adsabs/AstroCLIMB huggingface dataset:
Paper: The Role of High-frequency Transverse Oscillations in Coronal Heating
Authors: Daye Lim, Tom Van Doorsselaere, David Berghmans, Richard J. Morton, Vaibhav Pant, Sudip Mandal
Figure caption: The distribution, s(ω), of spectral energy fluxes as a function of oscillation frequencies (top panels) and the number of oscillations for each frequency bin (bottom panels). The vertical bars show an uncertainty ﹩{\sigma }_{\omega }={\sigma }_{{F}_{i}}\sqrt{{n}_{{\omega }_{i}}}﹩). A bin size of ﹩\overline{\omega }=0.1﹩ has been considered. The best fits of distributions are shown in dashed lines. The fitting was only considered for bins with a number greater than 1 of cases. Blue, red, and black colors represent observations from AIA, EUI, and both of them, respectively. The power-law slopes are ﹩{\delta }_{\mathrm{AIA}}={0.75}_{-0.55}^{+0.56}﹩, ﹩{\delta }_{\mathrm{EUI}}=-{1.84}_{-0.39}^{+0.38}﹩, and ﹩{\delta }_{\mathrm{ALL}}=-{1.40}_{-0.33}^{+0.33}﹩, respectively.
Paper: The Role of High-frequency Transverse Oscillations in Coronal Heating
Authors: Daye Lim, Tom Van Doorsselaere, David Berghmans, Richard J. Morton, Vaibhav Pant, Sudip Mandal
Figure caption: Marginal posterior distributions of the power-law index and constant of the best-fitting shown in Figures 1 and 2. Each panel corresponds to the energy flux–frequency distribution (top), the number–energy flux distribution (middle), and the number–energy distribution (bottom). Solid lines show a maximum a posterior probability that we use as the slope results and dashed lines show 95% credible intervals.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Limb-darkening and brightness temperature comparison of MWR observations and forward model results at 600 MHz and 1.2 GHz for metallicities ranging from 0 to −7 at different ammonia vapor concentration profiles and opacities considering T 1bar = 175 K.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Pressure vs. temperature difference in the temperatures of Jovian adiabats constructed using T 1bar = 168.8 K and T 1bar = 166.1 K.
Paper: A Black Hole Kicked at Birth: MAXI J1305-704
Authors: Chase Kimball, Sam Imperato, Vicky Kalogera, Kyle A. Rocha, Zoheyr Doctor, Jeff J. Andrews, Aaron Dotter, Emmanouil Zapartas, Simone S. Bavera, Konstantinos Kovlakas, Tassos Fragos, Philipp M. Srivastava, Devina Misra, Meng Sun, Zepei Xing
Figure caption: Here we plot the final marginalized posterior over pre-SN primary mass and kick velocity, with the final, combined weights. The dotted, dashed, and solid contours enclose central 68%, 90%, and 99% probabilities.
Paper: The Luminosity–Area Relation of z > 2 Quasars’ Lyα Nebulae
Authors: Fabrizio Arrigoni Battaia, Aura Obreja, Tiago Costa, Emanuele P. Farina, Zheng Cai
Figure caption: Linear regressions for the case with comoving areas. The linear regression through all the observations (solid black line, black circles) and through the observations classified as inliers by MCD (dashed–dotted blue line, blue stars) for the reference SB threshold (top), and for a 2× (middle) and 4× (bottom) higher SB threshold. The red circles are the MCD outliers. The legends list the obtained slopes.
Paper: The Luminosity–Area Relation of z > 2 Quasars’ Lyα Nebulae
Authors: Fabrizio Arrigoni Battaia, Aura Obreja, Tiago Costa, Emanuele P. Farina, Zheng Cai
Figure caption: The linear regression for the whole sample with its rms (black line with shaded region) is compared to the simulated data (stars; Costa et al. 2022). Each of the simulated data points, color coded following their nebula asymmetry, represents a different host-galaxy inclination i, and we vary their sizes as a function of ﹩\cos (i)﹩. The inset zooms on the simulated data points, with three of them highlighted by their i.
