adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...603A.105D (fulltext)
different dust sizes: Ohmic diffusivity (solid line), Hall diffusivity (dotted for positive and dash-dotted for negative), and ambipolar diffusivity (dashed line) vs. radius. The plots show at time kyr, when central temperature is K and central density cm. The asterix denotes that the mean dust cross section is minimal in model S5, and the mean dust radius is not a unique characteristic of dust properties. 4.1. Sign reversal of the Hall effect Owing to the total charge neutrality condition where is the charge of species, there are only three independent parameters that can be defined as , , and (see also Sect. 2 in Xu Bai 2016 ). In Fig. 3 we show the dimensionless parameters , , and . These parameters show how smaller dust size leads to larger inequality and the transition to the dust-ion regime and even dust-dust regimes, i.e. when main charge carriers are ions and (model S3), or and (model S5). Fig. 3 Various properties of charged species and magnetic diffusivities radial profiles shown for models S1 (black), S2 (red), S3 (blue), S4 (green), and S5 (lilac). The colours refer to the same models in all following figures. Plots show, from left to right , the density of ion vs. radius, the relation of electron density to the ion density, and the relative abundances of charged dust to the ion density ( bottom ). The Hall effect term, given in Eq. ( 2 ), becomes negative when the Hall conductivity is negative, . In case of single-charged dust and local charge neutrality, Eq. ( 4 ) can be rewritten as where we reduce the dust coupling parameter to because it is insensitive to the sign of the charge, i.e. . In our simulations, we observe that the coupling parameters everywhere, and and for radii AU (see also Fig. 4 ). Thus, we simplify the equation for further and derive the threshold value for , When , the Hall effect changes sign to the negative, which may lead to the interesting MHD effects during the collapse. The Hall effect becomes negative when the number of negative charge carriers, weighted over coupling parameter , dominates the number of -weighted positive carriers. This is likely to happen when both the negatively charged dust is abundant and the magnetic fields are strong enough for the strong coupling of ions (). Fig. 4 Top : relative number densities of positively and negatively charged dust, and , compared to the threshold value in Eq. ( 9 ). Middle : coupling parameter for electrons, ions, and dust (Eqs. ( 3 )–( 5 )). Bottom : magnetic diffusivities depending on the magnetic field strength with colours showing . From the previous equations we learn that both the amount of negatively charged dust and also the quality of coupling to the magnetic fields are important. It is now a natural question to ask what happens if we change the cloud magnetisation. We consider cases with . The various magnetisations are shown with colours in Fig. 4 for models S2 and S5. We observe that the radial position of the sign reversal in Hall effect shifts
Paper: 2020A&A...637A..94G (fulltext)
merger fraction in all galaxies is low, leading to the misidentification of non-mergers as mergers. Assuming 1000 galaxies in which 10% are real mergers, even a 90% accuracy will cause 100 galaxies to be incorrectly identified. In an extreme situation where these 100 galaxies are all non-mergers misidentified as mergers, then the final CNN-identified merger sample will include 200 galaxies (100 real mergers plus 100 misidentified galaxies), causing half of the sample to be contaminated by non-mergers. This contamination exists in both the AGN sample and the non-AGN control sample. To assess the influence of this contamination and given the fact that it would be extremely time-consuming to visually inspect all the mergers, we only visually inspected the 184 and 213 mergers in the MIR AGNs of the SDSS sample and the GAMA sample respectively. For the controls we also visually inspected the 1200 mergers in the non-MIR AGNs of the SDSS sample and randomly selected 600 mergers from the entire 1560 mergers in the non-MIR AGNs of the GAMA sample. The visual inspection was done independently by three of the co-authors. By selecting all mergers that have more than one, two, and all three positive votes, in Fig. 14 we find an increase of the merger excess in MIR AGNs relative to non-AGN controls for the SDSS sample and the GAMA sample. Fig. 14. Merger excess in MIR AGNs relative to non-AGN controls for the SDSS sample and the GAMA sample. The x -axis indicates the number of positive votes based on visual inspection. There are advantages and disadvantages in terms of merger identification methods using CNN and visual inspection. Our CNN merger identification method is affected by non-merger contamination, but it can work efficiently on a large sample. Visual inspection, even though it is not always reliable, is less likely to be affected by contamination, but it would have a bias toward more obvious mergers, and is very time consuming. Despite the non-merger contamination, our results are in qualitative agreement compared to previous studies using visual inspecting to identify mergers. In addition, when applying a stricter merger threshold and visually inspecting a smaller sample of mergers, our results are more consistent. 