adsabs/AstroCLIMB huggingface dataset:
Paper: A Search for Relativistic Ejecta in a Sample of ZTF Broad-lined Type Ic Supernovae
Authors: Alessandra Corsi, Anna Y. Q. Ho, S. Bradley Cenko, Shrinivas R. Kulkarni, Shreya Anand, Sheng Yang, Jesper Sollerman, Gokul P. Srinivasaragavan, Conor M. B. Omand, Arvind Balasubramanian, Dale A. Frail, Christoffer Fremling, Daniel A. Perley, Yuhan Yao, Aishwarya S. Dahiwale, Kishalay De, Alison Dugas, Matthew Hankins, Jacob Jencson, Mansi M. Kasliwal, Anastasios Tzanidakis, Eric C. Bellm, Russ R. Laher, Frank J. Masci, Josiah N. Purdum, Nicolas Regnault
Figure caption: Properties of the radio-emitting ejecta of the SNe in our sample for which we detect a radio counterpart (magenta dots, magenta cross, and orange cross), compared with those of GRB-SNe (red stars) and of relativistic-to-mildly relativistic SNe Ic-BL discovered independently of a γ-ray trigger (cyan stars). SN 2018hom is compatible with an ejecta speed ≳ 0.3c, though with the caveat that we only have one radio observation for this SN. SN 2020jqm has a double-peaked radio light curve and we estimate the radio ejecta speed associated with each of these two peaks. We note that the second radio peak places SN 2020jqm in the region of the parameter space occupied by radio-loud CSM-interacting SNe similar to PTF11qcj (orange cross). Finally, for SN 2021ywf we can only set a lower limit on the ejecta speed (≳0.19c) and we cannot exclude a faster ejecta similar to that of iPTF17cw. See Section 3.5.2 for discussion.
Paper: Data-constrained 3D Modeling of a Solar Flare Evolution: Acceleration, Transport, Heating, and Energy Budget
Authors: Gregory D. Fleishman, Gelu M. Nita, Galina G. Motorina
Figure caption: Reconstructed evolution of the parameters of flux tube I (a) and II (b) defining the distribution functions of the nonthermal electrons; all curves are labeled in the panels. In flux tube I ﹩{n}_{b,\max }(I)={10}^{8}﹩ cm−3 and ﹩{N}_{\mathrm{tot},\max }(I)=1.11\times {10}^{33}﹩, while in flux tube II ﹩{n}_{b,\max }(\mathrm{II})=1.2\times {10}^{10}﹩ cm−3 and ﹩{N}_{\mathrm{tot},\max }(\mathrm{II})=2.9\times {10}^{35}﹩. The blue dotted line in panel (a) displays the ratio ﹩{N}_{\mathrm{tot}}(\mathrm{II})/{N}_{\mathrm{tot},\max }(I)﹩ to be directly compared with the red line showing ﹩{N}_{\mathrm{tot}}(I)/{N}_{\mathrm{tot},\max }(I)﹩.
Paper: A Search for Relativistic Ejecta in a Sample of ZTF Broad-lined Type Ic Supernovae
Authors: Alessandra Corsi, Anna Y. Q. Ho, S. Bradley Cenko, Shrinivas R. Kulkarni, Shreya Anand, Sheng Yang, Jesper Sollerman, Gokul P. Srinivasaragavan, Conor M. B. Omand, Arvind Balasubramanian, Dale A. Frail, Christoffer Fremling, Daniel A. Perley, Yuhan Yao, Aishwarya S. Dahiwale, Kishalay De, Alison Dugas, Matthew Hankins, Jacob Jencson, Mansi M. Kasliwal, Anastasios Tzanidakis, Eric C. Bellm, Russ R. Laher, Frank J. Masci, Josiah N. Purdum, Nicolas Regnault
Figure caption: Approximate radio luminosity density for GRBs observed largely off-axis during the subrelativistic phase (black solid, dotted, dashed, and dashed–dotted lines) compared with the radio detections and upper limits of the SNe Ic-BL in our sample. Most of our observations exclude fireballs with energies E ≳ 1050 erg expanding in ISMs with densities ≳1 cm−3. However, our observations become less constraining for smaller energies and ISM density values. For example, most of our radio data cannot exclude off-axis jets with energies E ∼ 1049 erg and n ∼ 0.1 cm−3. See Section 3.5.3 for discussion.
