adsabs/AstroCLIMB huggingface dataset:
Paper: Bits Missing: Finding Exotic Pulsars Using bfloat16 on NVIDIA GPUs
Authors: Jack White, Karel Adámek, Jayanta Roy, Sofia Dimoudi, Scott M. Ransom, Wesley Armour
Figure caption: Single-precision acceleration templates—complex magnitude plotted.
Paper: Bits Missing: Finding Exotic Pulsars Using bfloat16 on NVIDIA GPUs
Authors: Jack White, Karel Adámek, Jayanta Roy, Sofia Dimoudi, Scott M. Ransom, Wesley Armour
Figure caption: Comparison of bit layouts of IEEE-754 single- and half precision with bfloat16.
Paper: Sample and Statistical Analysis of the Near-Earth Object Wide-field Infrared Survey Explorer Variability of the 6.7 GHz Methanol Maser Sources
Authors: Shi-Min Song, Xi Chen, Zhi-Qiang Shen, You-Xin Wang, Kai Yang, Dan Miao, Jiong-Heng Wu, Jun-Ting Liu, Yan-Kun Zhang
Figure caption: Representative light curves of different variables. From top to bottom in the left panels are the secular variables, including linear, sin, and sin+linear variables. In the right panels, from top to bottom, are the stochastic variables, including the burst type, drop type, and irregular type. Note that the red dashed line is the median W2 magnitude while the green dashed line marks the criteria for both the burst and drop variables. The source ID, classification, and fitting results are given at the top of each panel. The W2 light curves of all variables identified in this study are presented at https://github.com/Shermansong/NEOWISE_pic.
Paper: Sample and Statistical Analysis of the Near-Earth Object Wide-field Infrared Survey Explorer Variability of the 6.7 GHz Methanol Maser Sources
Authors: Shi-Min Song, Xi Chen, Zhi-Qiang Shen, You-Xin Wang, Kai Yang, Dan Miao, Jiong-Heng Wu, Jun-Ting Liu, Yan-Kun Zhang
Figure caption: Left panel: distribution of the differences between the maximum and minimum phases (ΔW2) vs. stochasticity (SD/σ) of the W2 flux density for the variable sources identified by the criteria in Section 2. SD is the standard deviation of the W2-band flux densities for a given light curve, and σ is the mean flux density uncertainty. The full sample data points, which are matched to the NEOWISE database, are shown in gray, and the secular and stochastic variable data points are shown in red and blue, respectively. The number of data points is indicated in parentheses in the upper right corner. Right panel: comparison of the 2D density distribution of ΔW2 vs. SD/σ between our high-mass YSO sample and the Park et al. (2021) low-mass YSO sample. P21 represents the Park et al. (2021) YSO sample shown in blue, and our sample is shown in orange.
Paper: Sample and Statistical Analysis of the Near-Earth Object Wide-field Infrared Survey Explorer Variability of the 6.7 GHz Methanol Maser Sources
Authors: Shi-Min Song, Xi Chen, Zhi-Qiang Shen, You-Xin Wang, Kai Yang, Dan Miao, Jiong-Heng Wu, Jun-Ting Liu, Yan-Kun Zhang
Figure caption: Comparisons of the cumulative distributions for different types of variables with different physical parameters. The dust temperature distribution is shown in the left panel, the bolometric luminosity is shown in the middle panel, and the hydrogen column density is shown in the right panel. The red, blue, and green solid curves represent the secular, stochastic variables, and nonvariability sources.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: An example of absolute flux calibration for the blue- and red-arm LAMOST spectra. Top panel: the original spectrum only with the relative flux calibration. The gray area represents the blue/red overlapping region that is masked during the fitting. A connection defect can be observed at this region. Middle panel: the spectrum after the absolute flux calibration. The green circles represent the flux densities in the g, r, i, and z bands, and the asterisks mark the flux densities that are used during the fitting. The lines in blue and red represent piecewise fits to the asterisks. The connection defect is improved after recalibration. The bottom panel shows the filter curves for the SDSS in g, r, i, and z bands. It is clear that the z band is not fully covered by the spectrum, so the photometric data in z band are not used during the spectral fitting.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The distributions of identified LAMOST quasars in the SDSS-WISE/UKIDSS color diagram. The dashed–dotted lines indicate the criteria used in the LAMOST QSO survey (Wu & Jia 2010; Wu et al. 2012). The WISE and UKIDSS magnitudes are in Vega magnitudes. The SDSS magnitudes in panels (a) and (b) are plotted in AB magnitudes, and those in panels (c) and (d) are plotted in Vega magnitudes. The SDSS AB magnitudes can be converted to Vega magnitudes using the following scaling (Hewett et al. 2006): g = g(AB) + 0.103, r = r(AB) − 0.146, i = i(AB) − 0.366, z = z(AB) − 0.533. The contours in pink show the distribution for common quasars between this work and SDSS, while the contours in gray show the distribution for unique quasars identified in this work. The mean (μ) and dispersion (σ) of each distribution are tabulated in corresponding plots.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The distribution in the magnitude−redshift space for the visually confirmed quasars for the previous LAMOST quasar survey (black contours) and in DR6−DR9 (blue). The absolute magnitudes M  i (z = 2) are normalized at z = 2, following the K-correction of Richards et al. (2006). The left and bottom panels show the absolute magnitude and redshift distributions, respectively.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The distribution of redshift difference (Δz) for common quasars between this work and SDSS vs. LAMOST spectral S/N.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: Examples for the deblending results of Hα−[N II]−[S II] (panel (a)), Hβ−[O III] (panel (b)), Mg II (panel (c)), and C IV (panel (d)) lines. The black lines represent the extinction-corrected spectra with the continuum subtracted. As for the fitted emission lines, the broad components are in red, while the narrow ones are in green, along with their sum (blue).