5. Conclusion We selected the SDSS DR7 spectroscopic data at redshifts 0.005 z 0.1 and the GAMA spectroscopic data at redshifts 0 z 0.6. We took advantage of deep leaning convolutional neural networks to identify mergers based on SDSS and KiDS images. We adopted two methods to classify AGNs, a WISE MIR color cut and optical emission line diagnostics, totaling 799 MIR AGNs and 5420 OPT AGNs for the SDSS sample, and 543 MIR AGNs and 2750 OPT AGNs for the GAMA sample. We also selected LERGs to analyze the connection between mergers and low accretion rate AGNs. We built a strictly matched control sample for each subgroup to investigate the connection between mergers and AGNs. Our findings are as follows: (1) In terms of AGN fraction in
Paper: 2021MNRAS.502.1026D (fulltext)
−0.61. This is approximately Δα ≃ 0.15 flatter than the measured average spectral index in radio α R ∼ −0.765. The flattest indices are obtained at the locations of assumed active particle acceleration with high shock velocities, such as near the forward shock and the south-west quadrant of the remnant. These locations also coincide with the regions that are brightest in non-thermal X-ray emission. The flattening of the spectrum at high frequencies is in qualitative agreement with non-linear DSA theory. The spectral index is, however, still steeper than the predictions of the non-linear models without magnetic field amplification (see Malkov Drury 2001 , and references therein). We assess the possibility of a cooling break being present in the remnant. We place the break frequency lower limit at ν br ∼ 5 × 10 12 Hz in the likely most affected regions. Considering 100 yr as the age of the shocked plasma, this indicates allowed magnetic field strength up to B ∼ 2–2.5 mG in the south-east/north-east and B ∼ 1 mG in the south-west of the remnant. This is higher than the average magnetic field limits inferred for Cas A, but probes the magnetic field values suggested locally by Atoyan et al. ( 2000 ). Using the age of Cas A (∼340 yr) as the age of the plasma puts the upper limits close to the magnetic field estimates obtained with other methods. Further investigations are still necessary and south-east/north-east regions are the best candidate locations for further investigation of the cooling break. Doing this may put constraints on the history of electron acceleration in Cas A. APPENDIX A: EXAMPLE OF OBTAINING SPECTRAL INDICES PER PIXEL USING MONTE CARLO SIMULATIONS In order to illustrate our procedure for measuring a spectral index for a single pixel and to prove the overall robustness of our results given the statistical and systematic errors, we show here our procedure for a given pixel, and how it leads to a likelihood range in the spectral index. We chose a pixel in the south-east location at coordinates (RA 23h 23m 391, Dec. 58 47′ 238) as our example pixel to provide a more complete picture of our data-handling process. The location is ideal to showcase the effect of the strongest effect of background subtraction as this region provides the lowest mid-infrared flux. We use a Monte Carlo approach in order to propagate the uncertainties and to obtain the result. The basis of this method is to take a value and its uncertainty of a given parameter and to simulate a normal distribution around it. In our case, we produce one-dimensional arrays of 10 000 elements. These arrays are then used as input into equations as if it would be a single value. However, as we do it on 10 000 elements we are saving the information about parameter’s possible values and effectively we also propagate its uncertainty. A.1 Background subtraction The flux in the chosen pixel is 190.1 × 10 −7 Jy px −1 with an uncertainty 9.1 × 10 −7 Jy px −1 . The mid-infrared
Paper: 2019MNRAS.489.2478N (fulltext)
