adsabs/AstroCLIMB huggingface dataset:
Paper: Effects of Barnett Magnetic Dipole–Dipole Interaction on Grain Growth and Destruction
Authors: Thiem Hoang, Bao Truong
Figure caption: Enhanced collision rate ﹩{({b}_{\max }/a)}^{2}﹩ of SPM grains driven by hydrodynamic turbulence (i.e., v gg = v turb). Small grains of a < 1 μm obtain a lower speed produced by small eddies (i.e., lower turbulence energy), resulting in the enhancement of grain growth by Barnett dipole interaction.
Paper: Effects of Barnett Magnetic Dipole–Dipole Interaction on Grain Growth and Destruction
Authors: Thiem Hoang, Bao Truong
Figure caption: Similar to Figure 5 but when the grain dipole is induced by stellar magnetic fields of 1 G–100 mG (see in Vlemmings 2019). Strong external magnetic fields could enhance the collisions of large grains a > 0.1 μm with high N cl within the inner regions of r < 1000 au.
Paper: Effects of Barnett Magnetic Dipole–Dipole Interaction on Grain Growth and Destruction
Authors: Thiem Hoang, Bao Truong
Figure caption: The enhanced collision rate, ﹩{({b}_{\max }/a)}^{2}﹩, of iron metallic grains moving at a thermal speed v th with T gas = 100 K (left panel) and T gas = 500 K (right panel). The Barnett dipole interaction is less efficient due to lower magnetic susceptibility produced by single-domain iron metallic inclusions, compared to the case of SPM grains in Figures 2 and 3.
Paper: Evolution of Negative Superhumps, Quasiperiodic Oscillations, and Outbursts in the Z Cam-type Dwarf Nova AH Her
Authors: Qi-Bin Sun, Sheng-Bang Qian, Min-Yu Li
Figure caption: Results of the analysis of the s25 light curve. (a) Two incomplete outbursts; the solid red line is the LOESS fit, and A–E represent the five different outburst and NSHs stages (see Section 4 for details); (b) Residuals of the LOESS fit; (c) CWT 2D spectrum of the light curve of the de-outburst trend; (d) Amplitude variation curve of the NSHs; (e) O − C curve of the NSHs maxima; (f) The red circle and cyan color are the declining and rising slopes of NSHs respectively; the blue and black solid lines are the LOESS fit; (g) Residuals of the LOESS fit in plate (f).
Paper: Evolution of Negative Superhumps, Quasiperiodic Oscillations, and Outbursts in the Z Cam-type Dwarf Nova AH Her
Authors: Qi-Bin Sun, Sheng-Bang Qian, Min-Yu Li
Figure caption: Outburst of AH Her observed by TESS.
Paper: Evolutionary Origins of Binary Neutron Star Mergers: Effects of Common Envelope Efficiency and Metallicity
Authors: Monica Gallegos-Garcia, Christopher P. L. Berry, Vicky Kalogera
Figure caption: Final outcomes of our simulations of NS–MS binaries with M NS = 1.4 M ⊙. We use two different prescriptions to determine if the explosion is a CCSN or ECSN: following Podsiadlowski et al. (2005; left) and following Tauris et al. (2015; right). First row: the models ran at solar metallicity Z ⊙ and α CE = 1; second row: Z ⊙ and α CE = 2; third row: Z ⊙ and α CE = 0.5; and bottom row: subsolar metallicity 0.1Z ⊙ and α CE = 1. The colors on the color scale indicate the fraction of BNS mergers. Regardless of the SN prescription, the simulations at subsolar metallicity have a higher fraction of forming BNS mergers, followed by the simulations at solar metallicity with α CE = 0.5.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: We train different models to perform likelihood-free inference on the value of the cosmological parameters. Top row: we use fully connected layers to infer the value of Ω m  from histograms that describe the distribution of void ellipticities. Middle row: we train fully connected layers to infer the value of Ω m  and σ 8 from the histograms that characterize the distribution of the density contrast of voids. Bottom row: we train deep sets on void catalogs to infer the value of Ω m , σ 8, and n  s . Our model takes a void catalog as input (containing all the voids in a given simulation), where every void is characterized by three properties: (1) ellipticity, (2) density contrast, and (3) radius. In all the cases, the models predict the posterior mean and standard deviation for each parameter. As expected, deep sets perform better than the histogram models. The bottom right part of the panels shows the accuracy metrics calculated from all the results obtained from the 198 values in the testing set. We find that the deep set model is able to infer the correct value of Ω m  (left column), σ 8 (middle column), and n  s  (right column) with a mean relative error of 10%, 3%, and 4%, respectively.