adsabs/AstroCLIMB huggingface dataset:
Paper: TESS Hunt for Young and Maturing Exoplanets (THYME). VI. An 11 Myr Giant Planet Transiting a Very-low-mass Star in Lower Centaurus Crux
Authors: Andrew W. Mann, Mackenna L. Wood, Stephen P. Schmidt, Madyson G. Barber, James E. Owen, Benjamin M. Tofflemire, Elisabeth R. Newton, Eric E. Mamajek, Jonathan L. Bush, Gregory N. Mace, Adam L. Kraus, Pa Chia Thao, Andrew Vanderburg, Joe Llama, Christopher M. Johns-Krull, L. Prato, Asa G. Stahl, Shih-Yun Tang, Matthew J. Fields, Karen A. Collins, Kevin I. Collins, Tianjun Gan, Eric L. N. Jensen, Jacob Kamler, Richard P. Schwarz, Elise Furlan, Crystal L. Gnilka, Steve B. Howell, Kathryn V. Lester, Dylan A. Owens, Olga Suarez, Djamel Mekarnia, Tristan Guillot, Lyu Abe, Amaury H. M. J. Triaud, Marshall C. Johnson, Reilly P. Milburn, Aaron C. Rizzuto, Samuel N. Quinn, Ronan Kerr, George R. Ricker, Roland Vanderspek, David W. Latham, Sara Seager, Joshua N. Winn, Jon M. Jenkins, Natalia M. Guerrero, Avi Shporer, Joshua E. Schlieder, Brian McLean, and Bill Wohler
Figure caption: Possible evolutionary tracks for TOI 1227b for a range of plausible initial masses (Mi) and cooling times (tKH). These correspond to high (green), medium (orange), and low (blue) entropy. In choosing these evolutionary curves we have assumed that the disk dispersed at a stellar age of 3 Myr. The red cross indicates the age and radius of TOI 1227b, which are used as the observables on the model fit.
Paper: TESS Hunt for Young and Maturing Exoplanets (THYME). VI. An 11 Myr Giant Planet Transiting a Very-low-mass Star in Lower Centaurus Crux
Authors: Andrew W. Mann, Mackenna L. Wood, Stephen P. Schmidt, Madyson G. Barber, James E. Owen, Benjamin M. Tofflemire, Elisabeth R. Newton, Eric E. Mamajek, Jonathan L. Bush, Gregory N. Mace, Adam L. Kraus, Pa Chia Thao, Andrew Vanderburg, Joe Llama, Christopher M. Johns-Krull, L. Prato, Asa G. Stahl, Shih-Yun Tang, Matthew J. Fields, Karen A. Collins, Kevin I. Collins, Tianjun Gan, Eric L. N. Jensen, Jacob Kamler, Richard P. Schwarz, Elise Furlan, Crystal L. Gnilka, Steve B. Howell, Kathryn V. Lester, Dylan A. Owens, Olga Suarez, Djamel Mekarnia, Tristan Guillot, Lyu Abe, Amaury H. M. J. Triaud, Marshall C. Johnson, Reilly P. Milburn, Aaron C. Rizzuto, Samuel N. Quinn, Ronan Kerr, George R. Ricker, Roland Vanderspek, David W. Latham, Sara Seager, Joshua N. Winn, Jon M. Jenkins, Natalia M. Guerrero, Avi Shporer, Joshua E. Schlieder, Brian McLean, and Bill Wohler
Figure caption: CMDs of the Crux-S (left) and LCC-B (right) groups from Kerr et al. (2021). Points included in our fit are labeled as blue circles, with blue squares representing stars excluded (mostly because of a high RUWE). Each point is shaded by the probability of being an outlier (i.e., not well described by the model). This may include nonmembers, nearby association interlopers, binaries, or simply stars with poor parallaxes/photometry.
Paper: Variable and Supersonic Winds in the Atmosphere of an Ultrahot Giant Planet
Authors: Anusha Pai Asnodkar, Ji Wang, Jason D. Eastman, P. Wilson Cauley, B. Scott Gaudi, Ilya Ilyin, and Klaus Strassmeier
Figure caption: 2D maps of transmission spectra, focusing on the six Fe II absorption lines in the PEPSI 2018 data set chosen for fitting Kp and vwind. The blue track is the planet’s atmospheric absorption while the red track is the Doppler shadow from the RME; both tracks only form during a transit. Top panel displays fully in-transit observations. Middle panel shows the best-fit model from MCMC sampling. The bottom panel presents the residuals (data—model).
