adsabs/AstroCLIMB huggingface dataset:
Paper: Comprehensive JWST+ALMA Study on the Extended Lyα Emitters, Himiko, and CR7 at z ∼ 7: Blue Major Merger Systems in Stark Contrast to Submillimeter Galaxies
Authors: Tomokazu Kiyota, Masami Ouchi, Yi Xu, Yurina Nakazato, Kenta Soga, Hidenobu Yajima, Seiji Fujimoto, Yuichi Harikane, Kimihiko Nakajima, Yoshiaki Ono, Dongsheng Sun, Haruka Kusakabe, Daniel Ceverino, Bunyo Hatsukade, Daisuke Iono, Kotaro Kohno, Kouichiro Nakanishi
Figure caption: SED of Himiko (left) and CR7 (right). Orange, blue, and green colors represent the clumps A, B, and C, respectively. The solid lines show the best-fit SEDs. The squares and error bars show the observed fluxes and their 1σ uncertainties, respectively. The circles show the best-fit photometry. We do not use the F090W fluxes in the SED fitting due to the contamination of Lyα emission and IGM absorption.
Paper: Comprehensive JWST+ALMA Study on the Extended Lyα Emitters, Himiko, and CR7 at z ∼ 7: Blue Major Merger Systems in Stark Contrast to Submillimeter Galaxies
Authors: Tomokazu Kiyota, Masami Ouchi, Yi Xu, Yurina Nakazato, Kenta Soga, Hidenobu Yajima, Seiji Fujimoto, Yuichi Harikane, Kimihiko Nakajima, Yoshiaki Ono, Dongsheng Sun, Haruka Kusakabe, Daniel Ceverino, Bunyo Hatsukade, Daisuke Iono, Kotaro Kohno, Kouichiro Nakanishi
Figure caption: [O III]λ5007, [C II] 158 μm, and Lyα emission lines of Himiko (left) and CR7 (right). [O III]λ5007 lines from G395H spectra are shown in gray, and clump IDs are added in the figure. [C II]158 μm and Lyα lines are presented in red and blue, respectively. [C II]158 μm lines in this figure are integrated over the regions where S/N > 3. Lyα spectra of Himiko and CR7 are taken from Keck/DEIMOS (M. Ouchi et al. 2009) and VLT/X-SHOOTER (D. Sobral et al. 2015), respectively.
Paper: Comprehensive JWST+ALMA Study on the Extended Lyα Emitters, Himiko, and CR7 at z ∼ 7: Blue Major Merger Systems in Stark Contrast to Submillimeter Galaxies
Authors: Tomokazu Kiyota, Masami Ouchi, Yi Xu, Yurina Nakazato, Kenta Soga, Hidenobu Yajima, Seiji Fujimoto, Yuichi Harikane, Kimihiko Nakajima, Yoshiaki Ono, Dongsheng Sun, Haruka Kusakabe, Daniel Ceverino, Bunyo Hatsukade, Daisuke Iono, Kotaro Kohno, Kouichiro Nakanishi
Figure caption: Results of the pixel-by-pixel SED fitting using NIRCam photometry. The top and bottom panels show Himiko and CR7, respectively. The left, middle, and right panels show the stellar mass density (Σ*) in M⊙ kpc−2, the star formation rate density (ΣSFR) in M⊙ yr−1kpc−2, and the specific star formation rate (sSFR) in yr−1, respectively.
Paper: Comprehensive JWST+ALMA Study on the Extended Lyα Emitters, Himiko, and CR7 at z ∼ 7: Blue Major Merger Systems in Stark Contrast to Submillimeter Galaxies
Authors: Tomokazu Kiyota, Masami Ouchi, Yi Xu, Yurina Nakazato, Kenta Soga, Hidenobu Yajima, Seiji Fujimoto, Yuichi Harikane, Kimihiko Nakajima, Yoshiaki Ono, Dongsheng Sun, Haruka Kusakabe, Daniel Ceverino, Bunyo Hatsukade, Daisuke Iono, Kotaro Kohno, Kouichiro Nakanishi
Figure caption: Integrated spectrum of Himiko taken with NIRSpec-IFU PRISM (R ∼ 100; left) and G395H (R ∼ 2700; right). From top to bottom, the spectra of Himiko-A to Himiko-E are shown. The black lines show the spectra, and the blue-shaded areas show their 1σ uncertainties. In the PRISM spectra of Himiko-A, there is a clear Lyα emission line.
