adsabs/AstroCLIMB huggingface dataset:
Paper: Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement
Authors: Shuainan Yan, Yanmei Cui, Bingxian Luo, Liqin Shi, Yanxia Cai
Figure caption: Error comparison of F10.7 predictions for short-term and medium-term forecasts. Panels (a) and (b) display the prediction errors for 3 and 27 day forecasts, respectively. In both charts, the yellow and blue lines represent the performance of the iTransformer model on the SG-F10.7 and SG-F10.7-All data sets, while the green and purple lines show the results of the Informer and Transformer models on the SG-F10.7-All data set.
Paper: Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement
Authors: Shuainan Yan, Yanmei Cui, Bingxian Luo, Liqin Shi, Yanxia Cai
Figure caption: The F10.7 index from 1998 to 2023. The red segment represents the training set, the green segment corresponds to the validation set, and the cyan segment indicates the test set.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: A comparison between our measured EWs and G. M. Sardane et al.’s (2014) measurements. While a few data points deviate, most measurements show excellent agreement (represented by the dashed orange line x = y). Left-hand panel: the comparison of our measurements (y-axis) with the corresponding values (x-axis) from G. M. Sardane et al. (2014). Right-hand panel: the fractional differences between our measurements and those from G. M. Sardane et al. (2014). The observed discrepancies in some absorption line parameters might be attributed to uncertainties in fitting the continuum for weak lines and the inherent complexity of certain absorption profiles.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Examples of parameter range constraints. For each plot, the solid-blue line represents the function formula fitted to the independent variable x and the dependent variable y. The green dashed line represents the first quantile fitted curve, and the red dashed line represents the 0.01 quantile fitted curve, both smoothed. Top left-hand panel: parameter correlation between EW and FWHM for λ3934. Top right-hand panel: parameter correlation between FWHM3934 and FWHM3969. Bottom left-hand panel: parameter correlation between EW3934 and Varflux. Bottom right-hand panel: parameter correlation between EW3934 and Meanerr. We only applied maximum upper bounds on parameters related to Varflux and Meanerr to expand the parameter range of the data.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Our network architecture diagram. Green squares within the Conv-block represent one-dimensional convolutions with a kernel size of 3 pixels. Numbers at the bottom of the maxpooling block indicate the kernel size (3 in this case). Numbers below other blocks represent the number of convolutional kernels used within those blocks. ResNet does not use CBAM, while ResNet-CBAM incorporates it within the architecture.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Example of a positive quasar spectrum sample with Ca II absorption lines in the test set (G. M. Sardane et al. 2014), shown in the absorber rest frame. The λ3934 and λ3969 lines are represented by red-solid lines, which also indicate the window range for Gaussian fitting. The top panel contains the quasar spectrum and the line fittings, while the bottom panel shows the residual after the fit is removed. The vertical red dashed lines indicate the wavelengths of the Ca II absorption lines, and the black solid lines represent the Gaussian profiles fitted to the two Ca II lines. The yellow line represents the error array of the spectrum.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Comparison of the EW distributions between simulated and real data. Data from the catalog by G. M. Sardane et al. (2014) are shown in red, while our simulated data are shown in blue. Left-hand panel: the EW distributions for the λ3934 line. Our simulated data contains a greater number of lower EWs for the λ3934 line, following I. Xia et al.’s (2022) settings, which enhances the network’s ability to represent weak absorption lines. Right-hand panel: the EW distributions for the λ3969 line. Both simulated and real data exhibit similar trends.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: The ROC curves depict the true positive rate (﹩\tfrac{{TP}}{{TP}+{FN}}﹩) vs. the false positive rate (﹩\tfrac{{FP}}{{FP}+{TN}}﹩).
