adsabs/AstroCLIMB huggingface dataset:
Paper: A Systematic Study of Associations between Supernova Remnants and Molecular Clouds
Authors: Xin Zhou, Yang Su, Ji Yang, Xuepeng Chen, Yan Sun, Zhibo Jiang, Min Wang, Hongchi Wang, Shaobo Zhang, Ye Xu, Qingzeng Yan, Lixia Yuan, Zhiwei Chen, Yiping Ao, Yuehui Ma
Figure caption: Integrated intensity and index-velocity maps of 12CO (J = 1–0) emission toward SNR IC 443. Both intensity maps in the left column are integrated over the velocity range of −15.9 to −1.6 km s−1, which covers most of the broad lines. The red dashed circle denotes the remnant region, defined by visual inspection according to the bright radio continuum shell of the remnant. Regions A, B, C, D, E, F, G, and H are adopted from Denoyer (1979a), Dickman et al. (1992), where shocked molecular gases were detected by 12CO (J = 1–0) emission. Region C′ is an additional shocked molecular clump, which is adjacent to region C. Locations of broad lines identified by full (upper) and partial (lower) criteria are denoted by red tiny stars. The minimum value of 12CO emission intensity shown in the colorbar is at 5σ confidence level. The beam is represented by a black circle in the lower left corner. This beam designation is used in all intensity maps in the paper. Index-velocity maps in the right column contain all significant 12CO (J = 1–0) emission lines in the 1.1 times enlarged remnant region, whose intensity is normalized and logarithmically scaled for better visibility. The index indicates the arrangement of the spectrum at each point, sorted by the distance of the point from the SNR center. Some distances are shown on the right axis. The center of each region adopted from previous works in intensity maps is also shown in index-velocity maps. Broad lines identified by full (upper) and partial (lower) criteria are in red, and remaining lines with FWHM over 6 km s−1 are in blue.
Paper: A Systematic Study of Associations between Supernova Remnants and Molecular Clouds
Authors: Xin Zhou, Yang Su, Ji Yang, Xuepeng Chen, Yan Sun, Zhibo Jiang, Min Wang, Hongchi Wang, Shaobo Zhang, Ye Xu, Qingzeng Yan, Lixia Yuan, Zhiwei Chen, Yiping Ao, Yuehui Ma
Figure caption: Test results of broad line identification. The spectrum comprising two Gaussian components is used for test, with standard deviations of σ 1 and σ 2 and the distance between their peaks of Δx 0. The FWHM of the first Gaussian component is set to 3, i.e., σ 1 is 1.27. Results are similar for other values of σ 1. Black contours show the distribution of the kurtosis ratio between the spectrum and a best-fitting Gaussian function. Red contours show the distribution of the square rooted variance ratio between the part of the spectrum larger than the best-fitting Gaussian function and the whole spectrum. Panels in the left column show cases that the broad Gaussian component is weaker than the narrow component, with peak ratios of 0.2, 0.5, and 0.8 from top to bottom, respectively. The right column is opposite, with the ratios of peaks between the broad and narrow components of 5, 2, and 1.2 from top to bottom, respectively. Gray-scale regions indicate where two Gaussian components can be resolved. Blue dashed lines denote regions selected by full broad line identification criteria.
Paper: A Systematic Study of Associations between Supernova Remnants and Molecular Clouds
Authors: Xin Zhou, Yang Su, Ji Yang, Xuepeng Chen, Yan Sun, Zhibo Jiang, Min Wang, Hongchi Wang, Shaobo Zhang, Ye Xu, Qingzeng Yan, Lixia Yuan, Zhiwei Chen, Yiping Ao, Yuehui Ma
Figure caption: Same as Figure 2, but for SNR HB 3. Both integrated intensity maps in the left column are in the velocity range of −57.3 to −26.8 km s−1. Regions (a1), (a2), (b1), (b2), (c), and (d) are adopted from Zhou et al. (2016), which indicate positions of shocked molecular clumps. All broad lines outside the SNR region are located in the star-forming region W3.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Cross-validation of the XGBoost parameters on the training sample (SDSS-APOGEE DR17 and Li et al. 2022). The plots show the results of the 20-fold cross-validation of the 5% portions of the training sample, withheld in the training. Rows from top to bottom show [M/H] residuals, T eff residuals, and ﹩\mathrm{log}g﹩ residuals. Columns from left to right show residuals versus the training sample’s [M/H], T eff, ﹩\mathrm{log}g﹩, and Gaia’s apparent G BP magnitude. The numbers in the top right corners quote the median absolute difference (MedAD) and the root mean square difference (RMSD). The density map is logarithmic. These plots illustrate the remarkable precision of the approach: 0.10 dex in [M/H], 50 K in T eff, and 0.08 dex in ﹩\mathrm{log}g﹩. Note that these variances still include all the uncertainties in the APOGEE estimates.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Completeness of the sample at a given G BP magnitude in several bandpasses that limit the application of XGBoost, which requires the full set of features for both training and testing. For G BP ≲ 17.7, CatWISE is the most severe limitation, while for G BP ≳ 17.7 the two narrow bandpasses synthesized with GaiaXPy in the far blue (e.g., Pristine_mag_CaHK and Jplus_mag_J0395) become severely incomplete.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Cross-validation of XGBoost on the training sample (SDSS-APOGEE DR17 and Li et al. 2022). The dependence of the test error for [M/H] on WISE A  K  extinction (panel (a)) and parallax (panel (b)). [M/H] shows no systematics with either.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Apparent magnitude distributions—G (black), G BP (blue), G RP (red), and W 1 (gray)—for the full application sample of 174,922,161 stars without any quality cuts. The bump in the G-band distribution at G ∼ 19 reflects contamination by galaxies, QSOs, and ultracool dwarfs.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Distributions of the [M/H] training sample terms of effective temperature, surface gravity, and metallicity. The dominant SDSS-APOGEE DR17 part of the sample is shown as the logarithmic density map, and the metal-poor training stars from Li et al. (2022) as black dots.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Distribution of [M/H] in the training sample of stars, which is drawn from SDSS-APOGEE DR17 (red) and the very-metal-poor stars from Li et al. (2022; blue).
