adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...637A..79L (fulltext)
minerals that formed in a water-rich environment, including phyllosilicates, sulfates, and hematite, which suggests changes in ancient aqueous environments. After exploring the top of Vera Rubin Ridge (VRR) in 2018, which mostly contains hematite signatures, the rover descended into a trough south of the ridge, dropping as much as 15 meters in elevation during the spring of 2019 to explore part of the clay-bearing unit, and Curiosity is currently investigating the clay-bearing unit on the side of Mount Sharp. Elemental abundances and mineralogical information has been obtained from the ChemCam, APXS, and CheMin instruments, which allows us to directly test the quantitative results from the orbital remote sensing data. Spectral unmixing modelling was performed on the CRISM single scattering albedo spectra to extract the abundances and grain sizes of both hydrous and anhydrous phases. Examples of spectral unmixing results over the hydrated sulfate and phyllosilicate units are shown in Fig. 6 . To investigate the spatial distribution of the secondary mineral abundances, we applied our spectral unmixing model to CRISM image HRL0000BABA to generate the mineral abundance map. Figure 7 a shows the distribution of the secondary phases mapped over Mount Sharp using the CRISM parameter map ( Milliken et al. 2010 ), and Fig. 7 b shows the results of the spectral unmixing analysis over the selected region enclosed in the white box in Fig. 7 a. From Fig. 7 b it is possible to resolve the hematite, phyllosilicate, and hydrated sulfate unit, and these units are consistent with the spatial distribution derived from the CRISM parameter map shown in Fig. 7 a. The spectral unmixing of the CRISM image cube over Gale indicates that there is 30% hematite, 5% phyllosilicates, and 20% hydrated sulfates. We note that the error of the retrieved abundance is around 5% following the error analysis approach used in Liu et al. (2016) . Fig. 7 Panel a : CRISM FRT0000BABA RGB mineral parameter map (red = Fe-minerals, green = Fe/Mg-clay minerals, blue = sulfates) overlain on CTX mosaic. Image adapted from Fig. 3 a of Milliken et al. (2010) . Panel b : Derived mineral abundance map over a region covering Curiosity’s traverse, outlined in the white box in Fig. 7 a. The derived mineral abundances from CRISM observations can be used to compare with the ground-truth data. Vera Rubin ridge (VRR) of Gale crater has the strongest spectral signature consistent with hematite detected from orbit, and the area has been explored by Curiosity rover intensively. Curiosity data show that hematite is present throughout most of the Murry formation and VRR, though it is more abundant in certain drill samples ( Rampe et al. 2019 ). The abundance of these iron oxides are around 20 wt%, as inferred from ChemCam and APXS instruments ( Milliken et al. 2019 ), and the value is slightly lower than what we derived from orbital data. In addition to abundance discrepancies, spatial distribution also
Paper: 2015ApJ...808..193Y (acknowledgments)
The anonymous referee is thanked for their very insightful and constructive comments and suggestions, which allowed us to improve the manuscript significantly. We are very grateful to the Tibet AS- γ collaboration for providing their CR maps in electronic format and especially Dr. Yi Zhang for helping us in understanding the data. We acknowledge the use of LAMBDA, support for which is provided by the NASA/GSFC, the software packages HEALPix ( http://healpix.sourceforge.net/ ), developed by A.J. Banday, M. Barthelmann, K.M. Gorski, F.K. Hansen, E. Hivon, and B.D. Wandelt, and CAMB ( http://camb.info ), developed by A. Challinor and A. Lewis. SNZ acknowledges partial funding support by Directional Research Project of the Chinese Academy of Sciences under project No. KJCX2-YW-T03 and by the National Natural Science Foundation of China under grant Nos. 10821061, 10733010, 10725313, and by 973 Program of China under grant 2009CB824800.
