adsabs/AstroCLIMB huggingface dataset:
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Scatter plots of fit X and Y corrections against time since launch (2006 September 22), day of year, and time of day. The color of each scatter plot point indicates the density at that location, estimated with a kernel density estimate. We additionally plot a kernel regression estimate, in red, of scale correction as a function of each covariate. The corrections have no trend, suggesting that the scale factor is constant and that causes driven by Earth or satellite orbit are not responsible.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Diagram of matching and transformation fitting. Given a Level 2 Hinode/SOT-SP scan and SDO/HMI scan, one can identify the contemporaneous part of the Hinode/SOT-SP scan and filter to the correspondences on contemporaneous Hinode/SOT-SP pixels. For clarity, we show only a subset of the matches that originate near the final fit. These have many outliers. After robust fitting, there are a far smaller number of inliers that are well described by a single model. For clarity, the diagram shows the Level 2 Hinode/SOT-SP scan as it is stored, a set of columns next to each other. In practice, each correspondence’s x coordinate is mapped to the slit position to account for the possibility of nonuniform spacing.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Fit on temporally adjacent SDO/HMI scans from the ME_720_fd10 series. In both cases, the fit scales are effectively unity and rotation zero. Translations have substantial scatter with the x-axis centered on a positive value and the y-axis centered on zero. This agrees with intuition, suggesting that the fitting procedure is effective even with temporal evolution.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: The matching setup. Given a correspondence ﹩[x,y]\leftrightarrow [x^{\prime} ,y^{\prime} ]﹩, we aim to find a transformation f, parameterized by  m , that maps [x, y] to closely match ﹩[x^{\prime} ,y^{\prime} ]﹩. Without outliers (i.e., correspondences that are drawn at random), one can safely minimize the squared distance ﹩| | [x^{\prime} ,y^{\prime} ]\,-f([x,y],{\boldsymbol{m}})| {| }_{2}^{2}﹩. With outliers, one instead maximizes the number of correspondences satisfying ﹩| | [x^{\prime} ,y^{\prime} ]-f([x,y],{\boldsymbol{m}})| {| }_{2}\lt \epsilon ﹩.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Locations of SIFT features found on SDO/HMI data. ORB features are found at similar distinctive regions in the image, but slightly different due to differences in the precise detection scheme. In both cases, the features can then be matched to features found on Hinode/SOT-SP data to generate correspondences between the two data sets. These correspondences contain both outliers and noise, but the fitting methods used are robust to both.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Histograms of the fit scale correction factor, weighted by the number of inlier matches. The 25% trimmed mean is drawn on the histogram as a black vertical line. There is a strong preference for X and Y scalings that are less than the theoretical Level 2 value on both normal and fast scans. There is substantially more scatter in the X direction, especially for normal scans.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Scatter plots of fit X and Y corrections against the range of the X and Y coordinates. Having more correspondences across the full image results in more stable estimate of the scales in the X coordinate.
Paper: Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Authors: David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, Marc L. DeRosa, J. Todd Hoeksema, K. D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Figure caption: Illustration of the nonuniform changes to j  R . Left two panels: we plot B  R  for context and the difference in j  R , along with zoom ins in between the images. The changes in j  R  are not spatially uniform but exhibit distinct spatial trends consisting of ridges. Right panel: the overall relative scaling between j  R  calculated with the updated pointing information compared to j  R  calculated with the pipeline pointing information. On the left hemisphere, the new j  R  is almost always bigger; on the right, it is almost always smaller.
Paper: Powerful Yet Lonely: Is 3C 297 a High-redshift Fossil Group?
