adsabs/AstroCLIMB huggingface dataset:
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Distributions of host star Teff (top) and distance (bottom) across all EEC survey simulations for EECs that could be probed for Earth levels of O2 within a 10 yr survey. 85% of the detectable simulated planets orbit M dwarfs, with a peak near spectral type M3. About three-quarters of the detectable simulated planets are within 13 pc of the Sun. The dashed–dotted blue lines show the distribution of all bright (GRP < 10) stars within 20 pc, which highlights that the GMT and ELT will be insensitive to O2 in the atmospheres of EECs orbiting stars above ∼5000 K, and is most sensitive to stars within about 13 pc.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: The inner working angles at 2λ/D for 0.6–2 μm are plotted for an ∼6 m JWST or anticipated HWO diameter aperture (orange solid line), the GMT (blue solid line), the TMT (green solid line), and the ELT (yellow solid line). The wavelengths of H2O (blue dashed), O2 (green dashed–dotted), CH4 (orange dotted), CO2 (pink dashed), and CO (black dotted) for this wavelength region are plotted as vertical lines. The plot considers distances of 5 pc (left) and 10 pc (right). The shaded horizontal regions show the conservative habitable zones for representative G2 V through M8 V stars. If a habitable zone region falls above the inner working angle of a telescope, an exoplanet and its biosignatures should be observable at that wavelength range. For example, below 1 μm, the GMT should be able to observe O2, CH4, and H2O for planets in the habitable zones of a hypothetical M4 V star at 5 pc. The GMT will only be able to access biosignatures between 1 and 2 μm if planets fall in certain regions of the habitable zone of this hypothetical star, and it will not be able to access biosignatures beyond 2 μm.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Simulation results for a survey of Earth-like concentrations of O2 at S/N = 5 for five known, and one candidate (LTT 1445 A d), habitable zone exoplanets within 20 pc with the ELT (blue) and the GMT (orange). The ELT and GMT should both be able to probe for O2 on Proxima Centauri b in less than a month. LTT 1445 A d is the only planet in the simulation for which Earth-like levels of O2 could not be probed with the GMT, but it could be probed with the ELT. More precise planet and orbital parameters would minimize the uncertainties on these observing simulations. The minimum mass (﹩{M}_{\oplus }\sin i﹩) of each planet is given above each relatively sized point (Anglada-Escudé et al. 2013; N. Astudillo-Defru et al. 2017; X. Bonfils et al. 2018; J. P. Faria et al. 2022; B. Lavie et al. 2023).
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Simulation results for a survey of Earth-like concentrations of O2 at S/N = 5 on EECs within 20 pc with the ELT (blue dots) and the GMT (orange stars). A survey of the same duration on both telescopes would allow the ELT to probe about 3 times more planets for O2 than the GMT. The large error bars are driven by the wide range of possible orbital parameters for our simulated exoplanets.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Simulation results for a survey of Earth-like concentrations of O2 at S/N = 5 on habitable zone Earth and super-Earth-sized candidates (0.8S0.25 < Rp < 1.8 R⊕) within 20 pc with the ELT (blue dots) and the GMT (orange stars). Similar to the exo-Earth simulation in Figure 4, the ELT could probe about 3 times more planets than the GMT. Overall, a super-Earth survey could probe about 1.5 times more planets than a survey of planets up to 1.4 R⊕.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Statistical power of the habitable zone oxygen hypothesis test on the ELT for different values of η⊕ and different fractions of EECs (left) and fractions of ESECs (right) with Earth-like levels of O2. Statistical power contours for 50% and 95% are shown in black. More planets with oxygen and/or higher values for η⊕ result in a greater ability to test this hypothesis, however, the inclusion of all super-Earths does not necessarily improve this ability. We note that these figures are supersampled to highlight differences.
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Simulation results for a survey of Earth-like O2 levels at S/N = 5 of EECs within 20 pc with different AO control systems and observing conditions for the GMT (upper left), additionally accounting for different deformable mirror actuator counts on the ELT (upper right), and considering an optimized setup for both the GMT and ELT but assessing the differences between R = 100,000 and R = 500,000 (lower panel).