Paper: 2021ApJ...909..159C (fulltext)
(black circles), and He i λ 3889 (red diamonds). Bottom panel: EW of the same emission lines (with the same color and marker combination as the top panel). The dotted blue lines indicate the value of the He ii line measured from the STIS spectra of 2000 February 09 (MJD 51584). The He i lines were not detected in that spectrum. On the X -axis we plot the Modified Julian Date of the observations. The FWHM and EW of the He ii line show a gradual decrease over the duration of our follow-up campaign: the FWHM decreases from ≈2700 km s −1 to ≈1350 km s −1 and the EW from ≈ 14 Å to ≈7 Å . Neither the FWHM nor the EW have returned to the values measured during the STIS spectrum. The evolution in the EW and FWHM of the He i lines is less clear, but especially that of the λ5876 line seems to follow a similar trend as that of He ii . In contrast, the other lines of similar width, that is, the broad bases of the other emission lines, do not show significant evolution with time in their FWHM, while the EW of some of them (mainly H β and [O iii ] λ 4959, λ 5007) show an increase between MJD 57280 and 58345 to subsequently decay back to the initial value (see Table 2 ). Table 2 Results of the Line Fitting of the Most Prominent Broad Lines Note. With ellipsis dots we indicate an epoch in which the line in question could not be fit. For the He i λ 5876, the values marked with an asterisk are the combination of the values measured for the narrow and broad component. We measure the offset of the lines with respect to their rest-frame wavelength. It is important to note that in good observing conditions (i.e., when the value of the seeing in arcseconds is smaller than the slit width, which is the case for many of our observations, see Table 1 ), the wavelength calibration of the source spectrum could be slightly shifted with respect to that derived by the arc lines (as the latter do fill the whole slit), due to possible imperfections in centering of the source in the slit. This in turn can affect the velocity offset measurements by a few times 100 km s −1 . However, the narrow lines do not show a significant offset with respect to their rest-frame wavelength (its absolute value is always below 200 km s −1 ), implying that the shifts in the wavelength scale caused by the potential imperfect centering of the source in the slit are smaller than a few hundred km s −1 . This is in line with the fact that the extended nature of the source acts as a mitigating factor for any small imperfect centering of the source in the slit. The broad bases of the emission lines are all blueshifted. We measure the shift of the broad bases with respect to the central wavelength of the narrow lines, assuming that these are at their rest-frame wavelength. Each broad line has a different blueshift, but none of them show a clear evolution with time. The velocity offset values are all ≲10 3 km s −1 . For the He i λ 5876 lines, we only separate the narrow and broader component of the
Paper: 2016ApJ...833...36F (fulltext)
grid cell, and then integrate the total photon flux density as in the photon energy range. We use 0.01 keV as , meaning 90 bins in total in the energy range. This process leads to the images shown in Figure 1 , right column. 3. RESULTS AND DISCUSSION 3.1. Asymmetric Footpoint Heating Inducing KHI The temporal evolution of the number density (left column), temperature (middle column), and synthesized SXR photon fluxes (right column) are shown in Figure 1 , covering a timespan of about 4.15 minutes. As described in Section 2.2 , we use H 1 to mimic the flare energy deposited in the upper chromosphere, and the consequent overpressure resulting from H 1 drives upward mass flows at a speed up to 700 km s −1 . We also witness a rapid thermodynamic change (due to thermal conduction) in the loop, reaching an extremely hot temperature around 18 MK. These upward chromospheric evaporation flows have been detected directly in imaging sequences as SXR emission propagating toward the loop-top sources (Silva et al. 1997 ; Liu et al. 2006 ; Nitta et al. 2012 ). This is also seen in our simulation, as in panel (f) of Figure 1 (and in the time period before this image, for which we refer to the corresponding movie). In the last several decades, simulations of chromospheric evaporation have been mostly carried out with symmetric energy input assumptions on both loop footpoints and these focused mainly on the UV-SXR spectral lines produced by the evaporated plasma (Fisher et al. 1985 ; Mariska et al. 1989 ; Yokoyama Shibata 2001 ; Allred et al. 2005 ; Liu et al. 2009 ). However, asymmetric energy deposition at footpoints is a much more likely scenario, and could be intrinsic to the acceleration process as pursued by McClements Alexander ( 2005 ). So, in our simulation we assume that the deposition of flare energy at two footpoints is slightly asymmetric, with a ratio