adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...847L...3O (fulltext)
of C/O is roughly uniform, unlike for unconstrained chemistry where it is double-peaked (Line et al. 2013 ). In chemical equilibrium, specifying the elemental abundances allows all of the molecular abundances to be computed, with no parametric freedom, given a temperature and pressure. We are agnostic about the terms “cloud” and “haze” and use them interchangeably for this study. 7 7 These terms are either used to distinguish between size (Earth science convention) or formation origin (planetary science convention), and there is no consensus within the exoplanet community on their usage. We implement the simplified cloud model introduced by Lee et al. ( 2013 ) and used by Lavie et al. ( 2017 ), which describes a monodisperse population of spherical cloud particles with radii , cloud volume mixing ratio and a single composition (represented by the parameter Q 0 ). Refractory and volatile cloud species have and ∼10, respectively. This cloud model accommodates both small and large particles, and correctly reproduces the limits of Rayleigh and gray scattering. It is based on the notion that curves of the extinction coefficient have a roughly universal shape (Pierrehumbert 2010 ). 3. Results We begin by presenting a pair of retrieval models that make the common assumption of log-uniform priors (Figure 3 ). For the measured emission spectrum of WASP-12b, we use the published data of Stevenson et al. ( 2014 ) as stated in their Table 3. For the model with unconstrained chemistry, we set log-uniform priors on the six mixing ratios. For the model with equilibrium chemistry, we set log-uniform priors on the elemental abundances of carbon, oxygen, and nitrogen. As an improvement over the work of Stevenson et al. ( 2014 ), we include clouds in our analysis as part of the retrieval (i.e., the cloud parameters are not fixed to preset values). The other parameters also have log-uniform priors, except for Q 0 , which has a (linearly) uniform prior. Figure 3. Posterior distributions of fitting parameters for cloudy retrieval models with unconstrained chemistry (top panel) and equilibrium chemistry (bottom panel) with log-uniform priors. has physical units of m 2 kg −1 , while is given in m. The rest of the parameters are dimensionless. In Figure 3 , the first thing to notice is that the cloud parameters display degeneracies that match our physical intuition: the mixing ratios are degenerate with cloud composition, particle radius, and number density. In particular, it is possible to set bounds on the cloud particle radius, but the cloud composition is essentially—and unsurprisingly—unconstrained. When we include only CO, CO 2 , CH 4 , and H 2 O in the retrieval, we reproduce the result of Line et al. ( 2014 ) and Stevenson et al. ( 2014 ) that unrealistically high abundances for CO 2 are obtained (not shown). (Heng Lyons 2016 have previously elucidated this implausibility using validated analytical formulae.) Such high abundances of CO 2 drive the
Paper: 2020MNRAS.499....7S (fulltext)
the eccentric disc at the centre of M31. In order to test whether the finding that σ z /σ R > 1 depends on our assumption that the velocity ellipsoid is aligned on cylindrical coordinates, we have repeated our analysis assuming that the velocity ellipsoid is aligned on spherical coordinates (see section 2.4 of Cappellari 2020 ). We found that models with σ θ /σ r > 1 are clearly favoured, which on the plane z = 0 corresponds to σ z /σ R > 1. Thus, the alignment of the velocity ellipsoid does not affect the results discussed here. There are two questions in relation to our finding that σ z /σ R > 1. The first is why are such values favoured by our models? Comparison of Fig. 9 with Fig. 13 shows that the reason is that a small value of b (i.e. a large σ z /σ R ) is needed to reproduce the drop in the observed μ obs near the centre (| l | ≲ ), which is present both in the APOGEE data and the SiO maser data (see middle row in Fig. 9 ). In Fig. 9 , which shows model 3 with b = 0.45, the drop is well reproduced, while in Fig. 13 , which shows the same model but for b = 1, the drop is not well reproduced. The ‘NSD only’ models favour a slightly larger value of b compared to the ‘NSD+NSC’ models because the absence of the NSC component in the middle lowers the velocity dispersion in the central regions compared to the outer parts. The ring models also favour a larger value of b (consistent with b ∼ 1, see Fig. A2 ) because the density is essentially zero for , and therefore those regions do not contribute to the integrals in equations ( 11 ) and ( 12 ). Assuming that the drop in the data is real (which ought to be confirmed with better data), this suggests that indeed b 1, or that our assumed tracer density population is not representative