adsabs/AstroCLIMB huggingface dataset:
Paper: Stellar Parameters and Spectroscopic Properties of TESS Objects Observed in the LAMOST Low- and Medium-resolution Spectral Survey
Authors: Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han, Gang Meng, Qingfeng Pi, Jiawei Yang
Figure caption: The distribution of RV and RV variation of TESS objects from LAMOST low-resolution spectra (left) and medium-resolution spectra (right).
Paper: Stellar Parameters and Spectroscopic Properties of TESS Objects Observed in the LAMOST Low- and Medium-resolution Spectral Survey
Authors: Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han, Gang Meng, Qingfeng Pi, Jiawei Yang
Figure caption: Examples of medium-resolution observations for irregular variation of the Hα emission line.
Paper: Time-variable Jet Ejections from RW Aur A, RY Tau, and DG Tau 
Authors: Michihiro Takami, Hans Moritz Günther, P. Christian Schneider, Tracy L. Beck, Jennifer L. Karr, Youichi Ohyama, Roberto Galván-Madrid, Taichi Uyama, Marc White, Konstantin Grankin, Deirdre Coffey, Chun-Fan Liu, Misato Fukagawa, Nadine Manset, Wen-Ping Chen, Tae-Soo Pyo, Hsien Shang, Thomas P. Ray, Masaaki Otsuka, Mei-Yin Chou
Figure caption: The same as the PV diagrams in Figures 1–3, but for 0.″2–1″ from the star and without positional offsets between the epochs. The yellow boxes show the possible stationary knots (see the text). As in Figures 1–3, the contour levels are arbitrarily chosen, to show the approximate locations of the jet knots. These are identical to Figures 2 and 3 for the jets from RY Tau and DG Tau, and slightly revised for RW Aur A, to better display the knots close to the base of the jet.
Paper: Time-variable Jet Ejections from RW Aur A, RY Tau, and DG Tau 
Authors: Michihiro Takami, Hans Moritz Günther, P. Christian Schneider, Tracy L. Beck, Jennifer L. Karr, Youichi Ohyama, Roberto Galván-Madrid, Taichi Uyama, Marc White, Konstantin Grankin, Deirdre Coffey, Chun-Fan Liu, Misato Fukagawa, Nadine Manset, Wen-Ping Chen, Tae-Soo Pyo, Hsien Shang, Thomas P. Ray, Masaaki Otsuka, Mei-Yin Chou
Figure caption: The correlation between the FWHMs across the jet and the peak intensities for the knots in the [Fe II] 1.644 μm emission. The FWHMs are measured without deconvolution of the observed PSFs. The vertical error bars are only shown when they are larger than the dots. The vertical dashed line at the left of each panel shows a typical angular resolution (0.″15).
Paper: Time-variable Jet Ejections from RW Aur A, RY Tau, and DG Tau 
Authors: Michihiro Takami, Hans Moritz Günther, P. Christian Schneider, Tracy L. Beck, Jennifer L. Karr, Youichi Ohyama, Roberto Galván-Madrid, Taichi Uyama, Marc White, Konstantin Grankin, Deirdre Coffey, Chun-Fan Liu, Misato Fukagawa, Nadine Manset, Wen-Ping Chen, Tae-Soo Pyo, Hsien Shang, Thomas P. Ray, Masaaki Otsuka, Mei-Yin Chou
Figure caption: Correlations between the tangential and radial velocities for the individual jets. The dashed lines are based on the average of the inclinations of the ejections. Only the error bars for the tangential velocities that we were able to measure are shown (see Section 4.1 and Tables 4–6).
Paper: Stellar Parameters and Spectroscopic Properties of TESS Objects Observed in the LAMOST Low- and Medium-resolution Spectral Survey
Authors: Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han, Gang Meng, Qingfeng Pi, Jiawei Yang
Figure caption: The left panel shows the distribution of the flare duration. The middle panel shows the relationship of the flare energy and the total duration. The right panel shows the relationship of flare energy and the decay time of the flare, where the red line represents the fit.