of 0.8 as described in Equation ( 5 ). As a result, the right evaporation flow rises quicker and is hotter than the left one as shown in panel (e). Asymmetric energy deposition guarantees that the two evaporation flows will not merely collide at the apex, a situation known to only generate reflected shocks. Panel (e) indeed shows that when our two evaporation flows run into each other asymmetrically, the right one has already crossed the loop apex. In addition, the hotter, right evaporation flow causes a bigger expansion of the loop cross-section in the right half of the loop. This configuration provides the conditions for the formation of shear flows, the trigger of KHI. As shown in panel (e), right after the collision of both flows, KHI begins to develop immediately, and the typical vortical structures of the KHI appear and grow up quickly from panels (e) to (g). The development of vortices in a magnetized plasma can further induce small-scale structure to develop, with a potential cascade of energy to establish a turbulent plasma state (Keppens et al. 1999 ; Baty et al. 2003 ; Elmegreen
Paper: 2019MNRAS.482.3089N (fulltext)
history. On the other hand, galaxies MW c and M31 c are the MW and M31 candidates of Nuza et al. ( 2014 ) (see also Scannapieco et al. 2015 ) that were extracted from a WMAP -5 cosmological simulation of the Local Group. As a result, these galaxies reside in an environment more similar to the actual one. The main properties of each model galaxy at = 0 are shown in Table 1 . The latter also includes a scaled-down version of g106, referred to as g106r, that was used as an MW candidate by MCM13 and MCM14 in their Galactic chemodynamical model. More details concerning the galaxies, as well as the main assumptions and techniques adopted to perform each simulation, can be found below. Table 1. Properties of the simulated galaxies at = 0. The columns show virial mass, virial radius, stellar mass (within optical radius), optical radius, bulge-to-total ratio, and references. Note . a Rescaled g106 used as an MW proxy by MCM13 and MCM14 (see Section 4.3 ). 2.1 g37 and g106 These galaxies are selected from the larger galaxy sample of Martig et al. ( 2012 ). The simulation technique involves basically two steps (see Martig et al. 2009 , for technical details). First, a dark-matter-only simulation is performed in a box of Mpc on a side using the adaptive mesh refinement RAMSES code (Teyssier 2002 ) to extract merger and accretion histories for the target haloes. Some properties such as time, mass, velocity, and spin of incoming satellites are then stored. In the second step, a new simulation is performed in which each halo recorded in the dark-matter-only run is replaced by a model galaxy made up of gas, stars, and dark-matter particles. Similarly, each diffuse particle of the cosmological simulation is replaced with a blob of higher-resolution gas and dark-matter particles. These model ‘seed’ galaxies are made up of a disc containing gas and stars, a stellar bulge, and a dark-matter halo following analytical prescriptions for the profiles in each case. The stellar mass of a galaxy in the simulation is set according to the Moster et al. ( 2010 ) relation as a function of halo mass and redshift. All model galaxies are assumed to be disc galaxies without a bulge, except for the most massive haloes with M ⊙ at 1, in which case the mass of the bulge is set to of the stellar mass. The gas content in the disc is also set according to an analytical scheme: at 1, the gas fraction (with respect to the total baryonic mass) is 0.3 for small galaxies ( M ⊙ ) and 0.15 for the massive ones. At higher redshifts, the gas fraction is chosen independently of the total mass, and is set to 0.5 for 1 3 and 0.7 for > 3. No hot gas halo is included, which should be a sensible approximation only for halo masses lower than a critical mass of M ⊙ , which is required for virial shocks to be stable (e.g. Birnboim Dekel 2003 ). The resimulation starts at = 5 inside a region of radius 800 kpc, i.e. spanning a few times the virial radius value of the main galaxy at = 0, and follows its
Paper: 2018A&A...619A.171B (acknowledgments)
Acknowledgements We thank the referee, Ruobing Dong, for constructive comments that helped improve the manuscript. We are thankful to A. Triaud, Z. Zhu, R. Nealon, C. Manara, and J. Huang for insightful discussions. We thank S. Barenfeld for sharing his ALMA data of HD 143006. M.B. and M.V. acknowledge funding from ANR of France under contract No. ANR-16-CE31-0013 (Planet Forming disks). This work has been supported by the diskSIM project, grant agreement 341137 funded by the European Research Council under ERC-2013-ADG. P.P. acknowledges support by NASA through Hubble Fellowship grant HST-HF2-51380.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS 5-26555. The figures were generated with the python -based package matplotlib ( Hunter 2007 ).