Paper: The Luminosity–Area Relation of z > 2 Quasars’ Lyα Nebulae
Authors: Fabrizio Arrigoni Battaia, Aura Obreja, Tiago Costa, Emanuele P. Farina, Zheng Cai
Figure caption: Top: luminosity vs. physical (left), normalized (center), and comoving (right) area of the QSOs’ Lyα nebulae. The data points are color coded following the asymmetry η. Small magenta dots indicate radio-loud quasars. The error bar in the bottom right corner of the left plot indicates the median error of about 10% and 4% on area and luminosity, respectively. Center: same as the top panels, but with data points color coded following the QSOs bolometric luminosity L bol. Bottom: Lyα SB maps (FAB19) of three nebulae with QSOs having L bol ∼ 1047.5 erg s−1. The IDs from QSO MUSEUM are indicated in the lower-right corner. The reference isophote for the current analysis is highlighted in yellow. The white circle denotes the virial radius for a DM halo of M DM = 1012.5 M ⊙ at z = 3 (R vir ∼ 107 kpc). All of the information used in this article for the reference luminosity–area relation is available as data behind the figure.(The data used to create this figure are available.)
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Comparisons are drawn between the Juno MWR observations and the results of the radiative transfer model for T  b  and L  d  at 0.6 GHz and 1.2 GHz, keeping the water abundance constant at ∼ five times solar abundance. (a) and (b) Jupiter’s atmosphere in the absence of alkali metals with only variations in the NH3 vapor profile and the NH3 opacity. (c) and (d) Jupiter’s atmosphere in the presence of alkali metals with variations in the NH3 vapor profile and the NH3 opacity. The NH3 profile of Jupiter’s atmosphere is varied using a scale from 0 to 1.5, to take into account the contribution of the nonuniform distribution of NH3 vapor observed by MWR (Li et al. 2017). The NH3 opacity at temperatures above 750 K undergoes power-law scaling as a function of atmospheric temperature (Equation (7)). In the absence of alkali metals, the changes in the NH3 vapor profile and the scaling in the NH3 vapor opacity deviate significantly from the Juno MWR observations at 0.6 GHz. However, in the presence of alkali metals of low metallicity, i.e., in the range of −2 to −5, there is an agreement between the model results and MWR observations. Observations at 1.2 GHz can be explained by variations in the NH3 vapor profile and the NH3 opacity independent of the opacity contributions from alkali metals.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Relative weighting functions at 0.6 GHz (black) and 1.2 GHz (gray) for a Jupiter adiabat considering the Hanley model (Hanley et al. 2009) for NH3 absorption. The functions peak at 100 bar and 30 bar at 0.6 GHz and 1.2 GHz, respectively, without the inclusion of alkali metals. The inclusion of alkali metals (orange, magenta, and blue lines) decreases the relative weighting function at ∼100 bar and produces a second peak that is observed at ∼1 kbar pressure, due to the opacity contributed by free electrons from alkali metal ionization. As the metallicity of the alkali metals increases, the global maximum of the weighting function shifts toward a lower pressure.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Condensation curves of NH3, H2O, H2S, and the alkali metals Na2S and KCl at solar abundance. Our calculations are based on the ECCM (Atreya et al. 1999) and saturation vapor pressure corresponding to Na2S and KCl (Visscher et al. 2006; Morley et al. 2012). The cloud bases are at the levels where the condensation curves cross the adiabat considering T 1bar = 166.1 K (Seiff et al. 1998).
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Limb-darkening and brightness temperature MWR observations compared with simulation results at 0.6 and 1.2 GHz, corresponding to Jovian adiabats at (i) T 1bar = 166.1 K and (ii) T 1bar = 168.8 K. (a) T  b  vs. latitude at 0.6 GHz. (b) L  d  vs. latitude at 0.6 GHz. (c) T  b  vs. latitude at 1.2 GHz. (d) L  d  vs. latitude at 1.2 GHz.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: (a) Electron density of Jupiter’s deep atmosphere at the solar abundance and [M/H] = −3 and −4. (b) Electrical conductivity of Jupiter’s deep atmosphere at the solar abundance and [M/H] = −3 and −4.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Limb-darkening and brightness temperature comparison of MWR observations and forward model results at 600 MHz and 1.2 GHz for metallicities ranging from 0 to −7 at different ammonia vapor concentration profiles and opacities. This showcases the sole effect of free electrons due to the ionization of Na, without considering any contribution from K.
