adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.471.4917J (fulltext)
width of the horseshoe region in front and at the rear of the planet in order to minimize the effect of an asymmetry, we prefer not to introduce a source of asymmetry of the horseshoe region. 4 The planet mass is introduced progressively over two orbital periods, and the width of the horseshoe region is measured 20 orbits after the introduction of the planet. We perform a systematic study for three values of the aspect ratio: h = 0.03, h = 0.04 and h = 0.05. For each of these values, we perform 30 calculations with a planetary mass varying in a geometric sequence from 0.05 h 3 to 4 h 3 . The width of the horseshoe region is determined automatically by dichotomy, averaging the results in φ = 1 rad and φ = −1 rad. We find that more accurate results are obtained if the disc is inviscid when q 0.4 h 3 , and viscous for larger masses. For lower mass indeed, a viscous drift of the disc can distort severely the horseshoe region (Masset 2002 ), whereas for high masses, the planet can trigger vortices on the edges of its horseshoe region if the disc is inviscid, which precludes an accurate determination of the width of the horseshoe region. For low masses, we have the scaling (Lega et al. 2015 ; Masset Benítez-Llambay 2016 ): whereas for large masses we recover the scaling found in 2D simulation with softened potential (Masset et al. 2006 ): Using the width normalized to the pressure length scale X s = x s /( hr p ) and denoting with Q the planetary mass normalized to the thermal mass : we can rewrite equations ( 24 ) and ( 25 ) as and The lack of an explicit reference to h in these expressions suggests that the normalized width of the horseshoe region has a universal dependence on the mass expressed in thermal masses. Fig. 6 confirms this expectation. We find that a function that gives the asymptotic behaviours of equation ( 27 ) and ( 28 ) and that matches satisfactorily the width behaviour for intermediate masses is a blend of the asymptotic values at low- and large mass with weights respectively ε and 1 − ε, where ε = 1/(1 + 2 Q 2 ). This yields or Figure 6. Normalized half width of the horseshoe region as a function of the mass in thermal masses. The data from the three different aspect ratios coincide nearly exactly. The solid curve corresponds to equation ( 29 ), the dotted curve to equation ( 27 ) and the dashed curve to equation ( 28 ). This figure should be compared to fig. 7 of Lega et al. ( 2015 ). From now on we will use equation ( 30 ) to evaluate the width of the horseshoe region. As a side result, this leads us to a reasonable definition of an intermediate mass protoplanet as a protoplanet that has a horseshoe width intermediate between the widths given by the low-mass and high-mass scaling (equations 24 and 25 ). Examination of Fig. 6 shows that this broadly corresponds to 0.2 h 3 ≲ q ≲ 2 h 3 . We assume that the corotation torque tends towards its linear value when dissipation (either viscous or thermal) is large, regardless of the
Paper: 2017ApJ...837...68C (fulltext)
in two galaxies (A and C), while velocity dispersion (third row) is unusually low in C for an elliptical galaxy. We present the rotation parameter as a function of radial distance from the galaxy center (bottom) defined as Equation ( 1 ). Galaxies A and C have a fast rotator profile, whereas B shows a typical profile of a slow rotator. Figure 1. Three examples of model galaxies at z = 0. First row: projected stellar density within 4 R eff in log scale. Second row: mass-weighted radial velocity map of the stellar component with projected stellar density contour. Third row: mass-weighted radial velocity dispersion of the stellar component with projected stellar density contour. Last row: radial profile of rotation parameter. Most of the model galaxies in our clusters are early type in morphology, yet their kinematic properties vary significantly, as found in recent IFU spectroscopic observations. Figure 2 shows the model galaxies in comparison with the ATLAS observational data in the rotation parameter versus ellipticity plane. In a simplistic case of circular symmetry, 0.1 if and if , while (dashed curve) has been suggested as a demarcation line between slow and fast rotators. The observational data and models consistently suggest a positive correlation between ellipticity and spin, in the sense that rounder galaxies tend to rotate more slowly. They present a similar sequence in the diagram, but the ATLAS data span a larger area in the parameter space. Our models are rounder and more slowly rotating than the ATLAS sample. This may be partly due to the fact that our galaxies are exclusively in clusters where environmental effects are more dramatic. Of the 1726 model galaxies, 138 (8%) are classified as slow rotators, which