adsabs/AstroCLIMB huggingface dataset:
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption:  ﹩{{\mathfrak{J}}}_{L}﹩ and ﹩{{\mathfrak{J}}}_{E}﹩ colatitude dependence at three radii r = 6, 10, 20rg. The top (bottom) two rows correspond to the simulation shown in Figure 11 (Figure 13). For each simulation, we show in the top (bottom) row the angular momentum (energy) current. The left column plots the total current, while the middle and right columns plot the electromagnetic and fluid contribution, respectively. In the MAD simulation, we see outward transport of energy and angular momentum flux in the jet carried by the collimated magnetic fields, while the fluid stresses are inward and peak in the disk. The total current closely resembles the fluid sector profile for the SANE simulation with some outward electromagnetic angular momentum flux in the disk.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: A heat map of the radial component of the magnetic field Br at the event horizon for the SANE a* = −0.5 simulation. Top panel: Br at a specific instant in time. Bottom panel: Br averaged over the interval t = [25, 30] × 103tg. In both panels, note that Br changes polarity within each hemisphere. This behavior is not accounted for in Equation (12), leading to an overestimation of ΦBH.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: Time series of the fraction of total zones where floors and primitive recovery failures occur for MAD a* = +0.5. Before accretion begins, geometric floors on ρ and u are the dominant contribution. As the evolution progresses and the funnel region becomes magnetically dominated, ∼10% of the zones have their rest-mass density set by a σ ceiling.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: The spin-up parameter “s” is given by Equation (30). The markers indicate the time-averaged value, and error bars represent 1σ. Red and blue markers are MAD and SANE simulations, respectively. The black dashed line is the expected thin-disk values (Equation (31)). The solid gray line is the quadratic fit (Equation (32)), and the dashed silver line is the fifth-order polynomial fit provided in R. Narayan et al. (2022).
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: A poloidal cut of an MAD simulation illustrating various definitions of a jet or outflow. The background color saturation represents rest-mass density in code units. The gold lines mark geometric boundaries at θ = 1 and θ = π − 1, which are used in this work to compute the total outflow power Pout. The blue line is the −ut = 1 contour, while the magenta line represents βγ = 1, the latter serving as the jet definition in this study. The green, plum, and red lines indicate contours where μ = 1, Be = 1 (fluid Bernoulli parameter), and σ = 1, respectively.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: Similar to Figure 12, but instead we plot the conserved current profiles at a fixed radius r = 10rg for all simulations in the library. The outward transport of angular momentum and energy in the jet strongly depends on the black hole spin for the MAD models. Negative values of ﹩\sqrt{-g}{J}_{L}^{r}﹩ at higher latitudes for the retrograde models are simply a reflection of the magnetic field lines and plasma in the jet corotating with the black hole.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: A time series of the dimensionless magnetic flux crossing the event horizon for all of the models in v3. Left column: MAD; right column: SANE simulations. Rows (from top to bottom): increasing spin from a* = −15/16 to a* = +15/16. The gray line plots the flux as computed according to Equation (12), while the gold and magenta lines plot ﹩{{\rm{\Phi }}}_{\rm{BH}\,}^{\,\rm{N}}﹩ (Equation (A1)) and ﹩{{\rm{\Phi }}}_{\rm{BH}\,}^{\,\rm{S}}﹩ (Equation (A2)), respectively.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: Same as Figure 17 but for SANE a* = +0.5. The magnetic fields in this case are much more ordered, resulting in a consistent sign of Br across both hemispheres.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: Same as Figure 11 but for SANE a* = +0.5
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Color–magnitude diagram for stars with −0.05 < [Fe/H] < 0.15 from the training sample. The black, blue, and red points represent identified single main-sequence, intermediate-, and high-mass-ratio binary stars, respectively. The single stars and intermediate-mass-ratio binaries are selected as a training sample. The G-band absolute magnitude (MG) is plotted against the dereddened color index ﹩{(G-{K}_{s})}_{0}﹩.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: A snapshot from the MAD a* = +0.5 simulation. Grid zones where σ exceeds ﹩{\sigma }_{{\rm{\max }}}﹩ are highlighted in red, while the blue contour indicates σ = 1. The underlying grayscale shading shows the logarithm of the rest-mass density in code units. The exponential radial coordinate focuses zones near the event horizon. Since σ peaks at the base of the jet, a substantial fraction of grid zones reach the σ ceiling in MAD simulations (see Figure 20).
