adsabs/AstroCLIMB huggingface dataset:
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: How the initial position of the accreting matter changes over time in the high-spin a* = 0.9 MAD (left) and SANE (right) simulations. Top: initial cylindrical radius rcyl of accreting matter (vertical axis) vs. the time it crosses the event horizon (horizontal axis), normalized within each time bin. Each horizontal slice indicates the time when matter at a given rcyl in the initial torus accretes through the event horizon. In the MAD flow, small radii are depleted more rapidly, and the characteristic source radius grows steadily with time; by t ≈ 50,000 GM/c3, the source region lies at large radii and shows little secular evolution. In the SANE simulation, once accretion begins from the radius of maximum initial mass, the growth rate of the mean source radius slows. Solid and dashed lines indicate the mean and standard deviation of each distribution, respectively. Bottom: same as above but showing the initial height z above the midplane. The width of the z-distribution reflects both turbulent vertical mixing and the radial variation of the initial torus scale height (see Figure 1).
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Temporal and spatial distribution of radial mixing in the same high-spin MAD (red) and SANE (blue) simulations shown in earlier figures. Left: growth of tracer particle distribution width σ over time, starting from a population of particles that occupies a particular radial shell (r0; top/bottom) at time offset = 0. The spread of the distribution grows superdiffusively at early times for both simulations, with average behavior over the later part of the simulation (bold lines) bracketed by analytic scalings σ ∝ t 0.95 (dashed) and σ ∝ t 0.75 (dotted). Fainter lines show results from individual time segments. At later times, changes in the width reflect the mixing history from smaller radii and do not approach a true steady state. Right: initial growth rates of σ(time offset), measured from the local slope of σ shortly near the point where time offset = 0, shown as a function of radius. Measurements near the horizon are excluded where truncation of the distribution prevents reliable recovery (see Appendix B).
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Mixing times as a function of radius for matter within the midplane in the high-resolution simulations. We define the mixing time as the time required for a narrow ring of tracer particles to spread radially to a standard deviation of 1 GM/c2, assuming a constant rate of spread equal to the initial rate near time offset Δt = 0. Mixing is consistently faster in MAD simulations than in SANE simulations. Retrograde flows also tend to exhibit shorter mixing times than their prograde counterparts. Mixing times at small radii generally decrease as matter approaches the black hole, although this trend is not strictly monotonic across all models and may reflect the plasma state being “frozen” at larger radii before plunging toward the black hole.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Same as Figure 4 but for the high-spin a* = 0.9, high-resolution SANE simulation. As can be seen in the top panels, the tracer particles are advected inward and spread more slowly than in the MAD case. The bottom panels similarly reflect this slower evolution, as the inferred advection velocity is closer to zero.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Same as Figure 7, but for the high-spin a* = 0.9 SANE simulation shown previously in Figure 5. The tracer distribution shows evidence of both inward advection and spreading, although both rates are slower compared to the MAD flow.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: An illustration of the role of the concept of the merger duration in our methodology. By identifying a descendant at snapshot tm,e (the postmerger time) and its pair of progenitors at snapshot tm,s (the premerger time), we consider the merger to have occurred at some indeterminate time in between (marked by thin black arrows). Since we assume the merger is observable for a time duration T that is centered around the actual merger occurrence time, namely as early as around tm,s (green) or as late as around tm,e (blue), any analysis time point ta in the range tm,s − T/2 ≤ ta ≤ tm,e + T/2 (red) could potentially be a time at which the merger is observable. The probability p that the analysis time ta is an instant that is actually covered by the T-long duration window equals the ratio of two particular time segments, as described by Equation (1) and illustrated here (dashed) for an arbitrarily chosen ta.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Initial distribution of coordinate-frame torus mass (﹩\rho {u}^{t}\sqrt{-g}﹩) as a function of black hole spin. Top: midplane mass density vs. radius. All tori peak within a narrow range, r ≈ 53–56 GM/c2, but those in higher-spin spacetimes extend farther outward. Middle: radial distribution of total mass, integrated over θ. Because the more extended tori place more material at large radii, the peak radius of the mass distribution ranges from r ≈ 105–135 GM/c2 across spins. Bottom: cumulative enclosed mass vs. radius. The a* = 0.9 torus contains nearly 2.5× as much mass as the a* = −0.9 case. The half-mass–radius also varies widely, from r ≈ 145 GM/c2 to r ≈ 235 GM/c2.