adsabs/AstroCLIMB huggingface dataset:
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The performance of BASSET from application to the three FAST-detected single-component pulses. The pulses in panels (a) and (b) are from FRB 20121102A (D. Li et al. 2021), while the pulse in panel (c) is from FRB 20201124A. The time–frequency regions searched by BASSET in the adaptive filter step are highlighted with red boxes. The frequency channels affected by RFI are masked and highlighted. The blue dashed line represents the time series processed by the standard pipeline, and the orange solid line represents the time series processed by BASSET. SNRA and SNRB represent the maximum SNR obtained by the standard pipeline and BASSET, respectively.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: BASSET performance in three typical RFI mode environments. Panel (a): calibration noise diode. The injected mock pulse has a width of 5 ms and a bandwidth of 300 MHz centered at 1250 MHz, both with a Gaussian shape, and an fluence SNR of 60. Parabolic sweep in the RFI is due to dedispersion. Panel (b): the stationary narrowband RFI. The injected mock pulse has the same width and bandwidth as panel (a), but with an SNR of 10. Panel (c): the narrowband RFI like a faint bulge. The injected mock pulse is the same as in panel (b) with the central frequency changed to 1150 MHz. The time–frequency regions searched by BASSET are highlighted with red boxes.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The framework of BASSET. The SNR of the narrowband pulses is improved on the time series using BASSET. The frequency channels affected by RFI are masked and highlighted. Remove RFI based on WT is described in Section 2.1. Optimize bandpass is described in Section 2.2. Adaptive filter and enhance on the time series are described in Section 2.3.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The accuracy of searching for time-frequency range is quantified using a 2D KDE of the residuals. Panels (a), (b), and (c) show the results of the sample subsets with different bandwidths and SNR. Panel (d) shows the result of the entire sample set. The percentages of samples with total residuals within 0.2 and 0.3 are shown in red.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: Comparison between the BASSET and the standard pipeline (PRESTO, S. M. Ransom 2001). The result is based on 2000 mock pulses. Panel (a) presents the distribution of the SNR improvement ratio (SNRA/SNRB). Panel (b) shows the recovery rate as a function of the injected SNR, where SNR90 represents the 90% completeness threshold. Panel (c) shows a comparison between SNRA and SNRB. The injected pulse SNR is defined as fluence SNR, which is obtained by averaging the pulse over the entire observational bandwidth. The searched SNR is obtained using the standard pipeline (SNRA) and BASSET (SNRB). BASSET has eliminated the nondetectable frequency band from the bandwidth averaging of the pulses during the search, which thus leads the SNRB higher than the injected SNR.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The bandwidth and SNR distribution of the newly detected pulses. Panel (a) shows the bandwidth histogram of the newly detected pulses by BASSET, compared to the previous detection by C.-H. Niu et al. (2022a). Panel (b) shows the bandwidth–SNR statistics. The newly detected pulses are marked with red stars, while the previous detection by C.-H. Niu et al. (2022a) are represented by gray stars. The 2D KDE is the distribution of FRB 20121102A (D. Li et al. 2021), utilizing the FAST database Blinkverse  (J. Xu et al. 2023). Other FRB repeaters are not considered due to the lack of SNR or bandwidth data.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: A gallery of the newly detected pulses from FRB 20190520B data reprocessing. The SNRA is obtained by searching the time series processed by the standard pipeline (blue dashed line), using single_pulse_search.py from PRESTO. SNRB is obtained by searching the time series processed by BASSET (orange solid line). The frequency channels affected by RFI are masked and highlighted.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The luminosity function of the FRB 20190520B. The red line and bars represent the 79 newly detected pulses using BASSET, while the gray line and bars are taken from C.-H. Niu et al. (2022a). The 90% completeness threshold is 0.023 Jy ms,which is the simulation result from D. Li et al. (2021) using FAST.
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The fluence–width distribution of the FRB 20190520B. The red dots and histograms are newly detected 79 pulses using BASSET, while the gray dots and bars are taken from C.-H. Niu et al. (2022a). The blue line indicates SNRB = 5, based on the simulation result of D. Li et al. (2021).
