adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.500.2786M (fulltext)
with the lowest velocities ( V 290 km s −1 ) are clearly divided into two groups. One group is an undisturbed low-velocity SW, the second group includes points near the heliospheric plasma sheet and inside weak and very slow ICMEs. Using the regression model with different slopes in regions of low and high velocity (Table 2 ), we calculated hourly values of the expected temperature T exp and the temperature index K T = T / T exp for all hourly data from 1965 July–2019 November. The distribution of the temperature index is presented in Fig. 2 . The distribution is unimodal, peaked, right-skewed, and has a long tail in the region of large values. In Fig. 2 the tail is cut off, because the maximum value of the temperature index is too large ( K T max = 39.5) and the overwhelming majority of the observations have temperature index K T 5. A large number of high K T values is understandable: as a rule, the observed temperature is higher than expected where different-speed SW streams interact (e.g. Elliot et al. 2005 ). There are many such areas: they are in front of almost all HSS from CHs and in front of all ICMEs with sufficiently high speed. In other words, the events in a distribution tail in a region of large values of temperature index may be associated with CIRs or sheaths before high-speed ICME. Figure 2. Distribution of the temperature index K T (from 1965 July–2019 November). Analysis of the minimal (for each FD) values of the temperature index K T min for a large number of events (766 FDs) allowed us to establish a correspondence between K T min and the type of solar source modulating cosmic rays. Fig. 3 presents the distribution of K T min for groups of FDs associated with different types of source (1, ejection from an active region accompanied by a solar flare; 2, CME associated with a solar filament eruption from a region beyond a sunspots group; 3, mixed events with contributions of HSS from CHs and CMEs; 4, clear influence of HSS from CH without other influences), and for the full group of events (group 5). It is clearly visible that the distribution of K T min values for group 4 (CH-associated events) differs significantly from the distributions for groups 1 and 2 (CME-associated events). The K T min means (0.36 ± 0.02 in group 1 and 0.38 ± 0.02 in group 2) and medians (0.28 and 0.31 respectively) are approximately equal for the two groups associated with CMEs. At the same time, these statistics differ significantly from the mean (0.59 ± 0.01) and median (0.58) in the CH-associated group. The distribution of the mixed events group (mean 0.50 ± 0.02, median 0.47) is between those of the CME and CH groups and is close to the distribution of the full group of events (group 5: mean 0.48 ± 0.01, median 0.49). The results obtained therefore reveal that we can use K T min as a tool to help in the identification of FDs with solar sources. Figure 3. Box plots of the temperature index minimal values K T min (from 2000 January–2016 November) in
Paper: 2015MNRAS.448.2921S (acknowledgments)
We acknowledge the anonymous referee for useful comments, and Tim De Zeeuw and Wyn Evans for additional comments. This material is based upon work of LC supported in part by the National Science Foundation under grant no. 1066293 and the hospitality of the Aspen Center for Physics. We acknowledge financial support from PRIN MIUR 2010-2011, project ‘The Chemical and Dynamical Evolution of the Milky Way and Local Group Galaxies’, prot. 2010LY5N2T.
Paper: 2018ApJ...858...33C (acknowledgments)
We thank the anonymous referee for valuable comments that helped to improve our manuscript. This work is supported by the National Natural Science Foundation of China under grants 11133001, 11333004, and 11303015, and the Strategic Priority Research Program of CAS under grant XDB09000000. Z.L. acknowledges support from the Recruitment Program of Global Youth Experts.
Paper: 2019ApJ...875...91D (acknowledgments)
We thank all the outstanding men and women who have made IBEX a successful mission. Work at the Los Alamos National Laboratory was carried out under the auspices of the U.S. Department of Energy. This research was carried out partly under the IBEX mission, which is part of the NASA Explorer Program. M.I.D. and E.J.Z. were supported in part by NASA grant NNX16AG83G. J.H. and N.P. were supported in part by NASA grants NNX15AN72G, NNX16AG83G, and 80NSSC18K1649NS, and NSF award OAC-1811176. The work at CBK PAN (M.B., J.M.S, and M.A.K.) was supported by Polish NCN grant No. 2015/19/B/ST9/01328.