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Probability density function (PDF) of the predicted probabilities (pb) for binary (blue region) and single (red line) stars in the test set. The black line shows the precision as a function of the adopted probability cutoff threshold, assuming a binary-to-single-star ratio of 1:2.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Probability density function (PDF) of the predicted binary probabilities (pb) for stars observed multiple times in the LAMOST survey. The blue line indicates potential binary stars (with ﹩{{\rm{\Delta }}}_{{\rm{rv,max}}}\gt 25﹩ km s−1), while the red line indicates likely single stars (with ﹩{{\rm{\Delta }}}_{{\rm{rv,max}}}\lt 5﹩ km s−1).
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: The fraction of binary stars as a function of mass ratio for different pth values. The shaded area denotes the regions of intermediate-mass-ratio binaries (corresponding to mass ratios between approximately 0.71 and 0.93), which is included in the training sample.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Probability density function (PDF) of the predicted binary probabilities (pb) for eclipsing-binary stars. The blue solid line corresponds to Algol-type (EA) binaries, while the red solid line corresponds to W Ursa Majoris–type (EW) binaries, demonstrating a high detection rate of 96.45% for EA and 96.40% for EW binaries.
Paper: A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
Authors: Vedant Dhruv, Ben Prather, George N. Wong, Charles F. Gammie
Figure caption: Similar to Figure 20, but for SANE a* = +0.5. Compared to the MAD model, there are fewer flow hits, with the largest contributions coming from the entropy and σ ceilings.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Receiver operating characteristic (ROC) curve for the test set. The solid blue line represents the performance of the trained CNN model, while the dashed blue line indicates the ROC curve for random selection. The area under the curve for the model is 0.949, demonstrating significantly better performance than random classification.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: The distribution of pb provided by the network for all spectra (blue) and individual stars (red) within the main-sequence star sample. The solid curves represent the number distribution of pb, while the dashed lines indicate the count of spectra (or individual stars) with as a function of the pth. For individual stars, pb corresponds to the highest value in cases of multiple observations.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Number density contour plot in the color–magnitude diagram of stars with −0.05 < [Fe/H] < 0.15. Stars with pb < 0.5 are shown in black contours, while those with pb > 0.5 are highlighted in blue.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Distribution of distances to the Sun for all stars (black) and binary stars (red). The histogram reveals that binary stars are found at a range of distances, with 115 binary stars situated beyond 10 kpc.
Paper: Half a Million Binary Stars Identified from the Low-resolution Spectra of LAMOST
Authors: Yingjie Jing, Tian-Xiang Mao, Jie Wang, Chao Liu, Xiaodian Chen
Figure caption: Distribution of binary stars in the R.A. and decl. plane.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The long-term light curves of S5 0716+714 in the UBVRI bands. The symbols and colors depicted in the chart correspond to the respective sources of the data. (1) Mt. Abu: 1.2 m Telescope at Mt. Abu Infrared Observatory. (2) Perkins: 1.8 m Perkins telescope of Lowell Observatory with PRISM camera. (3) LX-200: 40 cm LX-200 telescope in St. Petersburg. (4) AZT-8: 70 cm AZT-8 telescope of the Crimean Astrophysical Observatory of the Russian Academy of Sciences. (5) AZT11: 125 cm Crimean Astrophysical Observatory telescope. (6) Abastumani: The Abastumani Observatory. (7) Bohyunsan: 1.8 m telescope of Bohyunsan Optical Astronomy Observatory. (8) Greve: The telescope of Greve. (9) GBOS: Ground-based Observational Support of the Fermi Gamma-ray Space Telescope at the University of Arizona. (10) Monte