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: Examples of voids with different ellipticities. We use VIDE to find the Voronoi cells of a void and plot the Voronoi cells of each void to show the void geometry. Small spheres inside each void represent its Voronoi cells. We show four randomly selected voids and arrange them by their ellipticities. We note that there is a large variety in the morphology of voids, so the voids shown in the figure should not be seen as representatives of their class.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: Examples of voids with different density contrasts. The gray wireframes show the geometries of the voids formed by the Voronoi cells of each void, similar to Figure 2. The colored dots inside each void represent halos. Voids in the top row are arranged by increasing density contrast (from left to right). The larger void at the bottom allows a clearer view of a void with a density contrast of 1.285, the mean value in the catalogs.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: Visualizations of the two machine-learning architectures used in this paper. While fully connected layers are one of the most commonly used neural network architectures, deep sets are able to extract information directly from the void catalog regardless of the number of voids in the catalog. Functions f and g in the deep sets architecture are modeled with fully connected layers. ﹩\bigoplus ﹩ represents a permutation invariant operation. More details about the deep sets architecture are given in Section 3.3.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: Plots representing the results for the unconstrained parameters using deep sets with void radius, ellipticity, and density contrast all together.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: Plots representing the results for the unconstrained parameters using fully connected layers with density contrast histograms.
Paper: Machine-learning Cosmology from Void Properties
Authors: Bonny Y. Wang, 玥 汪, Alice Pisani, Francisco Villaescusa-Navarro, Benjamin D. Wandelt
Figure caption: Plots representing the results for unconstrained parameters using fully connected layers with ellipticity histograms.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: The X-ray emissivity ranging from 0 to 8.5e9 cm−3 s−1 in both panels as indicated by the legend at right, calculated by the BATS-R-US magnetohydrodynamic model. Left: a slice in the GSE-X = 0 plane. Earth is at the center of the plot, and the Sun is behind you. A typical viewing geometry is shown for the HaloSat spacecraft at local midnight, with the viewing region bounded by the white lines. The dark region in the center of the plot is the area inside the magnetopause where the solar ions are, to first order, excluded. (The irregularity of this boundary is due to the field tracing algorithms and the step size of the BATS-R-US model.) Right: a cut through the emissivity in the GSE-Z = 0 plane. Earth is indicated by the crosshairs near the center of the plot, and the Sun is to the left. The red rings indicate radial distances of 10R E and 20R E. Sun angles of 110° are shown in white.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: The densities of hydrogen (left) and helium (right) in the heliosphere centered on the Sun in 10−2 atoms cm−3. Red indicates higher densities, while blue and purple indicate lower densities, with the axis values given in au. The dashed circle in the middle of each panel indicates Earth’s orbit around the Sun. The numbers along the circles correspond to the observations marked with an asterisk in Table 4, and the thin white lines indicate the look directions of those observations. The cone-shaped feature in the right panel is called the He-focusing cone.