Paper: 2018MNRAS.478.4293C (fulltext)
with increasing radius). However, since we have high-resolution HST imaging for all our galaxies, we prefer to relax this assumption and provide a 2D SFR map. Unlike the emission-line ratios , the modelled emission-line luminosities do have an azimuthal dependence. We assume that the distribution of stellar light in the HST images provides a rough approximation for the relative SFR distribution. In the MUSE-Deep UDF fields we use the deep HST F775W imaging. In all other fields we use the F814W band. Although ideally we would like to use photometry that covers the rest-frame ultraviolet light in each galaxy, the necessary deep multiwavelength HST imaging does not exist for all fields. To strike a good balance, we use the F775W and F814W filters, since they are providing the S/N required. This has the additional advantage that, because these filters have similar pivot wavelengths, we also ensure a degree of consistency between the fields. We construct SFR maps of our galaxies by cropping the HST images to only include flux within an ellipse of radius 4 × r d along the major axis. This ellipse has the same morphology (RA, Dec, inc., PA) as above. We inspect each image and alter the mask if necessary to ensure that we include all flux from the object and to remove other objects or defects. When necessary, we interpolate over these rather than mask them. Negative flux values are set to zero. The final result is an SFR map that represents the relative spatial distribution of the SFR. The absolute SFR values are determined by normalizing the map to the total SFR, SFR tot , which is a free parameter in the model. From experience with our C17 work, we found that it was quite important to include the complex spatial variations of the SFR in our model. Using an SFR map (as opposed to simply assuming an exponential disc) appeared to improve the accuracy of our model. However, including an SFR map does not correct for all effects of clumpy star formation. Indeed, a limitation of our model is that, by construction, we do not accommodate for galaxies with strong azimuthal metallicity variations 12 ; we should anticipate that we may observe galaxies where the bright star-forming clumps have uncharacteristically low/high metallicities. However, it should be noted that, for the low-redshift galaxies where azimuthal metallicity variations have been quantified, the variations are typically found to be quite small (≲0.05 dex; e.g. Sánchez et al. 2015 ; Zinchenko et al. 2016 ; Sánchez-Menguiano et al. 2017 ), and there is only the odd example of a galaxy with variations as large as ≈0.2 dex (e.g. Vogt et al. 2017 ). That said, even if there were large azimuthal variations in our galaxies, it does not mean that we cannot still make inferences in these cases because, in effect, we likely still measure the radial metallicity profile of brightest star-forming regions (i.e. a quasi flux-weighted metallicity profile). Nevertheless, this would be in contrast with
Paper: 2019ApJ...882..176K (acknowledgments)
We thank an anonymous referee for a careful review of this paper, which has improved the clarity of its presentation. Funding for SDSS-III was 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/ . Y.S.L., acting as the corresponding author, acknowledges support from the National Research Foundation (NRF) of Korea grant funded by the Ministry of Science and ICT (No.2017R1A5A1070354 and NRF-2018R1A2B6003961). T.C.B. acknowledges partial support for this work from grant PHY 14-30152; Physics Frontier Center/JINA Center for the Evolution of the Elements (JINA-CEE), awarded by the U.S. National Science Foundation.
Paper: 2017ApJ...845...24K (acknowledgments)
We thank I. Reiss, I. Gurwich, B. Katz, M. Pohl, M. Ostrowski, F. Zandanel, R. Mukherjee, and L. Rudnick for useful discussions. The research of U.K. has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no PCIG09-GA-2011-293975 and from the IAEC-UPBC joint research foundation (grant No. 257), and was supported by the Israel Science Foundation (grant No. 1769/15) and by the GIF (grant I-1362-303.7/2016).
