adsabs/AstroCLIMB huggingface dataset:
Paper: Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method
Authors: Lichan Zhou, 立婵 周, Jianghui Cai, 江辉 蔡, Haifeng Yang, 海峰 杨, Ali Luo, 阿理 罗, Yinbi Li, 荫碧 李, Chenhui Shi, 晨辉 史, Boyu Zhang, 博宇 张, Jianing Tian, 佳宁 田, Yinan Yuan, 艺楠 员, Yuqing Yang, 雨晴 杨
Figure caption: Stellar activity of carbon star subclasses from LAMOST.
Paper: Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method
Authors: Lichan Zhou, 立婵 周, Jianghui Cai, 江辉 蔡, Haifeng Yang, 海峰 杨, Ali Luo, 阿理 罗, Yinbi Li, 荫碧 李, Chenhui Shi, 晨辉 史, Boyu Zhang, 博宇 张, Jianing Tian, 佳宁 田, Yinan Yuan, 艺楠 员, Yuqing Yang, 雨晴 杨
Figure caption: Carbon stars from LAMOST with emissions.
Paper: Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method
Authors: Lichan Zhou, 立婵 周, Jianghui Cai, 江辉 蔡, Haifeng Yang, 海峰 杨, Ali Luo, 阿理 罗, Yinbi Li, 荫碧 李, Chenhui Shi, 晨辉 史, Boyu Zhang, 博宇 张, Jianing Tian, 佳宁 田, Yinan Yuan, 艺楠 员, Yuqing Yang, 雨晴 杨
Figure caption: Interval representation of carbon stars. The yellow boxes mark the extraction areas for local features and global features of region_C2 and region_CN. The gray dashed box illustrates the interval construction process using one of seven subspectra as an example. The lower bound a is obtained using function F() based on Equations (A1) and (A2), while the upper bound b is determined using function F() based on Equations (A3) and (A4). The upper and lower values of red vertical solid lines denote the endpoints of these intervals, i.e., a, b, reflecting the main distribution of the data in each subregion. Obviously, the interval captures essential features of the data in each region (Y. Zhou et al. 2022). The subspectrum from the seven regions is input into the interval construction module, resulting in seven corresponding intervals, which are finally concatenated into the final interval, reflecting the distinctive features of carbon stars.
Paper: Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method
Authors: Lichan Zhou, 立婵 周, Jianghui Cai, 江辉 蔡, Haifeng Yang, 海峰 杨, Ali Luo, 阿理 罗, Yinbi Li, 荫碧 李, Chenhui Shi, 晨辉 史, Boyu Zhang, 博宇 张, Jianing Tian, 佳宁 田, Yinan Yuan, 艺楠 员, Yuqing Yang, 雨晴 杨
Figure caption: Two H-R diagrams of carbon stars from SDSS on M_G vs. G_BP-G_RP and M_G vs. effective temperature. M_G, G_BP, G_RP, and effective temperature are from Gaia DR2. The interstellar dust extinction and redden on M_G, G_BP, and G_RP are also corrected by geometric distances from C. A. L. Bailer-Jones et al. (2021), E(B − V) values of G. M. Green et al. (2019) and the extinction coefficient of S. Wang & X. Chen (2019). Shapes are used to present subtypes of carbon stars. Different colors represent the density of subtypes of carbon stars. Red shows the highest density among other colors. The red dotted line at M_G = 5 mag is often used to separate white dwarfs from carbon stars.
Paper: Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method
Authors: Lichan Zhou, 立婵 周, Jianghui Cai, 江辉 蔡, Haifeng Yang, 海峰 杨, Ali Luo, 阿理 罗, Yinbi Li, 荫碧 李, Chenhui Shi, 晨辉 史, Boyu Zhang, 博宇 张, Jianing Tian, 佳宁 田, Yinan Yuan, 艺楠 员, Yuqing Yang, 雨晴 杨
Figure caption: S/Ns on g, r, and i bands of carbon stars from LAMOST. Each row shows the S/N of old carbon stars, new carbon stars, and all carbon stars. Each column represents the S/N of subtypes of carbon stars.
Paper: Identifying Carbon Star Candidates from LAMOST DR11 and SDSS DR18 Based on a Parallel Feature Recognition Method
Authors: Lichan Zhou, 立婵 周, Jianghui Cai, 江辉 蔡, Haifeng Yang, 海峰 杨, Ali Luo, 阿理 罗, Yinbi Li, 荫碧 李, Chenhui Shi, 晨辉 史, Boyu Zhang, 博宇 张, Jianing Tian, 佳宁 田, Yinan Yuan, 艺楠 员, Yuqing Yang, 雨晴 杨
Figure caption: S/Ns on g, r, and i bands of carbon stars from SDSS. Each row shows the S/N of old carbon stars, new carbon stars, and all carbon stars. Each column represents the S/N of subtypes of carbon stars.
Paper: Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products
Authors: Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang
Figure caption: The architecture of the iTransformer flare-forecasting model.
Paper: Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products
Authors: Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang
Figure caption: The magnetogram images from SHARP, HMI, and FMG, respectively, for the same time and AR number.