Paper: Highly Depleted Alkali Metals in Jupiter’s Deep Atmosphere
Authors: Ananyo Bhattacharya, Cheng Li, Sushil K. Atreya, Paul G. Steffes, Steven M. Levin, Scott J. Bolton, Tristan Guillot, Pranika Gupta, Andrew P. Ingersoll, Jonathan I. Lunine, Glenn S. Orton, Fabiano A. Oyafuso, J. Hunter Waite, Amadeo Bellotti, Michael H. Wong
Figure caption: Latitudinal variation of the brightness temperature and limb darkening of Jupiter’s atmosphere at 0.6 GHz and 1.2 GHz at [M/H] = −3.
Paper: A Black Hole Kicked at Birth: MAXI J1305-704
Authors: Chase Kimball, Sam Imperato, Vicky Kalogera, Kyle A. Rocha, Zoheyr Doctor, Jeff J. Andrews, Aaron Dotter, Emmanouil Zapartas, Simone S. Bavera, Konstantinos Kovlakas, Tassos Fragos, Philipp M. Srivastava, Devina Misra, Meng Sun, Zepei Xing
Figure caption: Here we plot the possible kinematic histories of J1305. In blue we plot the trajectory corresponding to taking the median observed position and proper motion as initial conditions. In gray we draw random initial conditions from the corresponding posteriors. In the top two panels, we plot its trajectory in the X–Y and X–Z planes in Galactocentric coordinates. In the bottom panel, we plot a segment of the peculiar velocity as a function of time before the present. The rotated histogram on the right is the distribution of potential peculiar velocities at birth, inferred by sampling the trajectory at crossings of the Galactic plane that coincide (to within 10 Myr) with donor ages from our successful RLO2 binaries.
Paper: A Black Hole Kicked at Birth: MAXI J1305-704
Authors: Chase Kimball, Sam Imperato, Vicky Kalogera, Kyle A. Rocha, Zoheyr Doctor, Jeff J. Andrews, Aaron Dotter, Emmanouil Zapartas, Simone S. Bavera, Konstantinos Kovlakas, Tassos Fragos, Philipp M. Srivastava, Devina Misra, Meng Sun, Zepei Xing
Figure caption: Posteriors over parameters and kicks of the potential progenitor binaries just before and after SN. In blue we plot the unweighted posteriors. In green, orange, purple, and red we weight these posteriors individually according to the observed V pec,birth,obs, M 1,obs, q obs, and T eff,obs. In black we plot our final result, weighted by the product of the individual weights.
Paper: A Black Hole Kicked at Birth: MAXI J1305-704
Authors: Chase Kimball, Sam Imperato, Vicky Kalogera, Kyle A. Rocha, Zoheyr Doctor, Jeff J. Andrews, Aaron Dotter, Emmanouil Zapartas, Simone S. Bavera, Konstantinos Kovlakas, Tassos Fragos, Philipp M. Srivastava, Devina Misra, Meng Sun, Zepei Xing
Figure caption: The viable RLO2 parameter space explored with MESA. The colored shapes mark successful binaries (at different BH masses) that evolved to simultaneously satisfy all observational constraints on the BH and donor masses, donor effective temperature, and orbital period of J1305 to within 2σ. In black are unsuccessful binaries where magnetic braking was efficient, shrinking their periods throughout MT. In gray are unsuccessful binaries above the bifurcation period, expanding during MT.
Paper: A Black Hole Kicked at Birth: MAXI J1305-704
Authors: Chase Kimball, Sam Imperato, Vicky Kalogera, Kyle A. Rocha, Zoheyr Doctor, Jeff J. Andrews, Aaron Dotter, Emmanouil Zapartas, Simone S. Bavera, Konstantinos Kovlakas, Tassos Fragos, Philipp M. Srivastava, Devina Misra, Meng Sun, Zepei Xing
Figure caption: Example of a successful RLO2 system. The 1.8 M ⊙ donor fills its RL at a period of 0.75 days with an 8 M ⊙ BH. The orange line denotes the age of the donor at the observed period. The green shaded regions mark the 2σ confidence intervals on the observed BH mass, mass ratio, and donor effective temperature.