Paper: Stellar Parameters and Spectroscopic Properties of TESS Objects Observed in the LAMOST Low- and Medium-resolution Spectral Survey
Authors: Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han, Gang Meng, Qingfeng Pi, Jiawei Yang
Figure caption: The left panel is the relationship between the maximum flare amplitude of each star and ﹩{\mathrm{EW}}_{{\rm{H}}\alpha }^{{\prime} }﹩, where different colors represent the objects with different stellar spectral types. The middle panel is the relationship between the average amplitude of each star and the ﹩{\mathrm{EW}}_{{\rm{H}}\alpha }^{{\prime} }﹩. And the right panel is the relationship between ﹩{L}_{{\rm{H}}\alpha }^{{\prime} }﹩ and the flare energy.
Paper: Stellar Parameters and Spectroscopic Properties of TESS Objects Observed in the LAMOST Low- and Medium-resolution Spectral Survey
Authors: Li-yun Zhang, Tianhao Su, Prabhakar Misra, Xianming L. Han, Gang Meng, Qingfeng Pi, Jiawei Yang
Figure caption: Comparison of the ﹩{\mathrm{EW}}_{{\rm{H}}\alpha }^{{\prime} }﹩ of stars with flares and stars without flares. The left panel is the low-resolution result, and the right panel is the medium-resolution result.
Paper: RELICS: A Strong-lens Model of SMACS J0723.3-7327 
Authors: Keren Sharon, Mandy C. Chen, Guillaume Mahler, Dan Coe
Figure caption: Top: HST imaging of SMACS J0723.3−7327 in WFC3-IR/F160W, WFC3-IR/F140W, and ACS/F606W. The eight multiple-image sets that were used to constrain the RELICS model are overplotted and labeled in green. Model-predicted counter images that were not used as constraints are plotted in magenta. The critical curves for a source at z = 3 are shown in red. Bottom: the posterior distribution of source redshifts for sources 2–8 are shown in gray histograms (Source 1 was fixed to its photo-z, z phot = 2.2). The orange dashed line points to the best-fit model prediction. Solid blue lines mark the recently published spectroscopic redshifts from Mahler et al. (2022), which were not available at the time this model was computed (see also Golubchik et al. 2022). The rightmost panel compares the model redshift to the photometric redshifts from RELICS; this plot was used as a diagnostic tool in the modeling process. The coordinates of the lensing constraints are tabulated in Table 1.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The completeness–flux and F0.5 score–flux curves of our target detection framework and the SExtractor-based detection algorithm with and without the classifier in detection targets with different flux. We bin all targets with flux more than 200 mcrab as targets with 200 mcrab. As shown in this figure, we find that the framework is better than the SExtractor-based method for most targets, especially ultrabright targets and ordinary targets.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The schematic draw of training and deploying of the RF. We set the number of decision trees as 61, and the number of features is defined automatically by the RF algorithm in the Scikit-learn package.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The purity and the completeness of two Faster-RCNN detection models with different confidences. Panels (a) and (c) show the purity and the completeness of our framework in detection of targets with different confidence values in Faster-RCNN for bright target detection. Panels (b) and (d) show the purity and the completeness of our framework in detection of targets with different confidence values in Faster-RCNN for ordinary target detection. We find that the confidence of the bright target detection model is more sensitive than the ordinary target detection model.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The schematic diagram of further weak restriction conditions, which includes the photon distribution condition, the correlation condition, and the photometric condition.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: Confusion matrix for the RF classifier on test set.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: ROC curve of the RF classifier on the test set.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: (a) The level 2 image; (b) the level 2 image after we mask bright and ultrabright targets. As can be seen from the figure, ultrabright and bright targets are masked with our method.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The purity–completeness curve of our detection framework and that of SExtractor for targets with different flux ranges throughout the whole detection process.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: Schematic draw of the detection framework and the purity in different steps.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The structure of the modified ResNet50 used in this paper. ResNet50 is used as a feature extraction neural network. The structure of different layers is shown as blocks with size, channel, normalization method, and activation function. In the figure, “conv” represents the convolution layer, and the numbers represent the size and number of the kernel, respectively; “fc” represents the fully connected layer; “Bn” represents group batch normalization; “Relu” represents leaky ReLU; and “s” is stride, which represents the moving step. Block0 and Block1 represent two different connection blocks; the Block0 block has two input parameters, and the Block1 has one input parameter.