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: Comparison of the temperature and emission measure of the hot component. Panel (a) compares the fitted temperatures in keV, and panel (b) compares the emission measures of the hot components of the Galactic halo measured in this work (orange triangles) and Bluem et al. (2022b; black circles). The data were obtained from the VizieR catalog in Bluem et al. (2022b), with the identification numbers noted on the horizontal axis (67 = MSWCX1, 68 = MSWCX2, etc). All error bars represent 90% CIs. The emission measures and temperatures are consistent for the two fields measured by both works. Note that a hot temperature component is only reported from this work if the emission measure is more than three standard deviations above zero, where one standard deviation is defined as the lower limit of the confidence interval divided by the ratio 1.645.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: Observed vs. predicted O VII line intensities for m-SWCX flank observations. Vertical error bars indicate 1σ error bars calculated using the fitted 90% confidence intervals; horizontal error bars indicate 1σ errors on predicted values. The solid line shows the hypothesis of observed O VII line intensities equal to the predicted values. Markers above and inconsistent with this line are indicative of excess emission, optimistically due to magnetospheric SWCX emission along the line of sight. Yellow diamonds (red square) indicate observations with at least 3σ (4σ) excess emission.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: Comparison of the temperature and emission measure of the warm component. Panel (a) compares the fitted temperatures in keV, and panel (b) compares the emission measures of the warm components of the Galactic halo measured in this work (orange triangles) and Bluem et al. (2022b; black circles). The data were obtained from the VizieR catalog in Bluem et al. (2022b), with the identification numbers noted on the horizontal axis (67 = MSWCX1, 68 = MSWCX2, etc.; see Table 5). All error bars represent 90% confidence intervals. The emission measures are generally consistent, but the temperatures are shifted lower in that work.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: Spectral fit for MSWCX3 tail composite spectrum. Solid curves show the spectral components as labeled: heliospheric SWCX emission (SWCX); emission from the CXB, LHB, and a two-temperature Galactic halo (CXB+LHB+Halo); the three particle backgrounds (straight lines, fitted); and the total spectral model at the top. Plus signs show the data, and residuals are shown in the bottom panel. The Ne IX SWCX contributions are barely visible just below 1 keV.
Paper: Observations of Magnetospheric Solar Wind Charge Exchange
Authors: R. Ringuette, K. D. Kuntz, D. Koutroumpa, P. Kaaret, D. LaRocca, J. Richardson
Figure caption: Spectral fit for MSWCX6 tail composite spectrum. Solid curves show the spectral components as labeled: heliospheric SWCX emission (SWCX); emission from the CXB, LHB, and two-temperature Galactic halo (CXB+LHB+Halo); the three particle backgrounds (straight lines, fitted); and the total spectral model at the top. Plus signs show the data, and residuals are shown in the bottom panel. The Ne IX SWCX contributions are barely visible just below 1 keV.
Paper: Entrapment of Hypervolatiles in Interstellar and Cometary H2O and CO2 Ice Analogs
Authors: Alexia Simon, Mahesh Rajappan, Karin I. Öberg
Figure caption: TPD curves of hypervolatiles from CO2 (dashed) and H2O (solid) 50 ML ice matrices with an initial 10:1 mixing ratio showing the temperature of the first desorption peak, when hypervolatiles that are not entrapped desorb from the ice surface and out of ice pores.
Paper: Entrapment of Hypervolatiles in Interstellar and Cometary H2O and CO2 Ice Analogs
Authors: Alexia Simon, Mahesh Rajappan, Karin I. Öberg
Figure caption: Infrared spectra centered at the CO2 (ν 3) band (∼2345 cm−1) on the left, and H2O dangling O–H band (∼3600 cm−1) on the right. Each panel shows the four hypervolatiles at the 3:1 50 ML parameter with the associated pure 50 ML CO2 or H2O in light gray.
Paper: Entrapment of Hypervolatiles in Interstellar and Cometary H2O and CO2 Ice Analogs
Authors: Alexia Simon, Mahesh Rajappan, Karin I. Öberg
Figure caption: Histograms showing the percentage of entrapment in CO2 ice matrices. The bars are color-coded by hypervolatile, while a solid bar indicates a hypervolatile trapped between ∼70 and 100 K (last TPD peak), and a more transparent bar indicates a hypervolatile quantity trapped from ∼45 to 100 K (intermediate and last TPD peaks).
