adsabs/AstroCLIMB huggingface dataset:
Paper: What Physical Factors Influence the Transformation between Blue Supergiants and Red Supergiants?
Authors: Yun He, Hanfeng Song, Georges Meynet, Andre Maeder, Ruiyu Zhang, Ying Qin, Shitao Qi, Qingli Li, Qiong Zhan
Figure caption: The evolution of the ratio of surface mass fraction of 14N to 12C for the models with different initial stellar masses, metallicities, and rotational velocities. Panels (a1), (a2), (a3) are for 16 M⊙ stars with different initial metallicities and rotational velocities. Panels (b1), (b2), (b3) are the same but for 24 M⊙ stars. Panels (c1), (c2), (c3) are the same but for 32 M⊙ stars. Black, red, green, and blue lines indicate the models calculated with different initial rotational velocities of 0 km s−1, 200 km s−1, 300 km s−1, and 400 km s−1, respectively. The observations of Large Magellanic Cloud supergiants are from R. E. Luck & D. L. Lambert (1992) and I. Hunter et al. (2009). The observations of Galactic supergiants are from N. Przybilla (2002), I. Hunter et al. (2009), and C. Georgy et al. (2021). The filled pentagon represent α Cyg variables.
Paper: What Physical Factors Influence the Transformation between Blue Supergiants and Red Supergiants?
Authors: Yun He, Hanfeng Song, Georges Meynet, Andre Maeder, Ruiyu Zhang, Ying Qin, Shitao Qi, Qingli Li, Qiong Zhan
Figure caption: The evolution of stellar radius for models with different initial stellar masses, metallicities, and rotational velocities. Panels (a1), (a2), (a3) are for 16 M⊙ stars with different initial metallicities and rotational velocities. Panels (b1), (b2), (b3) are the same but for 24 M⊙ stars. Panels (c1), (c2), (c3) are the same but for 32 M⊙ stars. Black, red, green, and blue tracks indicate the models calculated with rotational velocities of 0 km s−1, 200 km s−1, 300 km s−1, and 400 km s−1, respectively.
Paper: What Physical Factors Influence the Transformation between Blue Supergiants and Red Supergiants?
Authors: Yun He, Hanfeng Song, Georges Meynet, Andre Maeder, Ruiyu Zhang, Ying Qin, Shitao Qi, Qingli Li, Qiong Zhan
Figure caption: Left panels: the mass of the hydrogen envelopes as a function of evolutionary age. Stars of mass (a) 16 M⊙, (b) 24 M⊙, and (c) 32 M⊙ with different initial metallicities and rotational velocities of 0, 200, 300, and 400 km s−1. Right panels: the same as left panels but for the evolution of the mass of the helium core.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: Error in total mass, kinetic energy, and internal energy after first-order nearest-neighbor interpolation of AREPO mesh data onto an AMR FLASH grid at various levels of maximum refinement. Solid lines represent the error from FLASH grids that are allowed to adaptively refine across the entire domain. Dashed lines are errors from FLASH grids that only refine in a region of interest 75 pc in radius, representing 4.12% of the original domain volume, as seen in Figure 5.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: Demonstration of VorAMR’s zoom-in capability. Any user-defined cubic region within the full computational domain (left) can be zoomed in to and evolved as a normal Torch simulation. Here, two zoom-in regions are shown: a 300% zoom (center) and a 700% zoom (right). All panels show projected gas density and have a maximum refinement level of 5, corresponding to a resolution of 5.6, 1.87, and 0.80 pc from left to right. Both zoom-ins are centered at the same point within the original computational domain to show the effect of enhanced detail while maintaining the same refinement level and criteria.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: Far left: gas density projection of AREPO source data. Middle left: Voronoi mesh generated with the scipy.voronoi library from every 4096th data point of AREPO source data (for visualization purposes). Middle right: empty FLASH AMR grid generated from all AREPO data with AMR blocks annotated and level 12 maximum refinement. Far right: populated FLASH AMR grid after VorAMR interpolation showing projected gas density; initial conditions of a Torch simulation. The domain is 1.43 kpc wide.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: Comparison between the AMR structure using the full input data set (left) and localized refinement parameter (right). Local refinement is centered at the same point as the zoom-in regions in Figure 4. The FLASH grid blocks are shown to demonstrate the reduction in total blocks necessary for a localized refinement.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: Gas column density at 3.22 Myr beyond the transfer time of (top) the source AREPO simulation and (bottom) the Torch simulation. The black points are projected star particles that have formed from the gas. Note that the color bars have different ranges.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: VorAMR data flow (see Section 2.1). VorAMR builds upon the existing Torch framework in AMUSE and allows AREPO data to be read in and processed into FLASH-compatible data.