Paper: Data-constrained 3D Modeling of a Solar Flare Evolution: Acceleration, Transport, Heating, and Energy Budget
Authors: Gregory D. Fleishman, Gelu M. Nita, Galina G. Motorina
Figure caption: Evolution of the longitudinal distribution functions of the nonthermal electrons obtained from Equation (4), solid black, and defined in Section 3.1, solid green. The corresponding injection profile is shown by the dashed solid lines. Each panel is scaled separately.
Paper: Data-constrained 3D Modeling of a Solar Flare Evolution: Acceleration, Transport, Heating, and Energy Budget
Authors: Gregory D. Fleishman, Gelu M. Nita, Galina G. Motorina
Figure caption: A subset of panels from Figure 3, but in the same scale normalized to the absolute maximum.
Paper: Data-constrained 3D Modeling of a Solar Flare Evolution: Acceleration, Transport, Heating, and Energy Budget
Authors: Gregory D. Fleishman, Gelu M. Nita, Galina G. Motorina
Figure caption: Comparison of the spatial peak of the injection function (dashed line; cm−3 s−1) and spatial peak of the nonthermal electron number density (solid line).
Paper: Data-constrained 3D Modeling of a Solar Flare Evolution: Acceleration, Transport, Heating, and Energy Budget
Authors: Gregory D. Fleishman, Gelu M. Nita, Galina G. Motorina
Figure caption: Comparison between the injection function smoothed over 3 s to match the Konus–Wind cadence (dashed blue) and the Konus–Wind HXR light curve (solid black).
Paper: ACA CO(J = 2–1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
Authors: Kazuyuki Muraoka, Ayu Konishi, Kazuki Tokuda, Hiroshi Kondo, Rie E. Miura, Tomoka Tosaki, Sachiko Onodera, Nario Kuno, Masato I. N. Kobayashi, Kisetsu Tsuge, Hidetoshi Sano, Naoya Kitano, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Kazuya Saigo, Rin I. Yamada, Fumika Demachi, Kengo Tachihara, Yasuo Fukui, Akiko Kawamura
Figure caption: Distribution of 848 molecular clouds in M33 superposed on the ACA+IRAM combined 12CO(J = 2–1) peak temperature map (left) and the map of Spitzer/IRAC 8 μm flux obtained by Dale et al. (2009; right). The molecular clouds are represented as ellipses, whose sizes and orientation indicate the extrapolated and deconvolved major and minor axes, and the measured position angle. Red ellipses represent high-mass clouds (M CO ≥ 105 M ⊙) and blue ellipses indicate low-mass clouds (M CO < 105 M ⊙). The black line indicates the eventually observed field by ACA.
Paper: ACA CO(J = 2–1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
Authors: Kazuyuki Muraoka, Ayu Konishi, Kazuki Tokuda, Hiroshi Kondo, Rie E. Miura, Tomoka Tosaki, Sachiko Onodera, Nario Kuno, Masato I. N. Kobayashi, Kisetsu Tsuge, Hidetoshi Sano, Naoya Kitano, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Kazuya Saigo, Rin I. Yamada, Fumika Demachi, Kengo Tachihara, Yasuo Fukui, Akiko Kawamura
Figure caption: Integrated intensity maps in 12CO(J = 2–1) from the stand-alone ACA data (left) and the ACA+IRAM data (right). The yellow rectangle indicates the NGC 604 region. A zoomed-in view of this region is shown in Figure 3. The synthesized beam is shown in the lower left corner.