Authors: Valentina Missaglia, Juan P. Madrid, Mischa Schirmer, Francesco Massaro, Alberto Rodríguez-Ardila, Carlos J. Donzelli, Martell Valencia, Alessandro Paggi, Ralph P. Kraft, Chiara Stuardi, Belinda J. Wilkes
Figure caption: Chandra image of 3C 297, obtained after merging the available observations, for a total of 212 ks. The image has been filtered in the energy range 0.5–7 keV, binned with a pixel size of 0.″123 pixel−1 and smoothed with a 3.″44 Gaussian kernel. VLA 8.4 GHz (restoring beam 0.″357 × 0.″233) black radio contours are drawn starting at 0.08 μJy beam−1, increasing by factors of 6. The position of the radio core, derived from the spectral index map, is marked with a white cross.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: Left: The retrieved densities based on NRLMSISE-00 models. Since the density of Ar is less than 1% when compared to N+O, only N+O density is shown here. For clarity, the retrieved densities of observation P0111605063 (the last data points) are shifted left by 4 months. Right: The density correction factors of the NRLMSISE-00 model (in blue) and NRLMSIS 2.0 model (in orange). For clarity, the correction factors obtained from observation P0111605063 (the last data points) are shifted left by 4 months.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Comparison with the spectroscopic redshifts for our photo-zs (left panel) and Gaia QSOC redshifts (right panel) for 47,451 spectroscopically confirmed quasars covered in both our DES photometric quasar sample and Gaia QSOC. The color map indicates the source number at each grid point. The photo-z accuracy R 0.1 is 93.4% and 61.1% for our photo-zs and Gaia QSOC redshifts, respectively. Thus, the catastrophic redshift failure rate is much higher for the Gaia QSOC.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: The light curves of one occultation and the corresponding best-fit models for HE and ME telescopes. The reference time is the time when the LOS altitude becomes 90 km. LE telescope is saturated in this occultation and its data is therefore unavailable. The light blue data points are source flux subtracted, so that the variation of background can be seen more clearly. Two vertical dotted lines indicate the OTI. Two types of residuals, ﹩{\sum }_{c}\left({C}_{c,i}-{C}_{c,i,{\rm{e}}}\right)/\sqrt{{\sum }_{c}{C}_{c,i,{\rm{v}}}}﹩ and ﹩{\sum }_{c}\left({D}_{c,i}-{M}_{c,i}\right)/\sqrt{{\sum }_{c}{M}_{c,i}}﹩ are also plotted to show the fitness of the model. Left: The best-fit model of the HE light curve passes the goodness-of-fit test, and the corresponding p-value is 0.19. Right: The best-fit model of the ME light curve fails to pass the goodness-of-fit test, due to the fact that the prominent variation of background cannot be well fit by a background model with only two free parameters for each channel.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: The photon flux of the Crab Nebula observed during a standard GTI (from 2018-02-08T16:28:45 UTC to 2018-02-08T16:29:42 UTC), which is the most adjacent GTI to the occultation data shown in Figure 2. The green line is the best-fit spectral model, i.e., WABS ∗ POWERLAW in XSPEC, with the parameters of nH = 0.36 and Γ = 2.10. The data points in blue are background subtracted, and the data points in orange are the corresponding background estimated from lebkgmap, mebkgmap, or hebkgmap. For clarity, these data points are binned every 30 channels for LE and ME telescopes, while every two channels for the HE telescope.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: Observed light-curve data (solid lines) and corresponding posterior predictive light curve ranges (shaded area). The blue, green, and orange data are obtained from the first, second, and third occultations of observation P0111605046. The shaded regions are obtained from 1000 posterior predictive simulations. For clarity, the data point and the corresponding predictive light curves are shifted up by 100 (LE), 200 (ME), or 400 (HE) for the second occultation, and are shifted up by 200 (LE), 400 (ME), or 800 (HE) for the third occultation.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: Observed spectral data (solid lines) and corresponding posterior predictive spectra ranges (shaded area). The blue, green, and orange data are obtained from the first, second, and third occultations of observation P0111605046. These spectra are generated by grouping data according to the LOS altitude, which is annotated in the upper right of each subplot. For clarity, the spectral data are binned into five points for each telescope. The shaded regions are obtained from the same 1000 simulations as in Figure 10.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: The data point between the dashed and dotted lines of Figure 2. The abscissa axis is transformed from time to the corresponding LOS altitude. It should be noted that the X-ray emission received at low LOS altitude is also attenuated by atmosphere layers above the corresponding altitude. For LE, ME, and HE telescopes, the observed X-ray emission is significantly attenuated when the LOS altitude is 90–100 km, 70–90 km, and 55–80 km, respectively.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: Left: The total X-ray cross section of five atoms, including the cross sections of Compton scattering and photoelectric absorption. Right: The vertical density profile of five atoms obtained from NRLMSISE-00. Here, the density profile of O includes the contribution from O, O2, and Anomalous O, while the density profile of N includes the contribution from N and N2.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: The marginal distribution of density correction factors  γ . The dashed lines in the 1D histogram are the 15.8%, 50%, and 84.1% quantiles of each parameter, which are also given at the top of the histogram. On the 2D plots, the two contours indicate 68.3% and 95.0% credible regions. The density correction coefficients for adjacent atmosphere layers exhibit the negative correlation as expected.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: The projections of LOS on the ground are drawn for every 0.5 s for three occultations in ObsID P0111605010, which make up the three colored areas in visual. For clarity, only the LOS projections within 40–550 km altitude are shown. The colored areas indicate the geographical regions whose atmospheric density will be retrieved. The red arrows indicate the viewing direction of telescopes.
