adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.460.2881N (fulltext)
give rise to metal absorption in gaseous haloes. Simcoe et al. ( 2006 ) estimated absorbers sizes around z ∼ 2.3 galaxies ranging from the sub-parsec to the kiloparsec. This is consistent with the transverse scales of C iv absorbers derived in Rauch et al. ( 2001 ) from multisightline analysis of lensed QSOs at sub-kpc scales (see also Petitjean et al. 2000 , based on Mg II). Based on photoionization modelling of C iv absorbers at similar redshifts, Schaye, Carswell Kim ( 2007 ) derived typical densities of n H ∼ 10 −3.5 cm −3 with sizes of ∼100 pc. Similarly, Crighton et al. ( 2015 ) observe metal absorption lines arising near a z ≃ 2.5 galaxy and derive a (100–500) pc size constraint (see also Arrigoni Battaia et al. 2015 ), which they argue is a ubiquitous size-scale for all low-ion halo gas in the circumgalactic environments of both low and high redshift galaxies. Pieri et al. ( 2014 ) conclude that a typical low-ionization absorber associated with the CGM of strong Lyman α forest systems at z ∼ 2.5 is ∼30 pc in size. Furthermore, the gas giving rise to various absorption lines may also have unexpected geometries with little resemblance to spherical clouds (Churchill et al. 2015 ). Obtaining ∼100 pc hydrodynamic resolution at the virial radius by simply running higher resolution simulations of isolated haloes will be prohibitively expensive. Following the observed scaling of the gas resolution in the halo, we would obtain a mean r cell ≃ 0.1 kpc physical at z = 2 with a L14-class simulation. That is, gas mass resolution approaching 30 M ⊙ , requiring ≳ 10 10 gas cells in a 10 12 M ⊙ halo. This is not a realistic goal for the near future. Even given such a simulation, the minimum size of resolved clouds would be a few times larger than the size of individual resolution elements. On the other hand, numerical methods may enable novel ways to focus resolution within the halo regime. If we can avoid clustering the computational effort in the densest regions of space – within galaxies themselves – much higher spatial resolutions can be achieved in lower density media. In adaptive mesh refinement simulations, the refinement criterion can be chosen as desired, on the gradient of the neutral hydrogen fraction for instance (Rosdahl Blaizot 2012 ), or with a progressively refined uniform Eulerian grid which is volume filling (Miniati 2014 ). In our moving mesh simulations, we can enforce a target gas cell mass criterion of any type through adaptive refinement and de-refinement of Voronoi cells. In future work we will explore this possibility as a solution to the resolution issue, in addition to quantifying the rates and modes of accretion using the tracer particle information. 7 CONCLUSIONS In this first paper of the series, we present a suite of high-resolution cosmological hydrodynamic zoom-in simulations targeted at understanding the properties of halo gas and cosmological accretion in ∼10 12 M ⊙ haloes at z = 2. Using the moving-mesh code arepo
Paper: 2018MNRAS.475.3654Z (fulltext)
distances. We discuss the implications of this exception in Section 4.3.4 . Inserting equation ( 16 ) into equation ( 15 ) we use equations ( 3 ) and ( 9 ) to find the ram pressure for the Isothermal Sphere, and the NFW model, where . In the above expressions, f c is the cluster gas fraction, M v,clust and R c are the virial mass and radius of the cluster, respectively, and ε is a fudge factor, added to incorporate any uncertainties in the assumptions we made in our model, e.g. motion with velocities different than the virial velocity, and which also accounts for C ram . 4.3.2 Gravitational binding We wish to compare the ram pressure to the binding gravitational force per unit area acting on the satellite gas halo and determine the amount of mass stripped from the satellite. While expressing the ram pressure is relatively simple, as shown above, deter-mining the correct form of the gravitational binding force for the appropriate mass elements affected is not so straightforward. Ideally, one wishes to construct a simple spherical approximation for the binding force at a radius l in the satellite which will lead to a definition of a stripping radius l s . One option is to find the gravitational force which binds a spherical shell of width at a given radius l, divided by the cross-section of that shell where M (l) is the total mass profile and ρ(l) is the gas density profile of the satellite. This approximation suggests that the ram pressure ‘peels’ off spherical shells, like an onion, which is incongruous with the anisotropic nature