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: Left: the observed FUV image for M81 with the inner part of the galaxy marked as the area between the red and blue ellipses (the upper panel), where the yellow ellipse represents the aperture employed for photometry of the whole galaxy, and the spiral-arm image (the lower panel) segmented from the above FUV image. Right: flux ratios of the spiral arms (purple) and the inner area (pink) to the whole galaxy as a function of wavelength, with the inner area to the spiral arms overplotted as well (green).
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: Results of the surface-brightness fitting in the Spitzer-IRAC 5.8 μm image. The left panels show the observed (top panel), model (middle panel), and residual (bottom panel) images. The top-right panel shows the radial surface-brightness profiles of the observed galaxy and all of the components in the model, with the best-fit curves superimposed. The bottom-right panel shows the comparison between the observed data and the model, and the gray dotted line indicates the error. The legend is listed in the diagram. During the decomposition, values for the Sérsic index and the effective radius for the bulge and the disk in the model are determined according to Equations (3)–(6).
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: This figure presents the morphological parameters of the galaxy, including the Sérsic index (top-left panel) and effective radius (top-right panel) fitting based on the original S/N data, as well as the Sérsic index (lower-left panel) and effective radius (lower-right panel) fitting based on reduced S/N data. The black dots represent the fitting results for the integrated galaxy, which is a combination of all substructures.
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: Comparisons between the same best-fit parameters for the two Sérsic components and the one-Sérsic bulge plus one-exponential (i.e., the Sérsic index n = 1) disk. The left panel shows the comparison for the Sérsic index n; the right panel shows the comparison for the effective radius R  e . In each panel, symbols and lines are the same as assigned in Figure 8.
Paper: Multiwavelength Bulge–Disk Decomposition for the Galaxy M81 (NGC 3031). I. Morphology
Authors: Jun-Yu Gong, Ye-Wei Mao, Hua Gao, Si-Yue Yu
Figure caption: The same as Figure 3, but the morphological parameters at the Spitzer-IRAC 5.8 μm band are determined according to Equations (3)–(6), so there is no error bar for the data points of n and R  e  at this band.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Mass distributions along the z direction for different spiral arms. The distributions for molecular gas in the Mask 1 and Mask 2 regions are presented in (a) and (b), respectively. The Gaussian fittings to the distributions of 12CO gas (Mask 1) are shown in (c)–(f) and those of 13CO gas (Mask 2) in (g)–(j). The fitted parameters, μ and σ, are indicated in the top-right corners of the panels.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Locations of molecular clouds in the OSC arm superimposed on an artist’s conception of the Milky Way (R. Hurt: NASA/JPL-Caltech/SSC). The green circles, yellow circles, and red circles indicate clouds covering Galactic longitudes from l = 59.°75 to 74.°75 detected in this work, l = 13° to 55° detected by Dame & Thaddeus (2011), and l = 100° to 150° detected by Sun et al. (2015). The cyan line is a logarithmic spiral line fit to the Scutum–Centaurus arm (Reid et al. 2019). The Galactic center is marked with a star symbol and the location of the Sun is marked with a large red dot.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Same as Figure 4 but for 13CO line emission. The orange boxes in (a) and (c) mark the regions with high-column density.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Integrated-intensity maps (a1), (b1), and (c1) and average spectra (a2), (b2), and (c2) of three distant clouds. The red outlines represent the boundaries of molecular clouds. Two vertical dashed lines in each average spectrum indicate the velocity range of the cloud. The angular sizes in units of arcmin2 and the coordinates of the clouds in degrees are indicated in the top-left corner of the average spectra.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Same as Figure 2 but for 13CO line emission.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Velocity channel maps of 12CO from −87 to −45 km s−1 of the Outer arm. The velocity range of each channel is shown at the top-left corner of each panel.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Velocity channel maps of 12CO from 20 to 55 km s−1 of the Local Spur. The velocity range of each channel is shown at the top-left corner of each panel.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Velocity channel maps of 12CO from −45 to −8 km s−1 of the Perseus arm. The velocity range of each channel is shown at the top-left corner of each panel.
