adsabs/AstroCLIMB huggingface dataset:
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: Distribution of intrinsic B − V colors in the stellar parameter space derived using the standard-pair algorithm. The color-coded dots represent the intrinsic B − V colors obtained from the LAMOST DR11 through the standard-pair algorithm.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: Comparison of E(B − V) derived from LAMSOT and XP. The hollow points represent the median within each bin, and the red line is the linear fit to the hollow points.
Paper: An All-sky 3D Dust Map Based on Gaia and LAMOST
Authors: Tao Wang, 涛 王, Haibo Yuan, 海波 苑, Bingqiu Chen, 丙秋 陈, Maosheng Xiang, 茂盛 向, Ruoyi Zhang, 若羿 张, Bowen Huang, 博闻 黄, Hongrui Gu, 弘睿 顾, Shuaicong Wang, 帅聪 王, Jiawei Li, 佳伟 李
Figure caption: The number of independent molecular clouds along the line of sight is considered. The brown, yellow, blue, red, and green regions represent the presence of 0, 1, 2, 3, and 4 molecular clouds, respectively.
Paper: A Deep Learning–Based Multimodal F10.7 Prediction with Mamba
Authors: Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu
Figure caption: Architecture of the multimodal F10.7 prediction network. (a) The overall architecture of our model. It consists of the MaFI module, the MaSI module, the embedding fusion module, and the prediction module. (b)–(d) Thow the details of the Mamba block, Vision Mamba block, and cross attention, respectively.
Paper: A Deep Learning–Based Multimodal F10.7 Prediction with Mamba
Authors: Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu
Figure caption: The prediction results of LSTM and our MaFI methods. Models are trained on the F10.7 index data set from 2010 October to 2023 May.
Paper: Imaging the Solar Corona from “Within”: A Cutting-edge Approach to Enhance PSP/WISPR Imagery
Authors: Guillermo Stenborg, Angelos Vourlidas, Russell A. Howard
Figure caption: Comparison of WISPR-I images processed with the LW- (left column) and LX- (right column) techniques taken during E14 (top row) and E17 (bottom row). The E14 (E17) LX frame was processed with a Case II (Case I) background model. The colored arrows and ellipses mark features that highlight the differences between the two techniques, as discussed in the text.
Paper: A Deep Learning–Based Multimodal F10.7 Prediction with Mamba
Authors: Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu
Figure caption: The prediction results of MaFI+ResNet, MaFI+Stripformer, and our MaFI+MaSI models. Models are trained on the F10.7, ADAPT-GONG images, and the HMI magnetograms data sets from 2010 October to 2023 May.
Paper: Imaging the Solar Corona from “Within”: A Cutting-edge Approach to Enhance PSP/WISPR Imagery
Authors: Guillermo Stenborg, Angelos Vourlidas, Russell A. Howard
Figure caption: Comparison of WISPR-I images taken during the outbound segment of E14 at two different time instances, processed with the LW (left column) and LX (right column) techniques. The LX frames were processed with Case II background models. The colored arrows mark features that highlight the differences between the two techniques, as discussed in the text.
Paper: A Deep Learning–Based Multimodal F10.7 Prediction with Mamba
Authors: Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu
Figure caption: Heatmaps of MAE and RMSE in each bin of the single-modal models.
Paper: Imaging the Solar Corona from “Within”: A Cutting-edge Approach to Enhance PSP/WISPR Imagery
Authors: Guillermo Stenborg, Angelos Vourlidas, Russell A. Howard
Figure caption: Comparison of a WISPR-I image taken during E19 processed with the LW (left panel) and LX (right panel) techniques. The LX frame was processed with the Case II background model. The colored arrows and ellipse mark features that highlight the differences between the two techniques, as discussed in the text.
Paper: A Deep Learning–Based Multimodal F10.7 Prediction with Mamba
Authors: Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu
Figure caption: Heatmaps of MAE and RMSE in each bin of the multimodal models.
