adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...641A.151S (fulltext)
of the PA and inclination angle. Since the residual clumps proved to be not sensitive to the choices of the assumptions, we preferred the best fit model with PA major = 330° and i = 30°. The results from the kinematical analysis are presented in Fig. 6 , where the comparison between the model and the data in terms of intensity, velocity, and velocity dispersion maps (zeroth, first, and second moments, respectively) is shown. The model and data (smoothed by a factor of (2) are in excellent agreement. The kinematics of the molecular gas, traced by the CO(2–1) emission line, are clearly dominated by purely circular rotational motions around the nucleus. This is the first successful attempt to model the kinematics of the molecular gas in NGC 7213, thanks to the software 3D BAROLO, which is able to handle the available data, despite the sparse information that limited previous attempts to model the kinematics (e.g. Ramakrishnan et al. 2019 ). As is clearly visible in Fig. 7 , we found that the data cube central velocity was offset by an additional δV sys = 36 ± 10 km s −1 . This means that the systemic velocity of the source with respect to our rest frame has to be V sys = 1716 ± 10 km s −1 , which is a mean value between the systemic velocity obtained from the study of the stellar kinematics by Schnorr-Müller et al. (2014) , and the results by Ramakrishnan et al. (2019) . Fig. 7. PV diagrams on the major ( top panel ) and minor ( bottom panel ) axis, respectively. On the x -axis, the angular distance from the centre of the source along the direction fixed by the angle φ is reported. On the right y -axis, the velocity along the line of sight (in km s −1 ) is shown as it appears in the input data cube. On the left, the velocity along the line of sight (in km s −1 ) is shown, once the systemic velocity ( v sys = 36 ± 10 km s −1 ) has been subtracted. The data are represented in grey scales with blue contours, while the best fit model is identified by red contours. Upper panel : the blue circles represent the projected best fit rotational velocity at different radial distances from the centre, associated with each ring of the disc. Bottom panel : the signature of the potential outflow associated with Region C (bordered in green; see Sect. 4.3 ) is clearly visible as an excess of CO emission over 100 km s −1 with an offset of ∼1′′ from the centre of the rotation. Looking at the lower panels, the velocity dispersion in the best-fit model is ∼10−15 km s −1 : values that are expected from a rotation-dominated disc. The highest values of the velocity dispersion ( v disp ∼ 70 km s −1 ) are not reproduced by the best fit model. The high-dispersion residual clumps proved to be insensitive to our assumptions (e.g. PA, i , v rad ), therefore we suggest that the residuals are associated with non-rotational motion, as we discuss in Sect. 4.3 . 4.3. Complex structures in the CO emission The results of the modelling of the gas kinematics presented in Sect. 4.2
Paper: 2021A&A...651A..69K (fulltext)
a clear separation can be made between most stars and extragalactic objects, the separation between galaxies and quasars is less abrupt, reflecting the range of central-engine-dominated and host-dominated emission in these objects. As simple colour-magnitude diagnostics show, our classification generalises simple colour-magnitude cuts that have been proposed in the literature ( Stern et al. 2005 , 2012 ; Assef et al. 2013 ; Chehade et al. 2018 ), but it also deals with the overlap of different classes, especially towards the faint end in WISE magnitudes and towards higher-redshift quasars. The three VEXAS optical+IR classified tables, with object IDs, coordinates, optical and infrared magnitudes, including the imputed ones (which are flagged), and the probability to belong to each class, are publicly released and available for the scientific community as part of the VEXAS Data Collection (DR2) via the