Paper: Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Authors: Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan, Claude Cournoyer-Cloutier, Hui Li, Maite J. C. Wilhelm, Simon Portegies Zwart
Figure caption: Left: the full AREPO galaxy simulation from which the source data in this work are sampled. This galaxy is the SFE10 model detailed in H. Li et al. (2020). The squares of high resolution are the various zoom-in regions modeled. Right: the zoom-in region used as source data for this work.
Paper: Formation of Multiple Dynamical Classes in the Kuiper Belt via Disk Dissipation
Authors: Tommy Chi Ho Lau, 智昊 劉, Til Birnstiel, Sebastian M. Stammler, Joanna Drążkowska
Figure caption: Eight key timestamps of one of the fiducial simulations. In each panel, the snapshot at the denoted time is shown. The solid and dashed lines, respectively, show the gas surface density Σg and dust surface density Σd with respect to the distance from the star r. The eccentricity e and semimajor axis r of the N-body particles are shown by the black dots, for mass m ≤ 0.1M⊕, and, otherwise, blue circles with the linear sizes proportional to m1/3 and the provided scale. Upon the formation of massive planets, selected locations of mean motion commensurable with respect to the outermost one are also denoted in the top axis. The gray line in r − e traces the orbits that just cross the outermost planet’s apastron.
Paper: Formation of Polyoxymethylenes in Extraterrestrial Ice Analogs of Formaldehyde Exposed to Ionizing Radiation
Authors: Qi'ang Gong, Xilin Bai, Jinghui Lu, Jiuzhong Yang, Yang Pan, Zhenrong Sun, Ralf I. Kaiser, Tao Yang
Figure caption: SVUV-PI-ReTOF-MS data recorded during the TPD phase of H2CO ice (left) and D2CO ice (right). The TPD profiles at (a) C2H5O2+ (m/z 61), (b) C3H7O3+ (m/z 91), (c) C4H9O4+ (m/z 121), (d) C2D5O2+ (m/z 66), (e) C3D7O3+ (m/z 98), and (f) C4D9O4+ (m/z 130) were obtained by exploiting the ReTOF spectrometer at a photon energy of 11.00 eV. The product signals from the irradiated H2CO experiment (15 nA, 10 minutes; blue circles) were compared with the blank experiment (no irradiation; green circles) and irradiated D2CO experiment (15 nA, 10 minutes; black circles). The data are fitted with Gaussian peaks (red lines).
Paper: Formation of Polyoxymethylenes in Extraterrestrial Ice Analogs of Formaldehyde Exposed to Ionizing Radiation
Authors: Qi'ang Gong, Xilin Bai, Jinghui Lu, Jiuzhong Yang, Yang Pan, Zhenrong Sun, Ralf I. Kaiser, Tao Yang
Figure caption: Adiabatic ionization energies (IEs) and relative energies (ΔEs) of (a) methoxymethanol (CH3OCH2OH), (b) (methoxymethoxy)methanol (CH3OCH2OCH2OH), and (c) (methoxymethoxymethoxy)methanol (CH3OCH2OCH2OCH2OH) calculated at the CCSD(T)/CBS//B3LYP/cc-pVTZ level of theory, as well as their potential energy surfaces involving the associated dissociative channels into the hydrogen-loss fragment ions.
Paper: Formation of Multiple Dynamical Classes in the Kuiper Belt via Disk Dissipation
Authors: Tommy Chi Ho Lau, 智昊 劉, Til Birnstiel, Sebastian M. Stammler, Joanna Drążkowska
Figure caption: Origins of mass of the N-body particles at the end of the fiducial simulation. “Planetesimal” refers to the mass from the initial planetesimal formation and subsequent planetesimal accretions, “pebble” refers to the mass accreted from the dust in the disk, and “gas” refers to the mass accreted from the gas in the disk. For bodies above 0.1M⊕, the individual compositions are shown with the semimajor axis r shown on the horizontal axis. For the remaining 3283 minor bodies (≤0.1M⊕), the sum of their compositions is shown instead, which is noted by “≤0.1M⊕” on the horizontal axis.
