adsabs/AstroCLIMB huggingface dataset:
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: Power spectrum multipoles of the baseline sample compared with the theoretical templates with different photo-z damping terms. We exclude data points in the gray regions during the fitting process. Right: correlation coefficients of the measured baseline sample multipoles.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: Window function multipoles evaluated from the random catalog corresponding to the NGC subbaseline sample. We use the random samples whose sizes are equal to 2, 1, and 0.5 times the group sample size N group. The lower panel shows the fractional difference between those of “2 × N group” and “1 × N group” (blue lines) or “0.5 × N group” (green lines). Right: theoretical masked dipoles and their fractional differences.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: Measured ﹩\bar{\tau }﹩ profiles of the baseline sample by choosing different AP filter radii (θ AP). The blue dots with error bars represent the ﹩\bar{\tau }﹩ profile of the baseline sample, and the black dots with error bars show the results of the null test by targeting a random sample having the same size of the baseline sample. On top of the figure, we label the comoving radius corresponding to θ AP. Right: corresponding detection S/Ns.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: The pairwise kSZ dipole measurement of the baseline sample compared with the theoretical template (solid line). By applying ﹩{\theta }_{\mathrm{AP}}=4\buildrel{\,\prime}\over{.} 2﹩, the best-fitted ﹩\bar{\tau }=(3.96\pm 0.76)\times {10}^{-5}﹩ with the measurement S/N = 5.2. Right: correlation coefficients of the measured pairwise kSZ power dipole from the baseline sample using the JK method by applying ﹩{\theta }_{\mathrm{AP}}=4\buildrel{\,\prime}\over{.} 2﹩.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: higher order multipoles of the window function for the NGC subsample of the baseline sample. Right: corresponding theoretical kSZ dipoles. The red dashed line represents the true dipole without convolution with the window function, the blue solid line is the masked dipole calculated by Equation (C1), and the green dotted–dashed line is the one given by Equation (43). The relative difference Diff = Equation (43)/Equation (C1) –1 is of the order of  0.1% within the k range of our interest.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: the pairwise kSZ dipole measurement of the baseline sample without (data points with error bars) and with (dashed line) the imaging-systematics correction. Right: The multipoles of redshift space density power spectrum without (data points with error bars) and with (dashed line) the imaging-systematics correction. The lower panel shows the fractional difference between those of “corrected” and “raw” ones. Measurements are made from the baseline sample.
Paper: Detection of Pairwise Kinetic Sunyaev–Zel’dovich Effect with DESI Galaxy Groups and Planck in Fourier Space
Authors: Shaohong Li, Yi Zheng, Ziyang Chen, Haojie Xu, Xiaohu Yang
Figure caption: Left: window function multipoles evaluated from the random catalog corresponding to the NGC subbaseline sample. Right: theoretical masked and unmasked power spectrum multipoles and their fractional differences.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: Comparative analysis of loss curves and F1-scores for StellarGAN and CNN using the SDSS data set, with only 1% of the data set employed in the training process. Utilizing the SDSS data set, StellarGAN, benefitting from pretraining, exemplifies accelerated convergence relative to CNN in terms of loss evolution. Notably, in a majority of equivalent epochs, StellarGAN outperforms, demonstrating superior F1-scores that underscore its enhanced precision and recall capabilities.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: The training loss curves of G (blue solid line) and D (red solid line) in StellarGAN. In StellarGAN training, observing the D loss indicates its improving accuracy in discerning real from generated samples. Concurrently, the G loss oscillation around 10 epochs exemplifies the adversarial adjustment in response to D’s refinements, portraying the typical adversarial dynamic where G continuously strives to enhance data generation quality against D’s discriminations.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: The schematic structure of StellarGAN in the pretraining phase. The StellarGAN models architecture comprises two essential stages, each contributing uniquely to its training. Initially, in the pretraining stage, noise vectors serve as inputs, channeled into the generator (G), culminating in the creation of synthetic spectra. These generated entities are distinctly labeled “0,” signifying their nonauthentic origins. In succession, these synthetically generated spectra, along with the genuine spectra (labeled “1,”) are directed toward the discriminator (D), fostering its ability to distinguish synthetic from authentic spectra. Progressing to the second stage, fine-tuning is pivotal. Herein, D transitions into a sophisticated multiclassification network, seamlessly integrating parameters acquired from the preliminary pretraining stage.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: Examples of spectra observed by SDSS and APOGEE. The left panels cover different types of stars from SDSS (from top to bottom: O, B, A, F, G, K, and M types). The right panels cover different types of stars from APOGEE (from top to bottom: A, F, G, K, and M types). Prominent hydrogen Balmer lines (H) are visible for A-type stars, while H lines and neutral metal lines (Ca, Fe) are prominent for F-type and G-type stars. The absorption lines from neutral metals (Fe, Ca, and Mg) dominate the spectra of K-type and M-type stars.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: The comparison of confusion matrices for different algorithms on the SDSS data set. During the fine-tuning process, a random subset comprising 10% of the labeled data set is utilized for training, while the remaining 90% serves as the test data.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: The generated spectra from StellarGAN trained on an SDSS data set. Panels (a)–(d) display the data generated by G at different epochs, with each spectra maintaining a consistent dimensionality of 2000. It can be seen from the figure that the spectrum generated by G is random noise at the beginning. As the number of epochs increases, the G can generate data similar to the spectrum.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: Signal-to-noise ratio (S/N) distribution of SDSS spectra (left panel) and APOGEE spectra (right panel). A total of 107,800 labeled and 59,240 unlabeled data are selected from SDSS, while 28,200 labeled and 21,810 unlabeled data are selected from APOGEE. The blue and orange histograms denote the labeled and unlabeled data, respectively.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: Variation of F1-score with convolutional layer count. During the fine-tuning phase, a random subset comprising 10% of the labeled data set is employed for training, while the rest serves as the test set. The depicted blue curve captures the trajectory of StellarGAN’s F1-score on the SDSS data set as a function of convolutional layers. Similarly, the red curve charts out the performance trend of StellarGAN on the APOGEE data set against the convolutional layer count.
