adsabs/AstroCLIMB huggingface dataset:
Paper: Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-period Transiting Exoplanets
Authors: Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Figure caption: Comparison of the young transiting planet population in our sample (color-coded by age) and the Gyr-old transiting planet population (gray contours). Young gas-rich planets appear to be puffier and “shrink” as they age, thereby populating gaps in the Gyr-old distribution, as indicated by our statistical analysis of this sample.
Paper: Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-period Transiting Exoplanets
Authors: Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Figure caption: Skye excess metric for TESS EM1’s sectors 27–39.
Paper: Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-period Transiting Exoplanets
Authors: Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Figure caption: Skye excess metric for TESS EM1’s sectors 40–55.
Paper: Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-period Transiting Exoplanets
Authors: Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Figure caption: Comparison of rms of eleanor, SPOC, and QLP light curves for the planet hosts depicting that, on average, SPOC light curves have the least amount of noise, thereby making them ideal for planet searches.
Paper: Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-period Transiting Exoplanets
Authors: Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Figure caption: Comparison of occurrence rates for sub-Neptunes and Neptunes from different surveys, with the various underlying mechanisms and their timelines depicted with dashed arrows.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Distributions of uncertainty normalized parallax difference ﹩{\rm{\Delta }}\pi /{\sigma }_{{\rm{\Delta }}}\,=({\pi }_{\mathrm{GDR}3}-{\pi }_{\mathrm{Orb}})| /\sqrt{{{\sigma }_{{\pi }_{\mathrm{GDR}3}}}^{2}+{{\sigma }_{{\pi }_{\mathrm{Orb}}}}^{2}}﹩ for 132 filtered binaries. The quantity would be expected to follow a Gaussian distribution with σ = 1 (dotted lines) if the formal parallax uncertainties were accurate. σ > 1 points toward underestimated parallax uncertainties. Solid black line shows the Gaussian fit, with σ = 2.07.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: The PZPO from various studies and the final selection in this work, plotted against the median magnitude of the objects used in each study.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Difference of GDR3 parallax minus external parallax (Δπ) plotted against magnitude, Bp − Rp, and effective wavenumber for the final selection of this work (TW, 44), VLBI (45), and HST (52), respectively.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The figure demonstrates photometry and detection results obtained from images with different levels of background noises. We can find that simple global median methods cannot adapt to complex background variations, whereas our background estimation function effectively reduces the impact. Panel (a) shows that as the background level increases, the F1-score rate decreases, but with the background estimation and removal methods, we can obtain steady results. Panel (b) shows that the background estimation method effectively reduces photometric measurement errors.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The figure demonstrates the effectiveness of our image alignment function by comparing the residual between the template and observation images under different offset levels. Compared to the unaligned case, the rms of the background is significantly reduced after alignment, achieving better subtraction results.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The process of training, validating, updating, and deploying the image quality assessment function. As shown in the figure, we first train an autoencoder using high-quality images, validate and update the model periodically, and then deploy the trained model to filter out high-quality images.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The diagram of the pipeline designed to carry out data preprocessing for the GWAC.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The process of performing parallel convolution on the template image and generating the difference image. By leveraging the parallel computing power of the GPU, the time required for convolution can be significantly reduced.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The process of obtaining the coordinate mapping. Since each pixel coordinate undergoes the same calculation, leveraging the advantages of GPU parallel computing can significantly reduce the time required for this process.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The flowchart of the source extraction algorithm based on GPU parallel computing. By leveraging the parallel computing power of the GPU, the time spent on the four stages—detection, decomposition, cleaning, and photometry—has been significantly reduced.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The residual error comparison between images aligned using SWarp and our GPU-based image alignment method shows similar processing results. Our method achieves alignment that is more than 10 times faster while maintaining processing accuracy.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: A comparison of transmitter rate vs. ﹩{\mathrm{EIRP}}_{\min }﹩ for this work with previous searches, where blue, red, green, and yellow stars correspond to searches of the L, S, C, and X bands, respectively. The three vertical lines mark the EIRP of the Arecibo S-band radar, the power level of a Kardashev Type I civilization, and the total power budget of a Kardashev Type II civilization. The results of J. E. Enriquez et al. (2017), D. C. Price et al. (2020), M. A. Garrett & A. P. V. Siemion (2023), and Y. Uno et al. (2023) are corrected for findings by C. Choza et al. (2024) that a turboseti search run with a SNR threshold of 10 in fact is only sensitive to signals with a SNR ≳ 33. Other results shown come from C. D. Tremblay & S. J. Tingay (2020), G. R. Harp et al. (2016), A. P. V. Siemion et al. (2013), P. R. Backus & Project Phoenix Team (2004), and P. Horowitz & C. Sagan (1993).