of RPS. However, if the satellite halo is rotating with respect to its direction of motion, such that all parts of the outer shell experience the ram pressure, this approach may be justified. Another option is to examine a cylindrical shell of radius l and width (), oriented in the direction of motion of the satellite, and thus the direction of the ram-pressure force. McCarthy et al. ( 2008 ) employed this method by finding the maximal value of the restoring gravitational acceleration in the direction of the motion (and, by definition, the cylinder axis) as well as an upper limit of the projected surface density, resulting in an overestimation of the binding force which ensures that one finds an upper limit to the stripping radius. For a power-law spherically symmetric density profile ρ ∝ r −α , the maximal restoring acceleration is 2 and the surface density is The upper limit of the integral is formally but can be taken to L → ∞ when the integral converges (or to some set scale otherwise) to achieve an upper limit for the surface density. The gravitational binding force is then where ν is a factor of order unity For an Isothermal Sphere profile, we find ν = π/2. Yet another option is to assume that the gas halo is in hydrostatic equilibrium within the gravitational potential of the dark matter halo of the satellite, and use the gas pressure as a proxy for the binding force per unit area. For a power-law density
Paper: 2017ApJ...839..130K (acknowledgments)
A.K.H.K. is supported by the Ministry of Science and Technology of the Republic of China (Taiwan) through grants 105-2112-M-007-033-MY2, 105-2119-M-007-028-MY3, and 106-2918-I-007-005. C.Y.H. is supported by the National Research Foundation of Korea through grants 2014R1A1A2058590 and 2016R1A5A1013277. J.T. is supported by NSFC grants of Chinese Government under 11573010 and U1631103. Facilities: NuSTAR , Swift .
Paper: 2016A&A...585A..30C (acknowledgments)
We acknowledge the anonymous referee for their comments. The ATLASGAL project is a collaboration between the Max- Planck -Gesellschaft, the European Southern Observatory (ESO) and the Universidad de Chile. C.E.C. acknowledges the kind hospitality of M. Rubio and her family during her stay in Chile, and C. Kobulnicky for having suggested studying this object. L.C. acknowledges support from DIULS. V.F. acknowledges support from ESO-Chile Joint Committee and DIULS and would like to thank Ivan Valtchanov, Bruno Altieri, and Luca Conversi for their support and valuable assistance in Herschel data processing. V.F. also acknowledges support from the Faculty of the European Space Astronomy Centre (ESAC). This project was partially financed by CONICET of Argentina under project PIP 0356, UNLP under project 11/G120, and CONICyT of Chile through FONDECYT grant No. 1140839. This work is based [in part] on observations made with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. The MSX mission was sponsored by the Ballistic Missile Defense Organization (BMDO).
Paper: 2019MNRAS.489.4926G (fulltext)
feedback on the cold neutral gas. For some sources we could only search for intervening H i absorption (e.g. gas intervening DLAs along the line of sight), which is an alternate cause for obscuration of the background radio quasar. Lastly, for some sources observed we had no prior redshift information. We report three H i absorption detections in our sample, one towards PKS 1829−718 that was previously unknown, another towards PKS 1740−517 (Allison et al. 2015 ), and the third a re-detection of one towards PKS 0500+019 (Carilli et al. 1998 ). All three detections are made towards sources selected by optical faintness, and we see no H i absorption towards sources selected by their WISE mid-infrared colours. However, the lack of detections in the WISE selected sub-sample is not yet statistically significant, and further analysis during the full FLASH project is required. Through optical spectroscopic follow-up and information within the literature for PKS 1829−718, PKS 1740−517, and PKS 0500+019, we find that the detected H i absorption is associated with the host galaxy of the AGN in each case. These three AGN are obscured by material in the host galaxy, e.g. a dusty torus formed following a galaxy merger event, or perhaps in a larger disc on the scale of ∼100 pc. We do not detect any H i absorption towards our higher redshift targets ( z > 1). This is not necessarily unexpected given the predicted number of DLAs along the line of sight for our redshift range of 0.4 z 1. One factor which negatively contributed to this include the occasional correlator block error resulting in corrupted segments