Alves Batista and J. Devriendt for useful comments on the manuscript.
Paper: 2019A&A...630A.130D (acknowledgments)
Acknowledgements We thank Andreas Irrgang and Simon Kreuzer for the development of the SED fitting tool, Monika Schork for measuring the radial velocities of HZ 44 from HIRES spectra, and Markus Schindewolf for providing preliminary atmosphericparameters of HZ 44. M.L. acknowledges funding from the Deutsche Forschungsgemeinschaft (grant DR 281/35-1). Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the data archive (prop. ID GO5305) at the Space Telescope Science Institute. STScI is operated by the Association of Universities for Research in Astronomy, Inc. under NASA contract NAS 5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. Based on INES data from the IUE satellite. Based on observations made with ESO Telescopes at the La Silla Paranal Observatoryunder programme ID 074.B-0455(A). This research has made use of the Keck Observatory Archive (KOA), which is operated by the W.M. Keck Observatory and the NASA Exoplanet Science Institute (NExScI), under contract with the National Aeronautics and Space Administration. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The TOSS service ( http://dc.g-vo.org/TOSS ) used for this paper was constructed as part of the activities of the German Astrophysical Virtual Observatory. We acknowledge the use of the Atomic Line List ( http://www.pa.uky.edu/~peter/newpage/ ). We also thank the referee, C. Moni Bidin, for his helpful comments.
Paper: 2018ApJ...866...97B (fulltext)
; Margutti et al. 2013 ; D’Avanzo et al. 2014 ; Zaninoni et al. 2016 ). In order to check whether XRFs, XRRs, and C-GRBs in our sample follow this correlation, respectively, we collect the E γ ,iso and E X ,iso data of the GRBs in our sample from Amati et al. ( 2008 ), Margutti et al. ( 2013 ), and Liang et al. ( 2015 ). First, the relation between E X ,iso and E γ ,iso is shown in Figure 9 . We perform the maximum likelihood method and obtain the fitting of with the extrinsic scatter of σ = 0.57 ±0.04. The E X ,iso – E γ ,iso relation is consistent with that derived by Margutti et al. ( 2013 ). Second, we also investigate the correlation between E peak, z and E γ ,iso . The result is shown in Figure 10 . The correlation fitted by the maximum likelihood method is (keV) = (2.17 ± 0.04) + (0.46 ± 0.03) with the extrinsic scatter of σ = 0.26 ± 0.02. Our result is consistent with the correlation reported by Amati ( 2006 ). Third, in Figure 11 , we present the relation between E X ,iso and E peak, z by the maximum likelihood method with the fitting of = (49.69 ± 0.27) + (0.75 ± 0.11) , and the extrinsic scatter is σ = 0.73 ± 0.04. Our result is consistent with that of Margutti et al. ( 2013 ). Finally, the E X ,iso – E γ ,iso – E peak, z relation is shown in Figure 12 . The result with the fitting of = (4.78 ± 2.79) + (0.92 ± 0.06) and the extrinsic scatter of σ = 0.44 ± 0.04 produced by the maximum likelihood method is consistent with that of Margutti et al. ( 2013 ) as well. Figure 9. Correlation between E X ,iso and E γ ,iso in the Swift -BAT3 sample. The green solid line is the best fit with the function of ( E X ,iso ) = (13.31 ± 2.73) + (0.73 ± 0.05) log( E γ ,iso ), and the extrinsic scatter is σ = 0.57 ± 0.04. The green dashed lines are marked for the 2 σ regions. The pink dashed–dotted line is the best-fitting function of log( E X ,iso ) = (10.0 ± 20.6) + (0.79 ± 0.01) log( E γ ,iso ) reported by Margutti et al. ( 2013 ). XRFs, XRRs, and C-GRBs are marked as black squares, red dots, and blue triangles, respectively. The GRBs labeled as outliers in the figure are L-GRBs, and the exception is short GRB 090510. Figure 10. Correlation between GRB peak energy E peak, z in the rest frame and GRB isotropic-equivalent energy E γ ,iso . The green solid line is the best fit with the function of log( E peak, z ) (keV) = (2.17 ± 0.04) + (0.46 ± 0.03) , and the extrinsic scatter is σ = 0.26 ± 0.02. and the green dashed lines are marked for the 3 σ regions. The pink dashed–dotted line is the best fit with the function of E peak, z = 95 keV( E γ ,iso /10 52 erg) 0.49 reported by Amati ( 2006 ). XRFs, XRRs, and C-GRBs are marked as black squares, red dots, and blue triangles, respectively. Figure 11. Correlation between GRB X-ray energy E X ,iso and GRB peak energy E peak, z . The green solid line is the best fit with the function of , and the extrinsic scatter is σ = 0.73 ± 0.04. The green dashed lines are marked for the 1 σ regions. The pink dashed line
Paper: 2019MNRAS.488.4477D (acknowledgments)
ACKNOWLEDGEMENTS ND is supported by a Vidi grant awarded by the Netherlands organization for scientific research (NWO). LO, RW, and AP are supported by an NWO top grant, module 1, awarded to RW. DP is partially supported by the Consejo Nacional de Ciencia y Tecnologíaa with a CB-2014-1 grant #240512. DA acknowledges support from the Royal Society. JMM and JH acknowledge support from Chandra grant GO7-18031B. This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester. We also made use of MAXI data provided by RIKEN, JAXA, and the MAXI team, and public light curves from the Swift /BAT transient project.