Paper: ACA CO(J = 2–1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
Authors: Kazuyuki Muraoka, Ayu Konishi, Kazuki Tokuda, Hiroshi Kondo, Rie E. Miura, Tomoka Tosaki, Sachiko Onodera, Nario Kuno, Masato I. N. Kobayashi, Kisetsu Tsuge, Hidetoshi Sano, Naoya Kitano, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Kazuya Saigo, Rin I. Yamada, Fumika Demachi, Kengo Tachihara, Yasuo Fukui, Akiko Kawamura
Figure caption: Three-color composite image constructed from the B band (blue), V band (green), and Hα (red) images of M33 taken with the Mayall 4 m telescope (Massey et al. 2006, 2007). The yellow line indicates the observed field of the project code 2018.A.00058.S, and the green line indicates that of 2017.1.00901.S and 2019.1.01182.S.
Paper: ACA CO(J = 2–1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
Authors: Kazuyuki Muraoka, Ayu Konishi, Kazuki Tokuda, Hiroshi Kondo, Rie E. Miura, Tomoka Tosaki, Sachiko Onodera, Nario Kuno, Masato I. N. Kobayashi, Kisetsu Tsuge, Hidetoshi Sano, Naoya Kitano, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Kazuya Saigo, Rin I. Yamada, Fumika Demachi, Kengo Tachihara, Yasuo Fukui, Akiko Kawamura
Figure caption: Zoomed-in view of the NGC 604 region in Figure 2. Diffuse molecular-gas components between bright GMCs are well recovered. The synthesized beam is shown in the lower left corner.
Paper: ACA CO(J = 2–1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
Authors: Kazuyuki Muraoka, Ayu Konishi, Kazuki Tokuda, Hiroshi Kondo, Rie E. Miura, Tomoka Tosaki, Sachiko Onodera, Nario Kuno, Masato I. N. Kobayashi, Kisetsu Tsuge, Hidetoshi Sano, Naoya Kitano, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Kazuya Saigo, Rin I. Yamada, Fumika Demachi, Kengo Tachihara, Yasuo Fukui, Akiko Kawamura
Figure caption: Frequency distributions of the ratio between the extrapolated value and the observed value for the second moment of the major axis (σ maj;  left), that of the minor axis (﹩{\sigma }_{\min };﹩ center), and velocity dispersion (right), respectively.
Paper: ACA CO(J = 2–1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
Authors: Kazuyuki Muraoka, Ayu Konishi, Kazuki Tokuda, Hiroshi Kondo, Rie E. Miura, Tomoka Tosaki, Sachiko Onodera, Nario Kuno, Masato I. N. Kobayashi, Kisetsu Tsuge, Hidetoshi Sano, Naoya Kitano, Shinji Fujita, Atsushi Nishimura, Toshikazu Onishi, Kazuya Saigo, Rin I. Yamada, Fumika Demachi, Kengo Tachihara, Yasuo Fukui, Akiko Kawamura
Figure caption: Integrated intensity map in 13CO(J = 2–1) emission of M33 obtained by the ACA 7 m array (left) and its zoomed-in view of the NGC 604 region (right). The synthesized beam is shown in the lower left corner.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Left: volume filling factor against compressive strength of this work and previous studies. The monomer radii are r 0 = 0.6, 0.75, 0.85, 2.4, and 9 μm from dark to light colors in all line types. The thick and thin solid lines show the numerical results of silicate aggregates of this work and Seizinger et al. (2012), respectively. The other parameters of silicate are listed in Table 1, but we adopt that C v = 3 × 10−7. The dashed and dashed–dotted lines show the experimental results of SiO2 aggregates with a monodisperse (Güttler et al. 2009) and polydisperse monomer size distribution (Omura & Nakamura 2021), respectively. We use Equation (17) and Equation (19) as the fitting results of Güttler et al. (2009), Omura & Nakamura (2021), respectively. We also use the values listed in Table 2. The dotted lines show the analytical formula ϕ comp (Equation (12)) of silicate aggregates. Right: the dotted lines are the same as those of the left panel, but we change the surface energy to be γ = 210 mJ m−2.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Top: volume filling factor against compressive strength of dust aggregates with different monomer radii and materials. The colors show 0.1 μm radius ice (blue), 1.0 μm radius ice (green), 0.1 μm radius silicate (orange), and 1.0 μm radius silicate (red). The parameters are listed in Table 1. The solid, dashed, and dotted lines show the calculated volume filling factor ϕ calc, the corrected analytical formula ϕ comp (Equation (12)), and the previous formula ϕ K13 (Equation (13)), respectively. Bottom: absolute error between the calculated volume filling factor and that of the corrected analytical formula (solid) and between the calculated volume filling factor and that of the previous formula (dotted).