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Initial location of material accreting through the event horizon near t = 40,000 GM/c3 across all simulations. MAD simulations are shown in red and SANE simulations in blue, with marker shape indicating resolution (circles for 8/8 and triangles for 16/8, offset for clarity). Top: mean and standard deviation of the initial rcyl distribution of accreting material. MAD simulations show systematically larger mean radii and broader spreads than their SANE counterparts. The mean radius for MAD models also increases from strongly retrograde to strongly prograde spin, although this may partly reflect differences in the initial torus size combined with larger accretion rates (see Figure 1). Bottom: standard deviation of the initial z-position for the same tracer populations. The trends in this vertical spread correlate with the initial rcyl shown in the top panel: As the initial radial position increases, the accretion flow is able to draw from a larger range of vertical positions, since the initial torus increases in height with radius.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: A demonstration of the dependence on the snapshot separation in a naïve approach to determining whether a merger is a multiple merger, i.e., between more than two galaxies at once. On the left, case (a) shows how small snapshot separations delineates the mergers in time into a sequence of two binary mergers, with one descendant at snapshot tm,e−1 and the final descendant in snapshot tm,e, implying this case would not be counted as a multiple merger. In case (b) on the right, however, a larger difference in time between adjacent snapshots tm,e and tm,e−2 implies that the three galaxies in snapshot tm,e−2 appear to be merging all together into one descendant at tm,e, naïvely appearing as a multiple merger. This demonstrates that relying on the connectivity of the merger tree for identifying multiple mergers is fraught, since that connectivity significantly depends on the snapshot separation.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Accretion rate as a function of time across simulations. Top: time-averaged accretion rates measured over 15,000–40,000 GM/c3. Filled circles show normalized rates, while “×” marks show the corresponding gross (unnormalized) values. Center and bottom: heat maps of accretion rate (color) vs. time for each standard-resolution simulation. Rates are normalized by the total initial torus mass (see Figure 15), so color corresponds to the fraction of disk mass crossing the horizon per GM/c3. MAD simulations accrete significantly faster than their SANE counterparts and show a pronounced transient burst between 5000 and 10,000 GM/c3, especially in retrograde, high-spin cases. Although the gross accretion rate in MADs peaks near low prograde spins, the normalized rate increases steadily toward a* = −0.9 because the initial torus mass decreases in that direction.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Example of how we estimate the mean and spread from truncated particle distributions near the event horizon. Panels show the evolution of four different particle distributions (colors) over time (lines of the same color) across four different simulations (panels). The distributions are color-coded based on which radial shell they occupy at zero-time offset Δt = 0. Each distribution function is plotted three times: once when the particles are within the target radial shell (top-hat shape) as well as one step before that time and one step after. As time advances, the distributions advect inward and broaden, and the left tails truncate as particles cross the event horizon. To correct for biases due to the truncation, we fit the untruncated portion of each distribution with a rescaled copy of the distribution one step before the particles are in the target radial shell (see main text). Solid lines show measured histograms; dotted lines show the reconstructed fits. This procedure recovers the centroid and width of the distributions despite the incomplete particle position data.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Comparison of grid zone size between Cartesian Kerr–Schild coordinates (CKS; blue) and exponential spherical Kerr–Schild coordinates (EKS; red). Left: grid zone sizes in the midplane showing the 16/8 CKS simulation grid and the EKS (nr, nθ, nϕ = 288, 128, 128) simulation grid often used in spherical-grid-based codes (Event Horizon Telescope Collaboration et al. 2019, 2021, 2022, 2023, 2024; G. N. Wong et al. 2022; V. Dhruv et al. 2025). Right: comparison between zone widths for CKS 8/8 and 16/8 grids against reference EKS grid resolutions 288, 128, 128 (top) and 384, 192, 192 (bottom). The EKS grids have nonzero aspect ratios, so zone widths in both the ﹩\hat{r}﹩- and ﹩\widehat{\phi }﹩-directions are included as separate lines. Circles mark the location of each grid zone.
