adsabs/AstroCLIMB huggingface dataset:
Paper: Volatile Element Depletion of Carbonaceous Chondrites—Insights from Mass-dependent Zinc, Cadmium, and Tellurium Isotope Variations
Authors: Elin M. Morton, Harvey Pickard, Frank Wombacher, Yihang Huang, Emeliana Palk, Rayssa Martins, Sven Kuthning, Maria Schönbächler, Mark Rehkämper
Figure caption: Plots of (a) Zn, (b) Cd, and (c) Te concentrations vs. matrix mass fractions (%); (d) δ 66Zn, (e) δ 114Cd, and (f) δ 130Te vs. inverse elemental concentrations. The plotted data denote the mean CC group compositions calculated from the results of this and previous studies (Table A2). Matrix mass fractions are from J. L. Hellmann et al. (2020). Error bars represent two standard deviations (2SD) of the mean CC group compositions. Black dashed lines represents bivariate best-fit regression lines. Gray dashed lines in (c), (d), and (e) represent the error envelope for the regressions.
Paper: Volatile Element Depletion of Carbonaceous Chondrites—Insights from Mass-dependent Zinc, Cadmium, and Tellurium Isotope Variations
Authors: Elin M. Morton, Harvey Pickard, Frank Wombacher, Yihang Huang, Emeliana Palk, Rayssa Martins, Sven Kuthning, Maria Schönbächler, Mark Rehkämper
Figure caption: Plot showing the ε 113Cd values of the chondrites analyzed by E. R. Toth et al. (2020) as a function of CRE age. The ε 113Cd values and CRE ages of the meteorites are as follows. Murchison: −0.23 ± 0.27, 1.5 Ma; Allende: −0.38 ± 0.27, 5.1 Ma; Indarch: −0.46 ± 0.27, 12 Ma; and Jbilet Winselwan: −0.56 ± 0.27, 6.6 Ma. The slope of the best-fit trend is used to estimate the ε 113Cd values of other meteorites from their measured or inferred CRE ages.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: 15 M ⊙ initial model (from C. E. Fields & S. M. Couch 2020), showing the structure and composition of the region near the core. The dotted vertical line shows the radius inside of which we refine to higher resolution. The solid vertical line shows the radius corresponding to our domain width.
Paper: Volatile Element Depletion of Carbonaceous Chondrites—Insights from Mass-dependent Zinc, Cadmium, and Tellurium Isotope Variations
Authors: Elin M. Morton, Harvey Pickard, Frank Wombacher, Yihang Huang, Emeliana Palk, Rayssa Martins, Sven Kuthning, Maria Schönbächler, Mark Rehkämper
Figure caption: Plot of the δ 114Cd corrections applied to the Cd isotope data of this study as a function of the ε 113Cd values. The ε 113Cd values were either available from E. R. Toth et al. (2020) or calculated using the correlation of Figure A1. The best-fit correlation of this plot was then used to determine the δ 114Cd corrections for the Cd isotope literature data of R. G. A. Baker et al. (2010). See main text for details of the correction procedure.
Paper: Volatile Element Depletion of Carbonaceous Chondrites—Insights from Mass-dependent Zinc, Cadmium, and Tellurium Isotope Variations
Authors: Elin M. Morton, Harvey Pickard, Frank Wombacher, Yihang Huang, Emeliana Palk, Rayssa Martins, Sven Kuthning, Maria Schönbächler, Mark Rehkämper
Figure caption: Rare-earth element concentrations of the CCs analyzed in this study, normalized to the CI and Fe concentrations of K. Lodders (2003).
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Time sequence of ﹩\bar{A}﹩ for the ms simulation, focusing on the region near the core. The black contour separates the region where the NSE table is used from the region where the full network is integrated.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Mach number sequence for the ms simulation focusing on the region near the core. The first frame is just after the drive initial convection phase. The black contour separates the region where the NSE table is used from the region where the full network is integrated.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Left: minimum Y  e  vs. time since the drive initial convection process ended for the seven different simulations. Right: relative difference in minimum Y  e  with the ms simulation for several cases.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Left: sum of the 28Si and 32S mass vs. time since the drive initial convection process ended for the seven different simulations. Right: relative difference in the total 28Si and 32S mass with the ms simulation for several cases.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Left: peak temperature vs. time since the drive initial convection process ended for the seven different simulations. Right: relative difference in peak temperature with the ms simulation for several cases.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Left: peak density vs. time since the drive initial convection process ended for the seven different simulations. Right: relative difference in peak density with the ms simulation for several cases.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: Time sequence of the energy generation rate for the ms simulation focusing on the region near the core. The black contour separates the region where the NSE table is used from the region where the full network is integrated.