ESO Phase 3 13 . The machine-learning classification pipeline and the code are available on the VEXAS Github repository 14 . The density of extragalactic objects varies with the survey depth, quite expectedly. Considering only high-confidence objects for which the probability of belonging to the class is p class ≥ 0.7, our classifiers yielded 111 qso deg −2 for the VEXAS-DESW footprint (≈4900 deg 2 ) and 103 qso deg −2 for the VEXAS-PSW footprint (≈3800 deg 2 ). These numbers roughly meet the requirements of the 4MOST selection for baryon acoustic oscillations measurements. The density drops to ≈10 qso deg −2 for the VEXAS-SMW footprint (≈9300 deg 2 ). All in all, the combined survey depth is the limiting factor. The VEXAS-SMW footprint is limited by the shallow optical coverage of SkyMapper, which also has less uniform coverage than DES and PanSTARRS. A solution would be to consider only NIR and mid-IR magnitudes in the classification, which in turn requires more uniform coverage in the NIR and smaller uncertainties in the WISE magnitudes. This may be obtained thanks to unWISE, a re-processing of WISE, and NeoWISE imaging ( Lang 2014 ; Schlafly et al. 2019 ), or with forced photometry (on SkyMapper and unWISE cutouts) based on VISTA detections. We caution that these samples are only the very first step for cosmological measurements, which also require spectroscopic redshifts and well characterised coverage maps. The spectroscopic follow-up is already the aim of southern surveys (4MOST, de Jong et al. 2012 ; SDSS-V, Kollmeier et al. 2017 ). Since this paper is focused on the classification techniques and their application to VEXAS, the coverage maps are outside the scope of this work and will be part of a subsequent release. Appendix A: Testing the classification at the faint end of VEXAS-DESW As described in detail in Sect. 6.1 , the r -band magnitude ranges covered by the input tables are larger than those covered by the corresponding tables. This is particularly true for the VEXAS-DESW table, which reaches magnitudes as faint as r ∼ 25 m (see
Paper: 2015MNRAS.446.3669C (acknowledgments)
The authors acknowledge funding support for CMOR from NASA cooperative agreement NNX11AB76A, the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program. Visual IMO Eta Aquariid data provided by J. Rebdtel is gratefully acknowledged.
Paper: 2016ApJ...833..241S (acknowledgments)
We thank the anonymous referee for helpful comments that greatly improved the presentation of this paper. This work is supported by the 973 Program (No. 2015CB857002), national science foundation of China (grant Nos. 11128306, 11121062, 11233005, 11073017), NCET-11-0879, the Strategic Priority Research Program “The Emergence of Cosmological Structures” of the Chinese Academy of Sciences, Grant No. XDB09000000 and the Shanghai Committee of Science and Technology, China (grant No. 12ZR1452800). We also thank the support of a key laboratory grant from the Office of Science and Technology, Shanghai Municipal Government (No. 11DZ2260700). H.J.M. would like to acknowledge the support of NSF AST-1517528, and F.v.d.B. is supported by the US National Science Foundation through grant AST 1516962. A computing facility award on the PI cluster at Shanghai Jiao Tong University is acknowledged. This work is also supported by the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Shanghai Astronomical Observatory.
Paper: 2015A&A...577A.106S (acknowledgments)
I thank A. Partl, D. Streich, C. Vocks, H. Enke, and L. Strandberg for comments on the manuscript. C.S. was supported by funds of PTDESY-05A12BA1 and 05A14BA1, and the BMBF VIP program 03V0843. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Paper: 2015A&A...584A..19W (acknowledgments)
T. Rauch was supported by the German Aerospace Center (DLR) under grant 05 OR 1402. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France, and of NASA’s Astrophysics Data System Bibliographic Services. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST).