Paper: 2018MNRAS.479.1638S (acknowledgments)
ACKNOWLEDGEMENTS We thank an anonymous referee for thoughtful comments. We thank our colleagues (J. Schaye, M. Schaller, R. Crain, and R. Bower) for sharing with us the data from the eagle simulation. This study was funded by the Science and Technology Facilities Council (grant number ST/F001166/1) and by the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office (AP P7/08 CHARM). For this study, the DiRAC Data Centric system at Durham University was used. This system is run by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ); the equipment was funded by BIS National E-Infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC; as a part of the National E-Infrastructure. M.S. is supported by an STFC post-doctoral fellowship.
Paper: 2019MNRAS.483..599G (acknowledgments)
ACKNOWLEDGEMENTS We thank Dr. A. Y. Potekhin for useful discussions during the early stages of this work. DGC acknowledges the financial support by CONICYT Becas-Chile fellowship (No. 72150555). We are grateful to an anonymous referee for a careful reading and constructive comments on the paper.
Paper: 2019ApJ...876..107P (acknowledgments)
The authors thank Vivien Bonvin, Patrick Kelly, and Tommaso Treu for very helpful discussions of this paper. We would also like to thank the referee for their insightful comments, which certainly strengthened this work. Support for this work was provided by NASA through grant No. HST -AR-15050 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS 5-26555.
Paper: 2018MNRAS.478..342B (acknowledgments)
ACKNOWLEDGEMENT The authors thank the anonymous referee for his/her valuable comments, which helped them to improve the manuscript substantially.
Paper: 2019A&A...622A.208D (fulltext)
( Stimpfl et al. 2006 ). Model B (green curve) is constructed by matching the surface potential energy distribution shown in Stimpfl et al. (2006) . Here sites with binding energies smaller than 8420 K (70 kJ mol −1 ) are included, contrary to model A. Finally, model C (blue curve) includes 8% of surface sites with adsorption energies higher at 25 260 K (210 kJ mol −1 ), which were not taken into account in model A although claimed by the atomistic model of Stimpfl et al. (2006) . The surface energy potential distribution of the adsorption sites of the crystal surface determines the coverage of the adsorbed water molecules. As shown in the right plot of Fig. B.1 , in model C the surface coverage is 5% higher than in model A at 700 K and n H 2 O = 6 × 10 10 cm −3 . The surface potential energy distribution of model B allows only half the coverage than seen in model A and C, due to the higher hydrophobicity of the surface. We can conclude that the choice of the adsorption energy of the sites does not explain the factor of three difference in surface coverage between our work and the Muralidharan et al. (2008) simulations. Fig. B.1. Surface potential energy distribution ( left plot ) and surface site density ( middle plot ) of three models: A (red curve), B (green curve), and C (blue curve). The surface coverage for the three models is calculated at 700 K and n H 2 O = 6 × 10 10 cm −3 ( right plot ). Fig. C.1. Effect of water cluster on surface coverage data. Left panel : surface coverage (H 2 O nm −2 ) as a function of time (s) at n H 2 O = 6 × 10 10 cm −3 and T = 700 K, with cluster effect (pink curve) and without (light blue curve). Right panel : snapshots of the grid resembling the [1 0 0] forsterite surface showing the adsorption energy of the occupied sites without (middle plot) and with (right plot) cluster effect. The color scale indicates the adsorption energy in the range from 13 000 K (blue) to 25 000 K (yellow). Appendix C: Water cluster simulation In our MC models the water molecules randomly “walk” on the surface until they adsorb at a favorable site. As two water molecules sit on neighboring sites, a hydrogen bond links the two species, and a dimer can be created with the total binding energy higher than the ones of single molecules. This generates the formation of water clusters for which the binding energy increases with increasing size ( González et al. 2007 ). In two dimensions, the binding energies increase with the number of water molecules present in the cluster, while for a 3D cluster Lin et al. (2005) show that the binding energy mainly depends on the number of water molecules close to the surface. In this work we used a simple way to calculate the binding energies, which increase linearly with the number of neighboring water molecules ( Dartois et al. 2013 ), but we limited the number of water molecules to four. Water clusters have been implemented in previous MC simulations to study the formation of water on carbon grains