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates that contain ice monomers of 0.1 μm radius with different critical rolling displacements ξ crit. The critical rolling displacements are ξ crit = 2, 4, 8, 16, and 32 Å from dark to light colors. We adopt C v = 3 × 10−7, for ξ crit = 2, 4, 16, and 32 Å because of the calculation cost. The other parameters are fiducial values in Table 1. The solid, dashed, and dotted lines show the averages of the 10 runs, our corrected analytical formula (Equation (11)), and the analytical formula (Equation (9)) of Kataoka et al. (2013), respectively.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates with different monomer radii and materials. The colors represent 0.1 μm radius ice (blue), 1.0 μm radius ice (green), 0.1 μm radius silicate (orange), and 1.0 μm radius silicate (red). The parameters are listed in Table 1. The solid, dashed, and dotted lines show the averages of the 10 runs, our corrected analytical formula (Equation (11)), and the analytical formula (Equation (9)) of Kataoka et al. (2013), respectively.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Energy dissipations of (a) 0.1 μm radius ice, (b) 1.0 μm radius ice, (c) 0.1 μm radius silicate, and (d) 1.0 μm radius silicate. The energy dissipation mechanisms are the normal (orange dotted), sliding (green dashed), rolling (red solid), twisting (purple dashed), damping motions (brown dotted), and the total of all energy dissipations (blue solid). The parameters are listed in Table 1.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Top: upper limit of compressive strength for which monomer disruption can be neglected as a function of monomer radius (Equation (15)). The blue and orange lines are for ice and silicate, respectively. The parameters are listed in Table 1. Bottom: upper limit of volume filling factor for which monomer disruption can be neglected as a function of monomer radius (Equation (16)).
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Outline of our simulations. Each aggregate contains 16,384 ice monomers with a radius of 0.1 μm. Each white cube shows the computational box with periodic boundaries. We compress it three-dimensionally as shown by the white allows. We put each volume filling factor and size of the computational box above and below each snapshot, respectively. In our simulations, the final volume filling factor can exceed a value for the closest packing ﹩\sqrt{2}\pi /6=0.74﹩ because monomers can deform elastically.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates that contain ice monomers of 0.1 μm radius. The colored solid lines show the results for 10 different initial BCCAs. The black solid line shows the average of the 10 runs. The dashed line shows our corrected analytical formula (Equation (11)). The dotted line shows the analytical formula (Equation (9)) of Kataoka et al. (2013).
Paper: Reacceleration of Galactic Cosmic Rays beyond the Knee at the Termination Shock of a Cosmic-Ray-driven Galactic Wind
Authors: Payel Mukhopadhyay, Enrico Peretti, Noémie Globus, Paul Simeon, Roger Blandford
Figure caption: Proton intensity at the disk as a function of energy as the diffusion coefficient downstream of the GWTS is increased. The reference model assumes the downstream diffusion (D 2) is an order of magnitude smaller than the diffusion coefficient right upstream (D 1) of the shock. As the downstream diffusion coefficient is increased, the maximum energies achieved at the GWTS decrease, and the overall flux of reaccelerated particles back to the disk also decreases.