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The structure of roi alignment. Feature maps of different spatial scales and rois will be sent to the roi alignment part. In the roi alignment part, we carry out classification (the left branch) and bounding-box regression (the right branch) to obtain final detection results. In the figure, “feature maps” represents a set of multiscale feature layers, “Sort” represents sorting the scores of rois from large to small, “conv” represents the convolution layer and the numbers represent the size and depth of the convolution kernel in turn, “FC” represents the fully connected layer and the number represents the number of output nodes in the full connection layer, and “Relu” represents leaky ReLU.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The structure of RPN. The left branch is used to propose regions that may include targets. The right branch is used to calculate appropriate positions of proposed regions. Finally, we choose the suitable rois from them. In the figure, “feature_maps” represents a set of multiscale feature layers, “conv” represents the convolution layer and the numbers represent the size and depth of the convolution kernel in turn, “rpn_cls” represents the two categories predicted by the rpn layer, and “rpn_bbox” represents the two diagonal coordinates of the bounding-box predicted by the rpn layer.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: a) Two-dimensional stacked image containing a large number of targets with high flux. (b) Two-dimensional stacked image containing a large number of targets of moderate flux. The center of the green circle is the coordinate of these targets. Targets with moderate flux are hard to see, even when we use the histogram equalization method to show these figures.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: Stacked image with an ultrabright target. Cruciform arms of the bright target would be affected by gaps between microchannel plates (labeled by yellow boxes), which would introduce false detection results.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The structure of Faster-RCNN used in this paper. It includes three parts: feature extractor (Extractor), region proposal network (RPN), and region of interest alignment part (ROI Align).
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The diagram of the target detection framework. The whole process could be divided into four steps, and two images with different pixel scales are used for target detection.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: (a) The original level 1 image; (b) the same image after we mask ultrabright targets. As can be seen from the figure, bright targets are easier to detect after we mask ultrabright targets.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The position of the ultrabright target could be obtained after a series of steps discussed above.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Lifetime interpolant employed in this work and explained in Section 2.5. It follows the same structure as Figure 6, but for τ(M *, Z); i.e., the even rows represent the original domain, and the odd rows the transformed domain onto which the interpolation is computed. The top half of the image shows the lifetime interpolant, while the bottom half shows the derivative of the lifetime with respect to the mass. The 3D projections on the first column show the metallicity and lifetime in gigayears for the bottom x‒y plane, while mass is shown on the vertical z-axis. On the second column are the respective 2D contours. The color-coding of the scatter points is consistent with Figure 3.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Diagram of the GCE rationale. At each time step, the SFR, ψ(t), determines how much total gas mass is converted into stellar mass. The distribution of stellar masses M * follows the given IMF, ϕ(M *). At each subsequent time step, the GCE convolution integral determines how many stars die at t  n  and therefore how much enrichment these stars return to the gas mass. The stellar birth time ﹩t^{\prime} ﹩, the baby blue and magenta grid, and lifetime τ(M *, Z *), i.e., the curves connecting birth time with t  n , are updated at each time step. Each enrichment channel will cover a mass/lifetime range specific to its process (SNCC and LIMs are included in the graphic). Onto these enrichment channel grids we compute the integrals, as explained in Figure 5.