Paper: The Lyα and Continuum Origins Survey. I. Survey Description and Lyα Imaging
Authors: Alexandra Le Reste, Claudia Scarlata, Matthew J. Hayes, Jens Melinder, Alberto Saldana-Lopez, Aaron Smith, Axel Runnholm, Yu-Heng Lin, Ricardo O. Amorín, Hakim Atek, Sanchayeeta Borthakur, Cody A. Carr, Brian Fleming, Sophia R. Flury, Mauro Giavalisco, Alaina Henry, Anne E. Jaskot, Zhiyuan Ji, Intae Jung, Floriane Leclercq, Rui Marques-Chaves, Stephan R. McCandliss, M. S. Oey, Göran Östlin, Swara Ravindranath, Daniel Schaerer, Trinh X. Thuan, Xinfeng Xu
Figure caption: Optical RGB color-composite images of LaCOS galaxies. The F850LP data is shown in red, F547M in green, and F438W in blue. The intensity is normalized to the maximum of the emission in each filter in order to highlight structural differences across different bands, but the bulk of the optical flux is in the F438W filter. The panels are ordered from left to right and top to bottom by increasing ﹩{f}_{{\rm{esc}}}^{{\rm{LyC}}}﹩ (with value shown on the bottom right corner), and all display the galaxies on a scale of 20 kpc × 20 kpc, which encompasses the emission in all filters in most cases. Galaxies with emission extending beyond the scale presented in the panels are shown in full in Figure 13. LaCOS galaxies show very diverse morphologies and many exhibit signs of merger interaction.
Paper: The Lyα and Continuum Origins Survey. I. Survey Description and Lyα Imaging
Authors: Alexandra Le Reste, Claudia Scarlata, Matthew J. Hayes, Jens Melinder, Alberto Saldana-Lopez, Aaron Smith, Axel Runnholm, Yu-Heng Lin, Ricardo O. Amorín, Hakim Atek, Sanchayeeta Borthakur, Cody A. Carr, Brian Fleming, Sophia R. Flury, Mauro Giavalisco, Alaina Henry, Anne E. Jaskot, Zhiyuan Ji, Intae Jung, Floriane Leclercq, Rui Marques-Chaves, Stephan R. McCandliss, M. S. Oey, Göran Östlin, Swara Ravindranath, Daniel Schaerer, Trinh X. Thuan, Xinfeng Xu
Figure caption: Lyα maps of the LaCOS galaxies. The panels display a physical scale of 20 kpc × 20 kpc, and are ordered by increasing LyC escape fraction, displayed in the lower right corner of each panel. For all panels, the Lyα flux values are shown between 5 × 10−18 erg s−1 cm−2 (in black) and 2.5 × 10−16 erg s−1 cm−2 (in white), displayed on a logarithmic scale.
Paper: The Lyα and Continuum Origins Survey. I. Survey Description and Lyα Imaging
Authors: Alexandra Le Reste, Claudia Scarlata, Matthew J. Hayes, Jens Melinder, Alberto Saldana-Lopez, Aaron Smith, Axel Runnholm, Yu-Heng Lin, Ricardo O. Amorín, Hakim Atek, Sanchayeeta Borthakur, Cody A. Carr, Brian Fleming, Sophia R. Flury, Mauro Giavalisco, Alaina Henry, Anne E. Jaskot, Zhiyuan Ji, Intae Jung, Floriane Leclercq, Rui Marques-Chaves, Stephan R. McCandliss, M. S. Oey, Göran Östlin, Swara Ravindranath, Daniel Schaerer, Trinh X. Thuan, Xinfeng Xu
Figure caption: Illustration of LaCOS HST photometry coverage for galaxy J172010 (z = 0.29). The leftmost panel shows a Lyα map of the galaxy, with COS (white) and SDSS (gray) extraction apertures overlaid. The panel shows Lyα flux values between 5 × 10−18 erg s−1 cm−2 (in black) and 1 × 10−16 erg s−1 cm−2 (in white), displayed on a logarithmic scale. The middle panel shows the COS spectrum for wavelengths overlapping with ACS/SBC filter coverage, with filter throughput overlaid. The rightmost panel shows the SDSS spectrum with WFC3/UVIS filter responses overlaid.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Images for example case c in YSE field 257A. The same description as in Figure 7 applies. Here, the images have comparable seeing, but varying depths. We see that D21 has worse star subtraction that leaves significant residuals, shown in circles 2 and 3. Circle 1 follows an artifact that shows up in I2, but not I1. We expect to see this artifact in both difference images, but the artifact is masked in D12. D12 is the better difference image with good star subtraction and a uniform background.