is in rough agreement with the observations. The early SAURON data appeared to suggest a separated distribution between slow and fast rotators (Emsellem et al. 2007 ), but our data do not present such a dichotomy. Figure 2. Rotation parameter vs. ellipticity. The model galaxies in our clusters at z = 0 (blue dots and contours) are presented in comparison to the ATLAS data (red dots). All measurements were performed at 1 R eff . The black dashed line shows the empirical demarcation between slow (below) and fast (above) rotators (Emsellem et al. 2011 ). We present the evolution of the galaxy rotation parameter starting from z = 3, because it is nontrivial to extract the galaxy merger history accurately from a cosmological simulation at higher redshifts where galaxy interactions and mergers occurred in a more chaotic manner. At z = 3, most of the model galaxies were fast rotators (Figure 3 ). It seems that galaxies are indeed born generally as rotating disks, as tidal torque theory suggests. Galaxy mass growth was quick in early high-density peaks that later evolve to be galaxy clusters, and thus the cluster galaxies reached a stage where dispersion is more important than rotation () already at z = 3. The peak in the rotation
Paper: 2020MNRAS.496.2558F (fulltext)
of Zr 2 + initially in the excited level versus photon energy. For the online material, we provide the complete set of photoionization cross-sections from ground and excited levels of Sr, Y + , and Zr 2 + in a TOP format (The Opacity Project Team 1995 ), and the complete set of bound–bound photoexcitation transition coefficients for all the electric-dipole-allowed transitions within the same set of levels for these three parent ions. To ensure the files maintain a reasonable downloadable size, of the order of megabytes, we convoluted the cross-sections to reduce their size to around one thousand points in each initial level. The complete photoionization cross-sections, without any convolution, are available for any user under request. These cross-sections can be used for modelling the opacity of astrophysical objects, such as stellar atmospheres. 5 MODELLING OF STELLAR ATMOSPHERE Using sterne , we calculated models for the atmosphere of LS IV-14 116 assuming base parameters given in Table 6 for effective temperature T eff , surface gravity g , microturbulent velocity v turb , fractional abundances by number for hydrogen, helium, carbon, and nitrogen n i , and logarithmic abundances of heavier species relative to solar . For Sr, Y, and Zr, we used two values, one being scaled to the light metals Ca – Mn (the reference model), and the second enhanced by a factor of (+4.3 dex), which corresponds approximately to the overabundances observed in LS IV-14 116. Table 6. Model atmosphere parameters for LS IV-14 116. We construct 10 different models in three groups. All models include the photoionization cross-sections calculated here for Sr 0 , Y + , and Zr 2 + . The reference model has no enhancement in the abundances of these elements. There are four models that include abundances of Sr, Y, and Zr enhanced by +4.3 dex, each individually and all collectively. The third group of models includes a revised linelist with the calculated photoexcitation transitions of the parent ions Sr 0 , Y + , and Zr 2 + , and the ionization products Sr + , Y 2 + , and Zr 3 + . Again, Sr, Y, and Zr are enhanced by +4.3 dex, individually and collectively. In addition, two models were computed with the Zr 2 + and Zr 3 + lines included separately. Fig. 5 (top left-hand panel) shows the temperature structure of model atmospheres T versus optical depth τ 4000 for different abundances of Sr, Y, and Zr. Remaining panels show the temperature relative difference (per cent) to the reference model for various cases. Results imply that the new photoionization cross-section makes negligible difference to the total opacity, even with the high abundances adopted. The photoexcitation transitions make a modest temperature difference in the line-forming region (0.01 τ 4000 1), but only due to a number of very strong Zr lines. This difference corresponds to a temperature increase of up to . Figure 5. The temperature structure of model atmospheres with a -fold (+4.3 dex)
Paper: 2020MNRAS.491.1146Y (fulltext)
