adsabs/AstroCLIMB huggingface dataset:
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: WDNet overview. WDNet consists of four modules, as shown in ① ~ ④. ① The input module resizes the 1489 × 2048 pixels original image to 1504 × 2048 pixels. ② and ⑤ The backbone module extracts the feature maps P1, P2, P3, P4, and P5 at two times, four times, eight times, 16 times, and 32 times downsampled scales. ③ The neck module utilizes the BiFPN to perform the bidirectional fusion of feature maps. ④ The head module predicts the position, class, and confidence of three detection bounding boxes (“BBox”es) for each grid on the feature map. ⑥ The subgraph shows the basic structure of CASP. ⑦ The subgraph shows the DySample technology. ⑧ The post-processing process includes threshold filtering and NMS. The celestial objects in the above image are not real WDs; they are only drawn for illustration.
Paper: Broad-line Region Characterization in Dozens of Active Galactic Nuclei Using Small-aperture Telescopes
Authors: Catalina Sobrino Figaredo, Doron Chelouche, Martin Haas, Michael Ramolla, Shai Kaspi, Swayamtrupta Panda, Martin W. Ochmann, Shay Zucker, Rolf Chini, Malte A. Probst, Wolfram Kollatschny, Miguel Murphy
Figure caption: Same as Figure 10 but for delay values obtained with JAVELIN.
Paper: Broad-line Region Characterization in Dozens of Active Galactic Nuclei Using Small-aperture Telescopes
Authors: Catalina Sobrino Figaredo, Doron Chelouche, Martin Haas, Michael Ramolla, Shai Kaspi, Swayamtrupta Panda, Martin W. Ochmann, Shay Zucker, Rolf Chini, Malte A. Probst, Wolfram Kollatschny, Miguel Murphy
Figure caption: Difference between the observed delays (τPRM) and expected delays from the rBLR-L best fit (τFit) versus the fraction of the Hα line within the NB filter in percent (HαNB). The data derived from the RM formalism is represented by blue circles, while the estimation of the lag deviation due to continuum contamination (where the delay of continuum is about 15% of the line delay) is marked with a red dotted line. The mean value for the Hα contribution, along with the standard deviation, is highlighted as a gray area.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: A synthesized image in the U, G, and R bands containing a WD target. The image has a size of 2048 × 1489 pixels, and the WD occupies a pixel size of 8 × 8, accounting for 0.00210% of the full image area. The image to the right represents a proportional enlargement of the red-box region in the image to the left. The annotation box in this image has a size of 25 × 25, just for illustration.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Color–color diagram of the test and training sets. The left panel shows the color index u − g on the horizontal axis and the color index g − r on the vertical axis. The right panel shows the color index r − i on the horizontal axis and the color index i − z on the vertical axis.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Candidates' confidence distribution on the test set. At a confidence threshold of 0.4, all candidates have a probability of being a WD that is not less than 0.4, and the majority of candidates have a probability that is not less than 0.8.
Paper: Broad-line Region Characterization in Dozens of Active Galactic Nuclei Using Small-aperture Telescopes
Authors: Catalina Sobrino Figaredo, Doron Chelouche, Martin Haas, Michael Ramolla, Shai Kaspi, Swayamtrupta Panda, Martin W. Ochmann, Shay Zucker, Rolf Chini, Malte A. Probst, Wolfram Kollatschny, Miguel Murphy
Figure caption: Same as Figure 10 but for peak values.