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Examples of Level 2 blocks picked up by our pipeline.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Telescope pointings of the sources containing our Level 2 blocks in the L band.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Telescope pointings of the sources containing our Level 2 blocks in the C band.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Telescope pointings of the sources containing our Level 2 blocks in the S band.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Telescope pointings of the sources containing our Level 2 blocks in the X band.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Telescope pointings of the sources containing our candidate signals.
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: Left panel: RV histogram of ASCC 19. Right panel: RV histogram of ASCC 21. The black dashed lines in each panel denote the median of each distribution. It should be noted that the RV-to-error ratios of the members of both clusters, presented in these histograms, are greater than or equal to 3. The range within three times the median error (median absolute deviation) is marked by the red box in each panel. RVs of the two clusters are from Gaia DR3.
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: Member distributions of ASCC 19 and ASCC 21 in multidimensional parameter spaces. The figure includes only members with a membership probability of 1. The members of ASCC 19 are marked by red circles or filled pentagrams, while those of ASCC 21 are marked by blue circles or filled triangles. Top left panel: distribution of the members of both clusters in a 2D celestial coordinate system. Gray bars represent the errors of α and δ. Top right panel: Parallax histograms of the member stars for both clusters. Bottom left panel: member distribution of both clusters in proper-motion space. Gray bars represent the errors of proper motion in both directions. Bottom right panel: CMD for both clusters. All member data are sourced from M. G. J. van Groeningen et al. (2023).
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: Top panel: histograms of the metallicity ([Fe/H]) of ASCC 19, with the metallicity parameters sourced from the LRS of LAMOST-DR11 (green), the MRS of LAMOST-DR11 (purple), APOGEE-DR17 (blue), and GALAH-DR4 (yellow). Bottom panel: histograms of the metallicity ([Fe/H]) of ASCC 21, with the metallicity parameters derived from the same sources: the LRS of LAMOST-DR11, the MRS of LAMOST-DR11, APOGEE-DR17, and GALAH-DR4. The colored dashed lines are Gaussian fitting profiles for the metallicities of the two clusters, with their respective mean and variance shown in the labels.
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: Left panel: CMD of ASCC 19. Right panel: CMD of ASCC 21. The solid black lines represent the isochrones fitted using the PARSEC model. The red and blue circles indicate the members of ASCC 19 and ASCC 21, respectively. All member data are sourced from M. G. J. van Groeningen et al. (2023).
Paper: Gaia-4b and 5b: Radial Velocity Confirmation of Gaia Astrometric Orbital Solutions Reveal a Massive Planet and a Brown Dwarf Orbiting Low-mass Stars
Authors: Gudmundur Stefánsson, Suvrath Mahadevan, Joshua N. Winn, Marcus L. Marcussen, Shubham Kanodia, Simon Albrecht, Evan Fitzmaurice, Onė Mikulskytė, Caleb I. Cañas, Juan I. Espinoza-Retamal, Yiri Zwart, Daniel M. Krolikowski, Andrew Hotnisky, Paul Robertson, Jaime A. Alvarado-Montes, Chad F. Bender, Cullen H. Blake, J. R. Callingham, William D. Cochran, Megan Delamer, Scott A. Diddams, Jiayin Dong, Rachel B. Fernandes, Mark R. Giovinazzi, Samuel Halverson, Jessica Libby-Roberts, Sarah E. Logsdon, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Varghese Reji, Arpita Roy, Christian Schwab, Jason T. Wright
Figure caption: Visualization of the Gaia two-body solution orbit for Gaia-4b. (a) Astrometric orbit of the photocenter after subtraction of parallax and proper motion. The black points show the positions of the photocenter on the sky corresponding to each epoch from the Gaia GOST tool, with uncertainties corresponding to the expected along-scan errors for a star of this brightness scaled by the expected nine along-scan measurements per epoch. These points are for visualization purposes only. The blue curves show 1000 random draws of the Gaia orbit. The + sign shows the barycenter, and the red square shows the position of periastron. The dashed line shows the line of nodes, and the arrow indicates the direction of the motion along the orbit. (b) Same as (a) but showing the R.A. as a function of time. The along-scan uncertainties are scaled in the direction of the R.A. (c) Same as (a) and (b), but showing the decl. as a function of time, with the along-scan uncertainties scaled accordingly.