Paper: Strong Coupling of Hydrodynamics and Reactions in Nuclear Statistical Equilibrium for Modeling Convection in Massive Stars
Authors: Michael Zingale, Zhi Chen, Eric T. Johnson, Max P. Katz, Alexander Smith Clark
Figure caption: The state of the convection in the ms simulation in a large region of the star, 200 s after the drive initial convection. The full O shell is seen on this scale. The gray contour marks a 16O mass fraction of 0.5.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Illustration of the conditional probabilities ﹩P(\mathrm{ln}{J}_{z}| \tau )﹩ and ﹩P(\tau | \mathrm{ln}{J}_{z})﹩ in relation to the joint probability ﹩P(\tau ,\mathrm{ln}{J}_{z})﹩. The blue heat map represents the joint probability ﹩P(\tau ,\mathrm{ln}{J}_{z})﹩ and shows a simulated stellar sample in τ–﹩\mathrm{ln}{J}_{z}﹩ space generated from the mean posterior dynamical age model. The pink and red highlighted horizontal regions correspond to the conditional probability ﹩P(\mathrm{ln}{J}_{z}| \tau )﹩, shown on the top panel as probability density functions in the ﹩\mathrm{ln}{J}_{z}﹩ dimension. The light- and dark-green highlighted vertical regions correspond to the conditional probability ﹩P(\tau | \mathrm{ln}{J}_{z})﹩, shown on the right panel as probability density functions in the τ dimension. The ﹩P(\tau | \mathrm{ln}{J}_{z})﹩ probability density functions are analogous to stellar age predictions based on ﹩\mathrm{ln}{J}_{z}﹩ from Gaia.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Left:  CMD of the RGB calibration sample (red), MSTO calibration sample (blue), and a representative prediction sample (gray). The representative prediction sample consists of the first 100,000 stars returned from a Gaia search excluding objects with no radial velocity measurements and ϖ ≤ 0. Right:  vertical action distributions for the RGB (red), MSTO (blue), and representative Gaia sample (gray).
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Graphical model representation of the hierarchical Bayesian stellar age–﹩\mathrm{ln}{J}_{z}﹩ model. ﹩{t}_{\tau (\mathrm{ln}{J}_{z})}﹩ and ﹩{t}_{\mathrm{ln}\lambda (\tau )}﹩ refer to the fixed spline knot vectors for the ﹩\tau (\mathrm{ln}{J}_{z})﹩ and ﹩\mathrm{ln}\lambda (\tau )﹩ splines, respectively, and ﹩{ \mathcal S }({t}_{\tau (\mathrm{ln}{J}_{z})})﹩ and ﹩{ \mathcal S }({t}_{\mathrm{ln}\lambda (\tau )})﹩ represent the associated knot-value free parameters. A list of the model parameters and their priors is given in Table 1. As in Table 1, “[⋆]” denotes a stellar number density model parameter that we ignore when we apply the calibrated model to a prediction sample of stars.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Top left: 2D histogram of the RGB calibration sample in A. Stokholm et al. (2023) asteroseismic age vs. ﹩\mathrm{ln}{J}_{z}﹩ space. The mean posterior τ–﹩\mathrm{ln}{J}_{z}﹩ monotonic spline is overplotted, ± the square root of the best-fit intrinsic variance. Bottom left: 2D histogram of the MSTO calibration sample in A. B. A. Queiroz et al. (2023) isochrone ages vs. ﹩\mathrm{ln}{J}_{z}﹩ space. Top right:  generated age prediction distributions for a set of test ﹩\mathrm{ln}{J}_{z}﹩ values using the RGB-calibrated model. Bottom right: generated age prediction distributions for the same set of test ﹩\mathrm{ln}{J}_{z}﹩ values using the MSTO-calibrated model.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Top left:  combined dynamical age probability distributions for Kepler (solid), K2 (dashed), and TESS (dotted) stellar samples with T eff between 5500 and 5700 K. Bin widths arbitrarily set to 0.5 Gyr. Top right: same as top left, but for stars with T eff between 4200 and 4500 K. Bottom left: combined ﹩\mathrm{ln}{J}_{z}﹩ kernel density estimations (KDEs) for the Kepler, K2, and TESS stars with T eff between 5500 and 5700 K. Bottom right: same as bottom left, but for stars with T eff between 4200 and 4500 K.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Violin plot of selected open-cluster dynamical ages. Each cluster segment is labeled according to the open cluster it represents. Each segment represents the age probability distribution for the median ﹩\mathrm{ln}{J}_{z}﹩ value for cluster members (with arbitrary width), according to the RGB-calibrated dynamical age model. The position of each cluster segment on the x-axis represents the cluster age reported by W. S. Dias et al. (2021). The one-to-one relation is shown with the red dotted line. We show that there is broad consistency between the cluster-derived ages and dynamical age predictions, though dynamical age constraints are calibration limited and much less constraining than the cluster age measurements.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Left:  combined dynamical age distributions (calibrated with the MSTO sample) for the KIC sample, partitioned by T eff (from S. Mathur et al. 2017). Right: ﹩\mathrm{ln}{J}_{z}﹩ distribution for the entire KIC sample (green) compared to the ﹩\mathrm{ln}{J}_{z}﹩ distributions for the RGB and MSTO calibration samples (red and blue, respectively).