Paper: Broad-line Region Characterization in Dozens of Active Galactic Nuclei Using Small-aperture Telescopes
Authors: Catalina Sobrino Figaredo, Doron Chelouche, Martin Haas, Michael Ramolla, Shai Kaspi, Swayamtrupta Panda, Martin W. Ochmann, Shay Zucker, Rolf Chini, Malte A. Probst, Wolfram Kollatschny, Miguel Murphy
Figure caption: Same as Figure 12 but for peak values.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Analysis of reduced proper motion and color of detection capability. The left subfigure shows the reduced proper motion distribution of detected and undetected WDs. The reduced proper motion is a proxy of absolute magnitude. The right subfigure displays the color distribution, with the u − g color index on the horizontal axis and the g − r color index on the vertical axis. Undetected WDs tend to have fainter magnitudes, with a concentration around 33 mag. The undetected WDs exhibit colors in the range of g − r  >  0 and u − g  > 0.4.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Cross-validation of the candidates. The first plot represents the cross-validation with SDSS DR17 and shows matched candidates with the following labels: WD (4071), CalciumWD (3233), CarbonWD (18), Wdmagnetic (98), Wdhotter (296), and Wdcooler (104). The second plot represents the cross-validation with LAMOST DR10 and shows matched candidates with the following labels: DA (1184), DB (98), DC (10), and DZ (5). The third plot represents the cross-validation with the MWDD database and shows matched candidates with the following labels: DA (6570), DB (778), DC (195), DO (34), DQ (49), and DZ (39), corresponding to standard WD types.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Reduced-proper-motion and color distribution of candidates and training samples. The kernel-density-estimation curves represent the distribution of the reduced proper motion of candidates and the WD labels in the training set. The scatter plot is a color–color diagram of the candidates. The vertical axis represents the color index g − r, while the horizontal axis represents the color index u − g. The varying colors in the scatter plot reflect the density (frequency) of candidates, with the highest density (highest frequency) indicated by the yellow region.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Distribution of candidate parameters. The left plot displays the distribution of log g, while the right plot displays the distribution of Teff. Each plot has two y-axes. The left y-axis represents the density scale, whose scientific notation magnitude is 10−5. The right y-axis represents the frequency scale.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Feature selection by mutual information. The left plot shows the feature selection based on the log g label, while the right plot shows the feature selection based on the Teff label. The blue line represents the original flux corresponding to the original 3909 feature wavelengths. The red dots indicate the flux corresponding to the selected 2000 feature wavelengths. The dashed lines represent the four Balmer absorption lines Hα, Hβ, Hγ, and Hδ.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Detected and undetected WDs. The first row displays five images of detected WD samples. The second row shows five images of undetected WD samples.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: WDNet detected two WD candidates in an image. The confidence scores are 0.88 and 0.78, respectively. The image shown above is a cropped region of the original image.
Paper: A Multiple-detection-heads Machine Learning Algorithm for Detecting White Dwarfs
Authors: Jiangchuan Zhang, Yude Bu, Mengmeng Zhang, Duo Xie, Zhenping Yi
Figure caption: Confidence distribution of candidates.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: ϕ21(π) vs. ﹩\mathrm{log}P﹩ diagram for TESS variable stars with amplitudes larger than 0.006 mag. From short to long periods, there are DSCT (yellow green), HADS (pink), EW (cyan), EB (silver), EA (orange), RRab (black circles), RRcd (blue circles), and Cepheids (red triangles).
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Panel (a) shows an Amp (mag) vs. ﹩\mathrm{log}P﹩ diagram for high-amplitude TESS variable stars. Panel (b) shows an Amp (mag) vs. ﹩{({B}_{P}-{R}_{P})}_{0}﹩ (mag) diagram for low-amplitude TESS variable stars. Symbols are as in Figure 4. GCASs (yellow dots), ROTs (blue solid circles), UVs (spring-green dots), and YSOs (red solid circles) are also included.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: R21 vs. ﹩\mathrm{log}P﹩ diagram for TESS variable stars. Symbols are as in Figure 4.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: The left panel shows a kurtosis vs. R21 diagram and the right panel shows a K vs. W diagram. Symbols are as in Figure 4.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Feature importance as estimated using the random forest algorithm. Obviously, R31 is the most powerful feature for separating periodic variable stars. The importance of period is also very high, only second to R31.