adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...629A..87D (fulltext)
such as the peculiar motion of the halo clouds, the nonuniform spectrum of the clouds due to the radiation balance and radiative transfer details, and so on. However, obviously, at this stage, those are lower-order effects with respect the global halo rotation. Moreover, since we consider M 104 as being foreground to the CMB, we note that for the maps we study (with monopole and dipole extracted) the foregrounds could also be negative due to physical processes, for example, in the case of clouds, the particular molecular energy level transitions, metastable states, two-photon emission or absorption, and so on. 3. Conclusions Similar to the case of other galaxies of the Local Group considered previously (in particular the galaxies M 31, M 82, M 81, M 33, and Cen A have been analyzed using Planck data and a temperature asymmetry Δ T about 60−80 μ K of one side with respect to the other about the rotation axis of the galactic disks has been detected in all cases), we found a consistent north–south temperature asymmetry toward the M 104 galaxy, that reaches values up to about 65 μ K within 0.6° (about 100 kpc) in all considered Planck bands 3 , and then starts to decrease. We mention that Chandra observations in the X-ray band of the M 104 galaxy show the presence of a diffuse X-ray emission extending at least up to 30 kpc from its center ( Li et al. 2011 ), together with hundreds of point sources which are a mixture of objects in the M 104 halo (mainly X-ray binaries) as well as background Quasars ( Li et al. 2010 ). However, the presence of this X-ray-emitting hot gas cannot explain the temperature asymmetry detected in Planck data. It is straightforward to show that if the cold-gas cloud model is at the origin of the detected temperature asymmetry (however, as anticipated in the previous section, other emission mechanisms may play a role in the detected signal), a lower limit to the M 104 galaxy dynamical mass is given by where R is the considered galaxy halo radius in units of 100 kpc, Δ T is the measured temperature asymmetry (in μ k ), and τ eff is the effective cloud optical depth (which depends on both the cloud filling factor and the averaged optical depth within a given Planck band. Expected values for τ eff are about a few 10 −3 ( Tahir et al. 2019 ), meaning that the M 104 dynamical mass out to ∼100 kpc is seen to be M dyn ≃ 3 × 10 12 M ⊙ , in agreement with other measurements (see, e.g., Tempel Tenjes 2006 ). It is interesting to mention in this respect that this galactocentric distance appears close to the transonic point (at ≃126 kpc), determined recently by Igarashi et al. (2014) within the framework of the transonic outflow model in a dark matter halo applied to the Sombrero galaxy 4 . The detected temperature asymmetry is, as discussed in the previous section, almost frequency-independent and indicates that the M 104 galaxy halo is rotating with respect to the major symmetry axis of the galaxy disk (Variant 1). However, the
Paper: 2020MNRAS.491.3870B (acknowledgments)
ACKNOWLEDGEMENTS We used the nirvana code version 3.3, developed by Udo Ziegler at the Leibniz-Institut für Astrophysik Potsdam (AIP). For computations we used Leibniz Computer Cluster, also at AIP. We thank Dipankar Bhattacharya, Torsten Enßlin and Anvar Shukurov for very insightful discussions.
Paper: 2016MNRAS.461..304C (acknowledgments)
This work was supported under NASA Contract No. NNG08FD60C, and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software, and Calibration teams for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software ( nustardas ) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). RK acknowledges support from Russian Science Foundation (grant 14-22-00271). GP acknowledges the Bundesministerium für Wirtschaft und Technologie/Deutsches Zentrum für Luftund Raumfahrt (BMWI/DLR, FKZ 50 OR 1408).
