adsabs/AstroCLIMB huggingface dataset:
Paper: The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Authors: Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault, Jennifer A. Johnson, Jack T. Warfield, Dennis Stello, Yvonne Elsworth, Rafael A. García, Savita Mathur, Benoit Mosser, Marc Hon, Jamie Tayar, Guy S. Stringfellow, Rachael L. Beaton, Henrik Jönsson, Dante Minniti
Figure caption: A collection of plots describing the completeness of our sample through an analysis of the selection function, by first comparing the simulated and K2-GAP samples, then the K2-GAP and APO-K2 samples. Top left: a 10 × 10 grid showing the scaled fractional density of K2-GAP stars compared to the simulated sample in the mass [M ⊙] vs. radius [R ⊙] space. The title displays the number of stars in each of the samples overall; these values correspond to the scaling relation in the second term of Equation (7). The color bar shows the varying scaled density in each bin from an overabundance of simulated stars in red to an overabundance of K2-GAP stars in blue, as compared to expectations. Because ﹩\hat{D}﹩ contains an asinh scale, the color bar is centered around 0, which corresponds to an equal number of stars in both bins. The gray area, bordered in white, demarcates bins with real numbers allocated, against those that were calculated to have “nan” or “inf” values, due to the bins being populated by no star in either the simulated or the K2-GAP sample. Top right: the same as the top left but for the density of APO-K2 stars over K2-GAP stars, with an abundance of APO-K2 stars shown in green and an abundance of K2-GAP stars again shown in blue. Bottom left: three histograms showing the distribution in radius within 2 < R [R ⊙] < 30 of each sample (when cut on radius and mass): the simulated sample (red), the K2-GAP sample (blue), and the APO-K2 sample (green). . Bottom right: the same as the bottom left but showing the distribution of masses over 0 < M [M ⊙] < 2. In all plots, the simulated stars have been multiplicied by a scaling factor of 0.1, to account for the ovsersampling discussed in S22.
Paper: The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Authors: Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault, Jennifer A. Johnson, Jack T. Warfield, Dennis Stello, Yvonne Elsworth, Rafael A. García, Savita Mathur, Benoit Mosser, Marc Hon, Jamie Tayar, Guy S. Stringfellow, Rachael L. Beaton, Henrik Jönsson, Dante Minniti
Figure caption: Same as Figure 1 but in mass [M ⊙] vs. metallicity [dex] space. The scaling values can be determined from the plot titles. The metallicity histogram (bottom left) ranges within −2.5 < [M/H] [dex] < 0.5, and the mass histogram (bottom right) ranges within 0 < mass [M ⊙] < 4. See Figure 1 for more general information.
Paper: The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Authors: Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault, Jennifer A. Johnson, Jack T. Warfield, Dennis Stello, Yvonne Elsworth, Rafael A. García, Savita Mathur, Benoit Mosser, Marc Hon, Jamie Tayar, Guy S. Stringfellow, Rachael L. Beaton, Henrik Jönsson, Dante Minniti
Figure caption: Same as Figure 1 but in color (J −K ) vs. magnitude (Vmag) space. Again, the scaling value can be determined from the plot titles. The color histogram (bottom left) ranges within 0.5 < JKs < 1.3, and the magnitude histogram (bottom right) ranges within 8.5 < V mag < 16. See Figure 1 for more general information.
Paper: The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Authors: Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault, Jennifer A. Johnson, Jack T. Warfield, Dennis Stello, Yvonne Elsworth, Rafael A. García, Savita Mathur, Benoit Mosser, Marc Hon, Jamie Tayar, Guy S. Stringfellow, Rachael L. Beaton, Henrik Jönsson, Dante Minniti
Figure caption: Same as Figure 1 but in magnitude (Vmag) vs. ﹩{\nu }_{\max }﹩ [μHz] space. The scaling values can be determined from the plot titles. The ﹩{\nu }_{\max }﹩ histogram (bottom left) ranges within 0 < ﹩{\nu }_{\max }﹩ [μHz] < 240, and the magnitude histogram (bottom right) ranges within 8.5 < Vmag < 16. See Figure 1 for more general information.