Paper: Variable and Supersonic Winds in the Atmosphere of an Ultrahot Giant Planet
Authors: Anusha Pai Asnodkar, Ji Wang, Jason D. Eastman, P. Wilson Cauley, B. Scott Gaudi, Ilya Ilyin, and Klaus Strassmeier
Figure caption: Day-to-nightside winds measurements from Fe II line-by-line and cross-correlation analyses with Markov chain Monte Carlo (MCMC) sampling errors for (a) KELT-9 b and (b) KELT-20 b. We note that the Fe II λ5363.9 Å line is very weak with S/N = 1.6; for comparison, the Fe II λ5169 Å line has S/N = 9.0 (see Figure 1 for the observed relative line strength of KELT-9 b as an example). Thus, this line is less reliable for measuring day-to-nightside winds.
Paper: Variable and Supersonic Winds in the Atmosphere of an Ultrahot Giant Planet
Authors: Anusha Pai Asnodkar, Ji Wang, Jason D. Eastman, P. Wilson Cauley, B. Scott Gaudi, Ilya Ilyin, and Klaus Strassmeier
Figure caption: The sloped solid lines represent the center of the planet’s absorption signature over the course of the transit, corrected by the systemic velocity (see Figure 2 of Pai Asnodkar et al. 2022). The slope captures the orbital motion of the planet while the velocity at midtransit (ϕ = 0) corresponds to the day-to-nightside wind velocity. Second and third contact occur at orbital phases beyond the range of this plot. Our revised ephemeris for the KELT-9 system (1) shifts the atmospheric signature up in phase, resulting in (2) a more blueshifted measurement of day-to-nightside winds.
Paper: Variable and Supersonic Winds in the Atmosphere of an Ultrahot Giant Planet
Authors: Anusha Pai Asnodkar, Ji Wang, Jason D. Eastman, P. Wilson Cauley, B. Scott Gaudi, Ilya Ilyin, and Klaus Strassmeier
Figure caption: Day-to-nightside winds of KELT-9 b and KELT-20 b ordered chronologically along the x-axis.
Paper: Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the Baryonic Tully–Fisher Relation
Authors: James Schombert, Stacy McGaugh, and Federico Lelli
Figure caption: Baseline SF and chemical enrichment histories as a function of stellar mass. The red curves are for high-mass (M* > 1010 M⊙) spiral galaxies that follow the Speagle et al. z = 0 main sequence. The blue curves are adjusted to match the low-mass main sequence found by McGaugh, Schombert, & Lelli (2017) with near-constant SF over a Hubble time. The green line is an intermediate, canonical SFH for a Milky Way–sized system. The early, strong SF for the high-mass galaxies results in the formation of red disks for early-type spirals. The low-intensity, constant SF in low-mass galaxies results in low-density (i.e., LSB), low-metallicity stellar populations. A 10 Gyr burst model for a 1012 M⊙ bulge is shown as the red dashed line. The bottom panel displays the adopted chemical enrichment scenarios for the SFH in the top panel. The final metallicity is set by the mass–metallicity relation for star-forming galaxies (Cresci et al. 2019). Bulges are assumed to have very fast enrichment to present-day values; thus, no chemical evolution history is presented in the bottom panel.
Paper: Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the Baryonic Tully–Fisher Relation
Authors: James Schombert, Stacy McGaugh, and Federico Lelli
Figure caption: The top panel displays the average optical to near-IR colors for the SPARC and S4G samples by galaxy type. The error bars display the 3σ dispersion of the averages (not error in the colors). The green track is the baseline SFH model from SML. The dashed line is the new bulge+disk model that captures the redder colors of early-type spirals (see Section 5). Three burst models are shown for late-type galaxies: red for a declining SFR, green for a constant SFR, and blue for a weak increase in recent SFR. An inset histogram of UV colors displays the observed FUV–NUV colors matching the endpoint of each late-type model (where the mean FUV–NUV color matches the constant SF model). The bottom panel displays the effect of those same models on the deduced mass-to-light ratios. Here the solid lines represent the original models adjusted on the blue side for late bursts of SF and extrapolated on the red side (dashed line) to capture older and more metal-rich red disks. Some knowledge of the optical color or morphology of a blue galaxy can reduce the uncertainty in ϒ* by 15% on the blue end. The cyan band indicates the range in uncertainty in ϒ* owing to scatter in the mass–metallicity and main-sequence relations.