Paper: 2019ApJ...875L..25M (fulltext)
reveals a clear difference in the two moon orientations. Figure 6. Exoplanet orbit (blue) with two different exomoon orbits: the red solid line is prograde and coplanar to the planet, and the black dashed line is retrograde and misaligned to the planet. As seen by the observer (right) there is a degeneracy, as both moons has the same ( x , y ) position and ( v x , y y ) velocity, despite the side view (left) betraying a clear difference between the two orbits. L denotes the angular momentum vectors. This degeneracy may be broken by nodal precession, which would not occur for the coplanar orbit but would for the misaligned orbit. Fortunately, for a moon that orbits at a fair fraction of its planet’s Hill sphere, precession will be rapid, revealing the magnitude of the misalignment in just tens of orbits of the planet. Therefore, the dynamically evolving character of TDV will betray the prograde or retrograde character of the moon. If the planetary impact parameter is low then the “TIP–TDV” may be negligible and the magnitude of non-coplanarity may not be enough to break the degeneracy. In this case, higher-order dynamical effects that differ in sign between prograde and retrograde moons may need to be taken into account, as envisioned by Lewis Fujii ( 2014 ). Two alternative methods for breaking the degeneracy, practical only with Extremely Large Telescopes, were discussed by Heller Albrecht ( 2014 ). 5.2. The Prevalence of Large TTVs for Long-period Gas Giants According to the transit times of Table S3 of Teachey Kipping ( 2018 ), the planet Kepler-1625b has a mean absolute deviation from a constant-period model, normalized by the orbital period—a “scatter”—of s O − C / T P = 2.40 × 10 −5 . The timings have a median error bar normalized by the orbital period of σ / T P = 1.55 × 10 −5 . For the TTV measurements of Holczer et al. ( 2016 ), the data are more precise than that for 40 planets with T P > 100 days. Of those 40, 15 planets have larger TTV scatter, i.e., s O − C / T P > 2.40 × 10 −5 , and all of these are deemed significant at . The large amplitude and period of these signals makes them likely due to planet–planet perturbations. We conclude that Kepler-1625b may very likely have a TTV signal due to additional planets, which may be confused for exomoons, or at least contaminate the exomoon TTV signal. A repeated photometric transit signal of the exomoon, rather than the TTV induced on the planet, is likely a more reliable signature. 5.3. Overlapping Moon and Planet Transits There are two possible scenarios for overlapping moon and planet transits. First, the moon may be entirely in front of or behind the planet, in which case the photometric signal would be identical to that of an isolated planet transit and the moon would be hidden. Such an event is not explicitly considered in our equations. We estimate it to be rare though, with a likelihood on the order of ≈ R P / a M if I M = I P = 90°, and significantly less for inclinations that
Paper: 2019MNRAS.489.4862H (fulltext)
also the channel maps by using the 13 CO data cube of the Galactic Ring Survey (GRS; Jackson et al. 2006 ). The RRLs corresponding to discrete regions are then used in the analysis of resolving the KDA. 3.1 Bubbles with detections of formaldehyde absorption The formaldehyde (H 2 CO) traces cold and dense ( n ∼ 10 4 cm −3 ) molecular gas (e.g. Young et al. 2004 ). By its absorption features of six fine structure transitions of (1 10 –1 11 ), H 2 CO has been widely detected in our Galaxy against radio continuum sources or even cosmic microwave background (CMB; Rodríguez et al. 2006 ). The transitions of H 2 CO (4829.6412–4829.6710 MHz) 5 close to the rest frequency of H110α (4874.157 MHz) have long been used to resolve the KDA for regions in the inner Galaxy (e.g. Downes et al. 1980 ; Watson et al. 2003 ; Sewilo et al. 2004 ; Du et al. 2011 ). Our observation covered the H 2 CO (1 10 –1 11 ) transition lines towards each of the targeted Spitzer infrared bubbles. The TianMa beam size for observing H 2 CO lines is about 3.9 arcmin. The typical on-source time is 2.5 min. Data processing is the same as for a single hydrogen RRL as described in Section 2 . H 2 CO absorptions along the line of sight of 27 out of the 75 infrared bubbles were detected. The absorption feature(s) is fitted by Gaussian(s) after removing a polynomial baseline (typically third order; Fig. 3 ). For the bubbles with H 2 CO detections, the spectra of RRL and H 2 CO are given in Appendix B (Supporting Information) . The fitted LSR velocity, the FWHM line width, the velocity-integrated line intensity, and their errors are listed in Table 3 . The rms noise for each spectrum estimated from the line-free channels is also incorporated. Figure 3. Examples of Spitzer infrared bubbles with detections of both RRLs ( lower panels , stacked spectrum) and formaldehyde lines near 4829.66 MHz ( upper panels ). From left to right, we show the KDA solutions of three bubbles assigned at near kinematic distance, far kinematic distance, and at the tangent point, respectively, according to the H 2 CO absorption method (see Section 3.2 ). The red vertical lines indicate the tangent point velocities derived by using the rotation curve of Russeil et al. ( 2017 ) with Galaxy fundamental parameters of R 0 = 8.34 kpc and (Reid et al. 2014 ). Table 3. Spitzer infrared bubbles with detections of H 2 CO (1 10 –1 11 ) transitions near 4829.66 MHz by the TianMa 65-m radio telescope. Column 1 is the source name; column 2 lists the measured LSR velocity from the H 2 CO spectrum, and its 1σ uncertainty is given in column 3; columns 4 and 5 are the measured FWHM line width and its 1σ uncertainty; columns 6 and 7 are the velocity-integrated intensity and its uncertainty, respectively; column 8 is the rms noise of the H 2 CO spectrum, estimated from the line-free channels. 3.2 Solution to the kinematic distance ambiguity Distance is a key parameter for Galactic sources. The trigonometric parallax (e.g. Reid et al.
Paper: 2018ApJ...855..102C (acknowledgments)
We are grateful to the staff astronomers at Keck Observatory for their assistance with the observations, and to Jason X. Prochaska, Tom Barlow, and Michael T. Murphy for providing some of the software that was used to reduce the data. We also thank an anonymous referee who provided helpful suggestions that improved the presentation of this work, following a referee who was unable to respond in a timely manner. During this work, R.J.C. was supported by a Royal Society University Research Fellowship. R.J.C. acknowledges support from STFC (ST/L00075X/1, ST/P000541/1). C.C.S. has been supported by grant AST-1313472 from the U.S. NSF. This research was also supported by a NASA Keck PI Data Award, administered by the NASA Exoplanet Science Institute. Data presented herein were obtained at the W. M. Keck Observatory from telescope time partially allocated to the National Aeronautics and Space Administration through the agency’s scientific partnership with the California Institute of Technology and the University of California. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. We thank the Hawaiian people for the opportunity to observe from MaunaKea; without their hospitality, this work would not have been possible. This work used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( http://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 National E-Infrastructure. This research has made use of NASA’s Astrophysics Data System. R.J.C. thanks S.O.C. for her impeccable timing and invaluable insight. Facility: Keck(HIRES) . Software: Astropy (Astropy Collaboration et al. 2013 ), Matplotlib (Hunter 2007 ), NumPy (van der Walt et al. 2011 ).
