adsabs/AstroCLIMB huggingface dataset:
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 polar angle θ and azimuthal angle ϕ in the photoelectric effect. The axis labels are subscripted with γ to signify that the frame of reference is constructed from the photon characteristics. Specifically, the z-axis aligns with the incident direction, and the x-axis aligns with the polarization vector.
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: The modulation functions theoretically calculated for unpolarized light and fully polarized light at three polarization angles φ0 with each plot showing results for different inclinations δ.
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: Quantification results of forward bending at different energy points of photoelectrons. The two-dimensional color map shows the probability density as a function of the emission polar angle θ of photoelectrons for each energy point. The red curve indicates the maximum differential cross section, ﹩{ \mathcal D }(\theta )﹩, for each energy point. The dashed black line represents θ = π/2, corresponding to the plane perpendicular to the incident direction of photons.
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: Theoretical distribution of photoelectron emission polar angle ϑ at different azimuthal angles φ in the instrument reference frame for unpolarized and fully polarized light at three polarization angles φ0 with an inclination δ of 45°.
Paper: The Local Ultraviolet to Infrared Treasury. I. Survey Overview of the Broadband Imaging
Authors: Karoline M. Gilbert, Yumi Choi, Martha L. Boyer, Benjamin F. Williams, Daniel R. Weisz, Eric F. Bell, Julianne J. Dalcanton, Kristen B. W. McQuinn, Evan D. Skillman, Guglielmo Costa, Andrew E. Dolphin, Morgan Fouesneau, Léo Girardi, Steven R. Goldman, Karl D. Gordon, Puragra Guhathakurta, Maude Gull, Lea Hagen, Ky Huynh, Christina W. Lindberg, Paola Marigo, Claire E. Murray, Giada Pastorelli, Petia Yanchulova Merica-Jones
Figure caption: Spatial distribution (panels (a)–(d)) and color-magnitude distribution (panels (e), (f)) of sources detected in the DDO210 archival optical observations. The spatial distributions show sources which (a) pass the pipeline quality criteria for high quality stellar sources (GST); (b) do not pass the GST criteria and are therefore removed from the stellar sample; (c) pass both the GST criteria and an additional set of criteria designed to remove spurious detections associated with diffraction spikes; and (d) sources which are removed from the sample because they do not pass the diffraction spike removal criteria, but which do pass the GST criteria (Section 3.3). The CMDs show sources that (e) pass both the GST and diffraction spike criteria and (f) are removed because they do not pass the diffraction spike removal criteria (same detections as in panel (d)). While the standard pipeline quality (GST) criteria remove many of the spurious detections associated with diffraction spikes, the additional diffraction spike criteria we have implemented targets remaining spurious detections, while not removing sources that are likely associated with the stellar population of the target galaxy.
Paper: The Local Ultraviolet to Infrared Treasury. I. Survey Overview of the Broadband Imaging
Authors: Karoline M. Gilbert, Yumi Choi, Martha L. Boyer, Benjamin F. Williams, Daniel R. Weisz, Eric F. Bell, Julianne J. Dalcanton, Kristen B. W. McQuinn, Evan D. Skillman, Guglielmo Costa, Andrew E. Dolphin, Morgan Fouesneau, Léo Girardi, Steven R. Goldman, Karl D. Gordon, Puragra Guhathakurta, Maude Gull, Lea Hagen, Ky Huynh, Christina W. Lindberg, Paola Marigo, Claire E. Murray, Giada Pastorelli, Petia Yanchulova Merica-Jones
Figure caption: The location of stars in various stages of stellar evolution in NUV, Optical, and NIR CMDs (first three panels), along with their measured flux rates (when >0) in all available bands (right-hand panels), for two LUVIT galaxies (Section 4.2). Leo A is among the lowest metallicity LUVIT galaxies, while UGC 9240 is among the highest. Shown are the CMD locations and flux as a function of wavelength for a bright, massive MS star (blue crosses), a BHeB star (olive circles), a RHeB star (orange squares), and an RGB star (red stars; not detected in F275W). The SEDs of the stars can be fit to stellar and dust models to estimate stellar parameters and dust properties along the line of sight to each star, as described in Section 7.