of the WASP-South data for WASP-182 from three different years (left) along with folds of the data at the possible rotational periods (right). The folded data have been binned to 20 bins, each corresponding to 1.5 nights. The blue marks are at 30 and 15 d and the dotted horizontal line at 1 per cent false alarm probability (FAP). 4 SYSTEM PARAMETERS The full set of system parameters was modelled jointly using the discovery photometry, follow-up light curves, and RV data with the MCMC code described in detail in Collier Cameron et al. ( 2007 ) and Anderson et al. ( 2015 ). The analytic eclipse expressions derived by Mandel Agol ( 2002 ) are used with a four-parameter, non-linear limb darkening law of Claret ( 2000 , 2004 ). We have interpolated coefficients for stellar temperature and metallicity of each star, and in each photometric filter. The values used were perturbed during the MCMC via T L-D , the ‘limb-darkening temperature’, which has a mean and standard deviation corresponding to the spectroscopic T eff and its uncertainty. We ran the MCMC both with the eccentricity as a free parameter and fixed to zero, to check if the results are compatible with a circular orbit. We expect most giant planets in short-period orbits to have been circularized by tidal forces, and want to avoid overestimating the eccentricity in orbits that have none. Each circular model has six fitted parameters: orbital period, P , epoch, T C , transit depth in the absences of dark limb effects, ( R p / R s ) 2 , transit duration, T 14 , impact parameters, b , and stellar RV semi-amplitude, K 1 . The RV systemic velocity γ was fitted too, and in the case of WASP-171 along with a linear RV drift, . For WASP-182, where we have data from two different spectrographs, an offset between CORALIE and HARPS was modelled as well. For each target we ran 5000 MCMC steps as a ’burn in phase’ to initialize the main phase that was set to have 50 000 iterations. At each step the free parameters are perturbed and the models are refit. If the χ 2 of the fit is better than the previous step, the current parameters are accepted, if the fit is worse, the parameters are accepted with a probability proportional to exp (−Δχ 2 ). We used Gelman–Rubin statistics (Gelman et al. 2003 ; Ford 2006 ) to check how well the chains converge. In our case the Gelman–Rubin statistics indicated that all fitted parameters were well mixed. Continuing our practice from recent discovery papers (e.g. Hellier et al. 2019 ) we treat the stellar parameters through a two-step process; we first estimate the stellar density, ρ s , from the transit duration alone, independently of stellar models. Secondly, we obtain stellar masses by using ρ s , T eff , and [Fe/H] in the stellar evolution model bagemass , as explained in Section 3.2 . The resulting stellar mass estimate and its uncertainty are finally used as input in the MCMC to derive stellar radii. The stellar density for WASP-182 is poorly constrained by the
Paper: 2019ApJ...880...69Y (fulltext)
of UV radiation (e.g., Jonkheid et al. 2007 ). Because the observed HCO + gas gap at a radius of 30 au coincides with the dust gap, there is a deficit of dust grains in the HCO + gas gap (Okuzumi et al. 2016 ; Pinte et al. 2016 ). Thus, the gas-to-dust mass ratio is higher in the gap, if there is no actual deficit of H 2 gas. As demonstrated in chemical models, the HCO + abundance in a protoplanetary disk with a higher gas-to-dust mass ratio of 1000 could be a factor of ten lower than in a disk with a gas-to-dust mass ratio of 100 (Jonkheid et al. 2007 ). Therefore, the HCO + column density possibly decreases in these dust gaps, even if there is no deficit of H 2 gas in the dust gaps. Future observations to measure the column density of neutral molecules in the HL Tau disk, whose abundances are less sensitive to the gas-to-dust mass ratio and UV radiation, are needed to examine this possibility. 5.2. Origins of the Infalling Spiral An infalling gas stream toward HL Tau is detected in the HCO + (3–2) emission with our ALMA observations, and its rotational motion gradually becomes dominant over its infalling motion. We note that the HCO + (3–2) line is optically thick (Figure 13 ), and the observed line-of-sight velocity of the spiral structure is inconsistent with the Keplerian velocity at radii larger than 03 (Figure 16 ). In addition, no counterpart of this spiral is detected in the continuum emission, which traces the disk mid-plane. These results could suggest that the infalling spiral is not in the mid-plane but is located above the disk surface. The velocity of the infalling spiral matches with the Keplerian velocity at a radius of 03, and there is a bright peak at a radius close to 03 (Figure 1 ). Thus, the infalling spiral could merge with the HL Tau disk at a radius of 03. Future observations of shock tracers, such as SO, SO 2 , and CH 3 OH, are needed to examine this scenario and the presence of accretion shock (Ohashi et al. 2014 ; Sakai et al. 2014 ; Yen et al. 2014 ; Artur de la Villarmois et al. 2019 ). Spiral structures connected with central disks have been observed in millimeter continuum emission and infrared reflected light around several young stellar objects (Liu et al. 2016 , 2018 ). Infalling motion along spiral structures has also been detected around other protoplanetary disks (Tang et al. 2012 ; Yen et al. 2014 ). An infalling spiral could form when a gas clump falls freely toward the center with a conserved angular momentum (e.g., Ulrich 1976 ). Free-falling material with a conserved angular momentum follows a parabolic trajectory, which appears as a spiral structure after the projection on the plane of the sky. This freefall model has successfully explained the morphology and velocity of the infalling spirals observed in the protostellar source, L1489 IRS (Yen et al. 2014 ). In HL Tau, we found that the morphology of the outer part of the infalling spiral at a radius larger than 05 can indeed be described by a