Paper: Comprehensive JWST+ALMA Study on the Extended Lyα Emitters, Himiko, and CR7 at z ∼ 7: Blue Major Merger Systems in Stark Contrast to Submillimeter Galaxies
Authors: Tomokazu Kiyota, Masami Ouchi, Yi Xu, Yurina Nakazato, Kenta Soga, Hidenobu Yajima, Seiji Fujimoto, Yuichi Harikane, Kimihiko Nakajima, Yoshiaki Ono, Dongsheng Sun, Haruka Kusakabe, Daniel Ceverino, Bunyo Hatsukade, Daisuke Iono, Kotaro Kohno, Kouichiro Nakanishi
Figure caption: Same as in Figure 5 but for CR7. In the PRISM spectra of CR7-A, a clear Lyα emission line is detected.
Paper: Examining the Gap in the Chirp Mass Distribution of Binary Black Holes
Authors: Vaibhav Tiwari
Figure caption: (Left) The mass estimates of BBHs measured from the GWs stacked a two-dimensional density plot. Only binaries measured with a mean chirp mass larger than 5 M⊙ and smaller than 20 M⊙ have been included. The stars are the median values measured for the individual BBHs. (Right) The posterior predictive distribution obtained using the model ﹩{\mathbb{M}}﹩. The black curve shows a constant ﹩{ \mathcal M }=11.0{M}_{\odot }﹩ line.
Paper: Examining the Gap in the Chirp Mass Distribution of Binary Black Holes
Authors: Vaibhav Tiwari
Figure caption: The measured chirp mass and redshift value of the 69 BBH observed with a false alarm rate of once per year or less. Observations cluster around a chirp mass value of 8 and 14 M⊙, leaving a gap in between.
Paper: Examining the Gap in the Chirp Mass Distribution of Binary Black Holes
Authors: Vaibhav Tiwari
Figure caption: The chirp mass distribution inferred from the models ﹩{{\mathbb{M}}}_{\mathrm{pl}}﹩ and ﹩{{\mathbb{M}}}_{\mathrm{pl}+\mathrm{gap}}﹩. The solid line represents the mean distribution, and the shaded region or dashed line indicates the 90% credible interval. As expected, the orange curve does not exhibit features in the chirp mass range of 9.5–20 M⊙. It is used as the fiducial chirp mass distribution for creating simulated catalogs. The models infer almost identical distributions except in the range 9.5–20 M⊙.
Paper: Examining the Gap in the Chirp Mass Distribution of Binary Black Holes
Authors: Vaibhav Tiwari
Figure caption: The chirp mass distribution inferred from the models ﹩{{\mathbb{M}}}_{\mathrm{pl}+\mathrm{gap}}﹩ and ﹩{{\mathbb{M}}}_{\mathrm{pl}}﹩. The solid line represents the mean distribution, and the shaded region or dashed line indicates the 90% credible interval. The models infer a similar distribution except in the range 9.5–20 M⊙.
Paper: Examining the Gap in the Chirp Mass Distribution of Binary Black Holes
Authors: Vaibhav Tiwari
Figure caption: The log Bayes factor (left) and χ2 values (right) between models ﹩{{\mathbb{M}}}_{\mathrm{pl}+\mathrm{gap}}﹩ and ﹩{{\mathbb{M}}}_{\mathrm{pl}}﹩ for the 500 simulated catalog. The red lines indicate the estimated values for the GW observations.
Paper: Examining the Gap in the Chirp Mass Distribution of Binary Black Holes
Authors: Vaibhav Tiwari
Figure caption: This figure plots the function defined in Equation (B3) for several values of hyperparameters. The power law truncates at 5.0 and 25 M⊙ and has a spectral index of −0.4.
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: We show the total surface flooding for a given topography as a function of Mg/Si and FeO for a 1 M⊕ planet with Earth-like molar ratios (i.e., Fe/Mg, Ca/Mg, and Al/Mg). We use cubic interpolation between data points for smoothing. The colorbar indicates the surface flooding in Earth oceans. We show the compositions of solar system planets with the following notation: Earth (⊕) , Venus (), Mars (), and Mercury (), using compositional values from R. G. Trønnes et al. (2019). (A) Earth-like topography shows three distinct “islands” correspond to the increase in water storage capacity. (B) Venus-like and (C) Mars-like topographies show similar structure in the water coverage fraction. (D) Mercury-like topography reaches complete flooding at low OM.