Paper: 2019A&A...630A..17L (fulltext)
thermal stress during the perihelion passage could trigger the observed repetition of dust jet activities. On the other hand, the penetration of the heat wave to the bottoms of some of the dormant pits might reactivate the sublimation process. Figure 9 shows a projection of the jet source number density distribution in Imhotep and Bes onto the shape model of the comet, oriented to match the viewing geometry of an NAC color-composite image. This illustrates the possible correlation between the two main clusters of activity in Imhotep and the prominent water-ice-enriched areas in the same region, as described in Oklay et al. (2017) . These flat areas contain exposed surface material with high volatile material content, which can sublimate away when illuminated. The cliffs surrounding the source terrain focus the gas flow that is released from the ice patches and lead to the jet formation, a process described in Vincent et al. (2016a ) for other regions of 67P. Fig. 9 Comparison of the two higher concentration regions in Imhotep and Bes with an NAC color-composite image from Oklay et al. (2017) . It is interesting to note that almost every cliff on 67P is likely to produce a jet, as long as a gas flow exists near by. The source of the gas flow is typically the boulder talus at the foot of a collapsed cliff. Although the process is similar here, we note that the two cliffs in Imhotep do not show much rubble in their taluses and are considerably brighter than other areas. In addition, the terrain at the foot of the cliffs appears smooth and subdued, and the low spectral slope (typical of water-rich content) has persisted for extended periods (>1 yr, see Oklay et al. 2017 ). This means that the source of volatiles in these two areas is not simply fresh debris from the collapsing cliffs, but probably local water reservoirs buried just below these areas that constantly feed the jets. This would explain why these areas remain spectrally distinct for a long time, and why more jets can be generated here than in other regions with similar morphologies. In a recent paper, Kramer et al. (2017) have also identified localized regions of enhanced activity using gas-monitoring data from the ROSINA instrument. Although the uncertainty of their source locations is large because the collimation factor of gas flows is lower than that of dust jets, they also found clustering of activity in zones that correspond to our inversion. 6 Summary and discussion We have examined the time evolution of the spatial distribution of the dust jet sources on comet 67P from November 2014 to March 2016 based on an analysis of the OSIRIS imaging data. The main results are listed below. 1. Using a ray-tracing method ( Spitale Porco 2007 ; Farnham et al. 2013 ; Lin et al. 2015 ; Vincent et al. 2016a ), we identified 3530 collimated jets by visual inspection. We find that the location of the jet source regions is correlated with theseasonal variation of the sunlit latitude of 67P. 2.
Paper: 2018ApJ...867...71L (fulltext)
(Note that this model is therefore agnostic to the light curve, but we included such a light curve in the fiducial flare for use in time-dependent models.) The impulsive model tracks the absorption and scattering of the flare photons and terminates when all photons have been either absorbed within the atmosphere, absorbed by the ground (assumed to be perfectly absorbing), or exited the top of the atmosphere (scattered out). The result represents an upper limit on the dissociation of various species: in reality, recombination reactions will be happening during any true flare, reducing the maximum quantities of dissociated species. However, an exception to this could occur if secondary reactions amplify dissociation, such as catalytic destruction of O 3 by NO x species. We use the inactive star fiducial flare model of Section 7 to set the spectral content of the flare input. The modeled atmosphere is that of Earth (1 bar surface pressure) and receives a bolometric flux equivalent to Earth’s insolation. We consider only the substellar point in the atmosphere. Its compositional profile is a combination of several empirical models of Earth’s atmosphere covering different height regimes and including different constituents: 1. NRLMSISE-00 (Picone et al. 2002 ) is a global atmospheric model commonly used for engineering purposes. We use it to specify N 2 , O 2 , O, H, N, Ar, and He densities