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Comparison between the neural network prediction and the Gaussian fitting results for EW values. The left-hand panel shows the relationship between the EW of the 3934 Å line, and the right-hand panel shows the relationship for the 3969 Å line. The orange dashed line indicates the ideal 1:1 relationship, illustrating that the neural network predictions closely match the Gaussian fitting results across both spectral lines.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Examples of the artificial spectra generated. The red dashed lines represent 3934.78 Å and 3969.59 Å, respectively. Left-hand panel: positive sample of Ca II absorption lines that meet the SNR criteria. The black solid line represents the Gaussian profiles we generated. The green dashed line indicates the center of the Gaussian profiles. The blue dashed lines represent the ±3σ around the centers of the Gaussian profiles. Right-hand panel: negative sample without absorption lines.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Learning curves of three network models with varying stopping epochs due to the early stopping training mechanism.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: The confusion matrices of the three network models.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: (a) Schematic of the row readout order. The first row to be read is the first active pixel row on the detector (row 5). The active pixel rows are read with Nskip to allow for row chopping. The reference pixels are weaved between the active pixel rows independent of Nskip, and their spacing is calculated from the number of total rows given the number of visits to the reference rows Nvisit. The first reference row is read after M rows, which is approximately half of the spacing between the reference rows, e.g. given Nvisit = 4 and Nskip = 32, the spacing is 64 rows. In the read order, the first appearance of a reference row (row 1, read 1) is the 26th row, and the last instance of a reference row (row 2048, read 4) is the 32nd to last. (b) Schematic of how the phantom and additional reference pixels are packaged in a SPHEREx exposure. The phantom pixels span 128 columns, grouped into 32 column channels with 4 columns of pixels in width. The H2RG pixel rows (active + reference) are arranged by their physical location, while extra samplings of the reference pixels (if any) are appended after row 2048. The phantom pixels associated with each row are also rearranged to follow the H2RG rows. In our data reduction pipeline, the phantom and reference pixels are trimmed postprocessing so the science data consist of only the active optical pixels.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: The BBM unit inside a black light-tight box. In this optical mode, a Winston cone and two integrating spheres were used to increase light diffusion. The spheres are off-the-shelf Newport 819D-GL-3 with an average reflection of 95%–99%. The Winston cone is 490 mm in length, with radii 11. 5mm at the input/sphere port (top) and 70 mm at the output/detector port (bottom). The cone is gold coated inside for similar reflectance as the spheres, and its curvature is designed to produce an F/3 beam to simulate SPHEREx light cone. The BBM was cooled to 40 K in a two-stage cryostat with thermal fluctuation ≤5 mK.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: The best-fit factors g1 [e ADU−1], presented as percentages of the expected g1 (manufacturer). We estimate the gain factors at two levels: array-wide and quartile level by grouping every eight readout channels. While the array-wide gain is consistent with the mean of the quartile factors, the quartile factors can capture the small variations between the Video8 chips.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: Exposure of a broadband light source. (a) Using the expected gain factor inferred from manufacturer information, the first (columns 0–511) and the last quartiles (columns 1536–2047) are visibly different from the middle two. (b) The same exposure after applying the best-fit gain factor in every quartile.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: Measured ratios of the reference pixel gain factor relative to that of the optical pixels using all Nvisit sets. The measurement uncertainties are consistent with the channel-to-channel fluctuations, so we adopt a single array-mean factor αi = 0.91. This factor is divided from the reference pixels before calculating the reference mean (as seen in Figure 4).
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: Fitting the gain factor using only the readout channels corresponding to Video8 number 1–4 (a)–(d). The data suggest that we need different corrections to the g1 factor in each quartile, which match the visible differences in Figure 15(a).
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: Slope noise of the (a) reference pixels and (b) optically active pixels as functions of visits. The noises converge to nonzero values highlighting the limit of using only the reference pixels to measure the channel offset.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: Comparing the noise in the phantom-corrected reference pixels against the optical pixels for channel i = 8 across Nvisit of = 1, 4, and 16. The best-fit αi (blue dashed) is consistently below 1 (red dotted line) in each case, suggesting a correction factor is needed.
Paper: Microlensing Discovery and Characterization Efficiency in the Vera C. Rubin Legacy Survey of Space and Time
Authors: Natasha S. Abrams, Markus P. G. Hundertmark, Somayeh Khakpash, Rachel A. Street, R. Lynne Jones, Jessica R. Lu, Etienne Bachelet, Yiannis Tsapras, Marc Moniez, Tristan Blaineau, Rosanne Di Stefano, Martin Makler, Anibal Varela, Markus Rabus
Figure caption: Same as Figure 5, but for select other 2.1 and later OpSims, with baseline metrics for reference. All OpSims are plotted in reference to baseline_v2.2_10yrs in this plot. Three representative twilight_neo runs are plotted. This has the OpSims on the y-axis and the metrics on the x-axis for ease of plotting.
Paper: Microlensing Discovery and Characterization Efficiency in the Vera C. Rubin Legacy Survey of Space and Time
Authors: Natasha S. Abrams, Markus P. G. Hundertmark, Somayeh Khakpash, Rachel A. Street, R. Lynne Jones, Jessica R. Lu, Etienne Bachelet, Yiannis Tsapras, Marc Moniez, Tristan Blaineau, Rosanne Di Stefano, Martin Makler, Anibal Varela, Markus Rabus
Figure caption: Same as Figure 5, but for select other v2.0 OpSims, with baseline metrics for reference. This has the OpSims on the y-axis and the metrics on the x-axis for ease of plotting.
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The distribution of BHB stars in the Teff vs. (BP − RP)0 plane from panel (a) to panel (c). Teff is estimated by training flux and color index (panel (a)), by training flux (panel (b)), and by M. Xiang et al. (2022) (panel (c)). The dashed line represents the theoretical results predicted by the PARSEC models with [Fe/H] = −2.0 and ﹩\mathrm{log}g﹩ = 3 (blue dashed line) and 5 (red dashed line), respectively. Panels (d)–(f) compare the theoretical (BP − RP)0 calculated from the predicted parameters and the observed color indexes ((BP − RP)0) after extinction correction.