Paper: SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications
Authors: Bingjie Wang, 冰洁 王, Joel Leja, V. Ashley Villar, Joshua S. Speagle, 佳士 沈
Figure caption: Here, we apply SBI ++ to JWST observations and compare its photometric redshift recovery to NS. (a) The scatter plot shows the measurement uncertainties as a function of magnitudes in the F444W band. The full sample is shown in gray, with the spectroscopic subsample highlighted in purple. The red shades indicate our toy noise model, while the blue shades indicates a “wider” noise model that encapsulates more data points. (b) Inferred photometric redshifts are plotted against the spectroscopic redshifts, which we take to be the truths. NS results are shown in black, while SBI ++ results are shown in red. Interestingly, SBI ++ appears to produce more realistic uncertainties. (c) The histograms show the residuals in the inferred redshifts. NS and SBI ++ exhibit comparable performance. However, the noisy SBI model, shown in blue, results in a scatter about twice as large as those of the other two methods.
Paper: SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications
Authors: Bingjie Wang, 冰洁 王, Joel Leja, V. Ashley Villar, Joshua S. Speagle, 佳士 沈
Figure caption: Here, we show the performance of the baseline SBI. (a) Runtime as a function of the number of posterior samples drawn in each SBI call. The dotted–dashed line shows the exponential fit to the data, whereas the dotted line shows the power-law fit in Equation (8). (b) Predictive-probability plot comparing the cumulative distributions of redshifts. The nested sampling result is shown in gray, while the SBI result is shown in red. Both lines follow along the diagonal, meaning both models are well calibrated. (c) Contours showing the median of the redshift posteriors as a function of true redshifts. Interestingly, the baseline SBI infers redshifts more accurately than NS. This suggests that the standard SBI performs exceptionally well on data closely following the training set distribution.
Paper: SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications
Authors: Bingjie Wang, 冰洁 王, Joel Leja, V. Ashley Villar, Joshua S. Speagle, 佳士 沈
Figure caption: The two panels on the left illustrate the changes in SBI/NS redshift posteriors estimated from the noisy photometry with respect to those from the unperturbed photometry. Similarly, the two panels on the right describe the changes in SBI/NS posteriors estimated from incomplete photometric data with respect to those from the complete data. Unit Gaussians are overplotted as gray dotted lines to guide the eye. It is evident that SBI ++ recovers the parameters with accuracy comparable to that of a standard inference methodology like NS. We also show results from improperly using the baseline SBI when the noise is OOD, to demonstrate the necessity of applying our method. The δ  σ,OOD < 0 group manifested in the second orange histogram shows naive SBI finds the wrong solution but with high confidence.
Paper: Dimming of Continuum Captured in Mrk 841: New Clues on the Nature of the Soft X-Ray Excess
Authors: Missagh Mehdipour, Gerard A. Kriss, Jelle S. Kaastra, Elisa Costantini, Junjie Mao
Figure caption: Swift/XRT soft X-ray flux (top panel) and the X-ray hardness ratio R (bottom panel), plotted vs. the Swift/UVOT UVW2 filter flux. The X-ray spectral hardness ratio (R) is defined as R = (H − S)/(H + S), where H and S are the Swift XRT count rate fluxes in the hard (1.5–10 keV) and soft (0.3–1.5 keV) bands, respectively. The displayed data span from 2007 to 2022. The data corresponding to the 2022 January ToO event are indicated with a magenta circle. The Spearman's rank correlation coefficient (r  s ) and the corresponding null hypothesis p-value probability (p null) from the two-sided t-test are given in each panel. There is a significant positive correlation between the soft X-ray and UV fluxes (top panel), and a significant negative correlation between the hardness R and the UV flux (bottom panel).
Paper: SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications
Authors: Bingjie Wang, 冰洁 王, Joel Leja, V. Ashley Villar, Joshua S. Speagle, 佳士 沈
Figure caption: Here, we show the runtime of SBI ++ under different settings. (a) Runtime as a function of the number of MC samples drawn in SBI ++, and each MC sample contains 50 posterior samples. The dashed line indicates the power-law fit of Equation (11). (b) Runtime as a function of the number of noisy bands. We draw 100 MC samples, where each MC sample contain 50 posterior samples. (c) Runtime as a function of the number of missing bands. The deviations from a power law shown in these two plots is likely due to the NN search. We additionally note that the runtime of SBI ++ does not depend on the exact band that is noisy/missing; that is, it takes SBI ++ roughly the same time to approximate a blue band as a redder band. We caution, however, these figures only serve as illustrations for the dependence of runtime, but not a guide for determining these parameters. The choice of these parameters depends on the data and the science question.