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Images for example case d in YSE field 257A. The same description as in Figure 7 applies. The images have comparable depths, but varying seeing. Here, our method and the standard practice agree since both would choose I2 (better seeing image) as the template. D21 is the worse difference image because it has bad star subtraction with PSF-ringing and a noisy background; see circles 2 and 3. D12 shows some residuals in circles 2 and 3 likely due to bright stars and the significant seeing difference, but still clearly the better difference image. Note that circle 1 follows an artifact that only shows up in image 2 and hence appears in both difference images.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Images for example case e in YSE field 257A. The same description as Figure 7 applies. This is a case where the images are completely different in both seeing and depth. D21 is the result of convolving a better seeing image to the worse seeing image in the standard way. D12 is the convolution of a worse seeing image to a better seeing image, but with significantly better depth. It is not as clear from a glance which difference image is better, but D21 does leave behind residuals in circles 1 and 2, whereas D12 does not. Circle 3 follows an artifact that only showed up in I1 and so appears in both difference images.
Paper: Optimizing Convolution Direction and Template Selection for Difference Image Analysis
Authors: Rodrigo Angulo, Armin Rest, William P. Blair, Jacob Jencson, David A. Coulter, Qinan Wang, Ryan J. Foley, Charles D. Kilpatrick, Xiaolong Li, Anthony L. Piro, César Rojas-Bravo
Figure caption: Images for example case b in YSE field 257A. The same description as in Figure 7 applies. This is a case where the images have different seeing and depth. D21 is the result of convolving a better seeing image to the worse seeing image in the standard way. D12 is the convolution of a worse seeing image, but with better depth to a better seeing image. Clearly, D12 is the better difference image with overall less residuals (see circles 1 and 2). Circle 3 follows an artifact that only shows up in image 2 and hence appears in both difference images.
Paper: The Lyα and Continuum Origins Survey. I. Survey Description and Lyα Imaging
Authors: Alexandra Le Reste, Claudia Scarlata, Matthew J. Hayes, Jens Melinder, Alberto Saldana-Lopez, Aaron Smith, Axel Runnholm, Yu-Heng Lin, Ricardo O. Amorín, Hakim Atek, Sanchayeeta Borthakur, Cody A. Carr, Brian Fleming, Sophia R. Flury, Mauro Giavalisco, Alaina Henry, Anne E. Jaskot, Zhiyuan Ji, Intae Jung, Floriane Leclercq, Rui Marques-Chaves, Stephan R. McCandliss, M. S. Oey, Göran Östlin, Swara Ravindranath, Daniel Schaerer, Trinh X. Thuan, Xinfeng Xu
Figure caption: Histogram of properties for the LaCOS (blue) and LzLCS (gray) samples. Vertical bars show the average properties for the two samples (calculated in real space for base 10 log values). The distribution of properties for the LaCOS sample is similar to that of the full LzLCS sample.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The distributions of physical jet parameters of BL Lacs (blue) and FSRQs (red).
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between particle energy γ3p and particle density ne (upper panels), and magnetic field B (lower panels). BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the best linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between the peak frequency and spectral curvature. The upper panels show the relationship between synchrotron frequency νs and synchrotron curvature bs, whereas the lower panels show the relationship between IC frequency νc and IC curvature bc. BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The intrinsic signature of stochastic acceleration, i.e., the relationship between EED peak energy γ3p and its curvature r. BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between magnetic field B and Doppler factor δ (upper panels) and particle density ne (lower panels). BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the best linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between synchrotron spectral curvature bs and source electron curvature r. The SSC blazars are shown by filled blue circles, and the EC blazars are shown by filled red circles. The solid lines represent the linear regression fit.