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The distributions of Teff vs. theoretical color indexes ((BP − G), (G − RP), (BP − RP), (J − H)). The color bar on the right represents ﹩\mathrm{log}g﹩.
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The crosses in the Teff–log g plane represent the model grids for A-type stars, while the gray-shaded area represents the linearly interpolated model grids that are employed for the training set.
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The distribution of scatter values for the predicted atmospheric parameters of Teff, ﹩\mathrm{log}g﹩, and [Fe/H] as a function of S/N values. Blue represents training flux only, and red represents training flux and color index.
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The upper panel shows the LAMOST low-resolution spectrum and the bottom panel shows the normalized and radial-velocity-corrected spectrum (blue line) and the theoretical spectrum (orange line) obtained based on the atmospheric parameters (Teff = 7783 K, ﹩\mathrm{log}g﹩ = 3.23, and [Fe/H] = −1.63) from SLAM.
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The distribution of BHB stars in Teff (left panel), ﹩\mathrm{log}g﹩ (middle panel), and [Fe/H] (right panel).
Paper: The Blue Horizontal-branch Stars from the LAMOST Survey: Atmospheric Parameters
Authors: Jie Ju, Bo Zhang, Wenyuan Cui, Zhenyan Huo, Chao Liu, Yang Huang, Jianrong Shi
Figure caption: The spatial distribution of BHB stars identified from LAMOST-LRS observations by J. Ju et al. (2024).
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: The redshift distributions of Ca II absorbers from our newly identified catalog (shown in blue) and the reference catalog by G. M. Sardane et al. (2014) (shown in red). Our result reveals a significant shift toward higher redshifts in the new catalog, as evidenced by the presence of more absorbers at mid-to-high redshifts and a notably higher average redshift compared to the G. M. Sardane et al.’s (2014) catalog.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Examples of EW fittings for weak absorption lines identified by our model. Each panel shows the observed wavelength on the x-axis, displaying the normalized flux (blue), Gaussian fits to the absorption lines (red), and the associated error array (yellow). The red dashed lines represent the wavelength of the Ca II absorbers.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: A comparison of the EW distributions for the Ca II absorption lines at λ3934 (left-hand panel) and λ3969 (right-hand panel). The distribution in our catalog exhibits a greater skew toward lower EW values, suggesting an improved ability to identify weaker Ca II absorbers.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Relationship between the SNR and EW for the Ca II absorption lines at 3934 Å (left-hand panel) and 3969 Å (right-hand panel). The red dashed line in the left-hand plot indicates an SNR threshold of 2.5, while the right-hand plot uses a threshold of 2.0.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Our model accurately identifies new candidate samples of absorption lines. The black dashed lines indicate the positions of Mg II absorption lines. The red dashed lines represent the Gaussian fit centers of Ca II absorption lines.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Training and validation loss. The blue curve represents the training loss, while the green curve represents the validation loss.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: Comparison between the neural network prediction and the Gaussian fitting results for EW values.
Paper: Deep Learning for Identification and Characterization of Ca II Absorption Lines: A Multitask Convolutional Neural Network Approach
Authors: Yang Liu, Jie Li, Linqing Gao, Haotong Zhang, Zhenghua Xu, Yu Wang, Wenbin Lin
Figure caption: The confusion matrices of the network model.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: Comparing different methods to correct for the channel-to-channel offset. Each dark current map here is coadded from 40 × 113 s exposures. (a) Conventional reference subtraction using only their mean as is. (b) The reference pixels are divided by 0.91 to account for the 10% difference from active pixels. (c) Instead of using only reference pixels, we use all pixels in a given channel to estimate the offset in this channel filter. This is the most effective at smoothing the channel-wide features. The visible row banding is the low-frequency noise of the electronics, modulated by the row chopping to 1/Nskip [row−1] or 42.35 Hz for Nskip = 32. A 4 pixel Gaussian kernel is applied to the plots to aid visualization.
Paper: Improving H2RG Performance in SPHEREx Brassboard Model
Authors: Chi H. Nguyen, Phillip Korngut, C. Darren Dowell, James Bock, Jill Burnham, Samuel Condon, Walter Cook, Grigory Heaton, Howard Hui, Branislav Kecman, Hiromasa Miyasaka, Kenneth Manatt, Hien T. Nguyen, Stephen Padin, Marco Viero
Figure caption: The noise maps corresponding to the three correction cases in Figure 6. We take the differences of the 40 exposures in Figure 6 pair-wise, divide by ﹩\sqrt{2}﹩, then coadd. (a) Reference subtraction as is. (b) Reference subtraction with 10% correction. (c) Channel filtering. A small number of bad pixels including those affected by transient events are masked in white. The low-frequency noise modulated by the row chopping is also visible here. A 4 pixel Gaussian kernel is applied to the plots to aid visualization.