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: A 2D histogram of the S. Mathur et al. (2023) sample of Kepler main-sequence stars in gyrochronological age–﹩\mathrm{ln}{J}_{z}﹩ space. The MSTO-calibrated dynamical ages for this sample are designated with the green line and shaded region. The solid green line represents the statistical mode of each dynamical age probability distribution associated with each star in the S. Mathur et al. (2023) sample, and the shaded region represents 68% of the distribution mass around the mode. The 2D histogram is row normalized to reduce the visual effect of the stellar density distribution on the τ-axis.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: A 2D histogram of the M. H. Pinsonneault et al. (2018) sample of red-giant-branch stars in asteroseismic age–﹩\mathrm{ln}{J}_{z}﹩ space. The RGB-calibrated dynamical ages for this sample are designated with the red line and shaded region. The solid red line represents the statistical mode of each dynamical age probability distribution associated with each star in the M. H. Pinsonneault et al. (2018) sample, and the shaded region represents 68% of the distribution mass around the mode. The 2D histogram is row normalized to reduce the visual effect of the stellar density distribution on the τ-axis.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Left:  2D histogram of the red-giant calibration sample, and the mean (ridgeline) of the model fit (red). Right: predicted density of stellar ages from model fit (red). While we fit and can regenerate the stellar number density along the age axis according to the calibration sample, we ignore this number density when predicting stellar ages, effectively using only the age–J  z  spline model represented in red.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Original and reduced RGB calibration samples. The original RGB calibration sample is represented by all points (both orange and teal). The orange points only represent the reduced calibration sample: stars in the RGB calibration sample that lie left of the line ﹩\mathrm{age}=7.5\times \mathrm{ln}{J}_{z}-12﹩ (red).
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Combined dynamical age distributions for the entire KIC catalog (green) and all KOIs (red).
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Age probability distributions for randomly selected open clusters from the W. S. Dias et al. (2021) sample. 15 clusters with 0 < τ < 2 Gyr and 25 clusters with 2 < τ < 14 Gyr according to W. S. Dias et al. (2021) were randomly selected to present a mix of young and old clusters. Each subplot shows the age probability distributions for each star associated with each cluster, with color denoting the membership probability according to W. S. Dias et al. (2021). The red vertical lines and shaded regions mark the cluster ages and 1σ uncertainties (respectively) presented in W. S. Dias et al. (2021).
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Standard deviation of stellar ages in 37 bins of ﹩\mathrm{ln}{J}_{z}﹩ vs. midpoint of each ﹩\mathrm{ln}{J}_{z}﹩ bin for the RGB calibration sample. The error bars on σ  τ  represent the standard deviation of individual stellar age uncertainties for the stars in each bin. The color represents the number of stars included in each bin; the small sample size for bins with ﹩\mathrm{ln}{J}_{z}﹩ <0 and >5 likely contributes to the large scatter in σ  τ  for low and high ﹩\mathrm{ln}{J}_{z}﹩ bins.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Comparison between original RGB age predictions (orange) and reduced RGB age predictions (teal) for six test values of ﹩\mathrm{ln}{J}_{z}﹩. For the values of ﹩\mathrm{ln}{J}_{z}﹩ that are well represented by both the original and reduced calibration sample (﹩\mathrm{ln}{J}_{z}=0﹩ and ﹩\mathrm{ln}{J}_{z}=1﹩), both age predictions appear similar. For larger values of ﹩\mathrm{ln}{J}_{z}﹩ that are not well represented in the reduced calibration sample, the absolute age predictions differ more significantly, while the relative age rankings remain consistent.