Paper: 2017ApJ...850..184C (fulltext)
factor is retained explicitly in order to track the resultant change if the ion wind does not completely fill the polar cap. The initial electron density will also be given by Equation ( 44 ). For ions, we scale , and we will use the midrange value of to make estimates. The primary beam electrons accelerate to with , curvature-radiate, and produce secondary pairs with the multiplicity ; thus, the initial Lorentz factor of the dense secondary pair plasma is of order . 6.2.2. Magnetic Field <inline-formula> <tex-math> </tex-math> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjaa984cieqn219.gif"></inline-graphic> </inline-formula> For the polar cap flux tube, in dipole (primed and starred) coordinates , , and, above the acceleration sheath, the essentially field-aligned current is . The perturbed magnetic field () can be obtained from Ampere’s law for the vector potential ,where H ( x ) is the Heaviside step function. Matching solutions inside and outside of the polar cap, we obtainThe perturbed magnetic fields within the polar cap are Since the wind equations are best formulated in the inertial coordinate system, in order to obtain the inertial perturbed azimuthal field , we must project onto the inertial -axis, , using the coordinate transformation relations To order , we obtainAveraging over the polar cap yieldsNote that in the inertial coordinate system, the dipole field does have a φ -component (), but that . 6.2.3. Polar Cap Poynting and Particle Energy Fluxes In the inertial coordinate system, the polar cap electric field , where is the electric field in the co-rotating frame. Since smaller than , we have . Integrating the outgoing Poynting flux () over the north and south polar caps yields the total radiated electromagnetic luminositywhere is the vacuum dipole radiated power. Integrating the ion energy flux over both polar caps yields the total ion luminosityThe pair plasma particle luminosity from the electron outflow half of the polar cap is much smaller than L i . Combining Equations ( 51 ) and ( 52 ) and taking , we obtain the initial global value of the ratio of electromagnetic to particle powerwhich is independent of . 6.2.4. Initial Wind Parameters We evaluate the wind-conserved energy parameter E by using the initial wind conditions to obtainFor the fiducial values of f and g , . The modified Kennel et al. ( 1983 ) conserved -parameter becomes 6.3. The Wind Z-parameter In their discussion of hot relativistic winds, Kennel et al. ( 1983 ) introduce the wind parameter that differentiates the types of wind solutions. Winds with are basically cold and centrifugally slung, corresponding to the classic pulsar wind. Winds with start with either a relativistic high temperature or high initial Lorentz factor. Clearly, for the above value of σ and , . Kennel et al. ( 1983 ) numerically show that high- winds experience very little radial acceleration with increasing distance. 6.4. The Alfvén Critical Point
Paper: 2018MNRAS.476.1756H (fulltext)
gas density was measured from either the neutral or ionized hydrogen and includes a correction factor of 1.36 in order to account for the contribution from helium. Since we are interested in a comparison with our 1D cosmic ray transport models, we neglect the error contribution from the variance of the density; thus, the error is solely determined by uncertainty of the velocity dispersion and is δ U kin = 2δσ v /σ v , equivalent to 50 per cent. We find that the kinetic energy densities are within a factor of a few in energy equipartition with the magnetic field. This is the expected result since the magnetic field in dwarf galaxies is usually assumed to be amplified by the turbulent gas motions via the ‘fluctuating dynamo’, which saturates when the energy densities become comparable (Schleicher Beck 2013 ). Interestingly in the central part, the magnetic energy density shows a peak that corresponds best to kinetic energy density of the ionized gas, whereas that of the atomic gas is practically constant. The cosmic ray energy density is important everywhere. Such a behaviour has been suggested by theoretical works of cosmic ray-driven galactic winds (e.g. Salem Bryan 2014 ), where the cosmic rays can diffuse outwards from star formation sites and thus create an overpressure in the surrounding area. A galactic wind can then be launched in the disc–halo interface, where the cosmic rays dominate the energy density and are able to transfer some of their energy and momentum to the ionized gas via the streaming instability. Figure 5. Minor axis profiles with distances from the major axis as seen on the sky. Shown are the energy densities of the magnetic field, warm neutral medium (H i ), warm ionized medium (Hα), and cosmic rays (CRs). The blue line shows the magnetic energy density for the accelerating wind model. The blue-shaded area indicates the range of magnetic field strengths for which we tested our models, where the upper limit is for energy equipartition and the lower limit is for almost freely escaping CREs (only radiation losses). Error bars show ±1σ uncertainties, which are assumed to be 50 per cent stemming solely from the uncertainty of the velocity dispersion (see text for details). While the diffusion model (Model I) fits our data best, the resulting diffusion coefficients are at least one order of magnitude lower than both estimates for the expected diffusion coefficient. On the other hand, the projected extent of CRE injection sources due to the mild inclination can account for the parabolic shape of the spectral index profile, where the spectral index remains flat out to distances of up to 0.5 kpc away from the major axis; so the abrupt steepening of the spectral index at | z | > 0.5 kpc that distinguishes diffusion from advection may be the result of geometry. Hence, we explored the possibility of dominating advection and found that constant wind speeds are only a factor of 2 below the escape velocity – the expected advection
Paper: 2017MNRAS.469.4428J (acknowledgments)
Acknowledgments CJ acknowledges a Durham COFUND Junior Research Fellowship (4958) supported by the DIFeREns2 project that has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 609412. This work was supported by the Science and Technology Facilities Council [ST/L00075X/1]. This work used the DiRAC Data Centric System at Durham University, operated by the ICC on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC Operations grant ST/K003267/1 and Durham University. DiRAC is part of the UK's National E-Infrastructure.