Paper: StellarGAN: Classifying Stellar Spectra with Generative Adversarial Networks in SDSS and APOGEE Sky Surveys
Authors: Wei Liu, Shuo Cao, Xian-Chuan Yu, Meng Zhu, Marek Biesiada, Jiawen Yao, Minghao Du
Figure caption: The performance of StellarGAN on the SDSS data set (left) and the APOGEE data set (right) evaluated across different S/Ns. It is noticeable that there is a correlation between the S/N and the models performance on the SDSS and APOGEE data sets. Specifically, an increase in S/N is associated with an enhancement in the model’s performance, elucidating the models sensitivity to the quality of the input data.
Paper: Detection of Solar-like Oscillations in Subgiant and Red Giant Stars Using 2 minute Cadence TESS Data
Authors: Jianzhao Zhou, Shaolan Bi, Jie Yu, Yaguang Li, Xianfei Zhang, Tanda Li, Liu Long, Mengjie Li, Tiancheng Sun, Lifei Ye
Figure caption: Comparison between the global seismic parameters measured in this work and those from previous literature. The black dashed lines in the top panel show the one-to-one relation between the two parameters. The bottom left panel shows the fractional residuals of ﹩{\nu }_{\max }﹩, calculated as ﹩\left({\nu }_{\max ,\mathrm{literature}}-{\nu }_{\max ,\mathrm{this}\ \mathrm{work}}\right)/{\nu }_{\max ,\,\mathrm{this}\ \mathrm{work}}﹩. Similarly, the bottom right panel displays the fractional residuals of Δν, calculated as ﹩\left({\rm{\Delta }}{\nu }_{\mathrm{literature}}-{\rm{\Delta }}{\nu }_{\mathrm{this}\ \mathrm{work}}\right)/{\rm{\Delta }}{\nu }_{\mathrm{this}\ \mathrm{work}}﹩.
Paper: Detection of Solar-like Oscillations in Subgiant and Red Giant Stars Using 2 minute Cadence TESS Data
Authors: Jianzhao Zhou, Shaolan Bi, Jie Yu, Yaguang Li, Xianfei Zhang, Tanda Li, Liu Long, Mengjie Li, Tiancheng Sun, Lifei Ye
Figure caption: Comparisons of radii from the asteroseismic scaling relations with radii from the SED fitting. The color bar represents the relative error of Δν. The bottom panel displays the fractional residuals between radius estimates, denoted as ΔR = R SED − R seismic.
Paper: Detection of Solar-like Oscillations in Subgiant and Red Giant Stars Using 2 minute Cadence TESS Data
Authors: Jianzhao Zhou, Shaolan Bi, Jie Yu, Yaguang Li, Xianfei Zhang, Tanda Li, Liu Long, Mengjie Li, Tiancheng Sun, Lifei Ye
Figure caption:                       ﹩{\nu }_{\max }﹩ vs. ﹩{\nu }_{\max }^{0.75}/{\rm{\Delta }}\nu ﹩ diagram. The horizontal axis shows ﹩{\nu }_{\max }﹩, while the vertical axis shows ﹩{\nu }_{\max }^{0.75}/{\rm{\Delta }}\nu ﹩, which is a mass proxy related to temperature. The gray triangles and crosses represent samples from the Kepler and K2 long-cadence data, respectively. The blue dots show the sample from previous TESS 2 minute data, as presented by Hatt et al. (2023), and the yellow dots show our sample.