Paper: 2016MNRAS.460.4258L (acknowledgments)
BL thanks Alex I. Maltz and David Hogg for useful discussions. BL and HVP were supported by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007- 2013)/ERC grant agreement no 306478-CosmicDawn. BL was also supported by the Simons Foundation and DJM by the STFC.
Paper: 2017MNRAS.467..524B (acknowledgments)
Acknowledgments The author is indebted to Sverre Aarseth of the Institute of Astronomy, Cambridge, for his continuous efforts in improving nbody6/7 , without which this study would not have been possible. SB is thankful to Jarrod Hurley of the Swinburne University of Technology for reviewing the changes made in the BSE s routines for this work, testing them independently, and also for interesting discussions. SB is thankful to Chris Belczynski of the Astronomical Observatory, University of Warsaw, for supplying the data corresponding to the ZAMS massremnant mass relations, as obtained from the StarTrack program (Fig. 1 ), and also for useful discussions. SB is thankful to the anonymous referee for helpful comments that improved some of the descriptions of the paper. Finally, but not the least, SB is indebted to the computing team of the Argelander-Institut fr Astronomie, University of Bonn, for their support and efficient maintenance of the workstations on which all the computations have been performed.
Paper: 2019A&A...626A..19E (acknowledgments)
The scientific results reported in this article are based on observations obtained with XMM-Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programmes 077.D-0764(A) and 297.D-5042(A). PE and ADL acknowledge funding in the framework of the project ULTraS (ASI–INAF contract N. 2017-14-H.0). FCZ is supported by a Juan de la Cierva fellowship.
Paper: 2018MNRAS.477.5139G (fulltext)
in their terms) appearing at approximately Σ ≳ 100 ⟨Σ⟩ with a slope ≈−1 ± 0.2. Similarly, Lin et al. ( 2017 ) see in the survey of clouds that the PDF becomes systematically shallower, approaching −1, as clouds (or cloud regions) become more actively star forming. We should also note that similar results have been seen in other numerical simulations (e.g. Burkhart, Collins Lazarian 2015 ). 4.4 Cluster mass profile Let us assume that some fraction of objects formed remain gravitationally bound to each other. We expect that the clustered substructures that formed from fragmentation will eventually merge together into a cluster with a density profile that decreases monotonically. Let us derive the power-law index of this profile. Using our model we can calculate the relation between the densities and the survivor masses before the objects rearrange themselves into clusters. For density, take their at-formation value (ρ n ). We can express the index n as which leads to Let us assume (for now) that after formation the objects rearrange themselves to form clusters, while preserving the local volume density – i.e. the local density (of stars) around a star does not change dramatically before/after the re-arrangement. This is motivated by the fact that during mergers, tidal shredding of an object with density ρ occurs at an orbital radius R , where the mean density enclosed within R is approximately ρ. This means that the amount of mass at different density levels must be the same as before the rearrangement, thus Let us assume the relaxed cluster has a power-law density profile: ρ cl ∝ R β − 3 . Also, ε ≪ 1 thus the right hand side of equation ( 25 ) is zero. This leads to thus β = 0, so the mass profile of a bound cluster that results from the assembly of substructures formed in a scale-free fragmentation cascade is ρ cl ∝ R −3 . We can repeat the same exercise while assuming that phase space density (ρ p ) is conserved instead of real space density, as per Liouville’s theorem. This is only true however if ρ p is resolved on infinitely fine scales, as elements of higher phase space density effectively get stretched out and diluted in phase space so that the final observed coarse-grained phase space density is generally lesser than the initial (Lynden-Bell 1967 ). However, we may still suppose that our self-similarity condition means that the evolution operator on the coarse-grained phase space density can only map an initially flat (d M /dln ρ p ∼ 0) phase space distribution into another flat distribution. We can approximate the phase space density as where σ is the velocity dispersion. Assuming that the collapsing clouds are virialized, we can write which leads to From here we can formulate the surviving mass per phase density (similar to equation 25 ): where, in the last step, we used the assumption that ε ≪ 1. Since we are interested in the asymptotic case at large radii. the mass enclosed is approximately converged ( M ≈ const.), thus so we get
Paper: 2015A&A...584A..81A (acknowledgments)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 637324 and from the Academy of Finland (projects 258963 and 267186). The calculations were performed using the Finnish Grid Infrastructure (FGI) project (Turku, Finland).