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: Comparisons between the measurements of the FWHM values in this work and S11 (R20). We show the plot of ﹩\mathrm{log}({\mathrm{FWHM}}_{\mathrm{LAMOST}}/{\mathrm{FWHM}}_{\mathrm{SDSS}})﹩ for broad Hα (top left), broad Hβ (top right), broad Mg II (bottom left), and whole C IV (bottom right). The mean (μ) and dispersion (σ) of each distribution are tabulated in corresponding plots. In this figure, only the emission lines with reliable fitting (LINE_FLAG = 0) are considered.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: Two examples for the spectral fitting results with (panel (b)) and without (panel (a)) the polynomial model. In each pair of panels, the upper panel shows the spectrum from SDSS, and the lower panel shows the spectral fitting results of the LAMOST spectrum: the black lines denote the dereddened spectra, yellow lines represent the continuum model of (f bpl + f poly), and the cyan lines represent Fe II templates. The details of emission-line components will be described in the later part.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The comparison of the monochromatic continuum luminosities (L 5100, L 1000, L 1300) and the estimated M BH based on Hβ, Mg II, and C IV between this work and S11 (R20).
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The distribution of M BH based on various broad emissions (Hβ, Mg II, and C IV) is plotted against the redshift. The quasars from S11 and R20 are represented by the pink and gray circles, respectively.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The comparison of the median S/N per pixel in the line-fitting region between this work and S11 (R20). The mean (μ) and dispersion (σ) of each distribution are shown in corresponding plots. Only the emission lines with reliable fitting (LINE_FLAG = 0) are considered.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: The distributions of median S/N per pixel around the line-fitting region are plotted as normalized histograms. Only the emission lines with reliable fitting (LINE_FLAG = 0) are considered.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: Same as in Figure 7, but for emission-line flux.
Paper: The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey: Quasar Properties from Data Releases 6 to 9
Authors: Jun-Jie Jin, Xue-Bing Wu, Yuming Fu, Su Yao, Yan-Li Ai, Xiao-Tong Feng, Zi-Qi He, Qin-Chun Ma, Yu-Xuan Pang, Rui Zhu, Yan-xia Zhang, Hai-long Yuan, Zhi-Ying Huo
Figure caption: Same as in Figure 7, but for EW values.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: Difference between the standard discriminator and relativistic discriminator. The improvement of relativistic GAN tends to calculate how much the real image is more realistic than the generated simulated image, and the simulated image is less realistic than the real value.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: The basic architecture of super-resolution models for enhancing the spatial resolution of X-EUVI 195 Å images.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: The magenta image is from SDO/AIA, and the blue image is from FY-3E X-EUVI. Panel (a) uses the calibration information provided by X-EUVI to process the EUV 195 Å image and linear stack with the EUV 193 Å images, and panel (b) uses the feature-based registration method to process the EUV 195 Å image and linear stack with the EUV 193 Å images.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: Combined SDO/AIA 193 Å and super-resolution enhanced FY-3E/X-EUVI 195 Å sequential EUV images from 00:00:01 to 00:00:30 on 2022 June 8.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: Considering the AIA 193 Å image, the corrected Bibubic interpolated X-EUVI image, and the corrected Real-ESRGAN super-resolution X-EUVI image, we sampled and analyzed the data of the two cuts, and then we found that their intensity values are close. The results of the Real-ESRGAN super-resolution are much closer to AIA than those of the Bibubic interpolation method.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: The generator network architecture of the Real-ESRGAN super-resolution model.