Paper: Bioverse: Giant Magellan Telescope and Extremely Large Telescope Direct Imaging and High-resolution Spectroscopy Assessment—Surveying Exo-Earth O2 and Testing the Habitable Zone Oxygen Hypothesis
Authors: Kevin K. Hardegree-Ullman, Dániel Apai, Sebastiaan Y. Haffert, Martin Schlecker, Markus Kasper, Jens Kammerer, Kevin Wagner
Figure caption: Statistical power of the habitable zone oxygen hypothesis test on the ELT for different values of maximum sampled planet size and different fractions of planet candidates with Earth-like levels of O2. Statistical power contours for 50% and 95% are shown in black. The maximum sampled planet size has a minor effect on the statistical power between ∼1.2 R⊕ and ∼1.8 R⊕. We note that this figure is supersampled.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Evolution of categorical and mean accuracy metrics as a function of the percentage of the microlensing alert data presented to the model. The left-side panels display individual categorical accuracies for the binary (top) and multiclass (bottom) classification tasks across training (Tr.), validation (Vl.), and test (Ts.) sets. The right-side panels show the corresponding mean accuracy for each set, computed by averaging the individual categorical accuracies. The model's performance improves steadily as it is exposed to more of the microlensing event, with sharp gains in the binary task observed between 0% and 40% visibility, while the multiclass task shows its most significant improvement between 60% and 100%. At 100% visibility, the models achieve peak accuracies of ~85% for the binary task and ~78% for the multiclass task, demonstrating the model's ability to effectively leverage full alert visibility in real-world scenarios.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Accuracy over different thresholds of the output neuron for the binary classification task. The horizontal axis represents the threshold applied to the model's output neuron, while the vertical axis shows the corresponding accuracy. Green curves denote the accuracy of the “No” category across the training, validation, and test sets, while red curves represent the accuracy for the “Yes”/“Maybe” category. Brown curves show the average accuracy across both categories, starting and ending at 50%. The peak of the average accuracy, observed at a threshold of approximately 0.45, indicates the optimal tradeoff between correctly classifying instances in both categories. However, operationally, a higher threshold is chosen to prioritize the purity of “Yes”/“Maybe” predictions, minimizing false positives despite a higher “No” classification rate. The vertical dashed lines mark the two thresholds utilized in the system, 0.45 for optimality and 0.955 for “Yes”/“Maybe” purity.
Paper: LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Confusion matrices of LensNet calculated using the nonaugmented data, showing the performance in the binary classification task (with the two threshold values discussed of 0.45 and 0.955) and in the three-class classification task. The accuracy rates for both tasks are displayed on the matrices for each set (training, validation, and testing). These results highlight the model's overall performance.
Paper: Giant Molecular Clouds in RCW 106 (G333): Galactic Mini-starbursts and Massive Star Formation Induced by Supersonic Cloud–Cloud Collisions
Authors: Mikito Kohno, 樹人 河野, Rin I. Yamada, 麟 山田, Kengo Tachihara, 研悟 立原, Shinji Fujita, 真司 藤田, Rei Enokiya, 玲依 榎谷, Kazuki Tokuda, 一起 徳田, Asao Habe, 朝男 羽部, Hidetoshi Sano, 栄俊 佐野, Takahiro Hayakawa, 貴敬 早川, Fumika Demachi, 史夏 出町, Takuto Ito, 拓冬 伊藤, Kisetsu Tsuge, 紀節 柘植, Atsushi Nishimura, 淳 西村, Masato I. N. Kobayashi, 将人 小林, Hiroaki Yamamoto, 宏昭 山本, Yasuo Fukui, 康雄 福井
Figure caption: The two-color composite image of the 12CO J = 1–0 integrated intensity distributions obtained by Mopra. (a) The blue and red image represent −72 km s−1 MCEAST and −54 km s−1 MCEAST, respectively. The integrated velocity ranges and yellow crosses are the same as Figure 8. The lowest contour levels and intervals are 10 K km s−1 and 5 K km s−1 in −72 km s−1 MCEAST, 50 K km s−1 and 40 K km s−1 in −54 km s−1 MCEAST, respectively. (b) The blue and red image represent −64 km s−1 MCWEST and −54 km s−1 MCWEST obtained by Mopra, respectively. The integrated velocity ranges and yellow cross are the same as Figure 10. The lowest contour levels and intervals are 30 K km s−1 and 30 K km s−1 in −64 km s−1 MCWEST, 10 K km s−1 and 10 K km s−1 in −54 km s−1 MCWEST, respectively. The green circles of A, B, C, and D show the position of spectra in Figures 7(a), and (b), Figures 9(a), and (b), respectively.