of the population from which the kinematics are drawn. We note that the observed kinematics directly constrain only the v R and v φ components of velocity: our constraints on the v z component come from the integral ( 11 ) of ρ and Φ along z . If the potential or density were much flatter than we have assumed, then the σ z given by ( 11 ) would increase and we could fit the observed kinematics with larger values of b . It is currently unclear whether such a strong flattening would be detectable given the extreme and strong differential extinction. Figure 13. Same as Fig. 9 , but for a model that is identical to model 3 except that b = 1. Note that the drop in second moment velocities at | l | ≲ is reproduced less well than in the model shown in Fig. 9 . Assuming that σ z /σ R > 1 then the second question is how would stars get such large vertical oscillations? In the solar neighbourhood stars are formed from gas clouds that move on almost closed orbits, beginning their lives with random velocities of the order of a few . There are a number of dynamical processes that inevitably cause these random velocities to increase over time (see Sellwood 2014 for a recent review). Each of these heating mechanisms has a different
Paper: 2016A&A...592A.103W (fulltext)
PN G233.010.1: ring-like PN. More images can be found in Saurer Weinberger (1987) . PN G236.010.6: object poorly studied, morphology type one-side (see Ali et al. 2000 ) with radial filaments. More images can be found in Hartl Weinberger (1987) . PN G236.701.6: our image shows two areas of lower brightness that are not visible in the H image of the MASH catalogue. PN G239.612.0: round with internal structure and intense patches. More images can be found in Fredrick West (1984) . PN G239.613.9: Balick (1989) classified this PN as elliptical (early-middle subtype). Additional images can be found in Schwarz et al. (1992) . PN G243.837.1: Boffin et al. (2012a) performed a morphological analysis of this PN. PN G247.504.7: there is an HII region towards the SW, and it is unclear whether it is part of the PN or is the optical counterpart of the molecular cloud BRAN 63. PN G249.305.4: ring-like PN with a very faint CSPN. It shows several irregularities in the brightness of the ring. PN G249.807.1: this seems to be two overlapping PN. It is difficult to explain the homogeneous and extensive overdensity of material towards the NW. PN G250.401.3: classical bipolar PN with an arc towards the NW. It is also visible in the [N ii ] images of Kerber et al. (2000) . PN G257.500.6: poorly studied planetary nebula with intriguing morphology. The PN displays a ring-like structure of low surface brightness towards the NE and a emission region towards the SSE that is difficult to explain. It probably is a bipolar PN. This object requires a more detailed study. PN G258.015.7: Gonçalves et al. (2002) show an [N ii ] image, but it is not as deep as ours. In both cases the huge jet-like features are clearly visible. Additional images can be found in Corradi et al. (1996 , 1999) . PN G259.100.9: it is difficult to perform a morphological classification of this Type I PN. A complete analysis of this object was made by Jones et al. (2014) . Our [N ii ] image does not show any differences with the [N ii ]+H published in that article. A H image is presented by Górny et al. (1999) . PN G260.703.3: the H image of MASH catalogue displays a more extended HII region than our image. According to its morphology, it would not be a PN. It could be a filament of the supernova remnant (SNR) Puppis A ( Goudis Meaburn 1978 ). PN G261.603.0: Corradi Schwarz (1995) classify this Type I PN as a possible/probable bipolar. Our [N ii ] image shows a well-defined bipolar morphology with a pronounced waist, thus we confirm this classification. It is not evident from our image if the two saturated regions define the torus of the waist. If this were the case, the waist would not be perpendicular to the bipolar lobes. To answer this question, it is necessary to perform a detailed spectroscopic study. PN G261.908.5: this Type I PN has been classified by Peimbert Torres-Peimbert (1983) as a filamentary bipolar. More images can be found in Vázquez (2012) , Bohigas (2003) and Dufour (1984) . PN
Paper: 2020MNRAS.493.2694S (fulltext)