Paper: Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images
Authors: Qinglin Yang, Zhou Chen, Rongxin Tang, Xiaohua Deng, Jinsong Wang
Figure caption: An example of the region images of the nearest interpolation, bicubic interpolation, RCAN, SwinIR, Real-ESRGAN, and AIA 193 Å on 2021 October 28, 10:22 UT.
Paper: Firefly: A Browser-based Interactive 3D Data Visualization Tool for Millions of Data Points
Authors: Alexander B. Gurvich, Aaron M. Geller
Figure caption: Performance benchmarks as a function of the number of particles in a test FIRE data set (m12b_res7100). Different numbers of particles are achieved using different levels of decimation. Dashed lines are models (defined by the black annotations in each panel) whose parameters are manually fit according to the .ffly data set in each panel. Top: the size of the .JSON and .ffly files when the data set is (re)written to disk. Middle: the amount of RAM being used by the browser after opening the .JSON and .ffly files. The size is expected to be larger than on disk as Firefly requires additional color and/or size, etc. arrays in addition the raw particle data. Bottom: the time it takes for Firefly to open the .JSON and .ffly files from disk and initialize the scene. In general, we see little difference between the .JSON and .ffly output types except when it comes to the size on disk, where .JSON is inflated by a factor ≈3×, as predicted. Finally, a rough rule of thumb for the amount of time it should take to open a data set is roughly 1 s for every 20,000 particles.
Paper: Firefly: A Browser-based Interactive 3D Data Visualization Tool for Millions of Data Points
Authors: Alexander B. Gurvich, Aaron M. Geller
Figure caption: Performance benchmarks for the frame rate of a Firefly visualization as a function of number of overlapping particles. In general, the bottleneck for performance during the visualization is how many particles in a pixel are overlapping (and for how many pixels this number is ≫1). Top: schematic diagrams of the two tests we perform (1) placing many particles at the origin (left) and (2) distributing many particles in a cube of fixed size. In both cases, we vary both the number of particles and their diameter. Scale bars illustrate the mapping between dataspace (in units) and screen space (in pixels) from a fixed camera distance 2 units for consumer grade monitor with a 1920 × 1080 resolution. In all tests, we move the camera along a predefined path orbiting the origin with radius of 2 units. Bottom: quantitative results of varying the number and diameter of particles for both the overlapping particle (left 2 panels) and cube (right-most panel) setup. In both of these extreme cases of particle density, we find that interactive frame rates are achievable.
Paper: Firefly: A Browser-based Interactive 3D Data Visualization Tool for Millions of Data Points
Authors: Alexander B. Gurvich, Aaron M. Geller
Figure caption: Performance benchmarks for preformatting sample FIRE data as an octree using the Firefly PDPP. Top: the number of nodes in the constructed octree as a function of the number of particles in the data set and the maximum number of particles in a node before it is refined into child nodes (yellow 103, red 104, and blue 105). Bottom: the time elapsed constructing the octree. The dashed lines in both panels show fitting functions (defined by the black annotations in each panel) to the data for each ﹩{N}_{\max \,\mathrm{node}}﹩. Generally, octrees with fewer but larger nodes take less total time to generate but appear more chunky than those with more numerous and smaller nodes.
Paper: Firefly: A Browser-based Interactive 3D Data Visualization Tool for Millions of Data Points
Authors: Alexander B. Gurvich, Aaron M. Geller
Figure caption: Demonstrating the usage of the redshift filter limits to find the maximum extent of disk galaxies in the SDSS data set, z ≈ 0.25.