Paper: The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Authors: Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault, Jennifer A. Johnson, Jack T. Warfield, Dennis Stello, Yvonne Elsworth, Rafael A. García, Savita Mathur, Benoit Mosser, Marc Hon, Jamie Tayar, Guy S. Stringfellow, Rachael L. Beaton, Henrik Jönsson, Dante Minniti
Figure caption: Footprint of each of the K2 campaigns in this sample, on a backdrop of the Milky Way, with axes representing the coordinates [deg]. Each point represents a star in our catalog and color indicates its metallicity. The position of the Kepler mission field (also colored by metallicity [Fe/H]) as taken from APOKASC-2 is shown for reference. Gaps seen in the telescope’s field of view correspond to CCD modules 3 and 7, which failed prior to the K2 mission. Each campaign is labeled with the campaign number in the format “C[number].” The metallicity color bar has been scaled between −1.0 and 0.5 to show the resulting metallicity distribution within each campaign. C9 and C19 are not used and therefore not shown. The background image was modified from ESA/Gaia/DPAC, and applied using the mw_plot Python module and the MWSkyProjection map “equirectangular.”
Paper: The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
Authors: Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault, Jennifer A. Johnson, Jack T. Warfield, Dennis Stello, Yvonne Elsworth, Rafael A. García, Savita Mathur, Benoit Mosser, Marc Hon, Jamie Tayar, Guy S. Stringfellow, Rachael L. Beaton, Henrik Jönsson, Dante Minniti
Figure caption: Each histogram shows the APOGEE [M/H] distribution for the K2 campaigns. The bottom right-hand plot, with hatched markings, is the metallicity distribution of the Kepler field taken from APOKASC-2. Color indicates the mean metallicity, with a darker blue corresponding to a higher mean metallicity. The title of each plot gives the campaign number, the number of stars in the campaign, the average metallicity, and the average Galactic latitude of the campaign field.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Positions of all (4850/6286) preprocessed Kepler test data light curves for Q10 plotted on the Kepler sensor in global pixel coordinates (11,000×11,000). Kepler modules are demarcated by bold dashed lines and labeled by module number. The (30 × 50) spatial grid is drawn in dashed lines with individual cells of size (220 × 220) pixels. All light curves with coordinates included in the rectangular gridded region are shown in blue. The subset of light curves that were gridded, and therefore processed by the spatial systematics algorithm, are shown in red at their original positions on the CCD. These light curves were either gridded to the (30 × 50) spatial cell in which they fall or to a nearby empty spatial cell, as described in the main text. After gridding, no spatial cell contained multiple light curves and there were no empty spatial cells. Light curves on modules excluded from the data grid are shown in gray. There are no targets in module 3 due to module failure.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Data analysis framework for numerical evaluation of the spatial systematics algorithm. The data flow includes input data (purple box), preprocessing (gray box; Section 2.5.1), and application of the spatial systematics algorithm (blue box; Sections 2.4 and 2.5).
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Representative sample of 15 light curves from Q10 shown before (a) and after (b) linear detrending and outlier removal. The x-axis index is in the unit of long-cadence time samples (Δt = 29.4 minutes). The dominant systematics visible are due to the quarterly roll and earth point recoveries (Stumpe et al. 2012).
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Visual representation of the coefficient matrix ﹩\bar{{\boldsymbol{C}}}﹩. Each image layer ﹩{\bar{{\boldsymbol{C}}}}^{k}﹩ represents the coefficient weights for a basis term k, (﹩{\left[\bar{{\boldsymbol{C}}}\right]}_{k}﹩) organized by 2D sensor cell position. The mapping of light curve i to cell position i → (x, y) is implicit in this notation, as described in the main text.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Noninjected light curves ﹩{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}}﹩ for which the spatial systematics algorithm produces the highest incidental correlation ﹩{\max }_{{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}}}\rho ({{\boldsymbol{a}}}_{[m]},{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}})﹩, m ∈ {s, t, f} between the nondetrended light curve ﹩{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}}﹩ and any injected astrophysical signal  a  [m] used in the simulation run. The top row shows the nondetrended light curve and the astrophysical signal (injected into another light curve) with which the incidental correlation is highest. The detrended light curves ﹩{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}}^{{\prime} }﹩ are shown as obtained by the reference PCA method (middle row) and the spatial systematics algorithm (bottom row). Simulated injected signals are overlaid in black. In these worst cases, it can be seen that the high level of correlation of the spatial detrended light curve ﹩{\boldsymbol{y}}^{\prime} ﹩ with an astrophysical signal  a  is incidental, as the original light curve  y  itself is incidentally correlated with  a . This suggests that high values for the metric of corruption ﹩| \bar{\rho }({\boldsymbol{a}},{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}}^{{\prime} })| /| \bar{\rho }({\boldsymbol{a}},{{\boldsymbol{y}}}_{{{\boldsymbol{a}}}^{c}})| ﹩ may result incidentally for the spatial systematics algorithm because it retains a higher level of astrophysical variability.