Paper: 2017A&A...608A..76V (fulltext)
quantifies in Fourier space the sum of the square of the difference between the data and the restored object, degraded with the current estimated individual transfer functions . In the usual case that the image data has a good signal to noise ratio (S/N), the regularization constant may be set to zero. Equation ( 1 )makes use of the assumption of additive, Gaussian noise, in which case the common estimate of the Fourier transform of object , , for a given set of transfer functions is given by (see e.g., Paxman et al. 1996 ) in which the object is obtained by deconvolution of the average of a purposely degraded dataset, with the average over the autocorrelation function of the individual transfer functions. Since the data have already been degraded by the , the additional degradation of the data with the complex conjugate of the results in a dataset that has been degraded with a PSF that is centered on the origin, and which can therefore be added to other such data, without further degrading the result. We note that it is implicitly assumed that the transfer function is independent of the spatial coordinates, so that the convolution operator can be conveniently expressed as a multiplication in Fourier transformed space. While this is in reality not quite true, the assumption leads to a considerable reduction in computational effort, and for a 1 m-class solar telescope it is normally a good assumption over an area smaller than about 5 5. In practice, an image is therefore divided in tiles no larger than approximately 5 5, restored on a tile-by-tile basis, and then re-assembled to a single restored image, although this may become more problematic with the large 4 m-class solar telescopes currently planned or under construction (DKIST, EST), where this area is expected to reduce to only 2 2. The initial assumption made by the MFBD process, that the PSF is the result of a pure phase error of the wavefront entering the telescope pupil, is clearly only true if the image is recorded significantly faster than the time scale over which the wavefront changes. This so-called seeing freezing time scale is variable, dependent on the telescope diameter, and has been determined in the past to typically lie at or below 10 ms ( Tarbell Smithson 1981 ) for a 1 m-class telescope. If this time is exceeded significantly, the assumption of the PSF being the result of a pure phase error is no longer valid, and the fitted wavefront cannot be unambiguously interpreted as a wavefront anymore, as it describes a PSF that has been produced by integration over time of several wavefronts, which is inconsistent with the formation model. Due to the non-linearity of the mapping from wavefront to PSF, the correspondence of PSF to wavefront is generally non-linear and not unique. The latter, however, can be addressed by providing additional constraints, such as phase diversity, that are effortlessly included in the formalism used here, and have been used to reduce or even remove
Paper: 2017MNRAS.466.4211G (acknowledgments)
Acknowledgments We thank to the anonymous reviewer for his/her careful reading of the manuscript and for critical and informative comments, which greatly improved and corrected this work. We are grateful to Polish National Science Centre for their support (grant no. 2011/01/D/ST9/00735). The European Very Long Base Interferomery Network (EVN) is a joint facility of European, Chinese, South African and other radio astronomy institutes funded by their national research councils. KG gratefully acknowledges the Poznan Supercomputing and Networking Centre (PCSS, Poland) for computing grant No. 195. This research has made use of the SIMBAD data base operated at Centre de Donnes astronomiques de Strasbourg (CDS), Strasbourg, France.
Paper: 2017MNRAS.469L.118J (acknowledgments)
Acknowledgments The research leading to these results has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement no. 670519: MAMSIE). The research presented here makes use of the MAST archive hosted by STScI in the USA and the Simbad astronomical database hosted in CDS, Stroudsbourg, France. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate.
Paper: 2020ApJ...888L...4T (fulltext)
The astronomical silicates and carbons are assumed from Draine ( 2003 ). The grain size distribution has a slope of −3.5 and goes from 0.03 μ m to 3 mm. To irradiate the disk the star is treated as an isotropic source with stellar spectral models obtained from the 5 Myr isochrones from Siess et al. ( 2000 ), with T eff ∼ 8000 K and a radius R ∼ 2.2 R ⊙ . The source is located at the distance of 117 pc, adopting the disk inclination and position angle of 13° and 5°. 