Paper: HOW-MHD: A High-order WENO-based Magnetohydrodynamic Code with a High-order Constrained Transport Algorithm for Astrophysical Applications
Authors: Jeongbhin Seo, Dongsu Ryu
Figure caption: Normalized magnetic energy, ﹩{B}^{2}/{B}_{0}^{2}﹩, of the circular magnetic loop advecting across a diagonal direction in the elongated 2D domain of [−1, 1] × [ − 1/2, 1/2] with 256 × 128 grid cells. The test results using the codes with CT6, CT4, and CTorg are shown after advecting the computational domain twice (t = 2; top panels) and 20 times (t = 20; bottom panels). The profiles of ﹩{B}^{2}/{B}_{0}^{2}﹩ along the white dotted lines are shown in the top panel of Figure 5.
Paper: HOW-MHD: A High-order WENO-based Magnetohydrodynamic Code with a High-order Constrained Transport Algorithm for Astrophysical Applications
Authors: Jeongbhin Seo, Dongsu Ryu
Figure caption: Schematic pictures displaying the quantities involved in our CT algorithm in (a) 2D and (b) 3D geometries. While the fluid variables in the state vector, including the magnetic field  B , are given at grid cell centers, the divergence-free magnetic field,  b , is defined at grid cell interfaces, and the advective flux, Ω, is defined at grid cell edges. The sequence of the CT algorithm is summarized in the bottom box.
Paper: Reacceleration of Galactic Cosmic Rays beyond the Knee at the Termination Shock of a Cosmic-Ray-driven Galactic Wind
Authors: Payel Mukhopadhyay, Enrico Peretti, Noémie Globus, Paul Simeon, Roger Blandford
Figure caption: Proton intensity at the disk as a function of energy as the terminal speed (v t = v gas) is varied. The reference model has v t ∼ 870 km s−1. Decreasing this speed to 200 km s−1 makes the contribution of the reaccelerated particles negligible at the disk. Increasing the speed to 1000 km s−1 increases the maximum energy achieved in the reacceleration mechanism.
Paper: HOW-MHD: A High-order WENO-based Magnetohydrodynamic Code with a High-order Constrained Transport Algorithm for Astrophysical Applications
Authors: Jeongbhin Seo, Dongsu Ryu
Figure caption:  L 1 errors due to the damping of three linear MHD waves—Alfvén, fast, and slow—at different resolutions. The waves propagate in 3D space at an oblique angle to the coordinate axes. Here and in the following figures, the results with the CT algorithm employing sixth-order FD (ν = 6 in Equation (27)) and fourth-order FD (ν = 4 in Equation (27)) are labeled as CT6 and CT4, respectively, and the results with the CT algorithm of Ryu et al. (1998) are labeled as CTorg. The lines showing the fifth- and second-order convergences are drawn for comparison.
Paper: HOW-MHD: A High-order WENO-based Magnetohydrodynamic Code with a High-order Constrained Transport Algorithm for Astrophysical Applications
Authors: Jeongbhin Seo, Dongsu Ryu
Figure caption: Top panel: normalized magnetic energy, ﹩{B}^{2}/{B}_{0}^{2}﹩, of the circular magnetic loops advecting across the 2D computational domain, along the white dotted lines shown in Figure 4. Bottom panel: time evolution of the average of the normalized magnetic energy of the 2D circular magnetic loops in test simulations with 256 × 128, 128 × 64, and 64 × 32 grid cells.
Paper: HOW-MHD: A High-order WENO-based Magnetohydrodynamic Code with a High-order Constrained Transport Algorithm for Astrophysical Applications
Authors: Jeongbhin Seo, Dongsu Ryu
Figure caption: Normalized magnetic energy, ﹩{B}^{2}/{B}_{0}^{2}﹩, of the circular magnetic loop advecting with a small angle of ﹩\tan \theta =1/100﹩ in the 2D domain of [−1, 1] × [ − 1/2, 1/2] with 256 × 128 grid cells. Here θ is the advection angle with respect to the x-axis. The test results using the codes with CT6 are shown at (a) t = 2 and (b) t = 45.
