adsabs/AstroCLIMB huggingface dataset:
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: Schematic showing an example path for the NEMESIS dayside-and-nightside radiative transfer calculation. The dayside portion of the atmosphere is represented by shades of red and the nightside by shades of blue. The intensity of the color indicates the atmospheric density, and it can be seen that the density and therefore the pressure on the nightside drop off much more rapidly as a function of altitude. The model altitude coordinate is continuous across the day–night boundary, but the pressure coordinate is not.
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: Fits to the white light curves using batman and catwoman models.
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: Allan deviation plots for the spectrophotometric transit-only light curves at both spectral resolutions. The transparent black lines show the rms residuals between the data and the models as a function of the number of bins and the red lines show the rms that would be expected if there were only white noise in the data.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Validation results for RGB stars at lower SNR (low-SNR field RGB-test). The Lux labels shown are determined by optimizing the latent representations for test stars using each star’s spectral fluxes. Here, we have chosen an SNR range that is expected for the SDSS-V Galactic Genesis survey. As in Figure 9, we show the mean ASPCAP uncertainty, bias, and RMSE values for each stellar label in each panel. Overall, the RMSE values obtained are reasonably low and the bias values are approximately equal to the average ASPCAP uncertainty, indicating that the Lux model is able to infer stellar labels at reasonable precision for lower-SNR stars. However, we do note that for some element abundance ratios (e.g., [O/Fe], [Ca/Fe], and [Ni/Fe]), there is a trend from the one-to-one line. See text in Section 4.6 for further details.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Validation results for RGB stars in the GALAH-APOGEE field giants-test set. The Lux labels shown are determined by optimizing the latent representations for test stars using each star’s spectral fluxes. We show the mean reported GALAH label uncertainty, bias, and RMSE values for each stellar label in each panel. Overall, the RMSE values obtained are reasonably low and the bias values are approximately equal to the average GALAH uncertainty, indicating that the Lux model is able to perform label transfer successfully between surveys observing at different wavelengths, such as GALAH and APOGEE. We do note however that some stellar labels show a wide scatter around the one-to-one relation (e.g., [O/Fe]), which may indicate that our simple Lux model is not powerful enough to learn well this stellar label. See text in Section 5 for further details.
Paper: The Dynamic Inner Disk of a Planet-forming Star
Authors: Benjamin R. Setterholm, John D. Monnier, Fabien Baron, Jaehan Bae, Jacques Kluska, Stefan Kraus, Nuria Calvet, Noura Ibrahim, Evan Rich, Narsireddy Anugu, Claire L. Davies, Jacob Ennis, Tyler Gardner, Aaron Labdon, Cyprien Lanthermann, Gail Schaefer
Figure caption: Square visibility (top) and closure phase (bottom) of all analyzed data. The large triangle shapes show the data in the spectral channel nearest 1.6 μm to highlight the number of observations, with the remaining data drawn at a smaller scale to demonstrate the total data volume. Displayed error bars are typical for other wavelengths. The resolution scale at the top indicates the θ = λ/2B scale of the [maximum] baseline separation.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Validation results on 1000 test stars with synthetic model Korg (A. Wheeler et al. 2022) spectra. The Lux labels shown are determined by optimizing the latent representations for test stars using each star’s synthetic (Korg) spectral fluxes. The model used to impute these validation results was trained on 4000 stars with APOGEE stellar labels and Korg spectra. In each panel, we show the mean ASPCAP uncertainty, bias, and RMSE values. Overall, the RMSE value is reasonably low for all labels, and is extremely low for ﹩\mathrm{log}\,g﹩ and [Fe/H] (RMSE value below 0.1). However, there are some weak trends in some stellar labels (e.g., [Si/Fe], [Mn/Fe], and [Ni/Fe], for example), which could be due to limitations in the Lux model.