Paper: Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products
Authors: Xuebao Li, Shunhuang Zhang, Yanfang Zheng, Ting Li, Rui Wang, Yingbo Liu, Hongwei Ye, Noraisyah Mohamed Shah, Pengchao Yan, Xuefeng Li, Xiaotian Wang, Yongshang Lv, Jinfang Wei, Honglei Jin, Changtian Xiang
Figure caption: The architecture of the CNN-BiLSTM flare-forecasting model.
Paper: A Search of Periodic Variable Stars in the LMC by JWST Photometry
Authors: Jiyu Wang, Xiaodian Chen, Jianxing Zhang, Ziming Yan, Shu Wang, Licai Deng
Figure caption: Distribution of EW-type eclipsing binaries, DSCT stars, and RR Lyrae stars in the period–luminosity diagram. Dashed lines indicate the best-fit PLR for each variable type. Different colors are used to distinguish between variable star types and, where applicable, between different pulsation modes (e.g., fundamental and first-overtone modes).
Paper: A Search of Periodic Variable Stars in the LMC by JWST Photometry
Authors: Jiyu Wang, Xiaodian Chen, Jianxing Zhang, Ziming Yan, Shu Wang, Licai Deng
Figure caption: The figure presents the amplitude detection threshold for identifying variable stars as a function of SNRs in a simulation. The results reveal a correlation with the amount of photometric data: the greater the number of measurements, the more sensitive the detection becomes to low-amplitude variability.
Paper: A Search of Periodic Variable Stars in the LMC by JWST Photometry
Authors: Jiyu Wang, Xiaodian Chen, Jianxing Zhang, Ziming Yan, Shu Wang, Licai Deng
Figure caption: Phase-folded LCs of variable stars exhibiting consistent periods across multiple bands. Blue and red curves correspond to observations in the F150W and F200W filters, respectively. For eclipsing binaries, the curves are folded using twice the derived photometric period to represent the full orbital cycle.
Paper: A Search of Periodic Variable Stars in the LMC by JWST Photometry
Authors: Jiyu Wang, Xiaodian Chen, Jianxing Zhang, Ziming Yan, Shu Wang, Licai Deng
Figure caption: Phase-folded LCs of variable stars observed in the F150W filter.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Waterfall plot of the uncalibrated TOD from one 5 hr scan taken by the spectrum backend of one beam and one polarization (M01, XX polarization). The color represents the value of data with no units because they are raw receiver readings with no direct physical meaning. The horizontal stripes are mainly caused by RFI contamination and bandpass shapes.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Time sampling for the spectrum backend (green dashed vertical line) and the pulsar backend (red dotted vertical line). The blue solid line represents the variation of data with periodic noise injection. Note that there are actually over 1000 noise-on/-off periods within 0.2 s. However, for clarity, we display only a few of them and use three dots to indicate the omission.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Time-averaged spectra of uncalibrated TOD of all 19 beams (XX polarization, ∼0.5 hr averaged). The x-axis shows frequency bands, and the y-axis (Vspec) shows the value of raw receiver readings with no unit. Different colors represent different beams. The gray shadow marks the range of RFI-contaminated frequency band (1150–1300 MHz).
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Comparison of the bandpass shape of nine 0.5 hr time blocks taken during one 5 hr observation. Upper panel: bandpass normalized by its median value. Lower panel: bandpass at each time block relative to the all-time-block averaged bandpass. Each colored line represents one time block of ∼0.5 hr. The black thick line is the average of all time blocks. The gray shadow between 1150 and 1300 MHz is the severely RFI-contaminated band, which is not used in our work. The significant outliers in the gray area are influenced by very strong RFIs, which means the data in these frequency bands are not reliable.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Sky coverage of CRAFTS from 2020 February until 2024 July 31. The gray shadow represents the Haslam radio continuum all-sky map at 408 MHz (M. Remazeilles et al. 2015) with darker areas indicating stronger foreground radiation. Stripes of different colors represent different observation periods. The red dashed box marks the data used in this work.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Noise-on (orange line) and noise-off (blue line) spectra from the pulsar backend for M01, XX polarization, ∼0.5 hr averaged. The values represent the raw receiver reading with no unit. The gray shadow marks the RFI-contaminated frequency band 1150−1300 MHz.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Frequency-averaged time stream of uncalibrated data of all 19 beams (XX polarization, 1300–1450 MHz averaged). The x-axis shows the time range, and the y-axis (Vspec) shows the value of raw receiver readings with no unit. Different colors represent different beams.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Flowchart of CRAFTS data processing procedures.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: A schematic plot of the CRAFTS drift scan survey. Different colors represent different days’ observations. The circles mark the position of 19 beams.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Left: noise level of calibrated TOD from 19 beams. The red line represents the noise level of the frequency band 1050–1150 MHz, while the blue line shows the noise level of the 1300–1450 MHz band. The red and blue dashed lines represent the averaged theoretical noise level at 1050–1150 MHz and 1300–1450 MHz, respectively. The red and blue shaded areas show the uncertainty in the theoretical noise level from the parameters we use to estimate the sensitivity. Right: the blue solid line shows the noise level of the map at each frequency channel at 1315–1415 MHz, with the gray solid line marking the RFI-contaminated channels. The blue dashed line represents the theoretical noise level, and the blue shadow is its error range caused by the uncertainty of fitting parameters.