best fit for Model 1 (χ 2 /ν = = 1.02) and Model 2 (χ 2 /ν = = 1.02) yield radii of 2.1 and 5.2 R, respectively. We have also performed another fit letting relxilllp represent both incident and reflected spectra alone (Model 3: diskbb+relxilllp ), in such a way that we can leave the reflection fraction free. The best fit (χ 2 /ν = = 1.02), which sets the inner radius at 1.7 R, provides a reflection fraction of R = 0.26. The power-law index – now unique for both incident and reflected spectrum – is Γ = 1.78. Other parameters, which are common to Models 1 and 2, have not changed much from the values quoted previously. In regard of spin and inclination, Fig. 6 presents the MCMC output distribution for these two parameters for the cases where R in was left free in Models 1, 2, and 3. For all three models, the spin behaviour differs very little from that of relconv(reflionx) , displayed in the bottom panel of Fig. 5 : it reaches the hard limit but is only poorly suggestive it might be ≳ 0.5 (1 σ); any value is still possible within 3 σ. As for the inclinations, they all coincide to be in the range of ∼ 60–85° (2 σ); values below ∼ 55° can be excluded with 3 σ confidence for Models 1 and 2, whereas for Model 3 this same confidence region encompass a broader range, with marginally lower inclinations. Model components, model fitted to the data, and its residuals for Model 2 are displayed in Fig. 7 . Aside from providing the best constraints on spin and inclination, the reason we elect to show this combination is that it includes the comptonization component, in which best-fitting values of τ and kT e are – as formerly discussed – in a more likely interval for 1E 1740.7−2942. For a comprehensive description of the model variables and dependencies, we also present, in Fig. 8 , the joint distributions of selected parameters for Model 2. Figure 6. MCMC output distribution for parameters spin and inclination for combinations relxill+comptt (Model 1; upper panel), relxilllp+comptt (Model 2; middle panel), and relxilllp alone (Model 3; bottom panel). The black plus symbols indicate the best-fitting values. Confidence contours of 1 (magenta), 2 (bluish green), and 3 (blue) σ are shown. The colour figure can be viewed online. Figure 7. Model components (upper panel), model fitted to the data (middle panel), and its residuals (bottom panel) for Model 2. Note: Vertical axis of bottom panel is in (data-model)/error , equivalent to , so magnitudes of XMM (blue), NuSTAR (magenta), and INTEGRAL (bluish green) residuals are comparable. The colour figure can be viewed online. Figure 8. Joint distribution of selected parameters for Model 2, calculated from the MCMC output. The red plus symbols indicate the best-fitting values. Confidence contours of 1 (magenta), 2 (bluish green), and 3 (blue) σ are shown. Note: Parameters and their units are: N H (× 10 22 cm −2 ), T 0 (keV), kT e (keV), h ( R g ), i (degrees), R in (R), (log erg cm s −1 ). τ and a are dimensionless.
Paper: 2017MNRAS.465.3291W (fulltext)
redshift z 2 to the present day, the black holes switch to the low-accretion regime again, remaining in a self-regulated state in which both the stellar and AGN feedback balance cooling. The relative importance of AGN over stellar feedback depends on halo mass. While stellar feedback dominates in haloes up to the size of the Milky Way, more massive haloes are mainly regulated through AGN feedback. The stellar mass fraction as a function of halo mass (Fig. 9 ) clearly shows the decrease in star formation efficiency with halo mass at the massive end, in good agreement with observations. We find it useful to compare the stellar mass including the diffuse intracluster light in the high-mass end in observations and theory. In this way, we are less sensitive to the choice of the aperture within which stellar masses are estimated. At the high halo-mass end, the data shown here are in reasonable agreement with Kravtsov, Vikhlinin Meshscheryakov ( 2014 ) who pointed out the importance of outer stellar profiles in high-mass haloes in this type of analysis. Additionally, we plot both the halo mass from the full physics simulation as well as the halo mass of the corresponding halo in the dark matter only run, where the latter, i.e. the dashed line and open circles, should be compared to the results from abundance-matching. Fig. 4 shows the ratio of these two masses as a function of halo mass. In particular at around M 200, c 10 13 M , it turns out to be crucial to take this effect into account. Figure 9. Stellar mass fraction as a function of halo mass for the high-resolution simulation. The stellar mass is calculated as the mass of all star particles within 10 stellar half-mass radii that do not belong to a subhalo. The black line is the corresponding fit to observations from Behroozi etal. ( 2013a , b ) including the intracluster light. We use the simulation values of b and m for both simulation and literature data. The dashed line and open circles correspond to the same simulation data, but the halo mass M 200, c is taken from the corresponding halo in the dark matter only simulation (see Munshi etal. 2013 , for a discussion). One of the conjectured effects of AGN is that they can prevent the most massive galaxies from being blue and star forming, instead making them red and having an old stellar population. We use BV colour and mean stellar age (Fig. 10 ) as a measure for the efficiency of the feedback in the low-accretion state to accomplish this. To probe the relevance of the kinetic feedback mode for this, we compare our fiducial simulation with a simulation without the kinetic mode. The panels of Fig. 10 clearly show the need for this efficient mode to get red and dead galaxies with old stellar populations on the massive end of the galaxy population. Figure 10. Top panel: BV colours as a function of stellar mass within twice the stellar half-mass radius. The red dots are from the fiducial simulations. The blue dots represent the haloes in the run