Paper: Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the Baryonic Tully–Fisher Relation
Authors: James Schombert, Stacy McGaugh, and Federico Lelli
Figure caption: Four ϒ* models for the optical and near-IR (V, I, K, and IRAC 3.6). The green lines are the SFH models from SML (extended to redder and bluer colors as discussed in the text). A 10 Gyr bulge model is shown as a purple line in each panel. The merged bulge+disk models are shown in red. As the models progress from the near-IR to the optical, the differences between the various models merge as the youngest stars dominate in the optical. The slope of the color−ϒ* relation steepens with bluer filters, meaning that errors in optical colors will result in larger ϒ* errors compared to the near-IR. Also shown in the B − V panel are previous color−ϒ* studies discussed in Schombert & McGaugh (2014; blue dashed lines) and in the V − I panel the relationship defined by Taylor et al. (2011) for SDSS i photometry. Bulge+disk models are needed to extend these older studies to redder colors but agree well with our newer models. The Taylor et al. relation is in agreement with intermediate colors but deviates significantly for red, early-type galaxies.
Paper: Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the Baryonic Tully–Fisher Relation
Authors: James Schombert, Stacy McGaugh, and Federico Lelli
Figure caption: A comparison of deduced stellar masses using SDSS g, i and Spitzer 3.6 photometry plus the new bulge+disk models from Figure 3. The SPARC sample is divided into red and blue galaxies based on colors greater or less than g − i = 1.05 or V − [3.6] = 2.9. The agreement across various optical and near-IR filters is excellent, with no obvious systematics. The dispersion increases slightly to bluer filters, which results in slightly higher slopes in color vs. Upsilon relations for bluer colors.
Paper: Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the Baryonic Tully–Fisher Relation
Authors: James Schombert, Stacy McGaugh, and Federico Lelli
Figure caption: A comparison of the original ϒ* prescription (SPARC; Lelli et al. 2016) on the y-axis vs. the three new color methods. The left panel uses the color of the separate disk and bulge components to derive ϒ* (using 3.6 luminosities). The middle panel uses the combined B+D model and total galaxy color. The right panel uses the galaxy type to predict the total color and then applies the B+D model. The top panels display the difference from unity in log space. Note that the disk model assumes a new SFH of constant SFH for very blue galaxies (see Figure 2) with a mean ϒ* of 0.41 vs. the assumed value of 0.5 in our previous papers. Red symbols are for galaxies with classic, r1/4 bulges, green symbols are for pseudobulges, and blue symbols are for pure disk systems. The most significant difference is the higher stellar masses for early-type spirals with the correct inclusion of bulges.
Paper: Stellar Mass-to-light Ratios: Composite Bulge+Disk Models and the Baryonic Tully–Fisher Relation
Authors: James Schombert, Stacy McGaugh, and Federico Lelli
Figure caption: The changes in the bTF using different ϒ* models. The top left panel is the original SPARC data set using EDD distances and flow models with Ho = 75. The top right panel is the same kinematic data and distances, but calculating ϒ* from the colors of the bulge and disk components. Galaxies without spatial color information were left out of the sample. The bottom left panel is the two-color B+D model, and the bottom right panel uses only the galaxy type to set the galaxy color. Symbol colors are the same as in Figure 5 for bulges, pseudobulges, and pure disks. The bTF is significantly more linear with either color model, but scatter around the C/TRGB fit is only reduced by 5%.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: The mth regression decision tree of GBDT fitting Teff with photometric data. The box represents feature selection according to the mean square error, and the ellipse represents the prediction result. The mth regression decision tree fits the residuals (the difference between the real value and the predicted value) of the (m − 1)th regression decision tree. With an increase in m, the fitting result of the mth regression decision tree tends to 0. The final fitting result of GBDT is the sum of the fitting results of samples in each regression decision tree.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Distribution of the sample in the range of Teff, log g and [Fe/H], including 21,964 dwarf (log g > 3.5) stars and 16,015 giant stars (log g < 3.5). The horizontal axis represents the range of stellar atmospheric parameters (Teff, log g, [Fe/H]), and the vertical axis represents the number of stars.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Experimental results on test set obtained through OPD + LightGBM. Colors code the density distribution of stellar atmospheric parameters as defined on the wedge. The first, second, and third columns represent the estimated results of Teff, log g, and [Fe/H] respectively. In the top row, the horizontal axis represents the predicted values, and the vertical axis represents the real values. The horizontal axis of the bottom row represents the predicted values of the model, and the vertical axis corresponds to the residuals. δ is RMSE.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: In this figure, different colors represent the difference of MIC. The numbers in each position are the MIC between two variables. The value of each position ranges from 0 to 1, and the closer to 1, the greater the MIC between them. The color bar represents the MIC values. Subgraphs A, B, and C, respectively, represent the MIC between the data transformed by OPD, CID, and PCA. From subgraph A (or subgraph B) to subgraph C, as the MIC between new variables decreases, and the MIC between new variables and stellar atmospheric parameters increases.