Paper: Formation of Polyoxymethylenes in Extraterrestrial Ice Analogs of Formaldehyde Exposed to Ionizing Radiation
Authors: Qi'ang Gong, Xilin Bai, Jinghui Lu, Jiuzhong Yang, Yang Pan, Zhenrong Sun, Ralf I. Kaiser, Tao Yang
Figure caption: Comparison of experimental and observed mass spectra for POMs. (a) PICCA spectra. (b) Ptolemy spectra. (c) Integrated mass spectra of POMs in the 180−320 K temperature range derived from TPD profiles measured by QMS.
Paper: Formation of Polyoxymethylenes in Extraterrestrial Ice Analogs of Formaldehyde Exposed to Ionizing Radiation
Authors: Qi'ang Gong, Xilin Bai, Jinghui Lu, Jiuzhong Yang, Yang Pan, Zhenrong Sun, Ralf I. Kaiser, Tao Yang
Figure caption: Experimental and reference PIE curves recorded at (a) m/z 61, (b) m/z 91, and (c) m/z 121. The black circles denote the experimental PIE curves, while the red line indicates the fitted reference PIE curve for the C2H5O2+ fragment of methoxymethanol. The dark gray and light gray shaded regions represent the 5% and 10% uncertainties from the reference PIE curve, respectively, while the error bars represent the experimental uncertainties (1σ).
Paper: Formation of Polyoxymethylenes in Extraterrestrial Ice Analogs of Formaldehyde Exposed to Ionizing Radiation
Authors: Qi'ang Gong, Xilin Bai, Jinghui Lu, Jiuzhong Yang, Yang Pan, Zhenrong Sun, Ralf I. Kaiser, Tao Yang
Figure caption: QMS data recorded during the TPD phase of the irradiated H2CO ice at m/z of 28 (black line) and 30 (red line), respectively.
Paper: Formation of Polyoxymethylenes in Extraterrestrial Ice Analogs of Formaldehyde Exposed to Ionizing Radiation
Authors: Qi'ang Gong, Xilin Bai, Jinghui Lu, Jiuzhong Yang, Yang Pan, Zhenrong Sun, Ralf I. Kaiser, Tao Yang
Figure caption: POM fragmentation patterns for CH3OCH2OH, CH3OCH2OCH2OH, and CH3OCH2OCH2OCH2OH (top); QMS data recorded during the TPD phase of the irradiated H2CO ices (bottom). Numbers in parentheses represent the multiplication factors of ion signal intensities.
Paper: Formation of Multiple Dynamical Classes in the Kuiper Belt via Disk Dissipation
Authors: Tommy Chi Ho Lau, 智昊 劉, Til Birnstiel, Sebastian M. Stammler, Joanna Drążkowska
Figure caption: Results of all random simulations. Each subfigure presents the results from the simulation with the denoted set of parameters: (a) fiducial parameters; (b) lower stellar X-ray luminosities LX = 5 × 1029 erg s−1; (c) LX = 2 × 1029 erg s−1; (d) doubled disk mass Mdisk = 0.05M⊙; and (e) halved gas envelope opacity κ = 0.01 cm2 g−1. In each subfigure, the top panel shows the semimajor axis r and eccentricity e of the particles for each random simulation, respectively. Similar to Figure 1, the eccentricity e and semimajor axis r of the N-body particles are shown by the black dots, for mass m ≤ 0.1M⊕, and, otherwise, colored circles with the linear sizes proportional to m1/3 and the provided scale. The bottom panel shows the radial distribution of the planetesimal mass at formation, with the colors corresponding to the random simulations in the top panel. The simulation denoted by the color blue in subfigure (a) corresponds to the one presented in Figures 1 and 3.
Paper: Formation of Multiple Dynamical Classes in the Kuiper Belt via Disk Dissipation
Authors: Tommy Chi Ho Lau, 智昊 劉, Til Birnstiel, Sebastian M. Stammler, Joanna Drążkowska
Figure caption: Comparison of the fiducial simulation result with the observed Kuiper Belt objects retrieved from the Minor Planet Center on 2025 May 17. The two sets of data are overlayed in a manner that the horizontal axis shows the period ratio with respect to the outermost giant planet of 145.8 M⊕ (blue circle) in our simulation and Neptune (red circle), respectively. The color map presents the number density of Kuiper Belt objects, and the blue dots present the result of our fiducial simulation as shown in the last frame of Figure 1. The linear sizes of the circles are proportional to m1/3. The blue and red lines trace the orbits that just cross outermost giant planet’s apastron and Neptune’s aphelion, respectively. Our result reproduced the major dynamical groups: the scattered group, the resonant group, and the dynamically cold group.