Paper: Molecular Clouds in the Galactic Plane from l = [59.°75, 74.°75] and b = [−5.°25, +5.°25]
Authors: Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin, Yang Su, Chong Li, Yan Sun, Xin Zhou, Ji Yang
Figure caption: Velocity channel maps of 12CO from −8 to 20 km s−1 of the Local Arm. The velocity range of each channel is shown at the top-left corner of each panel.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of R of resampling along different directions. V, LB, and LBV represent the statistics in data downsampled in the spectral channel, data downsampled in the spatial direction, and data downsampled in both the spatial direction and the spectral channel, respectively. Original  Par means that SRecursionLBV uses the same value as when detecting unresampled synthetic data, and V ariational Par means that SRecursionLBV varies according to the formula in Equation (B1).
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: Velocity-integrated intensity images of clumps in the high overlap area as shown in Figure 20. Each panel is presented in the same manner as in Figure 21.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of ΔX of FellWalker, LDC, ConBased, and FacetClumps for synthetic data of different density signal environments. ΔX is a function of S/N. The left panel shows the statistic of synthetic data in a high-density signal environment as shown in the top panel of Figure 22. The right panel shows the statistic of synthetic data in a low-density signal environment as shown in the bottom panel of Figure 22.
Paper: FacetClumps: A Facet-based Molecular Clump Detection Algorithm
Authors: Yu Jiang, Zhiwei Chen, Sheng Zheng, Zhibo Jiang, Yao Huang, Shuguang Zeng, Xiangyun Zeng, Xiaoyu Luo
Figure caption: The statistics of R of resampling with different resample factors and along different directions. SFactor = 1 means that no resampling is applied to the data. V, SFactor = 0.5 means that the data is downsampled with a resample factor of 2 in the spectral channel, and other labels have similar meanings. The plots are the statistics of utilizing variational parameters.
Paper: Searching for Galactic H II Regions from the LAMOST Database Based on the Multihead WDCNN Model
Authors: Mengxin Wang, Jingjing Wu, Bin Jiang, Yanxia Zhang
Figure caption: Aperture photometry process.
Paper: Searching for Galactic H II Regions from the LAMOST Database Based on the Multihead WDCNN Model
Authors: Mengxin Wang, Jingjing Wu, Bin Jiang, Yanxia Zhang
Figure caption: LAMOST spectra of H II region candidates. The upper blue spectra are the blue band part of LAMOST, and the lower red spectra are the red band part of LAMOST.
Paper: Searching for Galactic H II Regions from the LAMOST Database Based on the Multihead WDCNN Model
Authors: Mengxin Wang, Jingjing Wu, Bin Jiang, Yanxia Zhang
Figure caption: Structural diagram of the multihead WDCNN model.
Paper: Searching for Galactic H II Regions from the LAMOST Database Based on the Multihead WDCNN Model
Authors: Mengxin Wang, Jingjing Wu, Bin Jiang, Yanxia Zhang
Figure caption: LAMOST spectra of two Galactic H II regions. The upper panels are part of the blue wavelength, and the lower panels are part of the red wavelength.