Paper: 2021A&A...649A..84H (fulltext)
(e.g. Desert et al. 1990 ; Draine Li 2001 ; Compiègne et al. 2011 ). The grain structure and optical properties were therefore calculated by considering their composition and size, and the resulting spectra are directly relationed to these properties. In order to facilitate comparison with previous models and studies, we distinguish three sub-populations of a-C grains: a-C1: a-C nanoparticles with a ≤ 0.7 nm, which would be equivalent to the smallest PAH grains in Desert et al. (1990) , Draine Li (2001) and Compiègne et al. (2011) and represent ~10% of the mass for all models; a-C2: a-C with 0.7 a ≤ 1.5 nm, which would be equivalent to the largest PAHs in Desert et al. (1990) , Draine Li (2001) and Compiègne et al. (2011) ; very small grains (VSG): larger a-C particles with a > 1.5 nm, which would be equivalent to the VSG as defined in Desert et al. (1990) , the smallest carbon grains with graphite optical properties in Draine Li (2001) , and the small amorphous carbon grains (SamC) in Compiègne et al. (2011) . Fig. 5 Upper panel : measured FWHM in VISIR long-slit spectra. The ozone atmospheric absorption band is delimited by the grey shaded area. The positions of aromatic (blue) and silicate crystalline (red) main features are shown as dashed lines. The estimated 3 σ error on the FWHM is 2.5 10 −3′′ . The purple points are the measures for HD 100546. The same reference star (HD 91056, green points) as was used for photometriccalibration was used and is assumed to reflect the angular resolution response of the atmosphere combined to the telescope or instrument. Bottom panel : ratio between HD 100546 and standard star FWHM shown in the upper panel. The raw ratio obtained by crude division of the FWHMs was smoothed it with a three-pixel-wide boxcar having the spectral width of the actual spectral sampling in order to reduce pixel-to-pixel irregularities. The estimated 3 σ error on the ratio is about 1.2% (~0.02). Fig. 6 Top panel : 8.6 μm (brown) and 11.3 μm (magenta) aromatic emission bands and 10.4 μm (black) continuum emission plotted according to the distance to the star. The 1∕ d 2 function is also plotted (black dotted). Bottom panel : 8.6 μm/10.4 μm and 11.3 μm/10.4 μm band ratios plotted according the distance to the star. The transparent grey box on the left represents the distance up to which the cavity is extended in POLARIS simulations, ~ 0.12′′ (13 au). At large distances, the variations in the band-to-continuum ratios are not significant. The error bars on the band-to-continuum ratio are about 5%. The dust mass fraction of each THEMIS dust population and a-C sub-population, that is, the population dust mass M X to the total dust mass M total ratio, q X = M X ∕ M total are, q a-C1 ~ 10%, q a-C2 ~ 7%, q VSG ~ 6%, q a-C:H/a-C ~ 8% and q a-Sil/a-C ~ 69%. Finally, for simplicity, we considered only amorphous silicate grains in our model, while several signatures from crystalline silicates have clearly been observed towards HD
Paper: 2021MNRAS.500..889A (fulltext)
Telescopio Canarias (GTC) to obtain data on two further objects. Finally, we re-observed one S2 object with the second-generation Low Resolution Spectrograph (LRS2-B Chonis et al. 2014 ) at the Hobby-Eberly Telescope (HET). The high-quality data from LRS2-B allowed us to derive elemental abundances. The seven stars for which we have acquired data are listed in Table 2 . Table 2. Coordinates and stellar parameters from low-resolution analysis. This table is an example in which are included stars with further follow-up high-resolution spectroscopy. All the identified S2 members are shown in the appendix in Table B1 . Notes . is the average of the S/N of the entire low-resolution spectra. Inst. is the instrument used in the high-resolution observations. 