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: Transition zone (TZ) water storage capacity as a function of Mg/Si and FeO for a 1 M⊕ planet with a mantle potential temperature of 1600 K. We use cubic interpolation between data points for smoothing. The colorbar indicates the water storage capacity in Earth oceans. Earth’s composition is indicated by ⊕. The TZ water storage capacity increases with FeO. The drastic increase in water storage capacity at FeO ∼ 11 wt% corresponds with a pressure change in the transition of olivine to wadsleyite.(The data used to create this figure are available in the online article.)(The data used to create this figure are available.)
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: The normalized hypsometry of 1 M⊕ planet calculated using topographic data from Earth (green; C. Amante & B. W. Eakins 2009), Mars (orange; R. Fergason et al. 2018), Venus (purple; USGS Astrogeology Science Center 1993), and Mercury (pink; T. J. H. University 2015). Mercury and Earth have the most shallow topographies. Given the steep topographies of Mars and Venus, they are most useful when considering the surfaces of Earth-like or smaller (<1 R⊕).
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: TZ water storage capacity as a function of composition and mantle potential temperature assuming a 1 M⊕ planet. The colorbar indicates the water storage capacity in Earth oceans. We use cubic interpolation between data points for smoothing. Right: we show the variation of water storage capacity with temperature as a function of FeO. Decreasing temperature corresponds with a higher water storage capacity. Left: we show the variation of water storage capacity with temperature as a function of Mg/Si. Generally, (Mg, Fe)2SiO4 minerals (i.e., wadsleyite and ringwoodite) show a greater dependence on temperature than other minerals.
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: Planet sample as a function of radius and period. Our total sample consists of 689 rocky planets. We use a period-dependent radius gap (V. Van Eylen et al. 2018; C. S. K. Ho & V. Van Eylen 2023) to classify super-Earth and Earth-like planets.
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: Seafloor pressures as a function of surface oceans. We show seafloor pressures for our known planet sample assuming an FeO content of 8 wt% for differing topographies. We show Earth-like (teal), Mars-like (orange), Venus-like (purple), and Mercury-like (pink) topographies. The size of the point corresponds to planet radius.
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: Known planet water storage capacity as a function of Mg/Si. The colorbar corresponds to the planet radius. Here, we set the FeO to be 8 wt% assuming the oxidation state of Earth.
Paper: From Underground Oceans to Continents: A Glimpse into the Water Inventory on Rocky Planets Using Host Star Abundances
Authors: Kiersten M. Boley, Wendy R. Panero, Francesca Miozzi, Ashika Capirala, Johanna K. Teske
Figure caption: Water storage capacity as a function of planet radius and composition, assuming a Mg/Si = 1.2. We show the impact of FeO on the water storage capacities as a function of radius and their corresponding fits given by Equation (9).
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Left: predicted versus actual ﹩{\mathrm{log}}(\,\rm{age}\,)﹩; the panel structure is the same as in Figure 4. Right: histogram and KDE plots of predicted versus actual ﹩{\mathrm{log}}(\,\rm{age}\,)﹩, with JS = 0.1381.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Architecture of the UAFN. (Top left) ULR-spec module: 1D transformer-based residual network extracts features from ultra-low-resolution spectra (R ≈ 200). (Top right) Overall framework: two-stage architecture includes (1) pretrained Chem-Net branches for [α/Fe] and [C/N] estimation with Bayesian uncertainty and (2) physics-constrained fusion integrating spectral evidence (Fspec), chemical features (Fα, F[C/N]), and uncertainties (uα, u[C/N]). (Bottom) Physics-constrained fusion module: uncertainty-weighted cross-attention aligns physical priors (Gaussian mask) with spectral features.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Evaluation of uncertainty and prediction error for [α/Fe] and [C/N] across signal-to-noise ratios. The top panel analyzes [α/Fe], while the bottom panel analyzes [C/N]. In each panel, the top subpanel shows box plots depicting MAE, aleatoric uncertainty, and epistemic uncertainty as functions of SNR. Each plot divides the SNR into 10 equal-width bins, with x-axis labels representing the integer midpoints of these bins. The bottom subpanel depicts histograms showing the distributions of MAE, MAE divided by aleatoric uncertainty, and MAE divided by epistemic uncertainty, each with 50 bins.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Performance for [α/Fe] (left) and [C/N] (right) estimation. Upper: predicted versus reference values (KDE-colored, with regression line). Lower: residuals versus reference values (KDE-colored, with regression line).