Paper: 2017A&A...599A..27G (fulltext)
shape of the -band excess or to place meaningful constraints on the frequency of such events, as we are insensitive to many periods. Nonetheless, this opens up the possibility that some of the variations in the CoRoT and Spitzer light curves could be due to short-lived, and potentially recurring, hot spot emission or to stellar flares. We recall that the system’s average colours do not show a significant -band excess (Paper I, Sect. 4.5), implying a low-to-negligible average accretion rate consistent with the majority of the CFHT light curve. It is important to note that the raw CFHT light curves consist of short runs of 5 consecutive observations and these have been binned using a weighted average to give each data point seen in the figure. Hence, the three points indicating a -band excess are derived from 15 independent observations conducted in photometric conditions. 3.2. Spot model Young, low-mass stars tend to be heavily spotted (e.g. Donati et al. 2000 ) and can display photometric modulations not unlike the LAV-dominated parts of the CoRoT light curves. However, changes in such modulations from starspot evolution tend to occur smoothly on timescales of many days to weeks (e.g. Roettenbacher et al. 2013 ), rather than the sudden morphological changes we see at rHJD 4544 in the 2008 CoRoT light curve (when the LAVs appear) and at rHJDs 5910 and 5930 in the 2011/2012 CoRoT light curve (when the LAVs appear and disappear, respectively). Given that there are two stars in this system, both of which are likely spotted, an appealing solution could be constructive and destructive interference of starspot signals where different active regions have slightly different periods. This could arise due to either different stellar rotation periods (the system is young and not necessarily synchronised) or, even if the stars are synchronised, different latitudes (differential rotation) 4 . We therefore sought to understand how much of the large scale structure in the CoRoT and Spitzer OOE light curves could be attributed to the constructive and destructive interference of starspot signals. We set up a simple two-spot model following the formalism of Dorren (1987) . Due to the well known degeneracy of spot models we allow only two starspots and opt for a single spot on each star rather than two spots on one star 5 . Before modelling the data we need to: a) determine the flux contributions of each star in each band; b) determine the flux ratios between the spotted and unspotted stellar photospheres and c) mask variations that are clearly not due to spot modulation: a) the observed flux in each band is the sum of the stellar and dustcomponents. Our SED modelling indicated that the dustcontributes negligible flux in the CoRoT band but makes up asignificant fraction at 3.6 and 4.5 m. The stellar flux fractions are 0.62, 0.41 and 0.34 for the primary star in the CoRoT, Spitzer 3.6 and 4.5 m bands respectively, and 0.38, 0.25 and 0.21 in turn for the secondary.