Paper: Detection of Solar-like Oscillations in Subgiant and Red Giant Stars Using 2 minute Cadence TESS Data
Authors: Jianzhao Zhou, Shaolan Bi, Jie Yu, Yaguang Li, Xianfei Zhang, Tanda Li, Liu Long, Mengjie Li, Tiancheng Sun, Lifei Ye
Figure caption: Histogram of Tmag for all 2 minute cadence targets and seismic stars. The gray color shows all 2 minute cadence targets, the blue color shows oscillators with ﹩{\nu }_{\max }﹩, and the red color shows stars with Δν.
Paper: CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
Authors: Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang, Ravi Joshi, Shuo Zhang, Qiyue Wang, Jing Yang, FanLam Ng, Xingjian Liu, Yu Qiu, Rui Zhu, Huimei Wang, Christian Wolf, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai, Qinchun Ma, Xiaotong Feng, R. J. Bouwens
Figure caption: The absolute magnitudes M  i (z = 2) and redshift distributions of the CatNorth catalog and the DR16Q subsample with good redshifts with 421,959 sources. In the main panel (lower left), the CatNorth sources are shown as the two-dimensional histogram (density plot), over which the white contour lines based on two-dimensional kernel density estimation (KDE) are plotted. The DR16Q sources are shown as orange KDE contours. In the top and right panels, the blue-shaded areas denote the KDE probability density functions of the CatNorth catalog, and the orange-shaded areas denote the probability densities of the DR16Q sample.
Paper: CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
Authors: Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang, Ravi Joshi, Shuo Zhang, Qiyue Wang, Jing Yang, FanLam Ng, Xingjian Liu, Yu Qiu, Rui Zhu, Huimei Wang, Christian Wolf, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai, Qinchun Ma, Xiaotong Feng, R. J. Bouwens
Figure caption: The Gaia DR3 BP/RP spectra that are calibrated with GaiaXPy of four CatNorth quasars (in black). For each quasar, a template quasar spectrum of Vanden Berk et al. (2001) is shown as a blue line in addition to the BP/RP spectrum. The template spectrum is shifted to the same redshift of z xp_nn, and scaled to a similar flux level as the BP/RP spectrum. Some major emission lines of the template spectrum are marked with red dashed lines.
Paper: CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
Authors: Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang, Ravi Joshi, Shuo Zhang, Qiyue Wang, Jing Yang, FanLam Ng, Xingjian Liu, Yu Qiu, Rui Zhu, Huimei Wang, Christian Wolf, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai, Qinchun Ma, Xiaotong Feng, R. J. Bouwens
Figure caption: Color–color diagrams of sources from the CatNorth quasar candidate catalog (blue contours), 200,000 SDSS DR16Q quasars (gray contours), and 400,000 stars (red-shaded density plots) using photometric data from PS1, CatWISE2020, and Gaia DR3. The SDSS DR16Q subset and the star sample are the same as those in Figure 3. All magnitudes are in the AB system.
Paper: CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
Authors: Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang, Ravi Joshi, Shuo Zhang, Qiyue Wang, Jing Yang, FanLam Ng, Xingjian Liu, Yu Qiu, Rui Zhu, Huimei Wang, Christian Wolf, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai, Qinchun Ma, Xiaotong Feng, R. J. Bouwens
Figure caption: HEALPix (Górski et al. 2005) sky density maps of the CatNorth quasar candidate catalog (a), the CatNorth subsample with G < 20.5 (b), the full Quaia catalog (c), and the Quaia subsample with PS1 and CatWISE2020 data (d). The maps are plotted in Galactic coordinates, with the parameter N side = 64 and an area of 0.839 deg2 per pixel.
Paper: CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
Authors: Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang, Ravi Joshi, Shuo Zhang, Qiyue Wang, Jing Yang, FanLam Ng, Xingjian Liu, Yu Qiu, Rui Zhu, Huimei Wang, Christian Wolf, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai, Qinchun Ma, Xiaotong Feng, R. J. Bouwens
Figure caption: Upper panel: histograms of the apparent G magnitudes of CatNorth (blue bars) and the Quaia subsample with PS1 and CatWISE2020 data (Quaia×PS1×CatWISE2020; orange-filled step plot). Lower panel: same as the upper panel, but for apparent i P1 magnitudes.