Paper: 2019A&A...631L..12O (fulltext)
is not excluded when taking into account the noise level, but we do see a systematic trend across the velocity: a dip in the estimated α + around the [C II ] peak at position 1 and a gradient at 235–240 km s −1 at position 5. In addition, the estimated α + of 20–30 is lower than the measured 12 C/ 13 C in N159 ( Johansson et al. 1994 ). Therefore, the scenario of optically thick [ 12 C II ] discussed in Sect. 4.1 is more likely. Fig. 3. Estimate of α + = 12 C + / 13 C + when assuming [ 12 C II ] is optically thin. Asterisks indicate the derived α + together with the error bars. Blue lines show the [C II ] emission profiles. 4.3. Fractionation Because the fractionation reaction from 13 C + to 12 C + is an exothermal reaction (the exothermicity is 35 K; Langer et al. 1984 ), 13 C + tends to be underabundant with respect to 12 C + at low temperatures, namely at higher A V in a PDR. Röllig Ossenkopf (2013) calculated the fractionation in the clump integrated intensity of [ 12 C II ] and [ 13 C II ], and showed that only models with low UV fields ( χ ≤ 100) and high densities ( n ≥ 10 5 cm −3 ) trace the chemical fractionation, which would result in α α + , and that the derived [ 12 C II ] optical depth is a lower limit. On the other hand, it is unlikely that the fractionation is significant in the regions in this study because the estimated PDR properties are either low density and low UV field, or high density and high UV field ( Okada et al. 2019 ). 4.4. Metallicity effect Model predictions do not indicate a clear metallicity dependence of the [C II ] optical depth. For a high density PDR clump where the C + layer consists of a thin surface, the surface brightness of the [C II ] emission is almost constant with the metallicity ( Röllig et al. 2006 ) because the surface brightness is proportional to the thickness of the layer and the density of C + ions, with the former being proportional to the inverse of the metallicity due to the dust extinction and the latter being proportional to the metallicity as a first-order approximation. The derived optical depths in the LMC regions are similar to those of some Galactic regions (M 43 and Horsehead measured by Guevara et al. 2019 ); however, this study does not provide enough statistics to conclude that we do not see a metallicity dependence in the [C II ] optical depth. 4.5. Implications for the interpretation of the [C II] intensity The impact of the optical depth effect depends strongly on the actual geometry of the sources. Most PDR models compute the [C II ] intensity emerging from the surface of an externally illuminated clump or a face-on plane-parallel structure. For this geometry the radiative transfer computation takes the optical depth correctly into account. However, when the [C II ] emission stems from a mixture of components including surfaces illuminated from the back (negative temperature gradient toward the observer) or an ensemble of clumps we can no longer simply sum up the intensity
Paper: 2018MNRAS.477.5177D (acknowledgments)
ACKNOWLEDGEMENTS The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013/ under grant agreement number 607254. This publication reflects only the author’s view and the European Union is not responsible for any use that may be made of the information contained therein. KJD and HJAR acknowledge support from the ERC Advanced Investigator programme NewClusters 321271.
Paper: 2017ApJ...847..118G (acknowledgments)
Support by the Natural Sciences and Engineering Research Council (NSERC) of Canada is acknowledged.