Paper: HOW-MHD: A High-order WENO-based Magnetohydrodynamic Code with a High-order Constrained Transport Algorithm for Astrophysical Applications
Authors: Jeongbhin Seo, Dongsu Ryu
Figure caption: Top panel: L 1 errors at different resolutions due to the damping of a circularly polarized Alfvén wave propagating in 3D space at an oblique angle to the coordinate axes. The lines showing the fifth- and second-order convergences are drawn for comparison. Bottom panel: L 1 errors vs. CPU time on a computer with 20 cores.
Paper: Physical Evolution of Dark Matter Halo around the Depletion Boundary
Authors: Hongyu Gao, Jiaxin Han, Matthew Fong, Y. P. Jing, Zhaozhou Li
Figure caption: Evolution of MFR, radial velocity, density, and bias profiles stacked in a galactic-size halo bin (left column) and a cluster-size halo bin (right column) is displayed in the four rows from top to bottom. The legend for the top panel shows the number of the complete halo sample, as well as the range of halo mass at z = 0. Solid lines with different colors correspond to different redshifts, and the median halo mass at each redshift is also presented. The MFR profiles of the galactic-size halo bin are enlarged to show them more clearly. The r id, r cd, and r  t  are denoted as different markers. The MFR and velocity profiles are all scaled by the virial quantities at z = 0. As a reference, we also show ﹩\rho \left(r\right)\propto {r}^{-2}﹩ as a gray dashed line in the third row.
Paper: Physical Evolution of Dark Matter Halo around the Depletion Boundary
Authors: Hongyu Gao, Jiaxin Han, Matthew Fong, Y. P. Jing, Zhaozhou Li
Figure caption: Evolution of depletion radii. The left and right panels correspond to the galactic-size and cluster-size bins in Figure 1, respectively. r id, r cd, r  t , and r vir are marked as different symbols. The three stages of the outer halo are divided by regions with different gray levels. The ratio of each radius to the virial radius is also shown in the bottom panel. We also display 2.0 × r vir as the blue dotted lines, which agree well with the r id at each redshift. To compare with the expansion rate of the universe, we plot r ∝ a as the orange dotted line.
Paper: Physical Evolution of Dark Matter Halo around the Depletion Boundary
Authors: Hongyu Gao, Jiaxin Han, Matthew Fong, Y. P. Jing, Zhaozhou Li
Figure caption: Left: evolution and merger history of a pair of halos. The small halo is placed at the origin. The distributions of dark matter near the two halos are projected perpendicular to their alignment with a depth of 6 Mpc h −1. The black solid (dotted) inner circles represent the virial radii of the large (small) halo at different redshifts. The outer circle represents 2.5 times the virial radius, which is an approximate estimation of the characteristic depletion radius. The small halo eventually becomes a subhalo (highlighted with a red circle) of the large one at z = 0. Right: evolution of the density and radial velocity profiles around the small halo.
Paper: Physical Evolution of Dark Matter Halo around the Depletion Boundary
Authors: Hongyu Gao, Jiaxin Han, Matthew Fong, Y. P. Jing, Zhaozhou Li
Figure caption: Evolution of the feeding rate, MIR(r id), for galactic-size halos, scaled by the virial quantities at z = 0 (left axis and black line). The feeding rate peaks at around a = 0.35 (z ∼ 2). This coincides with the transition between fast- and slow-growth phases of the inner halo, which can be identified at the peak time of the V vir H(z)1/4 evolution according to Zhao et al. (2003). The V vir H(z)1/4 (right axis and blue line) is normalized by its z = 0 value. The three evolution stages of the outer halo are also shown in different shadings.