Paper: 2016A&A...595A..65S (acknowledgments)
We thank Carlos de Breuck, Katharina Immer and APEX operators for their support for the observations. We also thank the referee for helpful comments. The Atacama Pathfinder EXperiment (APEX) is a collaboration between the Max-Planck-Institut für Radioastronomie, the European Southern Observatory, and the Onsala Space Observatory. Based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme 60.A-9341A. We acknowledge financial support from “Programme National de Cosmologie and Galaxies” (PNCG) of CNRS/INSU, France.
Paper: 2019MNRAS.483.1224D (acknowledgments)
ACKNOWLEDGEMENTS The Westerbork Synthesis Radio Telescope is operated by the Netherlands Institute for Radio Astronomy (ASTRON) with support from The Netherlands Foundation for Scientific Research (NWO). LND and BWS acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 694745). We would like to thank Andrew Lyne, Marten van Kerkwijk, and James McKee for useful and interesting discussions. LND would like to thank the ASTRON/JIVE summer studentship programme for a fellowship in 2014. We thank the referee, assistant editor, and scientific editor for their constructive comments on the manuscript.
Paper: 2018MNRAS.474..547K (fulltext)
Residuals are in units of σ as in Fig. B1 . Following the method used in Appendix B1 , it was verified (but not shown here) that the mass gradients towards filaments after randomization of the distances D skel in bins of distances to the nearest wall D wall are substantially reduced. Only a very weak mass gradient (at a 1σ level at most) is detected after randomization even for d skel min = 2.5 Mpc. Similarly to what was found in Section B1 , increasing this parameter does not induce any substantial reduction of the gradient. Thus this distance was chosen as the limit for the exclusion region around filaments. APPENDIX C: SMALL-SCALE DENSITY-COSMIC WEB RELATION In this Appendix, the impact of the small-scale density estimator on the mass and type/colour gradients is presented. The density used here is DTFE, i.e. the density computed at the smallest possible scale. 10 As in Section 6 , the two methods, the reshuffling and density-matching, are applied. Fig. C1 shows the differential distributions of the distances to the nearest filament, D skel (normalized by 〈 D z 〉, for the same selections as in Fig. 12 . The contribution of the nodes to the measured signal is minimized, by removing from the analysis galaxies located closer to a node than 3.5 Mpc. Star-forming and passive galaxies have been matched in mass, as described in Appendix A1 . The vertical lines indicate the medians of the distributions, whose values, together with the error bars, are listed in Table F3 . Figure C1. Top rows: As in Fig. 12 , but using the DTFE density for both methods, reshuffling (Figure a) and density matching (Figure b). The numerical values of medians, shown as vertical lines, are listed in Table F3 . When the small-scale density, DTFE in this case, is used in the reshuffling method, the randomized (dashed lines) and original signal (solid lines) are nearly identical. Similarly, all gradients are almost completely erased, as expected. Bottom rows: Residuals are in unit of σ as in Fig. 12 . (a) Reshuffling. (b) Density matching. In Figure (a), the mass and type gradients are shown before (solid lines, as in 12 ) and after (dashed lines) applying the reshuffling of galaxies in the bins of overdensity (1+δ), where the number density corresponds to the DTFE density. The result conforms to the expectations. The reshuffling does not remove the observed mass and type/colour gradients, i.e. the distributions before and after the reshuffling are almost identical, suggesting that at the small scale, traced by DTFE, the density and cosmic web are closely correlated through the small-scale processes. Figure (b) illustrates the PDFs for samples that have been matched in overdensity (1 + δ), as described in Appendix A2 , where the density considered is DTFE. The density-matching technique yields qualitatively similar result than the above used reshuffling in that almost no mass and type gradients are detected when galaxies matched in the DTFE density. Qualitatively same