Paper: Gaia-4b and 5b: Radial Velocity Confirmation of Gaia Astrometric Orbital Solutions Reveal a Massive Planet and a Brown Dwarf Orbiting Low-mass Stars
Authors: Gudmundur Stefánsson, Suvrath Mahadevan, Joshua N. Winn, Marcus L. Marcussen, Shubham Kanodia, Simon Albrecht, Evan Fitzmaurice, Onė Mikulskytė, Caleb I. Cañas, Juan I. Espinoza-Retamal, Yiri Zwart, Daniel M. Krolikowski, Andrew Hotnisky, Paul Robertson, Jaime A. Alvarado-Montes, Chad F. Bender, Cullen H. Blake, J. R. Callingham, William D. Cochran, Megan Delamer, Scott A. Diddams, Jiayin Dong, Rachel B. Fernandes, Mark R. Giovinazzi, Samuel Halverson, Jessica Libby-Roberts, Sarah E. Logsdon, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Varghese Reji, Arpita Roy, Christian Schwab, Jason T. Wright
Figure caption: Corner plot for Gaia-5b showing parameters from Gaia-only sampling in red, RV-only sampling in blue, and Gaia+RV sampling in black. For the Gaia-only values, we add 180° to both ω and Ω, to bring them into agreement with the RV-derived values, breaking the known degeneracy in the argument of periastron and longitude of ascending node in the Gaia astrometric solutions. The parameters listed at the top of the 1D histograms are the 16th, 50th, and 84th percentile values for the Gaia+RV joint sampling posteriors. The 2D regions show the 1 and 2σ contours. We see that all parameters are in good agreement.
Paper: Gaia-4b and 5b: Radial Velocity Confirmation of Gaia Astrometric Orbital Solutions Reveal a Massive Planet and a Brown Dwarf Orbiting Low-mass Stars
Authors: Gudmundur Stefánsson, Suvrath Mahadevan, Joshua N. Winn, Marcus L. Marcussen, Shubham Kanodia, Simon Albrecht, Evan Fitzmaurice, Onė Mikulskytė, Caleb I. Cañas, Juan I. Espinoza-Retamal, Yiri Zwart, Daniel M. Krolikowski, Andrew Hotnisky, Paul Robertson, Jaime A. Alvarado-Montes, Chad F. Bender, Cullen H. Blake, J. R. Callingham, William D. Cochran, Megan Delamer, Scott A. Diddams, Jiayin Dong, Rachel B. Fernandes, Mark R. Giovinazzi, Samuel Halverson, Jessica Libby-Roberts, Sarah E. Logsdon, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Varghese Reji, Arpita Roy, Christian Schwab, Jason T. Wright
Figure caption: Corner plot for Gaia-4b showing parameters from Gaia-only sampling in red, RV-only sampling in blue, and Gaia+RV sampling in black. The parameters listed at the top of the 1D histograms are the 16th, 50th, and 84th percentile values for the Gaia+RV joint sampling posteriors. The 2D regions show the 1 and 2σ contours. We see that all parameters are in good agreement. The orbital period in the RV-only fit shows broad posteriors, likely due to only just covering a single orbital period of the system.