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: The light curves (left panels) and amplitude spectra (right panels) for CPD-62 2124, HD 67621, HD 89103, HD 118913, HD 119308, and HD 127453. In the frequency spectra, the significant peaks are marked with a blue inverted triangle, and the harmonics of the dominant frequency are marked with orange inverted triangles.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: The analysis results of HD 121743. The top-left panel shows the amplitude spectrum using the DFT method; we marked significant frequencies with blue inverted triangles. The bottom-left panel shows the logarithmic amplitude spectrum, and the best-fit result of Equation (3) is shown as a solid blue line. Red dashed–dotted and green dashed lines represent the red-noise and white-noise components, respectively. The right panel shows the posterior distributions from the MCMC for the parameters of the SLF variability.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: The frequency analysis result of HD 37479. In the top panel, the significant peak is marked with a blue inverted triangle, and the harmonics of the dominant frequency are marked with an orange inverted triangle. In the bottom panel, the residual amplitude spectrum is shown as the logarithmic diagram.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: The light curves (left panels) and amplitude spectra (right panels) for HD 138758, HD 157751, and HD 172690. The markers in the amplitude spectra have the same meaning as in Figure 7.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: We compared astrophysical parameters obtained from spectral energy distribution (SED) analysis with those derived from photometry. The top panel presents the effective temperatures derived from both methods, and a linear relation is shown by the red line. In the bottom panel, we compared the luminosities derived from SED analysis with those obtained from photometry, and their correlation is also displayed by the red line.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: Location of the stars in our catalog in celestial coordinates. The galactic plane (in red) is plotted for reference.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: The light curve and frequency spectrum of HD 3360. The light curve of HD 3360 is shown in the left panel, and the corresponding frequency spectrum is shown in the right panel. No significant variability is observed beyond 8 day−1. In the frequency spectrum, the significant peaks are marked with a blue inverted triangle.
Paper: Variability of Magnetic Hot Stars from the TESS Observations
Authors: Dong-Xiang Shen, Gang Li, Iskandar Abdusamatjan, Jian-Ning Fu, Chun-Hua Zhu, Jin-Long Yu, Yu Zhang, Guo-Liang Lü, Nan-Nan Zhai, Jin-Zhong Liu
Figure caption: The light curves of two stars in our sample. The top panel shows the light curve of the binary star HD 53921, which resulted from combining 10 sectors (sectors 4–13). In the bottom panel, we shown the light curve of the pulsating star HD 205021, which resulted from combining data from three sectors (sectors 16–18).
Paper: Unveiling the Dynamics of Dense Cores in Cluster-forming Clumps: A 3D Magnetohydrodynamics Simulation Study of Angular Momentum and Magnetic Field Properties
Authors: Shinichi. W. Kinoshita, Fumitaka Nakamura
Figure caption: Specific angular momentum, ﹩j\equiv {L}_{\mathrm{core}}/{M}_{\mathrm{core}}﹩, plotted against the core radius for all ﹩{ \mathcal M }=1.5﹩ models (left) and ﹩{ \mathcal M }=5﹩ (right) models, showing the best-fit power-law relation with 95% confidence bands for each setup. The black solid and dashed lines are j ∝ r 2 and j ∝ r 1.5 respectively. For ﹩{ \mathcal M }=1.5﹩, the slope is 2.83 (Rotation), 1.94 (collision (fast)), 2.13 (collision (fast)), 1.69 (w/o). For ﹩{ \mathcal M }=5﹩, the slope is of 2.88 (Rotation), 2.16 (collision (fast)), 2.17 (collision (fast)), 1.93 (w/o). When ﹩{ \mathcal M }=1.5﹩, j and the slope of best-fit in the Rotation Setup is relatively large. However, in ﹩{ \mathcal M }=5﹩, although the slope is large, j in the rotation setup is not significantly different from the core in other setups.