Paper: Neutral Atmospheric Density Measurement Using Insight-HXMT Data by the Earth Occultation Technique
Authors: Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong, Yong Chen, Shuang-Nan Zhang, Li-Ming Song, Shu Zhang, Ming-Yu Ge, You-Li Tuo, Hai-Tao Li, Dao-Chun Yu, Dong-Ya Guo, Jia-Cong Liu, Yan-Qiu Zhang, Chao Zheng
Figure caption: Schematic diagram of X-ray emission passing through several atmosphere layers along the LOS.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Completeness as a function of i-band magnitude (left panels) and redshift (right panels) for spectroscopically confirmed quasars. The top panels show the number distribution. The black diamonds/lines show the results from criteria (1)–(5). The blue circles/lines represent results from criteria (1)–(6).
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Comparison between photo-zs and the spectroscopic redshifts for quasars (left panels) and galaxies (right panels). The top panels show the results with the fewest bands, only using five-band DES photometry; the bottom panels show the results with the most bands, using all available photometry from optical, NIR, and MIR (i.e., the DES_YJHK_W1W2 combination). The color map indicates the source number at each grid point. For quasars, there is a degeneracy between z ∼ 0.8 and 2–3, and this problem is alleviated by combining optical data with infrared photometry.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: The 2D selection completeness map as a function of redshift (x-axis) and i-band magnitude (y-axis) using criteria P QSO > P Galaxy and P QSO > P Star on a sample of simulated quasars. The color indicates the completeness. The solid cyan line shows the location where the completeness is 80%.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Quasar candidate selection flowchart. For a given object with relative fluxes and magnitudes, we calculate the probability of the object being a quasar, galaxy, or star. We compare these probabilities and classify the candidate into quasar, galaxy, or star. We also obtain photoz-QSO for quasar candidates and photoz-galaxy for galaxy candidates.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Distributions of i-band magnitudes (left panel) and photometric redshifts (right panel) of the 1,352,947 quasar candidates in the DES wide footprint. The y-axes are in logarithmic scales. The blue histogram in the right panel shows quasar candidates with i < 21.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Sky coverage of the DES and NIR surveys in equatorial coordinates. WISE, 2MASS, and Gaia are all-sky surveys and not shown here. DES: shaded red area with black outline; VHS: shaded blue area with meshes; VIKING: shaded cyan area; UHS: shaded orange area with meshes; ULAS: shaded pink area with meshes. These NIR surveys (except for 2MASS) barely overlap with each other. For areas not covered by VHS, VIKING, UHS, or ULAS, we used 2MASS NIR data.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Completeness and efficiency as a function of r-band magnitude using spectroscopically confirmed quasars in the S82 region. The filled red circles and blue squares show results with selection criteria (1)–(5), and the open orange circles and green squares are results with one more criterion, P QSO > 0.7.
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Completeness (red solid line) and efficiency (blue dashed line) as a function of P QSO for spectroscopically confirmed quasars in the S82 region (r < 21.5).
Paper: A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Authors: Qian Yang, Yue Shen
Figure caption: Distribution of i-band magnitudes for spectroscopically known quasars (black). The red, blue, and green lines are sources classified as quasars, galaxies, and stars, respectively. The y-axis is in logarithmic scale.
Paper: A Possible Chemical Clock in High-mass Star-forming Regions: N(HC3N)/N(N2H+)?
Authors: Y. X. Wang, 友鑫 汪, J. S. Zhang, 江水 张, H. Z. Yu, 鸿智 余, Y. Wang, 尧 王, Y. T. Yan, 耀庭 闫, J. L. Chen, 家梁 陈, J. Y. Zhao, 洁瑜 赵, Y. P. Zou, 益鹏 邹
Figure caption: HC3N J = 10−9 spectra (upper panel of each pair: IRAM 30 m; lower panel of each pair: ARO 12 m) of 33 sources.
Paper: A Possible Chemical Clock in High-mass Star-forming Regions: N(HC3N)/N(N2H+)?
Authors: Y. X. Wang, 友鑫 汪, J. S. Zhang, 江水 张, H. Z. Yu, 鸿智 余, Y. Wang, 尧 王, Y. T. Yan, 耀庭 闫, J. L. Chen, 家梁 陈, J. Y. Zhao, 洁瑜 赵, Y. P. Zou, 益鹏 邹
Figure caption: These panels show the comparison of line widths between HC3N (J = 10−9, 12−11, and 16−15) and N2H+ (J = 1−0). The black dashed line means that both lines have the same line width.
Paper: A Possible Chemical Clock in High-mass Star-forming Regions: N(HC3N)/N(N2H+)?
Authors: Y. X. Wang, 友鑫 汪, J. S. Zhang, 江水 张, H. Z. Yu, 鸿智 余, Y. Wang, 尧 王, Y. T. Yan, 耀庭 闫, J. L. Chen, 家梁 陈, J. Y. Zhao, 洁瑜 赵, Y. P. Zou, 益鹏 邹
Figure caption:  N(HC3N)/N(N2H+) against the heliocentric distance; no significant variation can be found between them.