Paper: The Fermi-LAT Lightcurve Repository 
Authors: S. Abdollahi, M. Ajello, L. Baldini, J. Ballet, D. Bastieri, J. Becerra Gonzalez, R. Bellazzini, A. Berretta, E. Bissaldi, R. Bonino, A. Brill, P. Bruel, E. Burns, S. Buson, R. A. Cameron, R. Caputo, P. A. Caraveo, N. Cibrario, S. Ciprini, P. Cristarella Orestano, M. Crnogorcevic, S. Cutini, F. D'Ammando, S. De Gaetano, S. W. Digel, N. Di Lalla, L. Di Venere, A. Domínguez, V. Fallah Ramazani, S. J. Fegan, E. C. Ferrara, A. Fiori, H. Fleischhack, A. Franckowiak, Y. Fukazawa, P. Fusco, V. Gammaldi, F. Gargano, S. Garrappa, C. Gasbarra, D. Gasparrini, N. Giglietto, F. Giordano, M. Giroletti, D. Green, I. A. Grenier, S. Guiriec, M. Gustafsson, E. Hays, D. Horan, X. Hou, G. Jóhannesson, M. Kerr, D. Kocevski, M. Kuss, L. Latronico, J. Li, I. Liodakis, F. Longo, F. Loparco, L. Lorusso, B. Lott, M. N. Lovellette, P. Lubrano, S. Maldera, A. Manfreda, G. Martí-Devesa, M. N. Mazziotta, I. Mereu, M. Meyer, P. F. Michelson, T. Mizuno, M. E. Monzani, A. Morselli, I. V. Moskalenko, M. Negro, N. Omodei, E. Orlando, J. F. Ormes, D. Paneque, G. Panzarini, J. S. Perkins, M. Persic, M. Pesce-Rollins, R. Pillera, T. A. Porter, G. Principe, J. L. Racusin, S. Rainò, R. Rando, B. Rani, M. Razzano, S. Razzaque, A. Reimer, O. Reimer, M. Sánchez-Conde, P. M. Saz Parkinson, Jeff Scargle, L. Scotton, D. Serini, C. Sgrò, E. J. Siskind, G. Spandre, P. Spinelli, D. J. Suson, H. Tajima, D. J. Thompson, D. F. Torres, J. Valverde, T. Venters, Z. Wadiasingh, S. Wagner, K. Wood
Figure caption: The gray band marks the range of LAT on-sky exposures for each time bin of 3 day cadence. The pink bands, in order, mark the time ranges of the original rocking angle mission profile (details on the observatory sky-survey profiles can be found at https://fermi.gsfc.nasa.gov/ssc/observations/types/allsky/), the Galactic center monitoring, and the time gap between the solar panel anomaly and the beginning of the new survey mode. Note that some time bins have a minimum exposure value of zero: this means that some part of the sky was not observed during that time interval. The exposure maps are computed at the central energy in the 0.1–100 GeV band.
Paper: The Fermi-LAT Lightcurve Repository 
Authors: S. Abdollahi, M. Ajello, L. Baldini, J. Ballet, D. Bastieri, J. Becerra Gonzalez, R. Bellazzini, A. Berretta, E. Bissaldi, R. Bonino, A. Brill, P. Bruel, E. Burns, S. Buson, R. A. Cameron, R. Caputo, P. A. Caraveo, N. Cibrario, S. Ciprini, P. Cristarella Orestano, M. Crnogorcevic, S. Cutini, F. D'Ammando, S. De Gaetano, S. W. Digel, N. Di Lalla, L. Di Venere, A. Domínguez, V. Fallah Ramazani, S. J. Fegan, E. C. Ferrara, A. Fiori, H. Fleischhack, A. Franckowiak, Y. Fukazawa, P. Fusco, V. Gammaldi, F. Gargano, S. Garrappa, C. Gasbarra, D. Gasparrini, N. Giglietto, F. Giordano, M. Giroletti, D. Green, I. A. Grenier, S. Guiriec, M. Gustafsson, E. Hays, D. Horan, X. Hou, G. Jóhannesson, M. Kerr, D. Kocevski, M. Kuss, L. Latronico, J. Li, I. Liodakis, F. Longo, F. Loparco, L. Lorusso, B. Lott, M. N. Lovellette, P. Lubrano, S. Maldera, A. Manfreda, G. Martí-Devesa, M. N. Mazziotta, I. Mereu, M. Meyer, P. F. Michelson, T. Mizuno, M. E. Monzani, A. Morselli, I. V. Moskalenko, M. Negro, N. Omodei, E. Orlando, J. F. Ormes, D. Paneque, G. Panzarini, J. S. Perkins, M. Persic, M. Pesce-Rollins, R. Pillera, T. A. Porter, G. Principe, J. L. Racusin, S. Rainò, R. Rando, B. Rani, M. Razzano, S. Razzaque, A. Reimer, O. Reimer, M. Sánchez-Conde, P. M. Saz Parkinson, Jeff Scargle, L. Scotton, D. Serini, C. Sgrò, E. J. Siskind, G. Spandre, P. Spinelli, D. J. Suson, H. Tajima, D. J. Thompson, D. F. Torres, J. Valverde, T. Venters, Z. Wadiasingh, S. Wagner, K. Wood
Figure caption: Example of validation plots for the FSRQ 4C +28.07. The top panels show the case for which the spectral index (α for this source) is fixed. In all plots we are considering the 0.1–100 GeV energy range. The bottom panels show the case for which the spectral index is free to vary. The ratio of the flux to the flux uncertainty (left panels) is expected to be approximately proportional to the square root of TS. The middle panels show the distributions of the flux statistical uncertainties. Flux distributions are shown in the right panels. In this example, the distributions are good except for the outliers highlighted within green squares, which should be either further investigated or removed. In the histograms, the dashed lines represent the results from all time bins. The solid lines represent bins that did not converge. Notice that outliers are not necessarily the results from analyses that did not converge.