Paper: Formation of Multiple Dynamical Classes in the Kuiper Belt via Disk Dissipation
Authors: Tommy Chi Ho Lau, 智昊 劉, Til Birnstiel, Sebastian M. Stammler, Joanna Drążkowska
Figure caption: Distribution of planetesimal mass m at formation in distance from the star r (top), at the end of the fiducial simulation in Figure 1 (middle), and that in eccentricity e at the end of the same simulation (bottom). The colors denote the formation time. The mass bins are log-uniform in the horizontal axis. Only the mass from planetesimal formation is considered, while pebble accretion and planetesimal accretion are not shown here.
Paper: Far-ultraviolet Emission Line Investigation of Flares on AU Mic
Authors: Adalyn Gibson, Adam F. Kowalski, Adina D. Feinstein
Figure caption: Light curves of Flare B for the blue wing (blue; top left), the entire wavelength (wavelength from start of blue wing to end of red wing; black; top middle), and the red wing (red; top right) of the C II emission line (λcen = 1335.095 Å). We highlight the different responses based on the different components of the emission line. In particular, we find that the blue wing has a stronger response to the first peak in Flare B, whereas the red wing has a stronger response to the second peak. This highlights the unique emission line responses that occur during individual flare events.
Paper: microJAX: A Differentiable Framework for Microlensing Modeling with GPU-accelerated Image-centered Ray Shooting
Authors: Shota Miyazaki, Hajime Kawahara
Figure caption: Total computation time per magnification evaluation shown as a function of the number of light-curve points (epochs) for both uniform (left) and limb-darkened (right) source profiles. The results compare microJAX (stars) and VBBinaryLensing (circles) across different source sizes ρ. We set the relative error in VBBinaryLensing to 10−4 and adopt (Nr, Nθ) = (103, 103) in microJAX. All benchmarks were performed using an AMD EPYC 7452 CPU (2.35 GHz base frequency, single-threaded) for VBBinaryLensing and an NVIDIA A100 PCIe GPU (40 GB) for microJAX. All timings were measured with JIT compilation enabled and exclude compilation overhead.
Paper: Far-ultraviolet Emission Line Investigation of Flares on AU Mic
Authors: Adalyn Gibson, Adam F. Kowalski, Adina D. Feinstein
Figure caption: Identified flares from AU Mic from Visit 3. We present three light curves per visit: white light (top), Si III (λcen = 1294.55 Å; middle), and C III (λcen = 1175.64 Å; bottom). Flares are labeled and marked by vertical dashed lines. We identified seven new flares in this visit. The new flare parameters can be found in Table 1. We note that even though these observations were taken 1 yr after Visits 1 and 2, AU Mic shows a similar flaring rate (∼7 flares visit−1). The horizontal black line in each figure is the quiescent flux for each wavelength range.
Paper: microJAX: A Differentiable Framework for Microlensing Modeling with GPU-accelerated Image-centered Ray Shooting
Authors: Shota Miyazaki, Hajime Kawahara
Figure caption: The top right panel shows the synthetic light curve observed by Roman, where blue error bars are W149-band measurements and the orange solid line is the model curve. The inset in the upper right corner of the same panel displays the caustic geometry (red solid line) and the source trajectory (blue line). The corner plot shows the posterior distributions of the model parameters (blue) along with the Fisher covariance contours (red) evaluated at the best-fit parameters obtained with gradient-based optimization before inference. The ground truths (the injected parameters) are shown as black cross-hatched points.
Paper: microJAX: A Differentiable Framework for Microlensing Modeling with GPU-accelerated Image-centered Ray Shooting
Authors: Shota Miyazaki, Hajime Kawahara
Figure caption: An example of a lensing geometry, similar to Figure 1, where the error in magnification increases, with parameters (q, s, ρ) = (0.1, 1.1, 5 × 10−4) and wcenter = (−0.0765, 0). Each inset panel shows a zoomed-in view of each position. In this case, the angular resolution of the grid is too sparse to accurately determine the image boundary compared to the source size, illustrated in the inset in the lower right corner.
Paper: microJAX: A Differentiable Framework for Microlensing Modeling with GPU-accelerated Image-centered Ray Shooting
Authors: Shota Miyazaki, Hajime Kawahara
Figure caption: A schematic view of the integration method for a given r-row. The circles represent the grid points assigned for the inverse ray shooting, and the purple lines represent the image boundary. The orange grid points are fully inside the image, the blue (entering) and red (exiting) ones are partially inside, and the gray ones are outside (d > ρ). The boundary position of the image at each row, i.e., d = ρ, is estimated using cubic interpolation based on the distances of four nearest points to the boundary.