Paper: Unveiling the Dynamics of Dense Cores in Cluster-forming Clumps: A 3D Magnetohydrodynamics Simulation Study of Angular Momentum and Magnetic Field Properties
Authors: Shinichi. W. Kinoshita, Fumitaka Nakamura
Figure caption: Top row: Ratio between magnetic and gravitational energies, E mag/∣E grav∣, as functions of the cosine of the relative angle between ﹩{{\boldsymbol{L}}}_{\mathrm{core}}﹩ and ﹩{{\boldsymbol{B}}}_{\mathrm{core}}﹩, ﹩\cos \,\measuredangle [{{\boldsymbol{L}}}_{\mathrm{core}},{{\boldsymbol{B}}}_{\mathrm{core}}]﹩. Rotation Setup models with θ 0 = 0° are shown in the left panel, while θ 0 = 45° models are shown in the middle panel. The right panel shows the results of w/o Setup. Bottom row: Ratio between kinetic and gravitational energies, E kin/∣E grav∣, plotted against ﹩\cos \,\measuredangle [{{\boldsymbol{L}}}_{\mathrm{core}},{{\boldsymbol{B}}}_{\mathrm{core}}]﹩. The independence of ﹩\measuredangle [{{\boldsymbol{L}}}_{\mathrm{core}},{{\boldsymbol{B}}}_{\mathrm{core}}]﹩ with E mag/∣E grav∣ is confirmed. ﹩\measuredangle [{{\boldsymbol{L}}}_{\mathrm{core}},{{\boldsymbol{B}}}_{\mathrm{core}}]﹩ is also independent of E kin/∣E grav∣.
Paper: Unveiling the Dynamics of Dense Cores in Cluster-forming Clumps: A 3D Magnetohydrodynamics Simulation Study of Angular Momentum and Magnetic Field Properties
Authors: Shinichi. W. Kinoshita, Fumitaka Nakamura
Figure caption: Top row: Rotational parameter β as a function of bound core radius. The models with θ 0 = 0° and 45◦ are presented together. Results of Rotation Setup, Collision Setup(fast), Collision Setup(slow), and w/o Setup are shown from left to right. Bottom row: Ratio between rotational and total kinetic energies, E rot/E kin, plotted against the core radius. The distribution of β exhibits a large scatter with a typical value of β ∼ 0.05, and there is also a large scatter in the distribution of E rot/E kin. The Rotation Setup model tends to have a lower scatter and slightly larger E rot/E kin values than other setups. However, it is concluded that rotation is not the dominant motion in the dense core for any of the models.
Paper: Unveiling the Dynamics of Dense Cores in Cluster-forming Clumps: A 3D Magnetohydrodynamics Simulation Study of Angular Momentum and Magnetic Field Properties
Authors: Shinichi. W. Kinoshita, Fumitaka Nakamura
Figure caption: Top row: Ratio between kinetic and gravitational energies, E kin/∣E grav∣, as a function of core radius. The models with θ 0 = 0° and 45◦ are presented together. Note that, unlike the other figures, this plot includes the unbound (E thermal + E kin + E mag + E grav > 0) core. Results of Rotation Setup, Collision Setup(fast), Collision Setup(slow), and w/o Setup are shown from left to right. Middle row: Ratio between magnetic and gravitational energies, E mag/∣E grav∣, plotted against the core radius. Bottom row: Histograms of the energy ratio of the sum of turbulent, thermal field, and magnetic field energies to the absolute value of self-gravitational energy. In the Collision Setup, the magnetic fields and kinetic energy have a relatively larger impact on smaller cores, resulting in a higher ratio of unbound cores.
Paper: Unveiling the Dynamics of Dense Cores in Cluster-forming Clumps: A 3D Magnetohydrodynamics Simulation Study of Angular Momentum and Magnetic Field Properties
Authors: Shinichi. W. Kinoshita, Fumitaka Nakamura
Figure caption: Histograms of nearest neighboring separations. The Rotation Setup models are displayed on the top left and the Collision Setup (fast) models on the top right. The models with θ 0 = 0° and 45◦ are presented together. The Collision Setup (slow) models are shown on the bottom left, and the w/o Setup model on the bottom right. Vertical dashed lines with different colors indicate the average separation for each parameter model. Vertical black lines represent the thermal Jeans length ﹩{\lambda }_{{\rm{J}},\mathrm{clump}}^{\mathrm{th}}﹩ derived using the initial condition of the clump. The Rotation Setup models have higher peak positions, means, and variances in the histogram compared to the other models, indicating that the rotational motion of the clump has an impact on the fragmentation process.