of the spectrum. Another is that some targets had limited integration time resulting in higher optical depth limits, and corresponding higher H i column density upper limits (Table 3 ). This lowered our sensitivity to detecting H i gas in absorption. All detections are made towards GPS sources, and we find a 33 per cent H i associated absorption detection rate towards the GPS sources in our sample. These GPS sources are compact in linear extent and believed to be young or re-triggered in their AGN activity. We also find shallow and asymmetric H i absorption features in our survey, as seen in Carilli et al. ( 1998 ) and Ishwara-Chandra et al. ( 2003 ) towards red quasars, and low redshift studies towards young radio AGN. The full FLASH project, alongside other future all-sky H i absorption surveys (MALS and SHARP), will better increase our H i statistics. This in turn will improve our understanding of the impact of the radio AGN on its cold gas, and its evolution with redshift, during this crucial transitional stage in galaxy evolution. Intervening H i absorption studies alongside the other ASKAP survey science projects will increase our statistics of the obscured radio AGN population missed in optically selected samples, and understanding of the distribution of cold neutral gas with redshift for such sources. SUPPORTING INFORMATION Figure 9 . Averaged
Paper: 2017ApJ...838..112H (acknowledgments)
We are very grateful to an anonymous referee for a careful reading and constructive comments that helped us improve the clarity of the paper. We thank B-G Andersson for sending us a reference on graphene magnetism by hydrogen adsorption. We thank Bruce Draine, Alex Lazarian, Peter Martin, and Charles Telesco for insightful discussions at the initial stage of this work. Numerical calculations were conducted using a high-performance computing cluster at the Korea Astronomy and Space Science Institute (KASI).
Paper: 2019ApJ...873..111I (acknowledgments)
This material is based on work supported in part by the National Science Foundation through Cooperative Agreement 1258333 managed by the Association of Universities for Research in Astronomy (AURA), and the Department of Energy under Contract No. DE-AC02-76SF00515 with the SLAC National Accelerator Laboratory. Additional LSST funding comes from private donations, grants to universities, and in-kind support from LSSTC Institutional Members. This research has made use of NASA’s Astrophysics Data System Bibliographic Services. Facility: LSST.
Paper: 2017A&A...601A..79S (fulltext)
large sin . To check this we included provisional results for stars with initial masses higher than 15 that have evolved into O and B giants ( Ramírez-Agudelo 2017 ; Schneider et al., in prep.). A Kuiper test of the updated distributions for groups 2 and 3 found that the probability to be drawn from the same parent distribution is still 10%. This preliminary test indicates that this possible bias can probably not explain the lack of fast rotators below log g 3.9, although we remark that the parameters for some stars still have to be confirmed, and we will revisit this in a future study. 6.1.4. Helium abundances Helium abundances for our sample were also indicated in Fig. 11 . Most of our stars have Y(He) 0.12, i.e. normal abundances within the estimated uncertainties. There were 14 stars for which we could not obtain good determinations of Y(He), and where only upper limits of 0.08 could be estimated (with the best-fitting models being 0.06 or less). As explained in Paper XIII, these low abundances could be a consequence of undetected binarity, the effects of low signal-to-noise ratio, and/or nebular contamination in the spectra. We found five stars with enhanced helium abundances (Y(He) 0.12) and log g 3.9. Two, VFTS 724 and VFTS 285, are rapid rotators with sin 370 and 600 , respectively. The rapid rotation of these two stars could explain their high helium abundances (see e.g. Maeder 1987 ; Langer 1992 ; Denissenkov 1994 ; Herrero et al. 1999 ). The other three stars (VFTS 089, 123, and 761) have intermediate and low rotation rates. Rapid rotation might still explain the He excess of these stars given that their true rotational velocities might be higher because of projection effects. Equally, their He enhancements might indicate past mass transfer from an undetected binary (e.g. Hunter et al. 2008 ), and they could be magnetic stars (see e.g. Schneider et al. 2016 ). We note that stellar winds are unlikely to be the cause for the He enrichment, as such strong winds should generate a larger surface enhancement, with wind features also seen in the spectra. 