linear interpolation; see Fig. 2 . Based on these two cases, we expect that C ( u ) (and its derivatives) may be discontinuous at integer values of u when W is even, and may be discontinuous at half-integer values of u (i.e. values half-way between the integers) when W is odd. For W 3, visible discontinuities remain in the least-misfit gridding function C ( u ) at u 0.5 and u 1.5. This behaviour is a natural extension of the situation for W 1, and may also be understood by considering the set of three grid points on to which each visibility is distributed. According to which side of the mid-point between two grid points a baseline falls, the visibility is gridded on to a different set of three points. For example, if the baseline is u 0 0.49 (where u 0 is an integer), the visibility is distributed among u 0 1, u 0 , and u 0 1, whereas if the baseline is u 0 0.51, the visibility is distributed among u 0 , u 0 1, and u 0 2. Since the value of the gridding function at the edge of its support, i.e. C ( 1.5), is non-negligible, the way in which the visibility is distributed among the grid points is expected to change discontinuously as the baseline passes through u 0 0.5; this is because one grid point disappears from the set and is replaced by another. For W 4, these discontinuities in value and derivatives cease to be visible. In particular, the values of C ( u ) at the edge of the support u W /2 are very small. APPENDIX D: 34-SOURCE SIMULATED DATA Table D1 shows the location and flux of the 34 simulated point sources used in the numerical experiments. Sources are scattered about the phase centre with different fluxes across the full field of view. Table D1. Locations and fluxes of 34 simulated point sources. X and Y are specified in pixel numbers, and represent the distances from the image centre (0,0) to the corresponding point sources. Sources 27, 28, 29, and 30 are at four corners of the field, and are chosen to have the largest fluxes of the 34 sources. APPENDIX E: GENERATION OF THE LOOK-UP TABLE The look-up table for the least-misfit gridding function is assembled as follows: Create [0, 1) as a set of M s numbers spaced equally between 0 and 1. We refer to M s as the sampling rate in this paper. If W 11 then, for each value of , calculate a set of W gridding function values according to equation( 18 ) using matrix A , which can be used repeatedly as it is not dependent on . If W 11 then, for each value of , calculate a set of W gridding function values according to equation( B2 ) using matrix B . The B matrix must be recalculated for each , increasing the computational cost. APPENDIX F: EFFECT OF THE LOOK-UP TABLE Fig. 9 shows that, if the gridding function is calculated precisely, the RMS misfit between the DFT and the FFT is proportional to the square root of the map error ( x ), as expected from equation( 10 ). When, however, we store the gridding function C ( u ) for W /2 u W /2 in a table with WM s points over this range, and use
Paper: 2019ApJ...877...87Z (fulltext)
This channel is more common for those models with strong mass segregation effects, i.e., m BBH ≫ m ⋆ . Figure 7 shows an example of the evolutions of the BBH’s inner and outer orbits in model M2. The outer orbit of the BBH first gradually migrates from the outer parts of the cluster ( a 2 ∼ 10 5 au) into the inner regions ( a 2 ∼ 10 3 au). Along the way the binary–single encounters become more and more frequent. Each binary–single encounter changes the inner orbit slightly, as the incoming field star is much lighter than the components of the BBHs, i.e., m A , m B ≫ m ⋆ . From the top right panel of Figure 7 , we can see that the encounters gradually increase the e 1 of the BBH up to ∼0.8–0.9, and the SMA of the BBH also increases up to about 2 au. When the pericenter of the BBH’s outer orbit approaches to about tens of r t , the KL effects become important, which can increase e 1 rapidly. Note that simultaneously the binary–single encounters also change the inner orbits. Under these two effects, the inner orbit of the BBH finally reaches to a very high eccentricity, and eventually the BBH merges owing to GW radiation. Figure 7. Example of evolutions of BBH’s inner and outer orbits. Left panels: evolutions of the outer orbit of the BBH. The dashed cyan line in the top left panel shows the tidal radius for the BBH with a 2 = 2 au. In the bottom left panel, the evolutions of Ω 2 and I 2 are mainly driven by vector RR. Right panels: evolutions of the inner orbit of the BBH. The dashed lines in the top right panel show the GW timescales. “BySn” means the position after the binary–single encounter; “KL” means the position after the KL oscillations. The filled blue square symbol marks the starting position of the BBHs in each panel. The filled blue star symbol in all panels marks the position where the BBH is merged. The initial condition of the model is given in model M2. The bottom panels of Figure 7 show the evolutions of the orbital orientations of both the inner and outer orbits of the BBH. The orientation of the outer orbit evolves under vector RR and the KL effect (bottom left panel of Figure 7 ). The orientation of the inner orbit is changed cumulatively during multiple binary–single encounters and becomes quite rapid during the KL precesses (bottom right panel of Figure 7 ). The rapid oscillations of the inner orbital orientations can help to trigger the merging of BBHs by the KL effect. For example, before merging, I 2 ∼ π and I 1 ∼ π /2, the relative inclination between the inner and outer orbits is close to π /2, which can help to enhance the eccentricity oscillations and merging of the BBHs (Wen 2003 ). Figure 8 shows another example of the BBH’s evolutions in model M2. In Figure 8 , the SMA of the BBH always tends to decrease owing to multiple binary–single encounters. This is because the BBH is mostly remaining in the hard binary regions, and the binary–single encounters always decrease the SMA for a hard binary. A hard binary