Paper: Giant Molecular Clouds in RCW 106 (G333): Galactic Mini-starbursts and Massive Star Formation Induced by Supersonic Cloud–Cloud Collisions
Authors: Mikito Kohno, 樹人 河野, Rin I. Yamada, 麟 山田, Kengo Tachihara, 研悟 立原, Shinji Fujita, 真司 藤田, Rei Enokiya, 玲依 榎谷, Kazuki Tokuda, 一起 徳田, Asao Habe, 朝男 羽部, Hidetoshi Sano, 栄俊 佐野, Takahiro Hayakawa, 貴敬 早川, Fumika Demachi, 史夏 出町, Takuto Ito, 拓冬 伊藤, Kisetsu Tsuge, 紀節 柘植, Atsushi Nishimura, 淳 西村, Masato I. N. Kobayashi, 将人 小林, Hiroaki Yamamoto, 宏昭 山本, Yasuo Fukui, 康雄 福井
Figure caption: Detailed 12CO J = 1–0 longitude–velocity diagrams of (a) RCW 106 East and (b) RCW 106 West. The integrated latitude ranges of (a) and (b) are from −0﹩\mathop{.}\limits^{\unicode{x000B0}}﹩43 to −0﹩\mathop{.}\limits^{\unicode{x000B0}}﹩38 and from −0﹩\mathop{.}\limits^{\unicode{x000B0}}﹩41 to −0﹩\mathop{.}\limits^{\unicode{x000B0}}﹩34, respectively. The lowest contour levels of (a) and (b) are 0.09 K degree and 0.15 K degree. The contour intervals of (a) and (b) are 0.08 K degree and 0.15 K degree. The red-dotted rectangles and yellow arrows show the bridge features connecting two velocity components.
Paper: Giant Molecular Clouds in RCW 106 (G333): Galactic Mini-starbursts and Massive Star Formation Induced by Supersonic Cloud–Cloud Collisions
Authors: Mikito Kohno, 樹人 河野, Rin I. Yamada, 麟 山田, Kengo Tachihara, 研悟 立原, Shinji Fujita, 真司 藤田, Rei Enokiya, 玲依 榎谷, Kazuki Tokuda, 一起 徳田, Asao Habe, 朝男 羽部, Hidetoshi Sano, 栄俊 佐野, Takahiro Hayakawa, 貴敬 早川, Fumika Demachi, 史夏 出町, Takuto Ito, 拓冬 伊藤, Kisetsu Tsuge, 紀節 柘植, Atsushi Nishimura, 淳 西村, Masato I. N. Kobayashi, 将人 小林, Hiroaki Yamamoto, 宏昭 山本, Yasuo Fukui, 康雄 福井
Figure caption: (a) The location at the RCW 106 GMC complex superposed on the face-on illustration of the Milky Way (NASA/JPL-Caltech/ESO/R. Hurt). The distance to the Galactic center (R0) is adopted from the Very Long Baseline Interferometry (VLBI) astrometry results (M. J. Reid et al. 2019; VERA Collaboration et al. 2020). (b) Schematic illustration of our proposed massive star/cluster formation scenario in the RCW 106 GMC complex.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Two examples of the Gaussianity test performed in Figure 16. The top row shows examples of the residuals obtained with PCA and 4S. The pixels outside (2.5–4.5)λ/ were masked to extract the noise. Q–Q plots are shown in the bottom row. If the noise were perfectly Gaussian, we would expect all values to be on the gray diagonal line and the value of R2 to be close to 1.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Comparison of the Gaussianity of the noise in the residuals of 4S and PCA for data sets #1–#9. If the coefficient of determination R2 is closer to 1, the noise is better explained by Gaussian noise. We choose the number of principal components K and the regularization strength λ of 4S that maximize the contrast (optimal PCA).