Paper: 2019MNRAS.489.4741S (acknowledgments)
ACKNOWLEDGEMENTS We thank Qiusheng Gu, Paul Hewett, George Lansbury, Peter McGill, Leigh Smith, and Zhonglue Wen for helpful discussions. We thank the anonymous referee for a thoughtful report. YS was supported by the Royal Society – K. C. Wong International Fellowship (NF170995). SEK was partially supported by the National Science Foundation (NSF) grant AST-1813881 and Heising–Simons Foundation grant 2018-1030. This work made used of the Whole Sky Database (wsdb) created by Sergey Koposov and maintained at the Institute of Astronomy, Cambridge, by Sergey Koposov, Vasily Belokurov, and Wyn Evans with financial support from the Science Technology Facilities Council (STFC) and the European Research Council (ERC). This software made use of the q3c software (Koposov Bartunov 2006 ). This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. 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, and NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the National Aeronautics and Space Administration.
Paper: 2015MNRAS.453.1855M (acknowledgments)
We are very grateful to P. Jofre, A. Casey and C. Tout for fruitful discussion. This work was partly supported by the European Union FP7 programme through ERC grant number 320360.
Paper: 2018ApJ...862....1C (acknowledgments)
We thank the referee, D. C. Gabuzda, for a thorough reading of the manuscript and for many suggestions that improved the paper. We are grateful to D. E. Homan and S. Kiehlmann for reading the manuscript and making useful suggestions, and to the entire MOJAVE team for their years of work in assembling the data used here. This research has made use of data from the MOJAVE database that is maintained by the MOJAVE team (Lister et al. 2009 ); the MOJAVE program is supported under NASA-Fermi grant NNX15AU76G. Operations at UMRAO were supported by the University of Michigan, and research there was funded in part by NASA-Fermi GI grants NNX09AU16G, NNX10AP16G, NNX11AO13G, and NNX13AP18G and NSF grant AST-0607523. Y.Y.K. and A.B.P. were supported by the Russian Foundation for Basic Research (project 17-02-00197), the Basic Research Program P-28 of the Presidium of the Russian Academy of Sciences, and the government of the Russian Federation (agreement 05.Y09.21.0018). T.S. was funded by the Academy of Finland projects 274477 and 284495. This research has been supported in part by the Alexander von Humboldt Foundation, and it has made use of the Swinburne University of Technology software correlator, developed as part of the Australian Major National Research Facilities Programme and operated under license (Deller et al. 2007 ).
Paper: 2020MNRAS.491..858O (fulltext)
K1 (with PA 145 with respect to the core component), of the peak of the B3 component as observed in total intensity images. The polarization morphology resembles a limb-brightened structure. At 15GHz, the two polarized components are resolved from 2016 March, whereas in the first three epochs they are blended together, in agreement with what is found by Lister etal. ( 2018 ). At 43GHz, polarized emission from component K1 is detected during all epochs with the exception of 2015 October, whereas significant polarized emission from component K2 is detected sporadically. Polarized flux density of the sub-components of S50836710 are reported in Table 4 , while the full set of polarization images are presented in Appendix C (Fig.C1) . In the core component, we observe very high values of RM, which may exceed 5000radm 2 . The RM is highly variable with sign changes and its structure is patchy, indicating either opacity gradients and/or different components that evolve with time, expected in the case of a perturbed flow, which is moving along the jet. In the last epoch, when the radiation is optically thin at all three frequencies, we observe an RM of 1400500 rad m 2 , and an RM-corrected magnetic field direction of 585, roughly parallel to the jet direction. RM images and the associated error images at each observing epoch are presented in Fig. 11 . Figure 11. Rotation measure images (colour scale) for S50836710 overlaid with total intensity contours (left-hand column), and the associated rotation measure error images (colour scale; right-hand column). Vectors represent the RM-corrected magnetic field ( B ) vectors. Figure 11. continued. During the epochs in which component K2 has significant polarized emission at the three frequencies the RM is about 1950150 rad m 2 in 2015 August, and 1200400 rad m 2 in 2015 October and 2016 March. The RM-corrected magnetic field direction ranges between 160 and 175. Component K1 has RM values between 850120 and 180120 rad m 2 , with a tentative sign change observed in the last epoch, while the RM-corrected magnetic field direction ranges between 60 and 75, roughly parallel to the jet direction. We perform the analysis of the jet transverse structure using the data taken in March 2016, i.e. when polarized emission from both K1 and K2 components is clearly visible at all frequencies. Fig. 12 indicates the RM image and the slice considered for the analysis. A transverse RM gradient is clearly visible with K1 component showing positive RM values, while K2 component has negative RM values (Fig. 12 ). The shaded area marks regions with low polarization levels consistent with noise, where no reliable RM could be estimated. Total intensity and polarization profiles on the same transverse slice show a ridge-brightened profile and a limb-brightened profile, respectively. The transverse spectral index profile between 15 and 24GHz indicates a smooth flattening of the spectrum towards the ridge of the jet, with the