Paper: 2020MNRAS.495.3705N (fulltext)
are due to bending and breathing modes that might have been caused from the impact of Sagittarius dwarf falling into our Galactic disc (Gómez et al. 2013 ; D'Onghia et al. 2016 , Carrillo et al. 2019 ). Further Gómez et al. ( 2017 ) have shown that bending and breathing modes must be a common feature in most disc galaxies. 6.3 De-projection of corrugations seen in edge-on galaxies Simulations (Edelsohn Elmegreen 1997 ; Gómez et al. 2013 ) show that the bending waves generated by a Sagittarius-like perturber has both radial and azimuthal dependence. So far corrugations in other galaxies have all been detected in edge-on discs (all galaxies in Table 1 except Milky Way) where we see the combined effect of radial and azimuthal dependence. Looking for corrugations in less inclined discs is not an option, unless we look in the velocity space (Alfaro et al. 2001 ; Sánchez-Gil, Alfaro Pérez 2015 ). However, Kamphuis et al. ( 2013 ) combine the HALOGAS data with non-axisymmetric modelling (using a version of TIRIFIC) and deproject H i observations of edge-on galaxy NGC 5023. They find that their data are best explained by an edge-on disc showing radial corrugations (see their fig 10). Hence we stress the importance of non-axisymmetric modelling here as the need of the hour because, with the availability of high-resolution H i data on scores of nearby edge-on galaxies, a statistical study of corrugations is only a small step away. 7 CONCLUSION This study has focused mainly on characterizing corrugations in the dust lanes of a sample of five edge-on galaxies and understanding their physical state. Partial dust waves are seen in many galaxies but only a handful of galaxies show waves long enough to be characterized. This paper is one of the first attempts to give a quantitative estimate of their size and scale as well as address the issue of whether they are stable. In order to strengthen our analyses, we have added four more galaxies including the Milky Way where corrugations have been previously measured in stellar and gaseous discs. The fact that corrugations are seen in stars, gas and dust leads to the possibility of gravitational instability as the likely origin. In fact, five of the nine galaxies that we have tabulated are visibly perturbed (bar/tidal interaction). Our main conclusions from this work are the following: We present the first systematic study of dust corrugations in edge-on galaxies and characterize them by measuring their wavelengths and amplitudes. The amplitude of corrugation seems to depend on the host galaxy mass. The wavelength of corrugation does not show any dependence on host galaxy mass. It is roughly about the stellar disc scale length for most of our sample. By carrying out a stability analysis, we show that the corrugations with wavelengths larger than the scale length of the stellar disc, are stable. We find indications that corrugations can be as common as warps. We also find mounting evidence that corrugations play a
Paper: 2021A&A...645A..36H (fulltext)
wavelength. The blue line is a fit to the data. Extinction according to Cardelli et al. (1989) is shown for A v / E ( B − V ) values of 1.5 (green triangles, dashed line) and 3.1 (red diamonds, continuous line), respectively. Dirsch et al. (2005) estimated the maximum and the mean B -band attenuation in a 4′′ diameter area in the patch to be A ′ B ∼10 and 04, respectively. Their maximum attenuation is somewhat higher than the value A ′ B 005 obtained from the GMOS spectrum. This difference is possibly due to the positioning of the GMOS spectrum slit that does not pass exactly through the densest part of the patch, and also to the selected off position. Had the off position been chosen northeast of the patch, the attenuation would be 10% lower, but the wavelength dependence of the attenuation would not change. Our A ′ B /( A ′ B − A ′ R ) ratio (1.6 ± 0.11) is the same as (1.6 ± 0.25) reported in Dirsch et al. (2005) and it is, again, substantially lower than the corresponding value for the mean Milky Way extinction curve for point sources, A B /( A B − A R ) = 2.3. The background surface brightness attenuation in a clumpy foreground screen has been