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Three sample light curves across injected signal type  a  [m] m ∈ {s, t, f} (sine, transit, and flare) for Experiment B. The figure shows the simulated light curve before detrending  y  (upper row), the detrended light curve using PCA ﹩{{\boldsymbol{y}}}_{\mathrm{PCA}}^{{\prime} }﹩ (middle row), and the detrended light curve using the spatial systematics algorithm ﹩{\boldsymbol{y}}^{\prime} ﹩ (lower row). Simulated injected signals are overlaid in black. The x-axis is Kepler's long-cadence time sample index. This sample shows an instance where PCA detrending moderately removed the injected astrophysical sine and flare signals, while spatial detrending more clearly preserved those signals.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Gridded light-curve cells i → (x, y) ∈ (X, Y) where a simulated astrophysical signal  a  [m] was injected for Experiment B. The x- and y-axes are in units of gridded spatial cells. The column at left depicts the magnitude of the injected astrophysical signal ∥ a  [m]∥ as the color intensity of each cell on a log scale (blue color wedge at left). The middle and right columns depict coefficients ﹩{c}_{i}^{5}﹩ for light curve i → (x, y) ∈ (X, Y) at each spatial cell (from Figure 14) for the reference PCA method and the spatial systematics method, respectively. For these columns, the magnitude of the coefficient is shown as color intensity (color wedge at right). A black outline around a cell indicates that the corresponding light curve contained an injected astrophysical signal. The rank 5 coefficient was chosen for clarity of interpretation as this coefficient should be closer to zero. Coefficients fitted with PCA exhibit some overfitting of injected astrophysical signals.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Example coefficient vector  c   i  = 3 b  1 + 2 b   K  (blue), linearly transformed to vector Σ  B   T   c   i  = 3 b  1 + σ  K 2 b   K  (red), plotted on the 2D space spanned by  b  1 and  b   K  (brown). The normalized transformed vector is also shown (gray). A value σ  K  = 0.6 was adopted for this example (recall σ 1 = 1).
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Coefficients ﹩{c}_{i}^{k},k\in \{1,..,5\}(K=8)﹩ for light curve i → (x, y) ∈ (X, Y) for Experiment B. This figure shows the true simulated coefficients (top row), the coefficients obtained using the PCA method (middle row), and the coefficients obtained using the spatial systematics algorithm (bottom row). The coefficient index k is shown in the top left of each subfigure. The x- and y-axes are in units of gridded spatial cells. Coefficients fitted with PCA show speckling, indicative of the overfitting of injected astrophysical signals.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: CDPP6h  (ppm) vs. goodness metric ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩ per target light curve shown for the spatial systematics method and comparison standard detrending methods CBV, SFF, and PLD. Empirical distribution functions for each light-curve type are plotted over ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩ (top) and CDPP6h  (right). The CDPP6h  metric is broadly comparable across methods while the ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩ metric is more strongly dependent on the detrending method.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Metrics ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩ (left) and CDPP6h  (right) for the reference PCA method vs. the spatial systematics method. Both plots are per target light curve for a single run #0 of Experiment A. Empirical distribution functions for each light-curve metric are plotted over the spatial systematics method (horizontal axis) and the PCA method (vertical axis). Although correlated, there is significant scatter in the left plot due to the intrinsic nonlinear and pairwise nature of the metric ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩. It is accordingly more sensitive to light curves with poorly modeled systematics or greater retained astrophysical variability.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: Simulated light curve  y  before detrending (left), the detrended light curve using PCA ﹩{{\boldsymbol{y}}}_{\mathrm{PCA}}^{{\prime} }﹩ (middle), and the detrended light curve using the spatial systematics algorithm ﹩{\boldsymbol{y}}^{\prime} ﹩ (right), all at the position of the maximum value of ﹩{\left[{{\boldsymbol{C}}}_{\mathrm{PCA}}\right]}_{5}﹩ in Figure 17. Simulated injected signals are overlaid in black. The x-axis is Kepler's long-cadence time sample index. This shows an instance where PCA detrending overfits the injected astrophysical sine signal relative to the spatial systematics algorithm.
Paper: Robust Detrending of Spatially Correlated Systematics in Kepler Light Curves Using Low-rank Methods
Authors: Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
Figure caption: CDPP6h  vs. goodness metric ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩ per target light curve for a single run #0 of Experiment A. Three light-curve types are shown, including nondetrended preprocessed light curves (black), light curves detrended using PCA (red), and light curves detrended using the spatial systematics algorithm (blue). Empirical distribution functions for each light-curve type are plotted over ﹩{G}_{{\boldsymbol{y}}^{\prime} }﹩ (top) and CDPP6h  (right).