Paper: 2017A&A...602A..29B (fulltext)
of alignment of the outer disc with the equatorial plane of the BH. Caproni et al. (2004) and Liu Melia (2002) show that the misalignment between the rotation axis of the accretion disk and of the Kerr BH can explain jet precession in AGN as well. The authors successfully apply this scenario to quasars, Seyfert galaxies, and Sgr A*. However, Nixon King (2013) show that it is unlikely for any type of BH system to explain jet precession due to the result from a combination of the Lense-Thirring effect and accretion-disc viscosity. In general, the angular momentum of the disc is not sufficiently large, in comparison with that of a spinning hole, to cause jet precession on short timescales. AGN accretion events are assumed to be uncorrelated and this would cause changes in the jet directions on comparably large timescales of yr. Chaotic accretion is discussed in more detail by King Nixon (2015) – the effect of self-gravity limits the mass of any accretion-disc feeding event. They suggest that additional disc physics is needed to explain any jet precession on timescales short compared with the accretion time. Lubow et al. (2002) investigate the shape of a warped disc around a Kerr BH. In particular, they investigate this system under conditions such that the warp propagates in a wave-like manner. One of their results is, that in low-viscosity discs around a Kerr BH, the inner parts of the disc are not necessarily aligned with the BH. Because the shape of the inner disc depends sensitively on the radial dependence of the disc properties, a changing accretion rate can cause a change in the inner disc warp even without effecting the tilt of the outer disc. This finding contrasts with that of, for example, Nelson Papaloizou (2000) . Lai (2003) discusses the warping of an accretion disc due to magnetically driven outflows as a possible origin for jet precession. In particular, the disk might be subject to warping instabilities and retrograde precession driven by magnetic torques associated with the outflow. The growth timescale for the disk warp and the precession period would be of order the radial infall time of the disk. Caproni et al. (2006) study four mechanisms (tidal, irradiative, magnetic, and Bardeen-Petterson) that can cause warping and precession in galactic and extragalactic accretion disks. Eight X-ray binaries and four AGN are examined. The paper demonstrates how difficult it is to constrain the precession mechanism. The Bardeen-Petterson effect can produce the precession timescales compatible with the observed ones in all sources of their sample. However, the work by Caproni et al. (2006) shows that due to the many observational uncertainties other precession mechanisms can not easily be ruled out. In addition, more than one mechanism might be at work in a given source. Another alternative explanation of the observed effects could result from a magnetically-arrested disc (e.g., McKinney et al. 2012 ; Tchekhovskoy et al. 2014 ). Using
Paper: 2019ApJ...887...29R (fulltext)
the albedo of ice on M-dwarf hosted Earth-like planets () and the ice-albedo feedback becomes a negative, or stabilizing, feedback. We tested particular land distribution scenarios (i.e., configurations in which a nonuniform (by latitude) land fraction is used as input), including present-day Earth, and equatorial ocean and land belt configurations, that revealed some variation in surface temperature (∼5 K) and albedo (∼0.02), depending on the distribution of land in the model (see labeled markers in Figure 2 ). In particular, ocean-dominated planets and land planets with oceanic equatorial belts (with otherwise identical orbital parameters) have higher global mean surface temperatures when compared to a uniform land fraction distribution by latitude. Distinct zones of high and low albedo surfaces are shown in the simulations pertaining to planets orbiting F-, G-, and K-dwarfs (Figure 3 ), which represent frozen (yellow) and unfrozen surfaces, respectively. Note that these frozen surfaces exhibit different Bond albedos depending on SED, despite being comprised of the same 50% snow/ice mixture. Transitional boundaries between these zones are also evident, representing the maximum equator-ward extent of glacial conditions, i.e., a nominal “ice line” in latitude and F L parameter space, from which we can approximate the minimum land fraction required to force mid-latitude glaciations, assuming default model conditions: 0.46, 0.55, and 0.68 F L for planets around F-, G-, K-dwarfs, respectively. Figure 2. Mean global surface temperature (; left) and Bond albedo (; right) by average planetary land fraction ( F L ) for terrestrial planets orbiting FGK- and M-dwarf stars. The dashed lines labeled denote the albedos of the 50% ice-snow mixture on planets orbiting X -type star; the gray shaded area depicts a range of F L over which exceeds the blue shaded region illustrates temperatures below 271 K. Markers indicate some individual land fraction and latitudinal distribution configurations tested in the model: present-day “Earth” (E), a scenario in which a narrow band of equatorial ocean exists on an otherwise land-covered planet (Eq. O), an “oceanplanet” (O) configuration with some land at the poles, an equatorial land belt on an otherwise oceanic planet (Eq. L.), and a “landplanet” (L) distribution with some open ocean at the poles. See text for discussion. Figure 3. Contours of albedo (dimensionless) as a function of land fractional coverage ( F L ) and latitude ( φ ) for a planet orbiting an M-dwarf (top left), K-dwarf (top right), G-dwarf (bottom left), and F-dwarf (bottom right) stars. M-dwarf planets do not exhibit mid-latitude glaciations in any of these simulations, although albedos for high land fraction planets in orbit around these stars are proportionally higher in the mid-latitudes and equatorial regions than at the frozen polar regions. A general trend exists in that planetary broadband albedo increases with increasing F L regardless of