Paper: CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
Authors: Yuming Fu, Xue-Bing Wu, Yifan Li, Yuxuan Pang, Ravi Joshi, Shuo Zhang, Qiyue Wang, Jing Yang, FanLam Ng, Xingjian Liu, Yu Qiu, Rui Zhu, Huimei Wang, Christian Wolf, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai, Qinchun Ma, Xiaotong Feng, R. J. Bouwens
Figure caption: The HCT spectra of 10 randomly selected CatNorth quasar candidates that are not in Quaia.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Examples of point-source and extended-source removal on EPIC-MOS2 images. The sequential panels depict images from ObsIDs: 0675010901 (the left two panels) and 0304850901 (the right two panels). Each set presents the image before and after the removal of point sources (green ellipses) and either a galaxy cluster (A3718 in blue) or a nearby galaxy (NGC 253 in orange). See Section 2.2.5 for size determination of the extended sources.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Flowchart illustrating the data reduction process and observation classification. Explanations of the flags are provided in Table 1.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: All-sky maps depicting the MW soft X-ray emissions. From top to bottom: SWCX- and absorption-corrected O VII, O VIII, and Fe-L maps. These maps are centered on the Galactic center and smoothed with a Gaussian function of σ = 4°. Dashed lines indicate large-scale structures introduced in Figure 5. Black outlines hydrogen-dense areas where N H, dust > 5 × 1021 cm−2. The oxygen line emissions extend beyond the eRBs and decrease from the Galactic center. The Fe-L line emission closely matches the eRBs and Galactic disk. The emission intensity is proportional to ﹩{n}_{e}^{2}\epsilon (T)Z﹩, meaning that a higher intensity suggests an increased plasma density, metallicity, or emissivity at temperatures optimal for emission (i.e., ≈2 × 106 K, ≈3 × 106 K, and ≈7 × 106 K for O VII, O VIII, and Fe-L, respectively). The LHB emission is not corrected and may contribute to these line emissions, particularly for O VII (see Section 6).
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: From top to bottom: all-sky maps of observed O VII, O VIII, and Fe-L intensities, shown in Hammer projections centered on the Galactic center. These observations cover approximately 3% of the sky. Measurements (circles) and upper limits (triangles) are plotted according to their pointing directions. Dashed lines enclose five large MW structures: the Cygnus Superbubble (80°, 2°), the eRBs (centered at the Galactic center), the Vela and Monogem SNRs ((261°, −3°) and (197°, 10°)), and the Eridanus Superbubble (194°, −35°). The LMC and SMC are marked with stars.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Top: yearly shifts in observed Al and Si instrumental line centroids in both MOS1 and MOS2. These shifts are with an uncertainty of 5 eV, likely due to temperature variations in the instrument. Middle: calibrated Si line centroids derived from Al-inferred gain or offset adjustments. A smaller total χ 2 in the offset case suggests a closer relation between the shift and offset. Bottom: the observed and corrected E OVII over time. The correction is done using the Al-inferred offset results to minimize the instrument-induced variations. The resulting trend suggests a potential anticorrelation with solar activity, implying E OVII as a viable tracer of solar activity, even with limited spectral resolution.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Long-term variations in the observed O VII, O VIII, and Fe-L line intensities from 2000 to 2022. SSNs are smoothed and rescaled individually for better visualization. The circles represent intensities in half-year or 1 yr bins using the clean sample, adjusted by subtracting minimum values of 3.20, 0.54, and 0.46 LU, respectively. The reddish curves represent the medians and 1σ and 2σ uncertainties derived from the “close-pair” method. The variations in O VII and O VIII intensities correlate with the solar cycle, suggesting a strong association with SWCX. Moderate Fe-L correlation indicates that Fe-L is not primarily produced by SWCX (see text for details).
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Top: intensity distributions of O VII, O VIII, and Fe-L lines. Measurements and upper limits are shown in bar-type and dashed step-type histograms, respectively. Bottom: correlations of observed O VII, O VIII, and Fe-L intensities. Each panel displays the two-dimensional histograms of intensities (gray scale) using points from the MCMC chain. The blue dashed lines represent the CIE model predictions at different temperatures, while the contours (red) represent intensities measured inside eRBs. The eRBs contribute most of the high-intensity emissions, with some exceptions due to small X-ray structures or X-ray-bright phenomena outside the eRBs. Positive relations between emission lines imply a consistent temperature of the MW’s gas, with a relatively narrow temperature dispersion. A detailed study of MW temperature structures is provided in Paper II.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: An example of a joint-fitted SXRB spectrum and its best-fit model. Left: the paired spectrum extracted from ObsID: 0724770201. The O VII (≈0.56 keV), O VIII (≈0.65 keV), and Fe-L (≈0.80 keV) lines are modeled as three Gaussian functions and highlighted. Instrumental lines are covered by a gray shade. Right: MOS1 spectrum only. All spectral components are present, illustrating individual contributions to the modeled spectrum. The SPB is not folded through the instrumental response.
Paper: The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O VII, O VIII, and Fe L-shell Transitions
Authors: Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu
Figure caption: Example of MCMC analysis to derive long-term variations in O VII intensity from close pairs. Left: the medians of O VII intensity differences in half-year intervals. Middle: the intensity differences reconstructed after MCMC fitting. Right: residuals. The MCMC fitting process and results are detailed in Sections 5.1 and 5.2.