Paper: BASSET: Bandpass-adaptive Single-pulse Search Toolkit—Optimized Subband Pulse Search Strategies for Faint Narrowband Fast Radio Bursts
Authors: J. H Cao, P. Wang, D. Li, Q. H. Pan, K. Mao, C. H. Niu, Y. K. Zhang, Q. Y. Qu, W. J. Lu, J. S. Zhang, Y. H. Zhu, Y. D. Wang, H. X. Chen, X. L. Chen, J. H. Fang, Y. Feng, H. Gao, Y. F. Huang, J. Li, C. C. Miao, C. W. Tsai, J. M. Yao, S. P. You, R. S. Zhao, Q. Z. Liu, S. M. Weng, S. H. Yew, J. Zhang, L. Zhang, E. Gügercinoğlu, D. K. Zhou, W. W. Zhu
Figure caption: The dynamic spectra of the newly detected pulses from FRB 20190520B. The frequency channels affected by RFI are masked and highlighted.
Paper: The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS
Authors: Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Figure caption: Sensitivity of the ATLAS photometry to a series of 5 million randomly sampled M-giant stars across the sky (both Northern and Southern Hemispheres). The y-axis indicates the scatter of the flux across the entire light curve, and the x-axis is the apparent Gaia DR3 G-band magnitude.
Paper: The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS
Authors: Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Figure caption: Comparison of dominant periods (left) and amplitudes (right) measured from the OGLE I-band photometry (x-axis) and compared against the published catalog (y-axis). Note that the published amplitudes appear to be measured as peak-to-peak amplitudes, and are thus divided by 2 to match the semiamplitudes measured here. The discretization of amplitudes in the published catalog is likely due to the nonfitting nature of their prewhitening routine.
Paper: The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS
Authors: Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Figure caption: Comparison of measured and predicted orange and cyan (o/c) band magnitudes using the relations provided in Equation (1) with constants defined in Table 1 for the ugriz (left) and Gaia (right) passbands. The bottom panel shows the residual magnitudes, with the gray band highlighting the 0.02 mag nominal ATLAS photometric precision.
Paper: The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS
Authors: Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Figure caption: Examples of four randomly chosen variables in our sample, where the flux is corrected following Section 2.3. The left panels show the full ATLAS light curve in both passbands (orange, cyan), while the right panels show the light curve folded on the dominant measured period.
Paper: The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS
Authors: Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Figure caption: Gaia DR3 density plots of evolved stars around and within the LMC following the cuts made in Section 2.1. Left: the color–magnitude diagram of targets after removing contaminant foreground stars, showing the red-giant branch. Right: Same as left panel, but showing the Galactic coordinates for each source.
Paper: The Period–Luminosity Relation of Long-period Variables in the Large Magellanic Cloud Observed with ATLAS
Authors: Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Figure caption: Comparison of amplitudes for the same period in the OGLE and ATLAS data. The red dashed line marks the average lower bound of the ATLAS amplitude measurements, around 7.6 ppt.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: Left: diagram of a single neuron in an MLP network. Right: diagram of a single neuron in a KAN, where the boxes represent one-dimensional functions.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: The LSTM model introduced in this study is illustrated schematically in the figure. The leftmost box represents the input layer, which receives input data derived from the OHD. The central box, labeled “LSTM,” encapsulates the LSTM modules, each comprising 256 neurons. These modules are sequentially connected to a dropout layer and a batch normalization layer to enhance generalization and stabilize training. The final Batch-Normalization layer is linked to an FCL, which serves as the output layer for generating the estimated parameters. This structured design ensures robust feature extraction and effective parameter inference from the input data.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: The 2D plots and 1D marginalized distributions with 1σ and 2σ contours of cosmological parameters using the LSTM method (blue solid line), MCMC method (green dashed line), and PE-transformer method (red dashed line) from 32 real OHD.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: The comparison of results for reconstructing the Hubble parameter using different methods. The gray area and black line represent the 1σ confidence interval and the best-fitting values for the Ef-KAN model, the green area and green line indicate the corresponding results for the ANN model, the blue area and blue line represent the results for the LSTM model, and the red dashed line represents the fiducial ΛCDM model with the best-fitting values.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: The error of the mock H(z) reconstructed via linear regression. The red solid dots represent the observation error of H(z). The heuristic boundaries σ+ and σ− are shown by two green solid lines. The solid line represents the estimated average uncertainty σz.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: The LSTM model infers the posterior distribution by randomly selecting 32 sets of data from the reconstructed H(z) data (green solid line) and from the mock H(z) data (blue dashed line).