3. Results Our reference profile for the gas and dust distributions is the DALI thermochemical model from FD17 , that models the dust surface density profile Σ dust with two dust rings (20–35 au and 56–83 au) and the gas density profiles Σ gas with an inner cavity (13–56 au) reduced by a factor 30–40. However, we considered an update of the published model of FD17 , limited by the spatial resolution of the available data at the time. Based on the 13 CO J = 3–2 ALMA archive data from Honda et al. ( 2012 ), the gas cavity appears to be located at ∼20–56 au. Figure 1 shows the temporal evolution of the dust and gas surface density profiles, Σ dust and Σ gas , in our simulation. Going from black to red, Σ dust and Σ gas are displayed as a function of time, that we recall is normalized to the orbital period of the outer planet at the initial position (55 au, T orb ∼ 320 yr). Figure 1. Σ dust (top panel) and Σ gas (bottom panel) as a function of the radius R for increasing time. The initial surface density profiles are in black. Our time unit is normalized with the initial orbital period of the outer planet (∼320 yr). The inner planet carves a cavity in the dust and in the gas, with a depletion factor of ∼10 −4 and ∼10 −1 , respectively, inward of its orbit. The outer planet first migrates (see Figure 2 ) while accreting the material, then opens a gap in dust and gas (with depletion factor ∼10 −4 and ∼10 −1 , respectively, in dust and gas) at his “equilibrium” location. The dust ring produced between the two planets is preserved for at least ∼500 orbits, i.e., for over ∼2 × 10 5 yr. At the same time, the gas is accreted on the star and the planets. The density profiles of the gas and dust in our simulation are due to a combination of factors. For the gas, the “cavity” is produced by the torques exerted by the outer planet onto the disk, which prevents the gas outside the outer planet to refill the inner regions that are emptied by viscous accretion. For the dust, the two prominent rings are created by the usual trapping at pressure maxima (indeed, we have verified that the ring location coincides exactly with pressure maxima). This generates a gas cavity with a width of ∼50 au, lowering the gas surface density and increasing the value of the Stokes number that quickly becomes larger than unity, helping the dust trapping due to the pressure gradient in preventing the dust migration and thus preserving the dust ring. Figure 2. Dynamics of the two planets. Top-right panel: the
Paper: 2020A&A...644A.144D (fulltext)
from SIMBA are consistent at f gas 0.5, but significantly differ at f gas > 0.5 due to steeper decrease compared to our data. Such a sharp reduction in δ DGR translates to relatively low number of DSFGs with M dust / M ⋆ ≳ 0.01 produced in cosmological simulations. By investigating the connection between the δ DGR and Z gas in SIMBA, we find that very low δ DGR in galaxies from the highest-end of gas fraction is a result of their gas metallicities being lower by a factor of ∼4 relative to solar. The comprehensive treatment of different physical mechanisms that could be responsible for relative shortfall of model predictions, along with numerical limitations, is out of the scope of this paper. In the following we briefly emphasize their importance. The dust growth timescale is too long. One of the most critical parameters that describes the dust mass growth is the accretion timescale ( τ acc ). It is often modelled as: where a is a dust grain size which is usually assumed to be spherical with a typical size of ∼0.1 μ m ( Asano et al. 2013 ). The n H and T gas are the number density and temperature of the cold gas phase, and τ acc, 0 defines the timing of growth activation. The timescale for dust growth in the ISM changes as a function of gas surface density for different Z gas (see Popping et al. 2017 , their Fig. 1). If we apply M gas of our DSFGs, along with their compact ALMA continuum sizes (see the next section), we infer high median molecular gas surface density of ∼6.7 × 10 3 M ⊙ pc −2 . Such a high surface density implies short accretion timescales, on average τ acc ∼ 6 × 10 5 yr. These will be obtained if τ acc, 0 10 6 yr which is shorter than what has usually been adopted in cosmological simulations 6 . The short τ acc are proposed by Pantoni et al. (2019) , and are also reproduced in very recent semi-analytical models that claim fairly good overall match to the observations ( Triani et al. 2020 ). These are also in line with De Vis et al. (2017) , who found that variations in dust growth timescales might help to explain the M dust deficit at high gas-fractions in their large sample of nearby galaxies. Given the SIMBA’s ∼kpc resolution, a multiphase galaxy ISM cannot be resolved, which prevent us from knowing the exact dependence of the modelled dust content to the gas surface density. As a result, parameters such as the reference τ acc are tuned in order to boost the effective gas density. Thereby, it seems likely that use of shorter accretion timescales (equivalent to the increase in dust growth efficiency) in simulations could help partially overcome the shortfall to data. Dust destruction is too efficient. It is also possible that the dust destruction in simulations is too efficient. The total rate of dust mass destruction is given by Ṁ dest ∝ M dust / τ destr , where dust destruction timescale is usually approximated as ( Slavin et al. 2015 ): Here Σ M gas is the surface density of molecular gas mass, f ISM is the value