from 0 to 1000 km. 2. NASA Earth GRAM 2016 24 24 https://software.nasa.gov/software/MFS-32780-2 is a goal atmospheric model intended for environmental science. We use it to specify H 2 O, O 3 , CO 2 , CO, CH 4 , and N 2 O densities from 0 to 1000 km. 3. Hu et al. ( 2012 ) compiled atmospheric data from multiple sources on a variety of trace species to validate the general purpose atmospheric photochemistry model they developed. We use their profiles to specify OH, NO, and NO 2 densities from 0 to 80 km. H 2 and O( 1 D) were included purely as dissociation products. We use a grid of 400 layers, with thicknesses of 0.5 km up to 100 km and 4.5 km up to the top of the model at 1000 km. The high upper boundary to the model allows important resonant scattering by atomic H and O to be included. This yields scattering columns of 2.9 × 10 13 H and 8.5 × 10 17 O atoms cm −2 above 100 km (300 nbar) and 1.1 × 10 14 H and 1.1 × 10 18 O atoms cm −2 at ground level. Figure 21 shows the state of the atmosphere before and after exposure to flares of three representative equivalent durations. The δ = 10 4 s flare is akin to the largest observed in this survey, whereas the δ = 10 8 s flare is likely about an order of magnitude less energetic than the largest M dwarf flare observed to date (the DG CVn flare described by Osten et al. 2016 ; see Section 3.4 ). The effects progress from negligible to dramatic over this range. At the highest energy, the atmosphere exhibits clear dissociation fronts among the different species. A potentially surprising feature is the increase in CO
Paper: 2016ApJS..226....1J (fulltext)
different sub-types of carbon stars in line indices and multi-band photometric space. Finally, a short conclusion is drawn in Section 5 . 2. DATA 2.1. The LAMOST Data The LAMOST, also called the Guo Shou Jing telescope, is a 4 m reflective Schmidt telescope with 4000 fibers on a 20 square degree focal plane (Cui et al. 2012 ; Zhao et al. 2012 ). The unique design of LAMOST enables it to take 4000 spectra in a single exposure to a limiting magnitude as faint as r = 18 at the resolution R = 1800. The LAMOST survey will finally obtain more than 5 million low-resolution stellar spectra after its five year survey (Deng et al. 2012 ). Previous works have shown that LAMOST observations are biased more to giant than dwarf stars, since the limiting magnitude is relatively brighter (Liu et al. 2014a ; Wan et al. 2015 ). By the end of 2014, the LAMOST team had released the DR2 catalog, which contains 4,136,482 targets including 3,784,461 stars. In DR2, there are about 0.5 million late type giant stars (Liu et al. 2015a ), implying that there should be large numbers of carbon stars. 2.2. Line Indices The typical spectra of carbon stars are characterized by strong C 2 , CN, and CH absorption bands (Secchi 1869 ). Therefore, we measure the line indices of carbon related molecular lines to identify carbon stars. We show several sample LAMOST spectra for typical carbon stars in Figure 1 . The features of the C 2 swan bands at 4737, 5165, and 5635 Å, and red CN molecule bands at 7065 and 7820 Å are prominent in these spectra, while the detailed features are different in different sub-types. Compared to the traditional approach to classifying spectral types, using line indices allows a semi-automatic classification. With multiple spectral line indices, various spectral types may transitionally change from one to another, which naturally reflects the variation of the astrophysical parameters, e.g., effective temperature, surface gravity, and metallicity, from type to type (Liu et al. 2015b ). Figure 1. LAMOST spectra for typical carbon stars. The features of the C 2 swan bands at 4737, 5165, and 5635 Å and red CN molecule bands at 7065 and 7820 Å are indicated. In order to identify carbon stars, we adapt the Lick line indices defined by Worthey ( 1994 ) and Worthey Ottaviani ( 1997 ). For the LAMOST DR2 spectra, we use the measured results from Liu et al. ( 2015b ). The Ba ii (4554 Å), molecular bands C 2 (around 5635 Å), CN (7065 Å), and CN (7820 Å) are also measured based on the wavelength definition listed in Table 1 . Table 1 Line Indices Definition Used in This Work The line index in terms of equivalent width (EW) is defined by the following equation (Worthey 1994 ; Liu et al. 2015a ):where F λ and F C are the fluxes of the spectral line and continuum, respectively, which are functions of wavelength λ . The pseudo-continuum F C is estimated via linear interpolation of the fluxes located in the “shoulder” region on either side of each index bandpass. The line