Paper: Searching for Galactic H II Regions from the LAMOST Database Based on the Multihead WDCNN Model
Authors: Mengxin Wang, Jingjing Wu, Bin Jiang, Yanxia Zhang
Figure caption: Experimental flowchart.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Validation of the [M/H] estimates in the main sequence, using the Praesepe cluster. The cluster’s CMD of all 653 members with [M/H] is illustrated in panel (a). Their [M/H] estimates are shown as a function of color in panel (b). The horizontal dashed line indicates the metallicity of 0.16 (Z = 0.02) adopted by Gaia Collaboration et al. (2018). For 0.5 < G BP − G RP < 1.5 the metallicity agreement is excellent, whereas for 1.5 < G BP − G RP < 2.5 they are systematically too low by 0.15 dex. Outside of these color ranges the agreement is poor. We attribute the offsets and the poor estimates to possible systematics and poor sampling of the CMD space in the training sample. The [M/H] estimates for main-sequence stars with colors outside 0.5 < G BP − G RP < 2.5 are manifestly unreliable.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Comparison of the XGBoost [M/H] estimates with GSP-Spec’s calibrated metallicity estimates in Gaia DR3 (Recio-Blanco et al. 2022), with GALAH DR3 (Buder et al. 2021), and with SkyMapper DR2 (Chiti et al. 2021). For SkyMapper DR2, we impose the quality flag equal to 0. Numbers quote MedAD and RMSD. No quality cuts were applied, and the density maps are logarithmic. The comparison with GSP-Spec DR3 is very good, but the comparison is limited (mostly by GSP-Spec) to [M/H] ≳ −1. The comparison with GALAH DR3 is also very good, with a small systematic offset at [M/H] ≤ −1, already noted in Rix et al. (2022). Comparison with the photometric [M/H] estimates from SkyMapper DR2 shows substantially increased scatter. The good comparison of the XGBoost results with other surveys makes it likely that this is attributable to SkyMapper issues.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Differences of [M/H] between XGBoost for LAMOST DR6 as function of apparent G magnitude. The Gaia DR3 publication limit for XP spectra is G = 17.65. Black lines indicate the 16th, 50th, and 84th percentiles as function of G. Color maps indicate the logarithmic number density.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Distribution of [M/H] estimates from XGBoost for 5759 solar-analog candidates from Gaia Collaboration et al. (2022a). A Gaussian with zero mean and standard deviation of 0.1 is given by the dashed line, illustrating that the [M/H] estimates are precise and accurate on the main sequence at high metallicities and for T eff ∼ 5772 K.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: XGBoost input feature distributions of the application sample (color maps), overlaid with black contours of the training sample. The lowest contour is at one star per bin, i.e., it encloses the full training sample, and the other contours successively increase by factors of 10. This shows that our full sample extends across important portions of color–color space that are not covered by the training sample. The resulting parameter estimates in these regimes will be inevitably unreliable.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: [M/H] distributions resulting from XGBoost for all stars in our catalog (black), RGB stars (red, teff_xgboost < 5300 K, and logg_xgboost < 3.5), and RGB stars with high-quality parallaxes (orange). The differences in the distributions between the RGBs with and without high-quality parallaxes is physical, as the parallax cut eliminates mostly distance stars, often in the halo or the Magellanic Clouds, which are metal poor. The parallax cut on the RGB sample matters, as most of this subsample also has RVS radial velocities: for these stars orbits can be calculated (e.g., Rix et al. 2022).
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Comparison of the [M/H] estimates from XGBoost after filtering the input features by the mean metallicities of 36 open clusters from Gaia Collaboration et al. (2018). Black dots show the median [M/H] and the gray error bars show 16th and 84th percentiles in each cluster.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Comparison of the [M/H] estimates from APOGEE DR17 (Abdurro’uf & Aerts 2022) to the [Fe/H] estimates from GALAH DR3 (Buder et al. 2021; top panel) and to the [Fe/H] estimates from SkyMapper DR2 (Chiti et al. 2021; bottom panel). There are 22,662 stars in common between APOGEE and GALAH and 696 in common between APOGEE and SkyMapper. Numbers quote MedAD and RMSD.
Paper: Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
Authors: René Andrae, Hans-Walter Rix, Vedant Chandra
Figure caption: Comparison of the [M/H] estimates for the components of the 30,748 wide binaries from El-Badry et al. (2021). We quote RMSD divided by ﹩\sqrt{2}﹩ because we want to quantify the difference between two noisy [M/H] estimates.
Paper: A Two-element Interferometer for Millimeter-wave Solar Flare Observations
Authors: YongLin Yu, 永林 于, Shuo Xu, 硕 许, Lei Zhang, 磊 张, ZiQian Shang, 自乾 尚, ChengLong Qiao, 成龙 乔, ShuQi Li, 舒琪 李, Zhao Wu, 昭 武, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, FaBao Yan, 发宝 严
Figure caption: The interferometer output compared with the H and E single-antenna output.
Paper: A Two-element Interferometer for Millimeter-wave Solar Flare Observations
Authors: YongLin Yu, 永林 于, Shuo Xu, 硕 许, Lei Zhang, 磊 张, ZiQian Shang, 自乾 尚, ChengLong Qiao, 成龙 乔, ShuQi Li, 舒琪 李, Zhao Wu, 昭 武, YanRui Su, 艳蕊 苏, HongQiang Song, 红强 宋, Yao Chen, 耀 陈, FaBao Yan, 发宝 严
Figure caption: (a) Dynamic spectrum with 500 MHz bandwidth when the system is pointing to the Sun. (b) Comparison of the output of the interferometric system pointed to the cold sky and the Sun. The radiation flux of each frequency pointing to the Sun is approximately 3000 sfu more than that pointing to the cold sky, while the fluctuation of the radiation flux of each frequency point is basically controlled within 50 sfu when it is aimed at the Sun.