modelled by Natta Panagia (1984) . Clumping causes the reddening curve to flatten, meaning R ′ V and A ′ B /( A ′ B − A ′ R ) increase as compared to a homogeneous screen. Thus, clumping cannot explain the steeper wavelength dependence as observed for the patch. In several cases, dust lanes or patches have been observed in early-type galaxies with R ′ V values as low as 1.9 ( IC 4320 , Warren-Smith Berry 1983 ) and 2.15 ( NGC 5626 , Goudfrooij et al. 1994 ). Such cases have been interpreted as evidence for dust-grain size distributions with mean and maximum grain sizes smaller than those for the mean Milky Way extinction curve (with R V = 3.1). The dust mass for lanes and patches in galaxies has frequently been estimated using the following formula: where ⟨ A ′ B ⟩ is the mean attenuation as measured over the object’s area of Σ ( Sadler Gerhard 1985 ; van Dokkum Franx 1995 ). For the Milky Way’s mean dust properties, the value of the mass absorption coefficient Γ B is Γ = 4 × 10 4 mag cm 2 g −1 ∼ 8 × 10 −6 mag kpc 2 M ⊙ −1 . Adopting as the mean attenuation over the area of Σ = 4′′ × 4′′ of the patch, we obtain the following for its dust mass: M d = 4.8 × 10 4 ( d /50.7 Mpc) 2 M ⊙ . Goudfrooij et al. (1994) presented a method to estimate the value of Γ B for extinction (attenuation) curves with R ′ V values other than 3.1. For NGC 5626 and IC 4320, which have the smallest values of R ′ V in their sample (2.15 and 2.08), they found that the dust masses as obtained from the above formula have to be multiplied by a factor of ∼0.66. In the case of the patch with R ′ V = 1.5, we estimate that a correction factor of ∼0.5 could be appropriate. Thus the dust mass estimate for the patch becomes M d ∼ 2.6 × 10 4 ( d /50.7 Mpc) 2 M ⊙ , which corresponds to a molecular-gas-to-dust ratio of ∼55. If at the
Paper: 2016MNRAS.460.2919K (fulltext)
= 3.5 − 750 are displayed in Figs 14 (a, b). From Fig. 14(a) , it looks, as if, the acoustic modes are divided into two groups as follows. The first group of waves, which looks dispersive in nature, is localized in the domain, k = 10 −4 − 6 (). The phase velocity of this group increases radially outwards and acquires supersonic value with sub-sonic transition beyond about k = 6. The second group of waves, which is almost non-dispersive in nature within a wide-range of the ξ-space, is localized in a wide-range of k -domain [Fig. 14(a) and Fig. 15(a) ]. The propensity of weak dispersion is reflected near the SSB. These waves are found to travel within sub-sonic range of flow speed. From Fig. 14(b) , it is clear that the group velocity is almost constant in the k -space. Moreover, no distinction is noted between the two groups. However, the group velocity increases radially outwards with the asymptotic sonic speed value at 1 au. In Fig. 14(c) , we depict the time period plot in the k - and ξ-spaces. The categorization of the two distinct groups of waves is clearly visible. The time periods of the dispersive mode group are found to be larger than those of the non-dispersive wave group, but at different ξ. It also reveals that discreteness in the low k -value of the fluctuations results in discreteness in the oscillation period peaks. In the range of relatively high k -value and beyond, such discrete peaks in the oscillation period are not observed. In Figs 15 (a, b, c), we depict the phase velocity, group velocity and time period in the extended k -space. Also, the time period decreases towards shorter wavelength. Figs 16 (a, b) depict that the electrostatic potential fluctuation level, being higher, dominates over the SWP flow fluctuations. One more thing, to say, is that the amplitude profile of the flow fluctuations is the same as that of the SIP. Finally, Figs 17 (a, b) show the SWP fluctuation parameters [ and ] in the extended k -space. The electrostatic potential fluctuations increase towards the shorter wavelength regime. The flow fluctuations are not affected much in the shorter wavelengths. This is in conformity with the flow-pattern phenomena as observed in the SIP as well. Some specific features in addition, are enlisted as below. The phase velocity of the non-dispersive p-modes, as identified and discussed above in Figs 14(a) and 15(a) , varies in the sub-sonic range only. But, that of the dispersive branch of the p-modes, varies from sub-sonic to supersonic regime with maximum v p ∼ 200 km s −1 at 1 au. The phase velocity plot in the extended k -domain [Fig. 15(a) ] exhibits a similar type of behaviour of sub-sonic and supersonic speeds. The corresponding group velocity [Fig. 14(b) and Fig. 15(b) ] acquires sonic speed, v g ≈ 100 km s −1 , at 1 au in all the k -domains. The time period plots [Fig. 14(c) and Fig. 15(c) ] exhibit strong radial dependence with two distinct peaks, τ ≈ 20 min each, lying at ξ ≈ 30 ( away from the SSB) and