Paper: VLTI/GRAVITY Provides Evidence the Young, Substellar Companion HD 136164 Ab Formed Like a “Failed Star”
Authors: William O. Balmer, L. Pueyo, S. Lacour, J. J. Wang, T. Stolker, J. Kammerer, N. Pourré, M. Nowak, E. Rickman, S. Blunt, A. Sivaramakrishnan, D. Sing, K. Wagner, G.-D. Marleau, A.-M. Lagrange, R. Abuter, A. Amorim, R. Asensio-Torres, J.-P. Berger, H. Beust, A. Boccaletti, A. Bohn, M. Bonnefoy, H. Bonnet, M. S. Bordoni, G. Bourdarot, W. Brandner, F. Cantalloube, P. Caselli, B. Charnay, G. Chauvin, A. Chavez, E. Choquet, V. Christiaens, Y. Clénet, V. Coudé du Foresto, A. Cridland, R. Davies, R. Dembet, A. Drescher, G. Duvert, A. Eckart, F. Eisenhauer, N. M. Förster Schreiber, P. Garcia, R. Garcia Lopez, E. Gendron, R. Genzel, S. Gillessen, J. H. Girard, S. Grant, X. Haubois, G. Heißel, Th. Henning, S. Hinkley, S. Hippler, M. Houllé, Z. Hubert, L. Jocou, M. Keppler, P. Kervella, L. Kreidberg, N. T. Kurtovic, V. Lapeyrère, J.-B. Le Bouquin, P. Léna, D. Lutz, A.-L. Maire, F. Mang, A. Mérand, P. Mollière, C. Mordasini, D. Mouillet, E. Nasedkin, T. Ott, G. P. P. L. Otten, C. Paladini, T. Paumard, K. Perraut, G. Perrin, O. Pfuhl, D. C. Ribeiro, L. Rodet, Z. Rustamkulov, J. Shangguan, T. Shimizu, C. Straubmeier, E. Sturm, L. J. Tacconi, A. Vigan, F. Vincent, K. Ward-Duong, F. Widmann, T. Winterhalder, J. Woillez, S. Yazici
Figure caption: Posterior distributions of orbital elements in the orbital analysis presented in Section 3.1, and visualized in Figure 3. The median and 1σ CIs from the marginalized 1D histograms show here, for each parameter, are recorded in Table 3. Orbital parameters with respect to HD 136164 Ab are denoted with subscript “1,” and parameters with respect to HD 136164 A with subscript “0.” The distribution of orbital elements is unimodal, with no obvious degeneracies present between parameters, thanks to the combination of precise relative astrometry from GRAVITY, and long-duration baseline absolute astrometry from the HGCA considered in the fit.
Paper: VLTI/GRAVITY Provides Evidence the Young, Substellar Companion HD 136164 Ab Formed Like a “Failed Star”
Authors: William O. Balmer, L. Pueyo, S. Lacour, J. J. Wang, T. Stolker, J. Kammerer, N. Pourré, M. Nowak, E. Rickman, S. Blunt, A. Sivaramakrishnan, D. Sing, K. Wagner, G.-D. Marleau, A.-M. Lagrange, R. Abuter, A. Amorim, R. Asensio-Torres, J.-P. Berger, H. Beust, A. Boccaletti, A. Bohn, M. Bonnefoy, H. Bonnet, M. S. Bordoni, G. Bourdarot, W. Brandner, F. Cantalloube, P. Caselli, B. Charnay, G. Chauvin, A. Chavez, E. Choquet, V. Christiaens, Y. Clénet, V. Coudé du Foresto, A. Cridland, R. Davies, R. Dembet, A. Drescher, G. Duvert, A. Eckart, F. Eisenhauer, N. M. Förster Schreiber, P. Garcia, R. Garcia Lopez, E. Gendron, R. Genzel, S. Gillessen, J. H. Girard, S. Grant, X. Haubois, G. Heißel, Th. Henning, S. Hinkley, S. Hippler, M. Houllé, Z. Hubert, L. Jocou, M. Keppler, P. Kervella, L. Kreidberg, N. T. Kurtovic, V. Lapeyrère, J.-B. Le Bouquin, P. Léna, D. Lutz, A.-L. Maire, F. Mang, A. Mérand, P. Mollière, C. Mordasini, D. Mouillet, E. Nasedkin, T. Ott, G. P. P. L. Otten, C. Paladini, T. Paumard, K. Perraut, G. Perrin, O. Pfuhl, D. C. Ribeiro, L. Rodet, Z. Rustamkulov, J. Shangguan, T. Shimizu, C. Straubmeier, E. Sturm, L. J. Tacconi, A. Vigan, F. Vincent, K. Ward-Duong, F. Widmann, T. Winterhalder, J. Woillez, S. Yazici
Figure caption: The posterior distributions of atmospheric model parameters from our ATMO spectral fit, presented in Section 3.2. The contours of the posteriors from the fit with a uniform mass prior are plotted in red, and the contours of the posteriors from the fit with a dynamical mass prior are plotted in blue. The major takeaway is that ﹩\mathrm{log}(g)﹩ and mass remain relatively unconstrained in the uniform prior fit, but the other parameters remain effectively the same between the two.