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Mass interpolant employed in this work and explained in Section 2.5. The even rows represent the original domain, and the odd rows the transformed domain onto which the interpolation is computed. The top half of the image shows the actual mass interpolant, while the bottom half shows the derivative of the mass with respect to the lifetime. On the second column are the respective 2D contours. The 3D projections on the first column show the metallicity and lifetime in gigayears for the bottom xy plane, while mass (or lifetime derivative of the mass for the bottom panels) is shown on the vertical z-axis. The color-coding of the scatter points is consistent with Figure 3.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: The adopted (Portinari et al. 1998) metallicity-dependent stellar lifetimes, τ(M *, Z *), as a function of stellar mass (top panel) and the ratio of the top four metallicity bins normalized by the lowest one (Z = 0.0004, bottom panel). The four next metallicity bins on the bottom panel are 0.004, 0.008, 0.02, and 0.05 (represented in dotted, dashed, and solid blue lines, respectively). The data points of the metallicity tabulation are color-coded by the actual stellar lifetime as shown in the color bar (where the units are in log10 yr). In both the top and bottom panels, three arbitrary stellar masses are highlighted with red vertical lines (3, 6, and 9 M ⊙, solid, dashed, and dotted, respectively). The top-left panels in Figure 7 represent these same color-coded grid points in a full 3D graphic.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Example of interpolating yield tabulations. The current plot refers to 16O for the massive yields by Limongi & Chieffi (2018). The two plots on the bottom show the fit to the raw data. On the top is shown the interpolation on the transformed domain. This is the preprocessed interpolation curve, which is read inside the SNCC rate integral. An equivalent computation for LIMs is shown in Figure 9.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Example of interpolating yield tabulations. The current plot refers to 16O for the LIMs yields by Cristallo et al. (2015). Similarly to Figure 8, the bottom plot shows the interpolation on the raw data while the top plot shows the interpolation on the transformed domain. This latter curve is the preprocessed interpolation which is read inside the LIMs rate integral.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Schematic diagram representing the solution to the integrodifferential GCE equation. A description of the diagram can be found in Section 2.8.1.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: All the isotopes (451) tracked in the present run, including the ones coming from LIMs (Cristallo et al. 2015), massive stars dying as core-collapse SNe (Limongi & Chieffi 2018), Type Ia SNe (Iwamoto et al. 1999), and BBN (Galli & Palla 2013). The color bar represents the percentage of each isotope that composes each element in terms of solar abundances (Asplund et al. 2009). The subplot is a 3D projection of the figure’s histogram.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption:  GalCEM flowchart. A description of the figure is provided in Section 2.1.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: The performance of our framework in detection celestial objects from data obtained with different exposure times. Panel (a) shows the purity of all detection results. Panel (b) shows the completeness of our framework in detection of celestial objects with different flux intervals.
Paper: Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine-learning Algorithms
Authors: Peng Jia, Wenbo Liu, Yuan Liu, Haiwu Pan
Figure caption: Several detection results with our algorithm. Most targets with moderate brightness (some targets are hard to seen by the human eye) could be detected in our framework.
Paper: GalCEM. I. An Open-source Detailed Isotopic Chemical Evolution Code
Authors: Eda Gjergo, Aleksei G. Sorokin, Anthony Ruth, Emanuele Spitoni, Francesca Matteucci, Xilong Fan, Jinning Liang, Marco Limongi, Yuta Yamazaki, Motohiko Kusakabe, Toshitaka Kajino
Figure caption: Evolution of the global quantities in a GCE solution. The figure on the left represents mass quantities, while the figure on the right represents rate quantities. M tot is the total time-dependent baryonic mass. M star is the total stellar mass, or M *,tot in the text. M gas is the total gas mass. The sum of total stellar and total gas mass, M gas + M *,tot, as a sanity check coincides with M tot. M g,tot,i  is the total gas mass as inferred from the isotopic evolution output. As another sanity check, it should coincide with M gas. M H,g, M He,g, and M Z,g are the time-dependent masses of H, He, and all metals, respectively. M gal,f represents the final baryonic mass of the galaxy. When it comes to the rates, the infall is shown in the black solid line, the SFR is in the dashed orange yellow line, and the dotted dark blue, light blue, and magenta lines represent the SNCC, SNIa, and LIMs rates, respectively. The present-day Milky Way estimates of the rates are shown with the vertical segments at time 13.7 Gyr. The SFR is taken from CP11, Chomiuk & Povich (2011), while the SNCC and SNIa rates come from M05, Mannucci et al. (2005).