Paper: SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications
Authors: Bingjie Wang, 冰洁 王, Joel Leja, V. Ashley Villar, Joshua S. Speagle, 佳士 沈
Figure caption: Schematic diagram showing our algorithm, SBI ++, for dealing with missing data and OOD measurement errors. First, the SED on the top left has one band missing, rendering it inaccessible to our baseline SBI. Its approximate solution (middle left panel) is found by NN search along with MC integration. The resulting posteriors (top right) show good agreement with NS. Second, the violin plot on the bottom left shows one of our simulated SEDs, with Gaussian noise added to the true underlying SED. Given OOD uncertainties (black error bars), we marginalize over possible noise by MC integration. The bottom right corner plot shows the different posteriors from NS, the naive usage of baseline SBI, and SBI ++. Notably, our method performs similarly to the traditional NS method and markedly better than the naive SBI.
Paper: Dimming of Continuum Captured in Mrk 841: New Clues on the Nature of the Soft X-Ray Excess
Authors: Missagh Mehdipour, Gerard A. Kriss, Jelle S. Kaastra, Elisa Costantini, Junjie Mao
Figure caption: Comparison of the new (2022) and 2014 HST/COS spectra of Mrk 841 in the Lyα (top panel) and C IV (bottom panel) regions. No broad, blueshifted, UV absorption features appear in the new data, consistent with our no-obscuration interpretation. The gaps in the 2014 spectrum are due to the instrumental setup of that observation.
Paper: Isotopic Enrichment of Planetary Systems from Asymptotic Giant Branch Stars
Authors: Richard J. Parker, Christina Schoettler
Figure caption: Abundance ratios of 26Al against abundance ratios of 60Fe in N-body simulations where the stars have encountered ejecta from an AGB star. The colored points indicate different AGB progenitor masses; black are 7 M ⊙ stars, yellow are 6 M ⊙, blue are 5 M ⊙ and green are 3 M ⊙. The darker points show the values for all stars, whereas the lighter points show the values for Sun-like (0.5–1.5 M ⊙) stars. The horizontal dashed line indicates the initial 26Al/27Al value in the solar system inferred from CAI inclusions in chondritic meteorites (Thrane et al. 2006). The vertical dotted–dashed line (Mishra et al. 2016), and dotted line (Tang & Dauphas 2012), indicate the possible range of the initial 60Fe/56Fe value in the solar system (this measurement is more uncertain).
Paper: Isotopic Enrichment of Planetary Systems from Asymptotic Giant Branch Stars
Authors: Richard J. Parker, Christina Schoettler
Figure caption: The long-term internal geophysical heating of planetesimal, calculated form the relative abundance ratios of 26Al and 60Fe in our N-body simulations where the stars have encountered ejecta from an AGB star. The AGB ejecta is assumed to have been deposited immediately after the onset of star formation, and the heating, Q(t) is calculated at 1, 5, and 10 Myr (blue, hatched red, and gray histograms, respectively). The vertical dashed line indicates the likely heating value for the early solar system immediately after the inclusion of short-lived radioisotopes in the Sun’s protoplanetary disk (Moskovitz & Gaidos 2011). The highest degree of internal heating occurs at earlier times, before the majority of the 26Al and 60Fe has decayed.
Paper: Isotopic Enrichment of Planetary Systems from Asymptotic Giant Branch Stars
Authors: Richard J. Parker, Christina Schoettler
Figure caption: Sketch of the assumed geometry of an interloping AGB star in a star-forming region. The AGB star (green) has traversed the star-forming region, depositing a cylindrical shaped ejecta. Some stars (red) are enriched and then leave the ejecta region, whereas others remain. Blue stars are those that do not capture any of the ejecta (including fore- and background stars that reside outside of the ejecta). The hypothetical young Sun is shown by the yellow star.
Paper: Isotopic Enrichment of Planetary Systems from Asymptotic Giant Branch Stars
Authors: Richard J. Parker, Christina Schoettler
Figure caption: Gaia DR3 color–absolute-magnitude diagram of stars within 100 pc of NGC 2264 identifying RW (>30 km s−1, shown in red) and WW (>5 km s−1, shown in blue) stars. The WW at the top right of the giant branch (Gaia DR3 3131012157848982272) is a likely AGB star.