Paper: PrestoZL: A GPU-accelerated High-throughput Jerk Search Toolkit for Binary Pulsars
Authors: Kuang Mao, Zhaorong Tang, Qiuhong Pan, Pei Wang, Huaxi Chen, Scott M. Ransom, Di Li, Xuefei Tang, Qi Wang, Yi Feng, Lei Chen, Donghui Quan, Zujie Ren
Figure caption: GPU compute and memory throughput of the kernels in the jerk search GPU when processing an FFT file with search parameters zmax = 200 and wmax = 400. High throughput indicates that the kernel utilizes a large number of GPU compute units or performs extensive memory access operations. Each dot represents a single kernel call. The red dots indicate time-consuming kernels, which contribute to over 80% of the total kernel execution time.
Paper: PrestoZL: A GPU-accelerated High-throughput Jerk Search Toolkit for Binary Pulsars
Authors: Kuang Mao, Zhaorong Tang, Qiuhong Pan, Pei Wang, Huaxi Chen, Scott M. Ransom, Di Li, Xuefei Tang, Qi Wang, Yi Feng, Lei Chen, Donghui Quan, Zujie Ren
Figure caption: The time proportion of kernels in the jerk search GPU using search parameters zmax = 200 and wmax = 400. The meaning of each kernel is introduced in Table 1.
Paper: PrestoZL: A GPU-accelerated High-throughput Jerk Search Toolkit for Binary Pulsars
Authors: Kuang Mao, Zhaorong Tang, Qiuhong Pan, Pei Wang, Huaxi Chen, Scott M. Ransom, Di Li, Xuefei Tang, Qi Wang, Yi Feng, Lei Chen, Donghui Quan, Zujie Ren
Figure caption: GPU utilization of the jerk search GPU under eight concurrent processes.
Paper: PrestoZL: A GPU-accelerated High-throughput Jerk Search Toolkit for Binary Pulsars
Authors: Kuang Mao, Zhaorong Tang, Qiuhong Pan, Pei Wang, Huaxi Chen, Scott M. Ransom, Di Li, Xuefei Tang, Qi Wang, Yi Feng, Lei Chen, Donghui Quan, Zujie Ren
Figure caption: The elapsed time during processing the frequency bin blocks of one FFT file, with execution time recorded every 1000 blocks. The number of frequency bin blocks reflects the length of the time series being processed.
Paper: PrestoZL: A GPU-accelerated High-throughput Jerk Search Toolkit for Binary Pulsars
Authors: Kuang Mao, Zhaorong Tang, Qiuhong Pan, Pei Wang, Huaxi Chen, Scott M. Ransom, Di Li, Xuefei Tang, Qi Wang, Yi Feng, Lei Chen, Donghui Quan, Zujie Ren
Figure caption: The memory usage fluctuation during the processing period of the M5 data set sampled at 1 s intervals in the A100 40G GPU card.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Illustration of the merging of two units at Level 0 (left) into a single unit at Level 1 (right). At Level 0, ﹩{\rho }_{1}^{0}﹩ and ﹩{\rho }_{2}^{0}﹩ represent the density of the lower and upper units, with the corresponding zero-boundary potentials ﹩{[{{\rm{\Phi }}}_{1}^{0}]}_{3\times 3}﹩ and ﹩{[{{\rm{\Phi }}}_{2}^{0}]}_{3\times 3}﹩, respectively. Note that ﹩{\rho }_{\,\rm{int}\,}^{0}﹩ denotes the density in the intervening boundary at Level 0, which together with the screening charges ρ1,T and ρ2,B in the middle line at Level 1 produces the zero-boundary potential Ψ1.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Illustration of the merging process, where four units at Level 0, each with zero-boundary potentials ﹩{[{{\rm{\Phi }}}_{i}^{0}]}_{3\times 3}﹩ and line-density potentials ﹩{{\rm{\Psi }}}_{i}^{1}﹩ (left), combine into two units at Level 1 with zero-boundary potentials ﹩{[{{\rm{\Phi }}}_{i}^{1}]}_{3\times 7}﹩ (middle). These units are further merged, incorporating Ψ2, into a single unit at Level 2 possessing the zero-boundary potential ﹩{[{{\rm{\Phi }}}_{i}^{2}]}_{7\times 7}﹩ (right).