6.2. Mass-discrepancy problem As noted earlier, evolutionary masses () are typically estimated by placing a star in the H–R diagram (using results from spectral analysis) and interpolating between evolutionary tracks. Ideally, should agree with the spectroscopic mass (), calculated from , log g , and from the spectral analysis, but this is not always the case and is known as the “mass-discrepancy problem”. This discrepancy has been a challenge in the determination of masses in O-type stars since it was identified by Herrero et al. (1992) . Possible explanations have included uncertain distance estimates to Galactic stars, problems with stellar atmosphere models (e.g. underestimated gravities or the treatment of mass-loss), and problems in the evolutionary models (treatment of overshooting, rotation, and/or binary evolution). However, given a diversity of results across a range of
Paper: 2020ApJ...892L..21Z (fulltext)
panel) shows these “anomalous radial velocities” based on all three transit observations. Before making this plot, long-term trends in the PFS radial velocities were removed by subtracting the best-fitting quadratic function of time to the out-of-transit data. This trend need not be astrophysical; because the observations were obtained without the use of an iodine gas absorption cell, the radial velocities are susceptible to intra-night drift of the instrumental profile and wavelength solution. We computed a model for the anomalous radial velocity based on the system parameters in Table 1 , using code provided by Boué et al. ( 2013 ). The model agrees well with the data, even without any further tuning of the model parameters. With this way of displaying and analyzing the data, the effects of spots are not as obvious. This is because the spots migrate smoothly across a significant part of the stellar surface over the timescale of the transit. We note, though, that not accounting for the influence of spots can cause a systematic bias in the resulting derived obliquities from the Rossiter–McLaughlin effect. If one part of the star is dimmer due to the presence of a group of spots, then the overall weighted velocity variation induced by a planet will be affected by the specific part of the star the planet transits through. This bias is irrespective of any possible spot-crossing events. Systematic uncertainties arising from such issues will need to be accounted in the error budget in Rossiter–McLaughlin observations (e.g., Oshagh et al. 2018 ). A light curve motivated spot-modeling exercise, in conjunction with an Rossiter–McLaughlin observation, can encompass such systematic uncertainties (Montet et al. 2020 ). 4. Discussion The finding of a well-aligned star and planetary orbit would not have been surprising a decade ago. Since then, though, we have learned that stellar obliquities are sometimes very large (e.g., Winn et al. 2010a ; Albrecht et al. 2012 ), and no longer take spin–orbit alignment for granted. High-obliquity orbits have been interpreted as evidence for dynamical processes that tilt the orbit of a giant planet after its formation within the gaseous protoplanetary disk. The large majority of previous obliquity measurements have been made for close-in giant planets around mature-age stars (Figure 4 ). Figure 4. Age-obliquity distribution for planets with obliquity measurements. DS Tuc Ab is the youngest system with its orbital obliquity measured by nearly an order of magnitude. Other systems with ages are labeled. The vast majority of planetary systems have ages estimated from isochrone modeling. Note that Kepler-63 (labeled) has an age estimated from gyro-chronology and spectroscopic activity indicators (Sanchis-Ojeda et al. 2013 ). Kozai and secular dynamical interactions can form a portion of the close-in Neptune and Jovian population. Their representative timescales and resulting orbital obliquities are noted in the figure. DS
Paper: 2017MNRAS.465..834Z (acknowledgments)
This paper is based on Atacama Large Millimeter/submillimiter Array (ALMA) observations, program number 2013.1.00710.S. It makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00710.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan) and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. AZ and LAC acknowledge support from the Millennium Science Initiative (Chilean Ministry of Economy) through grant ‘Nucleus RC130007’. LAC also acknowledges support from FONDECYT-CONICYT grant #1140109. HC acknowledges support from the Spanish Ministerio de Economa y Competitividad under grant AYA2014-55840P. SP acknowledges financial support by FONDECYT grant #3140601. DAP acknowledges FONDECYT grant #3150550.