3.1 VLT observations Three of our targets (J0026 + 0037, J0049 + 1553, and J0206 + 0435) were observed with UVES at the 8.2- m VLT Kueyen Telescope in three observing blocks (OBs) of 1 h in service mode during two different nights, 2019 August 2 and August 28 under program ID 0103.B–0398(A). A 12 slit was used with 1 × 1 binning in grey sky conditions and an airmass of ∼1.4. Our settings used dichroic and provided a spectral coverage between 330 and 680 nm. We corrected each spectrum for the barycentric velocity. The average signal to noise per pixel in the spectra was ∼15, 30, and 35 at 390, 510, and 670 nm, respectively. The resolving power for this set up is R ∼ 45 000 for the blue part of the spectrum (330–452 nm) and R ∼ 41 500 for the red (480–680 nm). The seeing was 095 during the first and second OB and 105 in the third one. The data were reduced using the REFLEX environment (Freudling et al. 2013 ) within the ESO Common Pipeline Library. Two more targets (J0007–0431 and J2345–0003) were observed with X-SHOOTER during 2019 August 29 and September 24/26 under program ID 0103.B–0398(B). Due to the relative faintness of these targets, three OBs of 1 h each were allocated but only one per target was finally carried out. The second target has an additional half-time exposure that was aborted due to weather restrictions. We combined both spectra after barycentric corrections. Grey time, a maximum airmass of 1.3, and seeing of 1 were the observational conditions. We used slit widths of 08, 09, and 09 (for UVB, VIS, and NIR) leading to R ∼ 6200, 7400, and 5400, respectively. The average S/N obtained per pixel in stare mode was 30, 45, and 40 at 390, 510, and 670 nm, respectively. The data were reduced by the ESO staff and retrieved through the phase 3 query form. 3.2 GTC observations The observations with HORuS 1 on the 10.4-m GTC were performed in two different periods. Two 1800-s exposures of J0808 + 2418 were taken during the commissioning of the instrument in 2018 December 12. In addition, four 1800-s exposures for each of J0808 + 2418 and J0929 + 4105 were taken in queue mode in the period 2019 November 18 to December 18 under program 133-GTC130/19B. HORuS uses a 3 × 3 integral field unit
Paper: 2020A&A...642A.170V (fulltext)
this study as the approach leads to reasonable results for the limited energy range considered. Fig. 2 Sketch of the simulation setup as detailed in Dunzlaff et al. (2015) . The four different exit possibilities are colour-coded: the Sun (yellow), the Jovian magnetosphere (orange), the Heliopause (blue), as well as the option that the phase space trajectory is terminated by reaching a predefined end time (green). We note that the figure is not to scale. As described in great detail by e.g. Kloeden Platen (2011) , the stochastic nature of diffusion is treated within the SDE method as a Wiener process with ζ being a vector of Gaussian distributed random numbers. The resulting set of four integral equations, as they are derived by Strauss et al. (2011b ), is solved iteratively by applying the Euler-Maruyama scheme. This leads to a random walk type solution which is terminated if a spatial or temporal boundary is reached, as shown in Fig. 2 for various possible exit positions. A time-backward phase-space trajectory can either terminate at the assumed position of the heliopause (blue line), the Jovian magnetosphere (orange line), or after a pre-specified number of steps have been made without an encounter with either of the formerly mentioned structures (green line). As discussed in Sect. 2.2 , in the low-MeV energy range Jovian electrons dominate the spectrum. The solar population therefore can be assumed to be negligible during quiettimes, and thus trajectories exiting at the Sun are discarded. Galactic electrons, on the other hand are considered via the local interstellar spectrum according to Potgieter Vos (2017) , although the transport of the Galactic population into the inner heliosphere is much more sensitive to the drift effect, which is not considered here. Instead of solving Parker’s TPE directly to obtain a time-dependent distribution function f ( r , t ) covering thewhole phase space, the SDE method provides a chain of point-like solutions both in space and time. The solutions must be sampled at a large number of phase-space points to approximate a spatial solution covering the phase space of interest. In order for the computational results to be comparable with spacecraft data, the time-backward setup is solved as derived and discussed by e.g. Kopp et al. (2012) . A comparison between the time-forward and time-backward setup, made using a simpler 1D model, can be found in the review by Strauss Effenberger (2017) . The left panel of Fig. 5 shows histograms of the so called exit energy 1 (i.e. the energy of which pseudo-particles leave the computational domain) at times of good magnetic connection between the Jovian magnetosphere and the observational point, in contrast to Fig. 5 b, which illustrates the energy distribution for an observational point in opposition to the Jovian source. The distributions in Fig. 5 b are much broader. This, of course, is in agreement with Fig. 5 a being dominated by the much more efficient parallel