Paper: Gaia-4b and 5b: Radial Velocity Confirmation of Gaia Astrometric Orbital Solutions Reveal a Massive Planet and a Brown Dwarf Orbiting Low-mass Stars
Authors: Gudmundur Stefánsson, Suvrath Mahadevan, Joshua N. Winn, Marcus L. Marcussen, Shubham Kanodia, Simon Albrecht, Evan Fitzmaurice, Onė Mikulskytė, Caleb I. Cañas, Juan I. Espinoza-Retamal, Yiri Zwart, Daniel M. Krolikowski, Andrew Hotnisky, Paul Robertson, Jaime A. Alvarado-Montes, Chad F. Bender, Cullen H. Blake, J. R. Callingham, William D. Cochran, Megan Delamer, Scott A. Diddams, Jiayin Dong, Rachel B. Fernandes, Mark R. Giovinazzi, Samuel Halverson, Jessica Libby-Roberts, Sarah E. Logsdon, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Varghese Reji, Arpita Roy, Christian Schwab, Jason T. Wright
Figure caption: Same as Figure 9 but for Gaia-5b.
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: For different groups of hot Jupiters with Teq within different ranges, the bottom panel shows the values of γ (Rplanet, Tstar) for the power-law relationship between the effective temperature of their host star and their planetary radii, fitted using our two-level, two-parameter hierarchical model. Each blue point represents the mean Teq for a group of hot Jupiters along with the fitted mean value of γ (Rplanet, Tstar) for that group. The error bars along the γ (Rplanet, Tstar) axis represent the fitted 1σ locations, while the error bars along the Teq axis indicate the distribution range of Teq within each group. In the middle panel, each blue point and error bar show the mean effective temperature of the host stars for that group and the corresponding distribution range. The top panel displays the heating-efficiency curves for planets with varying Teq, as derived by D. P. Thorngren & J. J. Fortney (2018) and P. Sarkis et al. (2021).
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: The left panel displays the Teq of the hot Jupiters in the eight separate bins plotted against the effective temperatures of their host stars. The right panel shows the distribution of planetary masses for hot Jupiters across each of the eight bins, indicating that the masses of hot Jupiters with ultrahigh Teq are significantly larger.
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: The fitted values of γ (Rplanet, Tstar) for eight groups of hot Jupiters created using a separate-binning approach. The red dots represent the mean values of Teq along with the fitted mean values of γ (Rplanet, Tstar) for each group. The gray rectangles indicate the distribution ranges of Teq for each group of hot Jupiters in the horizontal direction, while in the vertical direction, they reflect the fitted 1σ ranges of γ (Rplanet, Tstar). The vertical labels denote the number of hot Jupiters in each group.
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: The flux–radius distributions of hot Jupiters (blue points) orbiting stars with varying effective temperatures, along with the fitted power-law relationships (red lines) derived from the two-level, two-parameter hierarchical models, are displayed. The red dashed lines indicate the 1σ regions of the relationships. The fitted power-law indices, γ (Rplanet, flux), for hot Jupiters orbiting host stars with effective temperatures in the ranges of 6500–7500 K, 5200–6000 K, and 3500–5200 K are 0.13﹩{}_{-0.01}^{+0.01}﹩, 0.10﹩{}_{-0.01}^{+0.01}﹩, and 0.08﹩{}_{-0.02}^{+0.03}﹩, respectively. The incident flux has been normalized to 100 times the amount received by the Earth.
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: The left panel displays the mass–radius distribution of hot Jupiters, with mass ranging from 0.5 to 5.0 MJupiter, along with the power-law relationship fitted using the two-level, two-parameter hierarchical models. The blue dots represent the hot Jupiters. The red line indicates the fitted power-law relationship, and the red dashed lines depict the 1σ regions of this relationship. The fitted power-law index, γ (Rplanet, Mplanet), is precisely equal to 0.00﹩{}_{-0.01}^{+0.01}﹩. The right panel shows the corner plot derived from parallel-tempering MCMC sampling.
Paper: The Heating Efficiency of Hot Jupiters from a Data-driven Perspective
Authors: Sheng Jin, Dong-Hong Wu, Yi-Xuan Cao, Zi-Yi Guo
Figure caption: A grid test was performed to evaluate the fitting of the two-level, two-parameter hierarchical model, using simulated data generated by various combinations of σy and ﹩{\sigma }_{{y}_{{\rm{obs}}}}﹩. In each panel, the blue dots represent the simulated data points, while the red line shows the power-law relationship given by the fitted values of log(C) and γ (y, x). The red dotted lines denote the 1σ regions of the power-law relationship, computed from the fitted σy.