Porzio: The 70 cm f/8.3 TRC70 telescope of Monte Porzio. (11) MT.L: The 1.0 m robotic telescope of Mount Lemmon Optical Astronomy Observatory. (12) Mt.M: The Mount Maidanak Observatory. (13) OCA: Observatoire de la Côte d'Azur. (14) Perugia: the 40 cm Automatic Imaging Telescope (AIT) of the Perugia University. (15) Roma: The University of Roma group. (16) YNO60cm: The 60 cm BOOTES-4 autotelescope at the Lijiang Observatory of the Yunnan Observatories. (17) SHAO: The 1.56 m telescope at the Sheshan station of the Shanghai Astronomical Observatory. (18) Skinakas: The 1.3 m telescope of the Skinakas Observatory. (19) Sobaeksan: The 0.6 m telescope of Sobaeksan Optical Astronomy Observatory. (20) WHO: The 1.0 m telescope at Weihai Observatory. (21) XL85cm: The 85 cm telescope located at the Xinglong Station. (22) MOBPOL: The MOBPOL program. (23) XLS: The Schmidt telescope at the Xinglong Station. (24) Torino: The 1.05 m telescope of the Torino Observatory. (25) Tuorla: Tuorla Observatory blazar monitoring optical light curves. (26) Vallinfreda: The 50 cm telescope of the Vallinfreda Station. (27) WEBT: The international Whole Earth Blazar Telescope. (28) XMO: The CSP telescope at the Xingming Observatory. (29) YNO1.02 m: The 1.02 m telescope of Yunnan Astronomical Observatory. (30) YNO2.4 m: The 2.4 m telescopes at the Lijiang station of the Yunnan Astronomical Observatories. (31) Zeiss-600(SAO): Zeiss-600 telescopes of the Special Astrophysical Observatory (SAO). (32) UVOT: The Swift’s Ultraviolet/Optical Telescope. (33) Zeiss 60-cm(SSSAI): the Zeiss 60 cm telescope at the Southern Station of the Sternberg Astronomical Institute.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The light curves of S5 0716+714 in the Sloan ﹩{i}^{{\prime} }﹩ and ﹩{r}^{{\prime} }﹩ bands. The difference in magnitude between the two comparison stars 5 and 8 are displayed at the bottom of the corresponding panel.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The light curve is partitioned into two segments based on the time of observation. The difference in magnitude between the two comparison stars 5 and 8 are displayed at the bottom of the corresponding panel.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: Results of the ACF analysis for ﹩{i}^{{\prime} }﹩-band IDV light curves. The red line is a polynomial least-squares fitting.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: Results of the ACF analysis for ﹩{r}^{{\prime} }﹩-band IDV light curves. The red line is a polynomial least-squares fitting.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The color–magnitude diagrams for each separate night observation of S5 0716+714. The red line is the linear fitting.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption:  ﹩{i}^{{\prime} }﹩-band intraday variable light curves of S5 0716+714.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption:  ﹩{r}^{{\prime} }﹩-band intraday variable light curves of S5 0716+714.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The result of correlation analysis between the Sloan ﹩{i}^{{\prime} }﹩- and ﹩{r}^{{\prime} }﹩-band light curves. The left and right picture is the result based on the observation data during MJD 8475-8540 and MJD 8826-8895, respectively. The red line is the Gaussian fitting to measure the time lag.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The V-band light curve and optical spectral curve of S5 0716+714.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The time lag between the optical variability and optical spectral index with the DCF. The red dashed line represents a confidence level of 3σ.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: Long-term optical spectral index vs. V-band flux for S5 0716+714. The red line represents the regression line, characterized by a slope of −0.601 ± 0.012, an intercept of 1.798 ± 0.015, and a Pearson coefficient of −0.737.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The REDFIT program analysis results for the V-band flux and optical spectral index of S5 0716+714. The top and bottom panels represent the results of the optical V band and spectral index, respectively.