Paper: Mass Transport, Turbulent Mixing, and Inflow in Black Hole Accretion
Authors: George N. Wong, Lia Medeiros, James M. Stone
Figure caption: Disk draining in the long-duration, high-spin a* = 0.9 MAD and SANE 8/8 simulations. Top: fraction of the initial torus mass that has left the domain (either through the event horizon or via outflows) as a function of time. Bottom left and bottom center: fraction of the initial mass at each radius (vertical axis) that has exited the domain over time. Contours indicate the times at which 10%, 20%, …, 90% of the initial mass at a given radius has been removed, highlighting how depletion rates vary across the disk. Bottom right: initial radial distribution of mass in the torus (same for MAD and SANE). The draining rate depends on both disk structure and magnetic configuration, with MAD systems depleting more rapidly at larger radii.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Merger fractions versus galaxy stellar mass as measured from the simulations with different methods at the fiducial point in parameter space. The fraction ft,RG15 derived from TNG100 following RG15 (cyan) is in good agreement with the RG15 fitting function ft,RG15 fit (blue) even though the latter was derived by RG15 for the original Illustris simulation, lending confidence to our implementation of the RG15 method. There is also very good agreement between the total merger fraction ft derived with our approach (green) and that of the RG15 methods, again providing a good check on our approach. One of the key results of this work is that the multiple merger fraction fm (red) is consistently 1–1.5 dex below the total merger fraction ft.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: A heatmap of the ratio of the multiple to total merger fraction fm/ft versus ft, based on the Poisson process toy scenario across the parameter space. At small values, fm/ft ≃ 0.5ft, namely at a fixed total merger fraction, the multiple merger fraction is smaller than in the actual simulated results shown in Figure 8, the average of which is repeated here (red). At values approaching unity, the slope increases toward fm/ft = ft = 1. The two black lines indicate two power-law relations, one linear that fits the Poisson model and one with an index of 0.68 that fits the simulation results.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: A heatmap of the ratio of the multiple to total merger fraction fm/ft versus ft, as measured from the simulations across our full parameter space, namely all galaxy mass bins, redshifts, merger minimum mass ratios, merger tree flavors, merger duration assumptions, etc. The red curve represents a running average. Albeit with some scatter, the results are best fit as ﹩{f}_{m}/{f}_{t}\approx 0.5{f}_{t}^{2/3}﹩.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Merger fractions versus stellar mass, broken down by the number of mergers the galaxy is undergoing at the analysis time. While the total (green) and multiple (red) merger fractions (repeated from Figure 6) include galaxies with at least one or two mergers (respectively), the other symbols represent fractions of galaxies undergoing a certain number of concurrent mergers exactly: one merger for “binary” (magenta), two for “trinary” (gray), and three for “quaternary” (yellow). Across the parameter space generally, and specifically for the fiducial case shown here, the total merger fraction is dominated by the binary fraction and the multiple merger fraction is dominated by the trinary fraction. Also, there is some suggestive vertical compression as the multiplicity increases, e.g., the logarithmic difference between the binary and trinary fractions is greater than that between the trinary and quaternary fractions.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Illustration of descendant and progenitor skips. On the left is a descendant skip, where the mass of the descendant (at time tm,e) is less than the total mass of its progenitors. Since galaxy masses typically increase over time, this is likely a skip, so we set the mass of the descendant to be equal to that of its progenitors at time tm,e−1. On the right, the mass of the progenitors at time tm,e−1 is much less than that of its descendant at time tm,e. This is a case of a likely progenitor skip, which we remedy by creating at time tm,e−1 a “virtual progenitor” galaxy with mass equal to the mass of the descendant that is in excess of the sum of the masses of its progenitors. Since there is some ambiguity with regards to when such a remedy is appropriate (since some mass growth can truly occur via unresolved objects), we generate and compare results both with and without these virtual progenitors.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Merger fractions versus stellar mass for the toy scenario where merger occurrence is a Poisson process. The total merger fraction in this scenario (light green) is very close to the actual total merger rate in the simulation (dark green; repeated from Figure 6). However, the multiple merger rate in this toy scenario (light red) is ≈0.5 dex lower than that measured in the simulation (dark red; repeated from Figure 6).
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: An illustration of the maximum past mass algorithm for determining the mass ratio of a merger, following RG15. The smaller progenitor (“next progenitor,” here NP) is followed backwards along its first progenitor branch and the time of its maximum past mass is determined (depicted here at tm,e−2 with the largest circle along the left branch). Its mass at that time is then compared to the mass of the primary galaxy (“first progenitor,” here FP) at that same time, and their ratio is recorded as the merger’s mass ratio.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: A comparison of the merger fractions measured from TNG100-1 (dark colors) and Illustris-1 (light colors), as a function of stellar mass. Notable differences are the greater second derivative of the mass dependence of the TNG merger fractions, and the tendency toward overall higher fractions in TNG, compared to Illustris, toward lower redshifts.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Illustration of the toy scenario where mergers occur at constant intervals on a galaxy’s timeline. Here tn+1 − tn = tn − tn−1 for any n and the merger rate is therefore R = 1/(tn+1 − tn). An analysis time ta is marked at an arbitrary location, representing the independence between the arbitrary times at which simulation snapshots are written and the merger occurrence sequence.