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Distribution in equatorial coordinates of stars observed at 2 minute cadence by TESS in 67 sectors. Upper: stars that were observed in only one sector of TESS observations (27.4 days). Lower: stars that were observed in multiple sectors of TESS observations (​​​​​​>54.8 days). The number in the color bars is normalized.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Distributions of the correct classification probability of 12 different variable stars.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Confusion matrix of the classifier.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: A scattered density map showing the ﹩{({B}_{P}-{R}_{P})}_{0}﹩ (mag)–﹩{M}_{{W}_{G}}﹩ (mag) distribution. The number in the color bar is normalized.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: A ﹩{({B}_{P}-{R}_{P})}_{0}﹩ (mag)–﹩{M}_{{W}_{G}}﹩ (mag) diagram for all TESS variable stars. Symbols in the upper panel are as in Figure 4. Symbols in the lower panel are as in Figure 6.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption:  ﹩\mathrm{log}P﹩–﹩{M}_{{W}_{G}}﹩ (mag) diagram for TESS variable stars. Symbols are as in Figure 4.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Example LCs for (a) DSCTs and (b) HADSs. From top to bottom, the phase difference ϕ21 increases. The mean magnitudes are fixed at 0.2i and 0.4i (i = 1, 2, ..., 10) for DSCTs and HADSs, respectively. Each LC has been merged into 1000 data points.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Comparison between our variability periods and the periods of four other variable star catalogs: ASAS-SN (top left), ZTF DR2 (top right), TSVC (bottom left), and TESS-EBs (bottom right). The solid gray line highlights the matching variability periods. The dashed gray lines show the lines where our periods are different from periods in other catalogs by factors of 2 or 4. The bottom right corner of each panel shows a zoomed-in view of the comparison characterized by 0 < P < 1 day.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Example LCs for (a) ROTs, (b) GCASs, (c) UVs, and (d) YSOs. From top to bottom, the amplitude increases. The mean magnitudes are fixed at 0.2i, 0.1i 0.2i, and 0.2i (i = 1, 2, ..., 10) for ROTs, GCASs, UVs, and YSOs, respectively. Each LC has been merged into 1000 data points.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Example LCs for (a) T2CEPs, (b) DCEPs, and (c) DCEPSs. From top to bottom, the phase difference ϕ21 increases. The mean magnitudes are fixed at 0.8i, 0.6i, and 0.3i (i = 1, 2, ..., 10), for T2CEPs, DCEPs, and DCEPSs, respectively. Each LC has been merged into 1000 data points.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Example LCs for (a) RRabs, (b) RRcs, and (c) RRcds. From top to bottom, the phase difference ϕ21 decreases. The mean magnitudes are fixed at 0.6i, 0.3i, and 0.4i (i = 1, 2, ..., 10), for RRabs, RRcs, and RRcds, respectively. Each LC has been merged into 1000 data points.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Example LCs for (a) EA-, (b) EB-, and (c) EW-type eclipsing binaries. From top to bottom, the amplitude increases. The mean magnitudes are all fixed at 0.6i (i = 1, 2, ..., 10). Each LC has been merged into 1000 data points.
Paper: Classification of Periodic Variable Stars from TESS
Authors: Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
Figure caption: Important parameters for variable star classification in machine learning. The upper panel includes pulsation stars and eclipsing binaries, and the lower panel includes GCASs, ROTs, UVs, and YSOs.
Paper: The Resolved Behavior of Dust Mass, Polycyclic Aromatic Hydrocarbon Fraction, and Radiation Field in ∼800 Nearby Galaxies
Authors: Jérémy Chastenet, Karin Sandstrom, Adam K. Leroy, Caroline Bot, I-Da Chiang, 宜達 江, Ryan Chown, Karl D. Gordon, Eric W. Koch, Hélène Roussel, Jessica Sutter, Thomas G. Williams
Figure caption: Distribution of ﹩{U}_{\min }﹩ (top row), γ (middle row), and ﹩\overline{U}﹩ (bottom row) from modeling integrated galaxies, as a function of sSFR (first column), SFR (second column), M ⋆ (third column), and ΔMS (fourth column). Shown are all of the integrated fits with acceptable residuals (which includes the light-gray points), and the darker points show those with log10(sSFR) > −11. The white circles are the running medians (on dark points only), with error bars as one standard deviation. The filled circles are those used to fit the scaling relations. The boxed text shows ﹩a\pm {\epsilon }_{a}^{\mathrm{fit}}\pm {\epsilon }_{a}^{\mathrm{bin}}﹩, with Y = a × X + b, where ﹩{\epsilon }_{a}^{\mathrm{fit}}﹩ is the error on the slope for the shown fit, and ﹩{\epsilon }_{a}^{\mathrm{bin}}﹩ is the standard deviation of slopes fit with a varying number of bins. These values are tabulated in Table 3.