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Feature importance of the stellar atmospheric parameters obtained by OPD + LightGBM. Feature importance quantifies the features extracted by the model. The larger the feature importance of each band is the more important the band is for parameter prediction. In each subgraph, the importance of features decreases from top to bottom.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Experimental results in test set obtained by using CID + LightGBM. Colors code the density distribution of stellar atmospheric parameters as defined on the wedge. The first, second, and third columns represent the estimated results of Teff, log g, and [Fe/H], respectively. In the top row, the horizontal axis represents the predicted values, and the vertical axis represents the real values. The horizontal axis of the bottom row represents the predicted values of the model, and the vertical axis corresponds to the residuals. δ is RMSE.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Feature importance of the stellar atmospheric parameters obtained by CID + LightGBM. The larger the value of each band is the more important the band is for parameter prediction. In each subgraph, the importance of features decreases from top to bottom.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Experimental results in test set obtained through PCA + LightGBM. Colors code the density distribution of stellar atmospheric parameters as defined on the wedge. The first, second, and third columns represent the estimated results of Teff, log g, and [Fe/H] respectively. In the top row, the horizontal axis represents the predicted values, and the vertical axis represents the real values. The horizontal axis of the bottom row represents the predicted values of the model, and the vertical axis corresponds to the residuals. δ is RMSE.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: Feature importance of the stellar atmospheric parameters obtained by PCA + LightGBM. The feature importance quantifies the features of model extraction. The larger the value of each band is the more important the band is for parameter prediction. In each subgraph, the importance of features decreases from top to bottom.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: The influence of GOSS on Teff prediction RMSE. Axis a and axis b represent the value range of a and b of GOSS algorithm, and the vertical axis represents the RMSE of Teff in the test set. The red dot in the figure indicates the position when the RMSE is the lowest, and different colors indicate the change of RMSE.
Paper: Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis
Authors: Junchao Liang, Yude Bu, Kefeng Tan, Jingchang Pan, Zhenping Yi, and Xiaoming Kong
Figure caption: The influence of max_bin on Teff training time. The horizontal axis represents the number of bins. The vertical axis represents the time consuming of LightGBM training.
Paper: APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Authors: Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, and Richard R. Lane
Figure caption: Stellar parameters derived from APOGEE spectra from various pipeline+data set combinations (listed clockwise from upper left): Synspec version of ASPCAP, DR17 (J. Holtzman et al. 2022, in preparation; [Fe/H] is not available for cool and hot dwarfs); The Cannon (Ness et al. 2015), DR14; The Payne (Ting et al. 2019), DR14; and APOGEE Net I (Olney et al. 2020), DR17.
Paper: APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Authors: Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, and Richard R. Lane
Figure caption: Distribution of Teff and ﹩\mathrm{log}g﹩ color coded by luminosity class (left panel) and spectral type (right panel) for sources in the literature in which both sets of these parameters are measured independently. Diamonds are the data, crosses are the computed grid.
Paper: APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Authors: Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, and Richard R. Lane
Figure caption: Distribution of Teff and ﹩\mathrm{log}g﹩ in the training sample classes. Yellow dots are the sources from APOGEE Net I, red circles are sources with Teff and ﹩\mathrm{log}g﹩ from SIMBAD for hot stars in a parameter space inaccessible to APOGEE Net I. Blue squares show the sources for which Teff and ﹩\mathrm{log}g﹩ were transformed from the corresponding spectral type and luminosity class combination.
Paper: APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Authors: Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, and Richard R. Lane
Figure caption: Architecture of the neural net model used in this paper. See also Appendix appendix for code.
Paper: APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Authors: Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, and Richard R. Lane
Figure caption: Derived Teff and ﹩\mathrm{log}g﹩ distribution for all ∼630,000 stars in the Milky Way in the APOGEE DR17 data.
Paper: APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Authors: Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim, and Richard R. Lane
Figure caption: Comparison of parameters between the training labels and the model predictions, using the reserved test sample that was not used in training the model. Left: comparison of ﹩\mathrm{log}g﹩, color coded by Teff. Middle: comparison of Teff, color coded by ﹩\mathrm{log}g﹩. Right: difference between the training labels derived from APOGEE Net I for cool stars with Teff < 6500 K and the model predictions for the same stars in this work, divided by the uncertainties measured both by APOGEE Net I and II added in quadrature, to demonstrate the typical magnitude of residuals, in σ.