Paper: The Local Ultraviolet to Infrared Treasury. I. Survey Overview of the Broadband Imaging
Authors: Karoline M. Gilbert, Yumi Choi, Martha L. Boyer, Benjamin F. Williams, Daniel R. Weisz, Eric F. Bell, Julianne J. Dalcanton, Kristen B. W. McQuinn, Evan D. Skillman, Guglielmo Costa, Andrew E. Dolphin, Morgan Fouesneau, Léo Girardi, Steven R. Goldman, Karl D. Gordon, Puragra Guhathakurta, Maude Gull, Lea Hagen, Ky Huynh, Christina W. Lindberg, Paola Marigo, Claire E. Murray, Giada Pastorelli, Petia Yanchulova Merica-Jones
Figure caption: Location in CMD space of the input ASTs for DDO210 (Section 5.2). (Top panels) Input ASTs are derived from a random selection of model SEDs from the full parameter space of the physics input grid, with extinction applied. (Bottom panels) Supplemental input ASTs are derived from randomly selected model SEDs with a more limited apparent magnitude range and a higher percentage drawn from the region of observed CMD space. ASTs are shown as points in regions of low density, and with a color scale increasing from yellow to green to blue (light to dark) in regions of higher density, with the range determined independently in each panel to provide maximum contrast.
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: Same as Figure 7, for fitted parameters q PAH (top row) and Σd (bottom row).
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: Same as Figure 5, for fitted parameters q PAH (top row) and M d (bottom row).
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: Left column: specific dust mass as a function of stellar mass on resolved (top) and integrated (bottom) scales. The light-gray points are those with ﹩{\mathrm{log}}_{10}(\mathrm{sSFR})\lt -11﹩. The medians are measured on the dark-gray points. Error bars show the scatter within the bin; the solid black line is the best-fit using the filled medians, whose slope is reported in the label; the first uncertainty is the statistical error on the fits, and the second uncertainty is the standard deviation on coefficients when fitting binned data with a varying number of bins. Orange dashed lines show the best fit from the literature: the best-fit from V. Casasola et al. (2022) for a few galaxies in the DustPedia sample, and from I. De Looze et al. (2020) for a large sample of integrated measurements. Right column: main sequence color-coded by the specific dust mass. In the resolved panel (top), the transparency shows all pixels, while the opaque bins cut out pixels with ﹩{\mathrm{log}}_{10}(\mathrm{sSFR})\lt -11﹩ and above the completeness threshold in Σ⋆. The line shows our main-sequence fit from Section 3.8.3. The bottom panel shows the integrated values from this work for the main sequence, and the white-dashed line shows the main-sequence fit by A. K. Leroy et al. (2019).
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: 2D histograms of ﹩{U}_{\min }﹩ (top row), γ (middle row), and ﹩\overline{U}﹩ (bottom row) fit for individual lines of sight at the SPIRE 250 μm resolution, as a function of sSFRR (first column), ΣSFR (second column), Σ⋆ (third column), and ΔMSR (fourth column), color coded by the number of hits. The vertical white-dashed lines are the limits of completeness (Section 4.4). The white circles are the running medians, with error bars as one standard deviation, and the filled circles are those used to fit the scaling relations. We mark the slope in 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: Comparison of the slopes (a) of resolved (black symbols) and integrated (gray symbols) fits presented in Figures 5–8 and Tables 3 and 4. We also show the fit to 〈SFR〉 and 〈M ⋆〉, galaxy-averaged values, shown in Figure 10. Here, each fit follows the form Y = aX + b, where we show only a in this figure. The open symbols mark where the high p-value signals no significant correlation between the axes. The behavior of the slopes of the ﹩{U}_{\min }﹩ and ﹩\overline{U}﹩ relationships are very similar, since under most circumstances the radiation field is dominated by the minimum radiation field.
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: Two-dimensional fits of ﹩\overline{U}﹩, q PAH, and Σd as a function of a combination of the resolved stellar mass and star formation rate. Top row: 2D histograms of the Σ⋆–ΣSFR plane, where the bin values show the average ﹩\overline{U}﹩ (left column), q PAH (center column), and Σd (right column). The light bins are calculated for all pixels passing the 3σ S/N cut, and the bright bins are calculated for pixels passing that same cut and that have Σ⋆ and ΣSFR above the completeness thresholds. The 2D fit uses the standard deviations within each bin as errors in the linear regression. Bottom row: the x-axis shows the best-fit using both Σ⋆ and ΣSFR. The y-axis shows the observed data. The best-fit coefficients are reported in Table 5.
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: Galaxy-averaged SFR and M ⋆, 〈SFR〉 (left) and 〈M ⋆〉 (right), variations with the dust parameters ﹩{U}_{\min }﹩, γ, ﹩\overline{U}﹩, q PAH, and galaxy-averaged dust mass, 〈M d〉. The symbols show the binned medians and lines are linear fits to the filled medians, similarly to Figures 5–8.
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: Two-dimensional histogram and scaling relation of the dust surface density, Σd, and the star formation rate surface density, ΣSFR. The orange medians and lines are derived from binning either in x or y, and the black line shows the mean of these slopes, with the reported equation.