Paper: The Resolved Behavior of Dust Mass, Polycyclic Aromatic Hydrocarbon Fraction, and Radiation Field in ∼800 Nearby Galaxies
Authors: Jérémy Chastenet, Karin Sandstrom, Adam K. Leroy, Caroline Bot, I-Da Chiang, 宜達 江, Ryan Chown, Karl D. Gordon, Eric W. Koch, Hélène Roussel, Jessica Sutter, Thomas G. Williams
Figure caption: Comparison of dust and radiation field parameters between Herschel-z0MGS and G. Aniano et al. (2020) for the KINGFISH galaxies (without NGC 5398 and NGC 5408). There is a good agreement between the two samples, despite different fitting approaches. The parameters and units are shown in each panel, with the offset, scatter, and MAD measured with respect to the 1:1 line (black dashed line), except for q PAH (second panel), where we find a best-fit slope of 0.49, shown in gray. Note that the Σd distribution crosses the 1:1 line, which leads to a different offset if we measure it below or above a threshold of ﹩{\mathrm{log}}_{10}({{\rm{\Sigma }}}_{{\rm{d}}}^{z0\mathrm{MGS}})=-0.6﹩.
Paper: The Resolved Behavior of Dust Mass, Polycyclic Aromatic Hydrocarbon Fraction, and Radiation Field in ∼800 Nearby Galaxies
Authors: Jérémy Chastenet, Karin Sandstrom, Adam K. Leroy, Caroline Bot, I-Da Chiang, 宜達 江, Ryan Chown, Karl D. Gordon, Eric W. Koch, Hélène Roussel, Jessica Sutter, Thomas G. Williams
Figure caption: Resolved comparisons of surface brightnesses, in MJy sr−1, for overlapping galaxies between the KINGFISH (G. Aniano et al. 2020) and Herschel-z0MGS samples. The top row shows the KINGFISH values as a function of the Herschelz0MGS surface brightness. The 1:1 relation is shown with the black dashed line, and we add offset lines (showing factors of 2 and 5 difference) to guide the eye. The bottom row shows the difference (Herschel-z0MGS-KINGFISH) in dex. We compute offset, scatter, and median absolute deviation (MAD) for the whole sample, shown in the upper-left corner of each panel, and for the “bright” pixels, chosen as ﹩\mathrm{log}({I}_{\nu }^{z0\mathrm{MGS}}\,[\mathrm{MJy}\,{\mathrm{sr}}^{-1}])\geqslant 0.75﹩, shown in the bottom-right corner of each panel.
Paper: The Resolved Behavior of Dust Mass, Polycyclic Aromatic Hydrocarbon Fraction, and Radiation Field in ∼800 Nearby Galaxies
Authors: Jérémy Chastenet, Karin Sandstrom, Adam K. Leroy, Caroline Bot, I-Da Chiang, 宜達 江, Ryan Chown, Karl D. Gordon, Eric W. Koch, Hélène Roussel, Jessica Sutter, Thomas G. Williams
Figure caption: Comparisons of the integrated photometry from the z0MGS sample compared to LVL (54 galaxies; top row), DustPedia (553 galaxies; bottom row). In general, we find good agreement between the integrated photometry between our measurements and those of DustPedia and LVL, with the largest scatter for fainter galaxies. Average noise values range between 2 and 4 × 10−3 Jy in each band, corresponding to ∼0.51, 0.36, 0.47, and 0.31 MJy sr−1 from PACS 70 to SPIRE 250.
Paper: The Resolved Behavior of Dust Mass, Polycyclic Aromatic Hydrocarbon Fraction, and Radiation Field in ∼800 Nearby Galaxies
Authors: Jérémy Chastenet, Karin Sandstrom, Adam K. Leroy, Caroline Bot, I-Da Chiang, 宜達 江, Ryan Chown, Karl D. Gordon, Eric W. Koch, Hélène Roussel, Jessica Sutter, Thomas G. Williams
Figure caption: Stellar mass vs. star formation rate for the z0MGS sample (gray points; A. K. Leroy et al. 2019) highlighting the galaxies with WISE and Herschel maps used in this study as black points. Above 109 M ⊙, the z0MGS sample is approximately complete for galaxies within 50 Mpc. The top panel shows the relative number of galaxies in our sample compared to the z0MGS sample in bins of M ⋆. We find that ∼10% of the z0MGS galaxies have Herschel coverage and are included in our sample. That fraction does not change substantially with stellar mass.