Paper: 2019ApJ...887...81R (fulltext)
is to get the power spectrum. The power spectrum obtained for the fiducial model is depicted in Figure 4 . To get the power spectrum, the 21 cm brightness temperature distribution, , is Fourier transformed and then the dimensionless power spectrum is obtained using the following prescription: The spherically averaged power spectrum is obtained by averaging Δ 2 ( k ) over all possible angles:where is the cosine of the angle that wavevector k makes with the line-of-sight direction. Figure 4. Power spectrum of 21 cm brightness temperature distribution for three redshifts. The power spectrum obtained is of expected magnitude (see, e.g., Mesinger et al. 2011 ) and also seems to show the expected trend with respect to the redshift. 2.5. Salient Features of the Method This method of generating 21 cm maps has the following features: 1. A simple and intuitive method where excursion set formalism is used only for getting the collapse fraction. 2. An explicitly photon conserving method where one can compute the mean neutral hydrogen fraction once one knows, the mean collapse fraction () and the other two reionization parameters ( n ion and n rec ). 3. The expression for mean neutral hydrogen fraction is: . 4. Method is fairly fast. It takes about 3–4 minutes to propagate 10 million photon rays and generate a temperature cube (64 × 64 × 64) on an ordinary intel core i5 machine with 8 GB memory. 5. It is precise even with a small number of photon rays as it automatically assigns more rays to more overdense regions or more luminous objects. 2.6. Stability Analysis To check if the reionization procedure is giving a convergent answer, two tests were performed. In the first test, the step size was halved, twice, from the value chosen earlier, that is, it was made to be 0.025 and 0.0125 of the grid cell size. And, in the second test, the number of photon rays was doubled, again twice, to 20 million and 40 million. The effect of these two procedures on temperature maps and power spectrum is summarized in Tables 1 and 2 respectively. In the case of temperature maps the quantity listed is , where is the absolute value of the difference in brightness temperature measured with respect to the fiducial case, that is, with respect to step size of .05 and ray count of 10 million case. In this case, indicates the average over all the grid cells. Table 1 Percentage Change in Temperature Maps with Respect to the Fiducial Case Table 2 Percentage Change in Power Spectrum with Respect to Fiducial Case As seen from Table 1 , the change in δT b averaged over the whole grid cell is small (≲5%). Also the increment in going from the double to the four times case is smaller than going from the fiducial to the double case. In the case of changing step size, the percentage change is nearly the same. In the case of the power spectrum, in Table 2 , the quantity listed is , that is a fractional change in 21 cm power spectrum averaged over all k values. The smallness of values in the
Paper: 2018MNRAS.480.1740D (fulltext)
that halos with M 10 9 M ⊙ were suppressed if located inside an ionized patch of the IGM (Iliev et al. 2007 , 2012 , 2014 ; Ahn et al. 2012 ; Dixon et al. 2016 ). Our ability to distinguish the progress and impact of reionization locally here has enabled us, not only to demonstrate the causal connection between reionization and low-mass halo suppression, but also to show that the time at which local reionization takes place is correlated with local matter overdensity and with the corresponding local variations of the halo mass function. In addition, we found that, with the exception of suppressed low-mass halos, the SFRs and gas-to-dark matter ratios of halos of any given mass are higher in regions that reionized earlier. For the low-mass halos, after their local reionization, when suppression takes place, curves of SFR and per halo vs thereafter decline by following a universal curve which is independent of re . We also found that relatively rare, highly overdense and, consequently, highly developed regions, which were the first to reionize, produced the vast majority of stars during the EOR, while not consuming more ionizing radiation than regions that