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Standard deviation of RGB calibration ages in 37 ﹩\mathrm{ln}{J}_{z}﹩ bins vs. median stellar parameter values in each bin (stellar metallicity, log(g), T eff, distance, stellar mass, stellar radius, α/Fe, J-band magnitude, maximum frequency ﹩{\nu }_{\max }﹩, and luminosity). All stellar parameters are published alongside the RGB calibration ages in A. Stokholm et al. (2023).
Paper:  zoomies: A Tool to Infer Stellar Age from Vertical Action in Gaia Data
Authors: Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi (Lucy) Lu, Ruth Angus, David W. Hogg
Figure caption: Comparison between age predictions (using the RGB calibration sample) generated with the Milky Way potential model (blue) vs. extreme custom galactic models, where the galaxy is composed entirely of a massive disk (orange), bulge (red), or halo (purple). Age predictions are shown for six test values of ﹩\mathrm{ln}{J}_{z}﹩ between −2 and 3.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: Spectroscopic redshift distributions for selected sources in DESI-EDR, BOSS-DR16, and total.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: Left: the architecture of the 1D CNN and BNN built upon ResNet blocks. Right: the structure of the ResNet block.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: The corresponding one-dimensional spectra extracted from spectral images of sources in Figure 4. The SEDs used in simulation are also illustrated. The black dashed–dotted line indicates zero fluxes. Additionally, the source information including coordinates, spectroscopic redshifts, morphological parameters, and SNR of each band are also shown.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: Two examples of simulated first-order slitless spectral images in GU, GV, and GI bands. The coordinates and spectroscopic redshifts are also shown. The corresponding extracted one-dimensional spectra and more information for the two sources are displayed in Figure 5.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: The results of the 1D CNN are illustrated, achieving accuracy σ NMAD = 0.00047 and outlier percentage η = 0.954%, respectively. The logarithmic SNRs of GI bands are indicated by the color bar.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: The distributions of true and predicted redshifts from the CNN and BNN for testing data.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: The SNR distributions of simulated slitless spectra in GU, GV, and GI bands and over the whole wavelength range.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: The distribution of four morphological parameters: effective radius r eff, Sérsic index n, axis ratio b/a, and position angle ϕ of sources from DESI-EDR and BOSS-DR16.
Paper: On the Influence of the Solar Wind on the Propagation of Earth-impacting Coronal Mass Ejections
Authors: Sandeep Kumar, Nandita Srivastava, Nat Gopalswamy, Ashutosh Dash
Figure caption: Solar wind velocity background at 21 R ⊙ of the events showing the longitudinal deflection. The top left and right panels show the background for Events 13 and 14, respectively. The bottom left and right panels show the background for Events 1 and 3, respectively. The black dotted mesh shows the position of the CME and GCS mesh, and velocities are indicated near the mesh. Events 13 and 14 are plotted with a lower half-angle and kappa than what was used in GCS fitting to show their propagation direction properly. θ CR and ϕ CR represent Carrington latitude and longitude, respectively.
Paper: On the Influence of the Solar Wind on the Propagation of Earth-impacting Coronal Mass Ejections
Authors: Sandeep Kumar, Nandita Srivastava, Nat Gopalswamy, Ashutosh Dash
Figure caption: Solar wind velocity background of Events 13 and 14 at 21 R ⊙, respectively, from left to right, plotted with the actual fitted parameters of GCS reconstruction.
Paper: Accurately Estimating Redshifts from CSST Slitless Spectroscopic Survey Using Deep Learning
Authors: Xingchen Zhou, Yan Gong, Xin Zhang, Nan Li, Xian-Min Meng, Xuelei Chen, Run Wen, Yunkun Han, Hu Zou, Xian Zhong Zheng, Xiaohu Yang, Hong Guo, Pengjie Zhang
Figure caption: The results of the BNN after uncertainty calibration. The error bars are displayed in light blue. Over the whole redshift range, the BNN can reach σ NMAD = 0.00063 and η = 0.92%, respectively. And the weighted mean uncertainty ﹩\overline{E}﹩ can achieve 0.00228.