Paper: Polarization Reconstruction Study of Wide Field-of-view Photoelectric Polarimeter for POLAR-2/LPD
Authors: Zu-Ke Feng, 祖科 冯, Hong-Bang Liu, 宏邦 刘, Di-Fan Yi, Li-Rong Xie, Cong Dai, Huan-Bo Feng, Jian-Chao Sun, Jiang He, Yuan-Hao Wang, Rui-Ting Ma, Bin-Long Wang, Qian-Nan Mai, Jiang-Chuan Tuo, Qian Zhong, Zhen-Yu Tang, Qian Liu, Fei Xie, Shuang-Nan Zhang, En-Wei Liang
Figure caption: Definition of the angles that describe the photon frame of reference, denoted as xγyγzγ, in relation to the instrument frame xyz. The inclination δ and the azimuth η determine the direction of incidence, while φ0 specifies the polarization angle. The plane ϒ contains both the direction of incidence and the normal to the detection plane. The frame ﹩{x}^{{\prime} }{y}^{{\prime} }{z}^{{\prime} }﹩ is defined as the coordinate system obtained by rotating xyz by η around the z-axis, while x″y″z″ denotes the frame further rotated by δ around the ﹩{y}^{{\prime} }﹩ axis.
Paper: Polarization Reconstruction Study of Wide Field-of-view Photoelectric Polarimeter for POLAR-2/LPD
Authors: Zu-Ke Feng, 祖科 冯, Hong-Bang Liu, 宏邦 刘, Di-Fan Yi, Li-Rong Xie, Cong Dai, Huan-Bo Feng, Jian-Chao Sun, Jiang He, Yuan-Hao Wang, Rui-Ting Ma, Bin-Long Wang, Qian-Nan Mai, Jiang-Chuan Tuo, Qian Zhong, Zhen-Yu Tang, Qian Liu, Fei Xie, Shuang-Nan Zhang, En-Wei Liang
Figure caption: Numerical solutions for the convolutions of ﹩{\cos }^{2}(\varphi )﹩ or ﹩{\cos }^{4}(\varphi )﹩ with the von Mises distribution, along with fitting results using “﹩A{\cos }^{2}(\varphi )+B﹩” or “﹩A{\cos }^{4}(\varphi )+B﹩,” where A and B are constants. Left-hand panel: ﹩{\cos }^{2}(\varphi )﹩. Right-hand panel: ﹩{\cos }^{4}(\varphi )﹩. The subplots below show the fitting residuals.
Paper: Polarization Reconstruction Study of Wide Field-of-view Photoelectric Polarimeter for POLAR-2/LPD
Authors: Zu-Ke Feng, 祖科 冯, Hong-Bang Liu, 宏邦 刘, Di-Fan Yi, Li-Rong Xie, Cong Dai, Huan-Bo Feng, Jian-Chao Sun, Jiang He, Yuan-Hao Wang, Rui-Ting Ma, Bin-Long Wang, Qian-Nan Mai, Jiang-Chuan Tuo, Qian Zhong, Zhen-Yu Tang, Qian Liu, Fei Xie, Shuang-Nan Zhang, En-Wei Liang
Figure caption: Polarization detection principle of the GMPD. When an X-ray photon enters the detector through the beryllium window and interacts with the working gas inside the GMPD, it causes the emission of photoelectrons. These photoelectrons ionize the gas molecules, generating primary electrons and ions. Under the influence of the electric field, the primary electrons drift toward the holes in the GMCP, where they undergo avalanche amplification as they move toward the anode. Some of these electrons reach the top of the silicon pixel chip, Topmetal-L, while others are collected by the electrodes at the bottom of the GMCP. By reconstructing the emission direction of the photoelectron tracks on Topmetal-L, the polarization of the incident X-ray can be determined. Additionally, information about the X-ray’s energy and timing is obtained by measuring the electrons collected at the bottom of the GMCP.
Paper: Polarization Reconstruction Study of Wide Field-of-view Photoelectric Polarimeter for POLAR-2/LPD
Authors: Zu-Ke Feng, 祖科 冯, Hong-Bang Liu, 宏邦 刘, Di-Fan Yi, Li-Rong Xie, Cong Dai, Huan-Bo Feng, Jian-Chao Sun, Jiang He, Yuan-Hao Wang, Rui-Ting Ma, Bin-Long Wang, Qian-Nan Mai, Jiang-Chuan Tuo, Qian Zhong, Zhen-Yu Tang, Qian Liu, Fei Xie, Shuang-Nan Zhang, En-Wei Liang
Figure caption: Reconstructed distribution of electron emission azimuthal angles for 3.4 keV electrons using the moment reconstruction algorithm. Left-hand panel: The electrons emitted parallel to the detection plane and in the same direction. The origin of the coordinate system is located at the center of the detection gas. Right-hand panel: The electrons emitted at the center of the detection gas, with the same azimuthal angles but different polar angles.