Paper: VLTI/GRAVITY Provides Evidence the Young, Substellar Companion HD 136164 Ab Formed Like a “Failed Star”
Authors: William O. Balmer, L. Pueyo, S. Lacour, J. J. Wang, T. Stolker, J. Kammerer, N. Pourré, M. Nowak, E. Rickman, S. Blunt, A. Sivaramakrishnan, D. Sing, K. Wagner, G.-D. Marleau, A.-M. Lagrange, R. Abuter, A. Amorim, R. Asensio-Torres, J.-P. Berger, H. Beust, A. Boccaletti, A. Bohn, M. Bonnefoy, H. Bonnet, M. S. Bordoni, G. Bourdarot, W. Brandner, F. Cantalloube, P. Caselli, B. Charnay, G. Chauvin, A. Chavez, E. Choquet, V. Christiaens, Y. Clénet, V. Coudé du Foresto, A. Cridland, R. Davies, R. Dembet, A. Drescher, G. Duvert, A. Eckart, F. Eisenhauer, N. M. Förster Schreiber, P. Garcia, R. Garcia Lopez, E. Gendron, R. Genzel, S. Gillessen, J. H. Girard, S. Grant, X. Haubois, G. Heißel, Th. Henning, S. Hinkley, S. Hippler, M. Houllé, Z. Hubert, L. Jocou, M. Keppler, P. Kervella, L. Kreidberg, N. T. Kurtovic, V. Lapeyrère, J.-B. Le Bouquin, P. Léna, D. Lutz, A.-L. Maire, F. Mang, A. Mérand, P. Mollière, C. Mordasini, D. Mouillet, E. Nasedkin, T. Ott, G. P. P. L. Otten, C. Paladini, T. Paumard, K. Perraut, G. Perrin, O. Pfuhl, D. C. Ribeiro, L. Rodet, Z. Rustamkulov, J. Shangguan, T. Shimizu, C. Straubmeier, E. Sturm, L. J. Tacconi, A. Vigan, F. Vincent, K. Ward-Duong, F. Widmann, T. Winterhalder, J. Woillez, S. Yazici
Figure caption: The posterior distributions of atmospheric model parameters from our SPHINX spectral fit, shown in Section 3.2. The contours of the posteriors from the fit with a uniform mass prior are plotted in red, and the contours of the posteriors from the fit with a dynamical mass prior are plotted in blue. The major takeaway is that, with a slightly enhanced metallicity and subsolar C/O, the sampler is no longer driven toward implausibly low ﹩\mathrm{log}(g)﹩.
Paper: Chemistry of Complex Organic Molecules in the V883 Ori Disk Revealed by ALMA Band 3 Observations
Authors: Yoshihide Yamato, Shota Notsu, Yuri Aikawa, Yuki Okoda, Hideko Nomura, Nami Sakai
Figure caption: Comparison of the COM abundance ratios with respect to CH3OH among different evolutionary stages including the V883 Ori disk measured in the present work. The data points with a down arrow indicate an upper limit. We compiled the literature values for the protostellar envelopes of IRAS 16293-2422 A/B (Lykke et al. 2017; Jørgensen et al. 2018; Manigand et al. 2020, 2021), the comet 67P/C-G (Rubin et al. 2019; Schuhmann et al. 2019), and the IRS 48 disk (Brunken et al. 2022). Note that, while all the cometary values are limited to be upper limits because cometary measurement (mass spectra) cannot distinguish the isomeric molecules with the same mass (e.g., CH3CHO and c-C2H4O pair and CH3COCH3 and s-C2H5CHO pair), the upper limit of CH3CHO in the IRS 48 disk is due to nondetection.