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: The structure of the Ef-KAN, which includes input layers and multiple hidden layers (Φ1, Φ2, Φ3, Φ4) and output layers. In the hidden layers, the connections between the nodes are represented by learned spline functions, which allow the network to better capture the complex relationships in the data.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: LSTM model iterative training flow chart.
Paper: Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks
Authors: Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang
Figure caption: Left: symbols of activations flowing through the network. Right: the activation function is parameterized with B-splines, which allows switching between coarse and fine grids.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Comparison of the XGBoost age estimates and reference (ground-truth) ages for our samples. The left panel shows results for the training sample, while the right panel shows results for the validation sample. Note the vertical axis refers to the averaged ages from different training realizations (see the text). Different tracers, namely wide binaries and open clusters (WB and OC), and field stars, are shown by different samples as marked in the figure. Different colors represent different temperatures, with red for Teff < 5400 K and black for Teff > 5400 K. The dispersion in the relative age difference is found to be 15% for the training sample and 18% for the validation sample.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: The wide binary sample. The left panel shows the distribution of separation angle of the wide binaries, with a lower separation angle cut of 3″. The middle and right panels show the comparisons of radial velocity and metallicity, respectively, between the individual component stars of the wide binary systems. The orange color delineates bona fide binaries selected from the radial velocity and metallicity criteria (see the text). The gray color delineates outliers, which are probably recognized as wide binaries because of chance alignment.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Distribution of wide binaries in the Teff–log g diagram. The left and right panels show, respectively, the primary and secondary components, defined in K. El-Badry et al. (2021). The colored dots are reference sample stars for age estimation in the current work. For these binaries, the primaries are in either subgiant of main-sequence (turnoff) evolutionary phase and their ages can be reliably determined with isochrone fitting, while the secondaries have a broad distribution in temperature and are adopted as the training set of our data-driven age estimation.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: An example for PCA reconstruction of the LAMOST spectra for denoising. From top to bottom, the first panel is the normalized LAMOST spectrum. The second panel is the PCA-reconstructed spectrum. The third panel is the residual by subtracting the PCA-reconstructed spectrum from the LAMOST spectrum. The fourth panel is the value for the first 20 principal components (PCs).
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: The difference in isochrone age estimates between the secondary and primary stars of wide binaries as a function of effective temperature of the secondary. All the primary stars have an effective temperature higher than 5400 K. The color represents the average age of the binaries. The shaded area delineates the regime where the isochrone ages are unreliable.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Distribution of the training samples. The upper panel shows their age distribution. Different colors represent different types of samples as marked in the figure. The black thick line represents the overall age distribution. The lower panel shows their distribution in the Teff–﹩{\mathrm{log}}\,g﹩ diagram.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: The relationship between the number of PCs and the precision of age estimates.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Same as Figure 6, but for stellar age estimates with alternative methods. The left panel shows the age estimated from an empirical chemical clock built in this work. The right panel shows the data-driven spectroscopic age estimates after masking Ca II H, K lines, which may be affected by stellar activity (see the discussions in Section 5).
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Validation of the age estimates for old Mg-poor stars. The left panel shows the τ–[Mg/Fe] relation for K dwarfs, from which we have selected old Mg-poor stars in the small box marked as red dots. The middle panel shows the box of [Fe/H] and [Mg/Fe] we have imposed in our selection. The right panel shows the τ–Teff relation for these old Mg-poor stars, while the white dots represent the comparison set, which is composed of stars in the same [Fe/H] and [Mg/Fe] box but with Teff > 5400 K. The background color coded map indicates the full stellar sample in our catalog.
Paper: Spectroscopic Ages for 4 Million Main-sequence Dwarf Stars from LAMOST DR10 Estimated with a Data-driven Approach
Authors: Jia-Hui Wang, Maosheng Xiang, Meng Zhang, Ji-Wei Xie, Jian Ge, Jinghua Zhang, Lanya Mou, Ji-Feng Liu
Figure caption: Relative age error as a function of age for K dwarfs (Teff = 5000 K). The orange and black dots are age estimates for two different metallicities, as labeled in the figure. As a comparison, the expected errors of isochrone age estimates for K dwarfs are shown in diamonds (see the text). The dashed line is a linear fit to the diamonds.