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Results from the updated orbit fit of AF Lep b. The corner diagram reports the posterior distribution of an MCMC-based orbit fit using orvara. The median with the 16% and 84% quantiles is reported. The top right inset shows all available relative astrometry measurements with the best orbital fit. To illustrate the confidence of the fit, 100 orbits are drawn from the fit posterior (gray). For a summary of all results, see Table 2.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: PCA saliency maps for different numbers of principal components K and separations from the star. The saliency maps show which information from the science frames is used by PCA to estimate the noise at the position marked by the white circle. Especially for large K and separations close to the star, only the information within the white circle is used, resulting in a substantial loss of the planet signal. The red star marks the center of the frame.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Verification of the astrometry and photometry estimated with the help of PCA. The top two panels show the residuals of 4S for AF Lep b (left) and after insertion of a negative fake planet (right) based on the results given in Table 2. In the middle row, fake planet residuals are shown that are inserted into the data at the same separation and contrast as AF Lep b, but at different position angles. The bottom panel summarizes the S/N values of the 40 fake planet experiments described in the text.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Residual images of the AF Lep data set shown in Figure 10 for several different settings of the algorithm’s hyperparameters (principal components K for PCA and regularization strength λ for 4S). The planet AF Lep b is barely visible in the PCA residuals. For 4S, any choice of parameters yields a clear detection, highlighting the robustness of the method.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Illustration of the noise estimation implemented by the linear postprocessing algorithms LOCI, PCA, and 4S. The noise estimate ﹩{\hat{x}}_{t,l}﹩ at time t and spatial position l is a weighted sum of the values marked in orange, weighted by the parameters in dark red. The dotted circle marks the position of the mask. PCA can be interpreted in two ways, as explained in the text.
Paper: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011
Authors: Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz
Figure caption: Comparison of the saliency maps of the masked version of PCA and 4S. The number of components for PCA was selected according to the best contrast performance at 2.5λ/D, which is reached for K = 90. The white plus sign represents the position for which the saliency map is calculated. The red star marks the center of the frame.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Weighted mean PZPO plotted against the number of the final selection and remaining binaries. At each step, the source with the largest absolute value of the parallax difference is removed. The gray dashed line represents zero value.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Parallax difference of GDR3 parallax minus orbital parallax for 132 filtered binaries, plotted against magnitude, Bp − Rp color, and RUWE. Black circles show the parallax differences of individual filtered sources.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Orbital parallax for the “good” binaries (93) plotted against GDR3 parallax. The size of each data point is proportional to the semimajor axis, while the color of the points corresponds to the orbital period.
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Histogram of the magnitude for binaries from TW (249), G23 (186), and overlapped sample (157).
Paper: Analysis of the Gaia Data Release 3 Parallax Bias at Bright Magnitudes
Authors: Ye Ding, Shilong Liao, Shangyu Wen, Zhaoxiang Qi
Figure caption: Map of the difference (Δπ) of GDR3 parallax minus external parallax in Galactic coordinate for the filtered samples from this work (TW, 132), VLBI (64), and HST (55). The dotted lines represent the Galactic latitude ±20°.
Paper: Deep Optical Images of the Ejecta Nebula around the Wolf–Rayet Star WR 8 (HD 62910)
Authors: Robert A. Fesen, Daniel Patnaude, Wei-Hao Wang, You-Hua Chu, Jason Sun, Manuel C. Peitsch, Martin Pugh, Scott Garrod, Michael Selby, Alex Woronow
Figure caption: Hα and [O III] images of the WR 8 nebula showing a comparison of the nebula’s relative brightness in these line emissions. The broad band of diffuse emission seen in the Hα image is unrelated background/foreground ISM emission. North is up, east to the left.
Paper: Deep Optical Images of the Ejecta Nebula around the Wolf–Rayet Star WR 8 (HD 62910)
Authors: Robert A. Fesen, Daniel Patnaude, Wei-Hao Wang, You-Hua Chu, Jason Sun, Manuel C. Peitsch, Martin Pugh, Scott Garrod, Michael Selby, Alex Woronow
Figure caption: Discovery Hα image showing faint nebulosity around and in front of WR 8 (from D. J. Stock & M. J. Barlow 2010).