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Illustration of our grid partitioning and level structure for the case of a 7 × 15 grid. Connected colored cells form units at each level, while white cells represent intervening boundaries at the same level. The boundaries of the entire domain are shaded in gray.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: The regions shaded in yellow and red correspond to the areas used for calculating the source-free interior potentials, ﹩{{ \mathcal L }}_{i}^{l-1}﹩, and source-free boundary potentials, ﹩{{ \mathcal L }}_{b}^{l}﹩, respectively. The purple-highlighted region denotes the area used to compute the middle-line potential, ﹩{{\rm{\Psi }}}_{\,\rm{mid}\,}^{3}﹩.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Structures of units and boundaries when the domain is discretized using a mesh with ﹩{N}_{r}={2}^{{n}_{r}}﹩ and ﹩{N}_{\theta }={2}^{{n}_{\theta }}﹩. The four units at Level L − 2 have 15 × 15 cells when nr = nθ = 5. The two polar grids in contact with the equator correspond to the lower and upper boundaries of the upper and lower units, respectively, while the outermost radial grid with ﹩i={2}^{{n}_{r}}﹩ coincides with the outer boundaries of the outer units in the radial direction.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Test results for a uniform slender spherical wedge. The numerical potential (circles) is compared with the analytical potential (line) along (a) the r-direction at θ = 0.5π and ϕ = 0.125π, and (b) the θ-direction at r = 2 and ϕ = 0.125π. The lower panels in (a) and (b) display the corresponding cut profiles of the relative errors. (c) Distribution of the relative errors on a logarithmic scale in the ϕ = 0.125π plane.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Results of the weak scaling test. The wall-clock time per cycle, twall, of the Poisson solver is compared with that of the MHD solver in FARGO3D as a function of the number of processors, Ncore.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: (a) Initial meridional distributions of the normalized ring density ﹩\hat{\rho }=\rho /{\rho }_{c}﹩ in the ϕ = 0 plane. (b) Comparison of the radial distribution of ﹩\hat{\rho }﹩ at ϕ = 0 and θ = π/2 between t/torb = 0 and 3.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: (a) Equatorial distribution of the ring density ﹩\hat{\rho }﹩ at t/torb = 19.2. (b) Temporal growth of the Fourier transform ﹩{{ \mathcal L }}_{m}﹩ of the integrated ring density for the modes with m = 3, 4, and 5. The m = 4 mode grows the fastest.
Paper: A Fast, Second-order Accurate Poisson Solver in Spherical Polar Coordinates
Authors: Jeonghyeon Ahn, Woong-Tae Kim, Yonghwi Kim
Figure caption: Convergence test results for the Poisson solver on the uniform slender spherical wedge with a density of unity, as described in Section 6.1. The computational domain is set to N × N × 4N for each N. The dashed line represents the ideal convergence slope of −2.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between spectral peak frequency and peak flux of a polynomial. The upper panels show the relationship between synchrotron peak frequency νs and synchrotron peak flux νsFs, whereas the lower panels show IC peak frequency νs and IC peak flux νcFc. BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the best linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between spectral frequency and the luminosity of a polynomial. The upper panels show the relationship between synchrotron frequency νs and synchrotron luminosity Ls, whereas the lower panels show the relationship between IC peak frequency νc and IC peak luminosity Lc. BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the best linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between synchrotron frequency νs and electron peak energy γ3p (upper panels) and parameter Bδ (lower panels), obtained by one-zone modeling. BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the best linear regression fit.
Paper: From Equipartition to Curvature: The Spectral Evolution of 4FGL Blazars
Authors: Muhammad S. Anjum, Shu-Jin Hou, Liang Chen, Zhigang Li, Minfeng Gu
Figure caption: The relationship between synchrotron luminosity Ls and Doppler factor δ (upper panels), and source size R (lower panels), obtained by one-zone modeling. BL Lacs are shown by filled blue circles, and FSRQs are shown by filled red circles. The solid lines represent the best linear regression fit.