Paper: microJAX: A Differentiable Framework for Microlensing Modeling with GPU-accelerated Image-centered Ray Shooting
Authors: Shota Miyazaki, Hajime Kawahara
Figure caption: The top right panel shows the observed light curve of OGLE-2014-BLG-0124. Gray error bars indicate the I-band photometry from the OGLE-IV survey, and the black solid line denotes the best-fit binary-lens model including orbital parallax. The inset in the upper right corner of the same panel displays the caustic geometry (red solid line) and the source trajectory (blue line). The corner plot shows the posterior distributions of the model parameters (blue) along with the Fisher covariance contours (red), evaluated at the best-fit solution obtained with gradient-based optimization before inference.
Paper: Far-ultraviolet Emission Line Investigation of Flares on AU Mic
Authors: Adalyn Gibson, Adam F. Kowalski, Adina D. Feinstein
Figure caption: An example of a nondetection of the OZ effect in Flare B in Visit 1. Here, an uneven enhancement of the red wing versus the blue wing of the Lyα emission line (1215.67 Å) during the impulsive phase of the flare is not observed. The OZ effect predicts an emission enhancement 5.6–17.7 Å redward of the core of the Lyα emission line compared to the blue wing (G. S. Kerr et al. 2023). The region in which an asymmetric red enhancement is expected is highlighted in gray. A similar displacement to the blue is indicated in gray, excluding the emission lines. In this flare we saw significant enhancement of the S III (λ = 1200.97 Å) and Si III ((λ = 1206.53 Å or λ = 1207.52 Å) emission lines labeled in this figure.
Paper: oMEGACat. VII. Tracing Interstellar and Intracluster Medium of ω Centauri Using Sodium Absorptions
Authors: Z. Wang, 梓先 王, A. C. Seth, M. Latour, J. Strader, M. Häberle, N. Neumayer, C. Clontz, S. Kamann, M. S. Nitschai, M. Alfaro-Cuello, A. Bellini, A. Feldmeier-Krause, M. Libralato, A. P. Milone, P. J. Smith, S. O. Souza, G. van de Ven
Figure caption: Comparison of EWD2,ISM with differential reddening measurements from the literature. In each panel, gray dots represent individual warm HB stars. The left and right y-axes show the EWD2,ISM measurements from HB spectra and the inferred E(B − V) using the empirical relation (Equation (5)), respectively. The x-axes indicate differential reddening interpolated from literature maps at the coordinates of the warm HB stars. Panel (a): comparison with photometric correction in the F275W filter from the oMEGACat photometry catalog (H24). This correction accounts for both differential reddening and CTE degradation, and the F275W band is expected to be dominated by differential reddening. Panel (b): comparison with differential reddening estimates derived from ground-based photometry by E. Pancino et al. (2024). Black triangles indicate the median of each x-axis bin, with the ranges obtained from 16th and 84th percentiles. The Pearson correlation coefficient and p-value are listed in each panel. A median uncertainty of the EWD2,ISM measurements is shown in the lower right in gray. The mild correlation supports the conclusion that sodium atomic gas is a reliable tracer of dust, and that the observed substructures in differential reddening are primarily due to foreground ISM.
Paper: oMEGACat. VII. Tracing Interstellar and Intracluster Medium of ω Centauri Using Sodium Absorptions
Authors: Z. Wang, 梓先 王, A. C. Seth, M. Latour, J. Strader, M. Häberle, N. Neumayer, C. Clontz, S. Kamann, M. S. Nitschai, M. Alfaro-Cuello, A. Bellini, A. Feldmeier-Krause, M. Libralato, A. P. Milone, P. J. Smith, S. O. Souza, G. van de Ven
Figure caption: EWD1,ICM distribution from the intracluster Na atomic gas of ω Cen, which is measured from individual warm HB spectra. Left panel: spatial distribution of warm HB stars color-coded by EWD1,ICM. Middle panel: histogram of the EWD1,ICM measurements in the left panel. Right panel: radial distribution of EWD1,ICM from the center of ω Cen, where the black line indicates the median of individual measurements, and the gray regions represent the 16th and 84th percentiles for each bin. The EWD1,ICM median uncertainty is also shown in the bottom right corner in black. This figure shows no significant spatial substructure and radial gradient of Na atomic gas inside ω Cen. The individual warm HB EWD1,ICM measurements used in this figure are available as a machine-readable table with columns listed in Table 1.