Paper: 2020MNRAS.491.3805J (acknowledgments)
ACKNOWLEDGEMENTS The authors are grateful for the valuable machine learning discussion with S. Wiechecki Vergara. Inspiration provided by C. Role. Research was partially supported by Perspecta, Inc. This material is based upon work supported by the NASA Florida Space Grant under 2018 Dissertation And Thesis Improvement Fellowship (No. 202379). The LINEAR program is sponsored by the National Aeronautics and Space Administration (NRA Nos. NNH09ZDA001N, 09-NEOO09-0010) and the United States Air Force under Air Force Contract FA8721-05-C-0002. This material is based upon work supported by the National Science Foundation under Grant No. CNS 09-23050.
Paper: 2016A&A...585A..24M (fulltext)
with elastic collision with the surface, reproduces the observations well. Although it is an empirical approach, it allows sorting among the products that should undergo the chemical desorption. The basic idea underlying our proposition is inspired by dynamic calculations in specific systems such as H+H on graphite ( Morisset et al. 2004 ; Bachellerie et al. 2007 ) or O+H on graphite ( Bergeron et al. 2008 ). We have illustrated the mechanism of this scenario in Fig. 4 . The first step consists of a collinear approach parallel to the surface. When the molecule is formed, the trajectories of both atoms adopt a more disordered motion symptomatic of the high internal energy in vibration and rotation of the newly formed molecules, up to a point where the molecule interacts with the surface and that a part of the motion is transferred perpendicularly to the surface that causes the desorption. Fig. 4 Illustration of the chemical desorption process. After an initial meeting (1), the molecule is highly excited; (2) and converts a fraction of its energy into vertical motion through interaction with the surface (3). We point out that the chemical desorption process seems to favor small molecules with low binding energies. Therefore, this process may be part of the reason COMS are present in dark clouds. We note that our prediction of about one percent of chemical desorption for methanol would be sufficient to sustain the observed value, as modeled by Vasyunin Herbst (2013) . The second part of our paper will examine the effect of the deriving our formulae in an astrophysical context. That the chemical desorption efficiency on water ice substrates is reduced is somehow to be expected. A similar behavior has been observed in photodesorption of CO. Pure CO ices exhibit an efficient wavelength-dependent photodesorption of up to one percent per incident photon ( Bertin et al. 2013 ) or possibly lower by a factor 3–11 ( van Hemert et al. 2015 ). Some authors have estimated the CO photodesorption on water substrate to be about 10 ( Öberg et al. 2009 ) or one order of magnitude lower ( DeSimone et al. 2013 ). However, even if the CO photodesorption process is poorly constrained, the water substrate clearly prevents or dramatically reduces its efficiency. This probably is due to the very fast transportation of energy in the water network and to the reduction of the indirect photodesorption. For chemical desorption on water substrate, the weakness of our coarse model probably lies in the assumption of elastic collisions, which are not adapted to the case of water. Many more experimental investigations and detailed computations would help to solve this problem. Finally, we note that even if water ice substrate seems to be a good trapping environment and can dramatically reduce the chemical desorption efficiency, CO layers in the central part of dark clouds are expected to cover an inner water layer around the solid seeds of dust. Therefore, another set of
Paper: 2016A&A...589A..35S (acknowledgments)
We thank the referee, J. Braine, for his comments and suggestions that improved the readability, clarity, and correctness of this paper. C.S. wants to thank F. Galliano for his input on the dust grain composition and for making his dust model available. Most of the numerical analysis conducted in this work have been performed using phy++, a free and open source C++ library for fast and robust numerical astrophysics (cschreib.github.io/phypp/). This work is based on observations taken by the CANDELS Multi-Cycle Treasury Program with the NASA/ESA HST, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. This research was supported by the French Agence Nationale de la Recherche (ANR) project ANR-09-BLAN-0224 and by the European Commission through the FP7 SPACE project ASTRODEEP (Ref. No: 312725). LC benefited from the thales project 383549 that is jointly funded by the European Union and the Greek Government in the framework of the program “Education and lifelong learning”. T.W. acknowledges support for this work from the National Natural Science Foundation of China under grants No. 11303014.