reionized later. These early-reionizing regions must have exported a surplus of ionizing radiation, therefore, which helped to reionize and/or maintain the reionization of the late-reionizing regions. Future observations of high-redshift galaxies which look for the inhomogeneous distribution of suppressed and gas-poor low-mass dwarf galaxies and partially-suppressed dwarf galaxies of intermediate mass predicted here will be an excellent probe of the EOR. Since the CoDa I simulation used a constrained realization of the initial conditions that represents the Local Universe, we can also apply the results found here to predict relic signatures of suppression in the present-day galaxies of the Local Universe. For example, the neighbourhoods of MW and M31 were found in CoDa I to have reionized at roughly the same redshift, a bit earlier than the global average reionization redshift, a result which was also found by CoDa I-AMR as described in Aubert et al. ( 2018 ). CoDa I-AMR used an improved calibration of subgrid star formation efficiency so as to finish global reionization by = 6.1, making it easier to relate simulation redshifts directly to observations. In Aubert et al. ( 2018 ), the reionization redshift field was based on the redshifts when each cell first reached X ion = 0.5 (rather than X ion = 0.9 as used here). As structure formation changes the comoving positions of mass over time, the time when a mass element first reaches a location that is 50 per cent ionized is defined as its reionization redshift, . The average reionization redshift of the mass elements that constitute MW and M31 at = 0 were found to be for both, while . The spread of reionization times within each galaxy was as large as ∼400 Myrs (max – min), with a 2σ spread of ∼100 − 150 Myrs. Our results here suggest that the
Paper: 2021MNRAS.503.1568R (fulltext)
arise from a broad range of temperatures at a given density (spanning up to ∼0.5 − 1 dex) as they are coming to the end of the rapid cooling phase, and they have not yet reached thermal equilibrium. Emission from molecules and low-ionization states such as [O i ] and [C ii ] that trace the 100 phase also cover a broad range of temperatures at each density, rather than simply tracking the thermal equilibrium temperature as a function of density. This is likely due to turbulent shock heating. We find that, in our simulations, the dense () gas clouds that condense within the outflowing shell after it cools typically have velocity dispersions up to a few tens of km s −1 . The shocks resulting from this turbulence can heat the gas up to a few thousand K. The photoionization models also assume that the ions and molecules are in chemical equilibrium. To test this assumption in our simulations, we computed the equilibrium abundances of each gas particle at fixed density and temperature, and then repeated the radiative transfer calculations using the equilibrium abundances. The most notable difference is in the [O iii ] line, which is ≈7 − 8 times higher when we set the abundances to equilibrium than when we use non-equilibrium abundances. Other emission lines also show modest deviations when we assume equilibrium abundances, in particular [Ne v ] (up to 80 per cent), [N ii ] (up to 62 per cent), [O iv ] (up to 60 per cent), [N iii ] (up to 44 per cent), [S iii ] (up to 39 per cent), [N ii ] (up to 35 per cent), H (up to 31 per cent), H (up to 29 per cent), and CO (up to 26 per cent). The remaining lines differ by 20 per cent when we use equilibrium abundances. 3.3 Comparison with observations We now compare the predictions from our simulations to observations of outflows in AGN host galaxies. Fig. 7 compares the line luminosity ( L line ) divided by the bolometric AGN luminosity ( L AGN ) plotted against L AGN for a subset of the infrared lines (the full sample can be found in Appendix A ). We divide by L AGN on the y -axis to reduce the dynamic range; a simple linear scaling between L line and L AGN would thus manifest itself as a horizontal trend in these plots. For the simulations ( RFG18 ; grey symbols), our radiative transfer calculations include only one quadrant of the spherical outflow (see Section 2.2 ). We therefore multiply the resulting luminosities by four to obtain the luminosity of the whole outflow. Figure 7. Line luminosity ( L line ) divided by bolometric AGN luminosity ( L AGN ) plotted against L AGN . Our simulations from RFG18 are shown by the grey symbols. In the left column, showing a subset of mid-infrared (IR) lines, we compare to the observations in Veilleux et al. ( 2009 ; V09 ; squares), which we divide according to the AGN classification as PG QSO, ULIRG Sy1, ULIRG Sy2, and ULIRG LINER, as indicated by the colour. In the right column, showing a subset of far-IR lines, we compare to the observations in Herrera-Camus et al.