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Uncertainty trends for [α/Fe] (top) and [C/N] (bottom), with histogram and KDE curves on the side. Left: data uncertainty. Right: model uncertainty. Orange dashed lines show trends (cubic polynomial fits).
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Histograms and KDE plots for [α/Fe] (left) and [C/N] (right). The JS divergence is shown on each plot.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Spectra before (blue, 4850 dims) and after (red, 164 dims) for two samples. Titles: LAMOST files.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Kiel diagram. Left: before screening. Right: after. Colors: [Fe/H].
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Age-dependent (left) and [Fe/H] (right) estimation errors. Upper: box plots of MAE across ﹩{\mathrm{log}}(\,\rm{age}\,)﹩ and [Fe/H] bins. Boxes span 25th–75th percentiles; whiskers show 5th–95th percentiles. Lower: binned average MAE trend line.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Aleatoric uncertainties (left) and epistemic uncertainties (right) on ﹩{\mathrm{log}}(\,\rm{age}\,)﹩ estimation. Upper: box plots of uncertainties across ﹩{\mathrm{log}}(\,\rm{age}\,)﹩ bins. Boxes span 25th–75th percentiles; whiskers show 5th–95th percentiles. Lower: binned average uncertainties trend line.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Evaluation of model performance and uncertainty across signal-to-noise ratios. Top row: box plots depicting MAE, aleatoric uncertainty, and epistemic uncertainty as functions of SNR. Each plot divides the SNR into 10 equal-width bins, with x-axis labels representing the integer midpoints of these bins. Bottom row: histograms showing the distributions of MAE, MAE divided by aleatoric uncertainty, and MAE divided by epistemic uncertainty, each with 50 bins.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Performance metrics (MAE, RMSE, sMAPE) as a function of label noise scale. The left, middle, and right panels correspond to [α/Fe], [C/N], and ﹩{\mathrm{log}}(\,\rm{age}\,)﹩, respectively. Each metric shows a gradual increase with noise scale, demonstrating the model’s robustness.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Left panels: test set performance of log(age) prediction with different Chem-Net branch training set sizes. Right panels: test set performance of the UAFN model for log(age) prediction across different training set sizes.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Resolution-dependent prediction performance for (a) [α/Fe], (b) [C/N], and (c) ﹩{\mathrm{log}}(\,\rm{age}\,)﹩. In these figures, the blue circle in the center represents the residual, while the length of the error bars above and below the circle corresponds to 1σ.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Test set performance of the Chem-Net branch across different training set sizes. The left and right panels display the results for [α/Fe] and [C/N], respectively.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Sky distribution of stellar samples. Blue: full LAMOST RGB catalog (156,794 stars). Green: cross-matched subsample with APOGEE/LAMOST labels (17,752 stars). Orange: original dataset from Section 3 (4816 stars).
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Architecture of the 1D-convolution-based model (top) and the Transformer-based model (bottom). The numbers below the layer indicate the output feature dimension of the layer.
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Left: number density distribution of LAMOST RGB stars in the age–[α/Fe] plane. Color indicates stellar density (log scaled). The red box shows high-α “young” stars ([α/Fe] > 0.18 dex, age < 6 Gyr). The bimodal thin/thick disk structure and anomalous “young” population match Y. Wu et al. (2019). Right: number density distribution of LAMOST RGB stars in the [Fe/H]–[α/Fe] plane. Color indicates stellar density (log scaled). Red dots indicate high-α “young” stars. The yellow dotted line shows the empirical boundary separating the thick disk (above) and thin disk (below).
Paper: Advancing Stellar Parameter Estimation: Uncertainty-Aware Astrophysical Fusion Network for Red Giant Ages and Abundances from Ultra-low-resolution Spectra—Applications to Galactic Archaeology
Authors: Jingzhen Sun, Yude Bu, Mengmeng Zhang, Jiangchuan Zhang, ShanShan Li, Yuhang Zhang, Ke Wang, Zhenping Yi, Xiaoming Kong, Meng Liu, Minglei Wu
Figure caption: Distribution of high-α “young” stars. Left: [α/Fe] distribution for stars age < 6 Gyr. The red line shows the Gaussian fit to low-α stars (mean fixed at peak). The magenta dashed line shows the selection threshold ([α/Fe] > 0.18 dex). Right: age distribution of high-α stars ([α/Fe] > 0.18 dex). The red line shows the Gaussian fit to old stars. The magenta dashed line shows the age cutoff (6 Gyr). Both panels show asymmetric tails inconsistent with pure measurement errors. Insets display logarithmic density scales.