Paper: Optical Monitoring and Long-term Optical Spectral Variability of BL Lacertae Object S5 0716+714
Authors: Huai-Zhen Li, Di-Fu Guo, Long-Hua Qin, Fen Liu, Hong-Tao Liu, Ting-Feng Yi, Quan-Gui Gao, Shi-Feng Huang, Xing Gao, Xu Chen
Figure caption: The result of long-term correlation analysis between the UBRI- and V-band light curves. The red line is the Gaussian fitting to measure the time lag. The correlation between the V and U bands, V and B bands, R and V bands, and I and V bands are depicted in (a), (b), (c), and (d), respectively.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The top row shows the distributions of dispersion measure (left) and scattering time at 600 MHz (right) of our ∼106 mock FRBs with fluence > 0.4 Jy ms, which are generated with parameter values given by the DM and τ combined MCMC simulation. The blue, orange, green, red, and purple histograms are the contribution of MW, IGM, extragalactic foreground halos and galaxies, host halos and galaxies, as well as the local environment, respectively. The pink shaded histogram indicates the distribution of the total (i.e., observed) DM and scattering time. For the sake of clarity, τ < 10−4 ms are set to 10−4 ms in the right panel. The bottom row shows the relative contribution of each component to the total DM and τ in each bin, with the error bars enclosing the central 68% of the samples. The meaning of different colored lines is the same as in the top row.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: Comparison between the distribution of observed dispersion measure (left) and scattering time at 600 MHz (right) for the selection-corrected FRBs in CHIME/FRB catalog 1 (blue), and our mock FRBs, which is generated with parameter values given by the DM and τ combined MCMC simulation (red). The error bar in blue histogram shows the 68% Poissonian confidence interval for each bin.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: Redshift estimated by our procedures, ﹩\hat{z}﹩ vs. the true redshift, z, for 32 localized FRBs, of which both their DM and scattering time are available. The top (bottom) row shows the results of the redshift estimator with combined DM and τ (DM-only) information. The left column indicates the method that estimates the redshift directly from the redshift of mock FRBs. The right column shows the results using an indirect method, through the DMIGM of mock FRBs and Equation (14). See Section 4 for more details. The insert in the bottom-right corner of each panel highlights the redshift range 0–0.2.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The top panel is the three-dimensional schematic diagram that illustrates the LOS to FRBs that have passed through a foreground halo. The red dashed line with an arrow shows the LOS and corresponding direction. The blue dots are gas particles in the simulation, which indicates the spatial distribution of halo gas. The outer sphere is the halo boundary with radius Rm200. The bottom panel shows the projection of the LOS to the plane that is perpendicular to them and across the halo center. “x” symbols in various colors represent the intersection points of different LOSs. The dotted circles mark radial bins.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The top panel shows the distribution of the redshift and total dispersion measure of our ∼106 mock FRBs with fluence above 0.4 Jy ms, superposed by 71 localized FRBs. The middle (bottom) panel shows the probability distribution of redshift (dispersion measure) of our mock FRBs with fluence above 0.4 Jy ms (blue) and localized FRBs (orange). The green histogram indicates the intrinsic redshift distribution of mock FRBs, i.e., the redshift distribution of mock FRBs in our model before applying a fluence limit >0.4 Jy ms.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The cumulative distribution function (CDF) of the dispersion measure and scattering measure caused by the foreground halos, i.e., ﹩{{\rm{DM}}}_{{\rm{Fore}}}﹩ (left) and ﹩{{\rm{SM}}}_{{\rm{Fore}}}﹩ (right), for halos with mass in the range 1010–1011, 1012–1013, and 1014–1015 M⊙ (from top to bottom), respectively.
Paper: The Dispersion Measure and Scattering of Fast Radio Bursts: Contributions from Multicomponents, and Clues for the Intrinsic Properties
Authors: Jian-feng Mo, Weishan Zhu, Long-Long Feng
Figure caption: The distribution of the observed scattering time at 600 MHz and total dispersion measure of our ∼106 mock FRBs with fluence >0.4 Jy ms (dots), and 33 localized FRBs (solid circles and downward triangles represent sources with a scattering time of true value and upper limits, respectively). Redshift is indicated by the color. The vertical and horizontal dashed lines illustrate one of the bins of DM and τ that we employed to select mock FRBs to estimate the redshift of an FRB with given DMobs and τobs. FRB 220610A is used as an example.