Paper: A Possible Chemical Clock in High-mass Star-forming Regions: N(HC3N)/N(N2H+)?
Authors: Y. X. Wang, 友鑫 汪, J. S. Zhang, 江水 张, H. Z. Yu, 鸿智 余, Y. Wang, 尧 王, Y. T. Yan, 耀庭 闫, J. L. Chen, 家梁 陈, J. Y. Zhao, 洁瑜 赵, Y. P. Zou, 益鹏 邹
Figure caption: The correlations between N(HC3N)/N(N2H+) and other evolutionary indicators, including dust temperature, bolometric luminosity, and luminosity-to-mass ratio.
Paper: A Possible Chemical Clock in High-mass Star-forming Regions: N(HC3N)/N(N2H+)?
Authors: Y. X. Wang, 友鑫 汪, J. S. Zhang, 江水 张, H. Z. Yu, 鸿智 余, Y. Wang, 尧 王, Y. T. Yan, 耀庭 闫, J. L. Chen, 家梁 陈, J. Y. Zhao, 洁瑜 赵, Y. P. Zou, 益鹏 邹
Figure caption: The cumulative distributions for N(N2H+) (top-left panel), N(HC3N) (top right) and their ratio values (bottom right) for HMSC, HMPO, and UC H II region samples. The N(HC3N)/N(N2H+) ratio is plotted against the N(HC3N) (bottom left) and one clear evolution trend of the ratio can be found, i.e., from HMSC and HMPO to UC H II region stages (our data shown by the red circles; half-filled and empty ones for those sources where N(HC3N) was derived using data from two HC3N lines and sources with only HC3N J = 10−9 line, respectively). N(HC3N) in HMSCs and HMPOs is taken from Taniguchi et al. (2019b), N(N2H+) in HMSCs and HMPOs is derived using the N2H+ (J = 1−0) data from Taniguchi et al. (2019b) and following the procedure of Purcell et al. (2009).
Paper: Erratum: “The First CHIME/FRB Fast Radio Burst Catalog” (2021, ApJS, 257, 59)
Authors: The CHIME/FRB Collaboration, Mandana Amiri, Bridget C. Andersen, Kevin Bandura, Sabrina Berger, Mohit Bhardwaj, Michelle M. Boyce, P. J. Boyle, Charanjot Brar, Daniela Breitman, Tomas Cassanelli, Pragya Chawla, Tianyue Chen, J.-F. Cliche, Amanda Cook, Davor Cubranic, Alice P. Curtin, Meiling Deng, Matt Dobbs, Fengqiu (Adam) Dong, Gwendolyn Eadie, Mateus Fandino, Emmanuel Fonseca, B. M. Gaensler, Utkarsh Giri, Deborah C. Good, Mark Halpern, Alex S. Hill, Gary Hinshaw, Alexander Josephy, Jane F. Kaczmarek, Zarif Kader, Joseph W. Kania, Victoria M. Kaspi, T. L. Landecker, Dustin Lang, Calvin Leung, Dongzi Li, Hsiu-Hsien Lin, Kiyoshi W. Masui, Ryan Mckinven, Juan Mena-Parra, Marcus Merryfield, Bradley W. Meyers, Daniele Michilli, Nikola Milutinovic, Arash Mirhosseini, Moritz Münchmeyer, Arun Naidu, Laura Newburgh, Cherry Ng, Chitrang Patel, Ue-Li Pen, Emily Petroff, Tristan Pinsonneault-Marotte, Ziggy Pleunis, Masoud Rafiei-Ravandi, Mubdi Rahman, Scott M. Ransom, Andre Renard, Pranav Sanghavi, Paul Scholz, J. Richard Shaw, Kaitlyn Shin, Seth R. Siegel, Andrew E. Sikora, Saurabh Singh, Kendrick M. Smith, Ingrid Stairs, Chia Min Tan, S. P. Tendulkar, Keith Vanderlinde, Haochen Wang, Dallas Wulf, A. V. Zwaniga
Figure caption: Markov Chain Monte Carlo samples of the posterior distributions for the all-sky rate and cumulative fluence distribution index α model parameters. The all-sky rate is for FRBs above 5 Jy ms, a DM above 100 pc cm−3, and scattering timescales less than 10 ms at 600 MHz. Overlaid in dashed blue lines are the mean parameter values, and in dotted blue lines are the 1σ statistical uncertainty. Systematic uncertainties on the rate and α are not included in the distributions. The fluence distribution is still consistent with the Euclidean expectation of α = −3/2.