Paper: Supersonic Turbulence in Primordial Halos: a Comparison with and without the Stream Velocity
Authors: Avi Chen, William Lake, Claire E. Williams, Blakesley Burkhart, Smadar Naoz, Shyam H. Menon, Federico Marinacci, Mark Vogelsberger, Naoki Yoshida
Figure caption: Comparison of kinematic properties of gas within halos in streaming (vbc = 2σvbc, blue) and no-streaming (vbc = 0σvbc, red), simulations at z = 20. Panels from top to bottom show turbulent velocity, rms velocity, radial velocity, and rotational velocity, each plotted against total halo mass (gas+DM). Shaded color bands indicate the 1σ scatter around the mean values. Streaming enhances turbulent and rms velocities in low-mass halos (M ≲ 106 M⊙) but suppresses them in high-mass halos (M  ≳  106 M⊙), with a clear turnover around the characteristic mass scale marked by the gray shaded band. Rotational velocities are amplified across all halo masses in the presence of streaming, while radial velocities are higher in high-mass halos without streaming.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: PDFs of the time-to-closest-merger Δt1 in various scenarios. The simulation results (gray), at our fiducial values (besides that Tfac = 2 instead of Tfac = 1, for visual clarity reasons; otherwise, the results are essentially the same), and their associated KDE (purple) are compared against toy models. The model that assumes a constant time between mergers (blue and green, for the cases that do not, or do, respectively, take into account the fact that only a fraction F of galaxies have any mergers with the fiducial mass ratio in their history) provides a reasonable match in this case but not at higher or lower redshifts (not shown). The model that assumes a Poisson process (red and orange, similarly accounting for F, respectively) provides a poor fit throughout the parameter space.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Histograms of the times to various close mergers. The histogram of the time elapsed to the overall second-closest merger Δt2 (gray) is presented along with distributions of the times elapsed to four other related types of mergers: the closest pre-ta merger (red), the second-closest pre-ta merger (blue), the closest post-ta merger (magenta), and the second-closest post-ta merger. The histogram area at values of Δt2 ≤ T/2 (left of the vertical dashed line) is of primary interest for the quantification of multiple mergers. In that regime, the second-closest merger is usually the first pre-ta merger (red). The first post-ta merger (magenta) usually comes close, but only in the bin containing Δt = 0 are they roughly equal.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: Histograms of the time-to-closest-merger Δt1, at our fiducial configuration (besides that Tfac = 2 instead of Tfac = 1, for visual clarity reasons; otherwise, the results are essentially the same). The overall Δt1 histogram (gray) is presented along with those for the closest previous (red) and closest subsequent (magenta) mergers. The Δt1 = T/2 threshold, which relates this histogram to the merger fraction, is shown by a vertical dashed line.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: PDFs of the time-to-second-closest-merger Δt2, analogously to the ones for Δt1 shown in Figure 14. The simulation results (gray) and their associated KDE (purple) are compared against our two toy models, which provide very poor fits to the data. The toy models, in particular, imply a zero or otherwise low value at Δt2 ≤ T/2, which is directly tied to the multiple merger fraction.
Paper: Supersonic Turbulence in Primordial Halos: a Comparison with and without the Stream Velocity
Authors: Avi Chen, William Lake, Claire E. Williams, Blakesley Burkhart, Smadar Naoz, Shyam H. Menon, Federico Marinacci, Mark Vogelsberger, Naoki Yoshida
Figure caption: Percent difference in the turbulence with and without streaming at different redshifts. Shaded color bands indicate 1σ uncertainties, computed via standard error propagation of the ratio of means. The percent difference generally declines as a function of redshift as the stream velocity decays. The turnover at M ∼ 106 M⊙ also shifts slightly toward higher masses at earlier times and is marked by the gray shaded band.
Paper: On the Frequency of Multiple Galaxy Mergers in ΛCDM Cosmological Simulations
Authors: Jonathan Mack, Shy Genel
Figure caption: A comparison between the merger durations we use in this work (blue), Tref (solid), 0.5Tref, and 2Tref (dashed), and the timescale over which the merger rate evolves (red), which we define as the ratio between the merger rate and its time derivative. The latter is shown for 16 distinct combinations of mass and mass ratio and calculated based on the RG15 formula, demonstrating that it is primarily a function of redshift and not of other parameters. Evidently, throughout our parameter space, the merger rate evolves on longer timescales than the merger duration, making the Poisson process assumption a good one locally at any given time.