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: Fitted q PAH from the resolved analysis as a function of ﹩{\mathrm{log}}_{10}(\mathrm{WISE}\,3/\mathrm{WISE}\,4)﹩ ratio. We fit a line through the bins that pass the same cuts used in other figures, for consistency, marked in black-filled symbols. For prediction purposes only, we show an additional fit in the form of a quadratic fit (see the text).
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: Distributions of the dust parameter values for M101 using the data from J. Chastenet et al. (2021). These values are extracted after fitting mid- to far-IR data at the SPIRE 500 resolution (∼36″) up to 500 μm, or up to 250 μm only. Top row: values from a fit using all data up to 500 μm (x-axis) vs. values from a fit using data up to 250 μm only (y-axis). The black dashed line shows the 1:1 relation. Only the radiation field is noticeably affected by wavelength coverage, and the difference becomes large only at high ﹩{U}_{\min }﹩ values. Bottom row: histograms for the dust parameters.
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: Distributions of the fitted dust and radiation field model parameters for all resolved pixel in this analysis passing the cuts (Section 4.4). The top plot of each column shows a marginalized 1D histogram of that parameter. The other panels show 2D histograms of all pixels in the resolved analysis. Note that ﹩\overline{U}﹩ is not a fitted parameter, but is derived from the combination of ﹩{U}_{\min }﹩ and γ.
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 SFR, M ⋆, and sSFR as a function of the integrated fit value of ﹩\overline{U}﹩, for the sample in this paper (gray points), with the best-fit linear relations shown as solid black lines (Figure 5 and Table 3). The purple symbols are from the main-sequence sample of M. Béthermin et al. (2015).
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: Radio data of two FRBs. The top and bottom rows show signals of two bursts, one strong and one weak, respectively. From left to right, the first column displays the original data from the radio telescope, with time on the horizontal axis and frequency on the vertical axis. The second column presents the time–dispersion measure (DM) signals obtained after dedispersing the original data using a series of DM values. The third column shows the time–frequency data after dedispersion with the optimal DM value. The fourth column depicts the time–frequency data after RFI mitigation from the data in the third column.
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: Data augmentation for binary classification. The first column is the input data, the second column is the data after enhancement, and the third and fourth columns are the data after adding interference and randomly combining.
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: Examples of detected signals using ResNet. A–H are eight instances of data classified as true by the classification model, where 1, 2, and 3 represent the original data, the regions in the image that significantly influence the network’s decision visualized using Grad-CAM++, and the result of superimposing these regions on the original data, respectively.
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: Data augmentation for object detection—random clipping. The figure illustrates the process of random clipping applied to the input data. The first column displays the original input images, the second column shows the images after random cropping with green boxes indicating the ground-truth labels, and the third column presents the center points after Gaussian scattering, which are used for CenterNet training.
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: The workflow of DRAFTS. The purple line segments below the figure divide the workflow into three steps, which are detailed in Section 3.
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: Examples of detected signals using CenterNet. The green boxes indicate the ground-truth labels, and the red boxes are the predicted labels.
Paper: DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline
Authors: Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu
Figure caption: Data augmentation for object detection—random combination. This figure demonstrates four examples of the random combination applied to the training data.
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: Calculation and correction process of off-axis modulation functions.
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: Left-hand panel: Fitting results of simulated data after azimuthal angle reconstruction using the theoretically calculated modulation function. Right-hand panel: Reconstruction results of polarization degree and angle using the least χ2 method, with the red cross indicating the input values. Black, yellow, and green rings represent the 1σ, 2σ, and 3σ confidence intervals, respectively. The incident photon energy is 4 keV, the inclination δ is 45°, the true polarization degree is 0.5, and the polarization angle φ0 is 90°. The number of events used for the reconstruction is 1,035,092. The results show that the fitting and the least χ2 reconstruction using the theoretically calculated modulation function both exhibit deviations.
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 reconstruction results of the simulated data after azimuthal angle reconstruction using the corrected modulation function and the least χ2 method. These nine figures represent incident photons with different configurations of polarization degree and polarization angle, with the center of the red cross representing the input values of the polarization degree and polarization angle in the simulator. The black, yellow, and green rings represent the 1σ, 2σ, and 3σ confidence intervals, respectively. The photon energy is 4 keV, and the off-axis angle is 45°. Each plot uses 1,000,000 events for the reconstruction. The inset in each plot is a magnified view of the confidence interval region.
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: The linear relationship between the polarization degree reconstructed from the simulated data and the true values input into the simulator. The three plots in the top row of the figure represent the results obtained using the uncorrected fitting function at three different polarization angles, while the three plots in the bottom row represent the results obtained using the corrected fitting function at three different polarization angles. The photon energy is 4 keV, and the off-axis angle is 45°. From the results in the figure, it can be seen that the results obtained using the corrected fitting function exhibit a good linear relationship and have smaller errors, accurately reconstructing the true polarization degree.