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Top: comparison of the flux of 447 continuum point sources measured by CRAFTS TOD to the NVSS catalog in the 1375–1425 MHz band. The black line marks equal flux. Different colors distinguish the source measured by beams at different positions, i.e., blue circles for the central beam (M01), red triangles for the inner-circle beams (M02–M07), and green squares for the outer-circle beams (M08–M19). Bottom: histogram of the relative flux error δS for the measurement by beams at the center, inner-circle, and outer-circle positions.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Spectrum (left) and time-stream data (right) of source NVSS J134335+413656 as an example of continuum point sources measurement with CRAFTS TOD. In the left panel, the blue line shows our measured spectrum, with the flux obtained by averaging the data within each 10 MHz frequency bin. The black dashed line shows the spectrum model by fitting the flux density measured by VLA at 1400 MHz and the Northern Cross Radio Telescope at 408 MHz, with the error of the model shown with the gray area. In the right plot, the blue line represents our continuum observation data, the orange line is the model with a Gaussian profile plus a second-order polynomial, and the red horizontal line marks the NVSS flux.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Sky map (left) and spectra (right) of the galaxy H I-MaNGA 8260-12703 as an example of H I galaxies measured by CRAFTS and GBT H I-MaNGA observation. Left: the sky map averaged over 1.4 MHz near this galaxy. The colors represent the intensity at each pixel, the cross marks the central position given by H I-MaNGA, and the black circle shows the aperture size we use for the flux integration. Right: comparison between the spectra from the CRAFTS map (red line) and H I-MaNGA (blue line).
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Top: Comparison of the 1375–1425 MHz flux of 447 continuum point sources measured from the CRAFTS averaged map and the NVSS catalog. The colors of these dots represent the angular distance between the position given by DAOStarFinder and the position in the NVSS catalog. The black line marks the equal flux relation. Bottom: histogram of the relative flux error δS between our measurement on the map and NVSS flux.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Left: comparison of the flux of 90 sources measured on the CRAFTS map and in the GBT H I-MaNGA catalog. The black line is the y = x relation, and each blue dot represents one source. Right: histogram of the relative error of the H I integral flux.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Noise level of the PCA modes removed maps. The black thick line is the noise level of the map containing all RFI-free frequency channels between 1315 and 1415 MHz, and each colored line represents an individual frequency channel. The gray dashed line and the gray shadow also show the averaged theoretical sensitivity at 1315 and 1415 MHz and its error from the fitting parameters' uncertainty.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Sky map averaged over RFI-free channels in the 1315–1415 MHz band after 30 foreground modes were removed by PCA. The blank areas are RFI-contaminated regions according to the discussion in Section 4.3.
Paper: CRAFTS for H I Cosmology. I. Data Processing Pipeline and Validation Tests
Authors: Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
Figure caption: Left: sky map of the mask array with the color bar representing the mask fraction at each pixel, averaged over 1315–1415 MHz. Right: mask fraction at different frequency channels in the 1315–1415 MHz band.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: Comparison of original and reconstructed black hole images in the additional test set. The figure presents six pairs of GRRT-simulated original black hole images (top row) alongside their BCDDM-reconstructed counterparts (bottom row). The parameters displayed above the original images represent the ground truth values used as initial inputs, while those below the reconstructed images show the output from the parameter corrector branch. Each reconstructed image is annotated with its normalized root mean squared error (NRMSE) and structural similarity index (SSIM) values compared to the original. All images are rendered in brightness temperature units (1010 K), calculated as T = Sλ2/(2kBΩ), where S denotes flux density, λ the observation wavelength, kB the Boltzmann constant, and Ω the solid angle of the resolution element.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: BCDDM architecture for black hole image generation. The diffusion process and denoising process utilize the same U-Net model, with detailed architecture illustrated in Figure 3.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: Workflow of constructing data sets from multiple sources and evaluating regression model performance. This figure outlines the process of constructing data sets from diverse sources and evaluating the performance of a regression model. It highlights two primary data sources: RIAF data sets and BCDDM data sets. The figure underscores the critical step of using a test set to validate the R2 metric, culminating in the output of the R2 score to assess the model’s performance.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: Comparison of the original and reconstructed black hole images, similar to Figure 5, but evaluated beyond the training data set’s parameter range. Parameters marked in green indicate values below the original range, while those in red denote values above the range.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: Encoding and decoding architecture embedded in BCDDM. The trainable components consist solely of the input encoded label vector and black hole image, along with the output predicted parameters and noise.
Paper: BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation
Authors: Ao Liu, Zelin Zhang, Songbai Chen, Cuihong Wen, Jieci Wang
Figure caption: Training loss and validation loss during the training process. The right panel displays two independent black hole images generated by BCDDM at epochs 100, 1000, 2500, and 5000, illustrating the performance progression of the generative model.