Paper: The Dynamic Inner Disk of a Planet-forming Star
Authors: Benjamin R. Setterholm, John D. Monnier, Fabien Baron, Jaehan Bae, Jacques Kluska, Stefan Kraus, Nuria Calvet, Noura Ibrahim, Evan Rich, Narsireddy Anugu, Claire L. Davies, Jacob Ennis, Tyler Gardner, Aaron Labdon, Cyprien Lanthermann, Gail Schaefer
Figure caption: Total uv coverage of the combined MIRC-X and PIONIER data set over all examined nights, shown as a function of the projected baseline separation in meters. Note that the CHARA north–south projected baselines are especially foreshortened compared to the east–west baselines, owing to HD 163296’s relatively low transit elevation of 34° at CHARA’s latitude.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Stellar labels determined using Lux for all 10,000 high-SNR RGB stars in the high-SNR field RGB-test set. Overall, the Lux labels look realistic and do not show any unusual trends. Moreover, the scatter around the labels is small, yielding a tight relation that reveals distinct structures both in stellar parameter and chemical abundance space, especially in the [Fe/H]-poor regime.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Components of a T-SNE dimensionality reduction (using two components) on the high-SNR field RGB-test set z latent parameters using a perplexity of 25. Each panel is color coded by one of the corresponding 12 stellar labels used to train the model for test set stars; these Lux stellar labels are computed by optimizing the test set latent representations using each star’s spectral fluxes. Overall, there is structure in the latent space that correlates with the stellar labels.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Median resulting total χ2 values from the K-fold cross-validation test, summed over all wavelengths, all labels, and all stars in the high-SNR field RGB-test set. We show the χ2 metric estimated by computing the test set z latent parameters using each star’s stellar fluxes (left) and labels (right). Overall, the model with P = 4 × M and Ω = 103 yields a good trade-off between latent dimensionality and regularization strength.
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: WASP-121 b's transmission spectrum obtained from the phase-curve analysis at a spectral resolution of R ∼ 600 and the GCM transmission spectra presented by W. Pluriel et al. (2020). Upper panel: the gray circles show the original data and the black circles show the data binned in wavelength. The model spectra presented by W. Pluriel et al. (2020) with varying degrees of molecular dissociation are plotted using lines. A wavelength-independent offset was added to the model spectra to fit the mean of the data between 2.9 and 3.7 μm. Lower panel: the residuals between the model spectra with an added constant and the data are depicted using colored circles with the error bars of the data propagated onto the residuals. A horizontal gray line at zero was added to guide the eye.
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: Same as Figure 11, but for the retrievals applied to the transit-only transmission spectrum.
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: Contributions of the different molecular absorbers to the NEMESIS model spectrum. Top panel: the black circles represent the transmission spectrum derived from the phase-curve analysis. The dotted magenta line shows the ML model and the solid blue, dashed orange, and dashed–dotted green lines show model spectra with all molecular opacities except for the opacity due to H2O, CO, and SiO set to zero, respectively. Bottom panel: magenta circles depict the data subtracted by the ML NEMESIS model with the data's error bars propagated onto the residuals. A horizontal gray line at zero was added to guide the eye.
Paper: WASP-121 b’s Transmission Spectrum Observed with JWST/NIRSpec G395H Reveals Thermal Dissociation and SiO in the Atmosphere
Authors: Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
Figure caption: Posteriors for WASP-121 b's C/O inferred from the retrievals enforcing equilibrium chemistry. The solid vertical lines depict the posteriors’ medians and the dashed vertical lines show the edges of the 1σ intervals. Two bars of the posteriors of the ATMO retrieval on the phase-curve transmission spectrum exceed the y-axis: their normalized probability values are ∼14.5 and ∼59.2 from left to right.
Paper: Comet 289P/Blanpain: Near-perihelion Activity and the Phoenicids
Authors: Toshihiro Kasuga
Figure caption: The median-combined images of 289P (center) in Visit B (MJD 58860: UT 2020 January 11–12, outbound). Both frames have a size of 200″ × 200″ with an integration time of 15.4 s. The left panel shows the W1-band image with FWHM θF = 6﹩\mathop{.}\limits^{{\rm{^{\prime} }}{\rm{^{\prime} }}}﹩​​​​6, while the right panel shows the W2-band image with θF = 6﹩\mathop{.}\limits^{{\rm{^{\prime} }}{\rm{^{\prime} }}}﹩​9. A 40″ scale bar is included. Rh = 1.01 au, Δ = 0.09 au, and α = ﹩70\mathop{.}\limits^{\circ }2﹩. North and east exhibit the cardinal directions. −V shows the direction of the negative heliocentric velocity vector and −⊙ shows the antisolar direction.
Paper: Comet 289P/Blanpain: Near-perihelion Activity and the Phoenicids
Authors: Toshihiro Kasuga
Figure caption: The ICORE coadded images of 289P (center) in Visit A (MJD 58786: UT 2019 October 30, inbound). Both frames have a size of 277″ × 277″. The left panel shows the W1-band image (38.5 s integration) with FWHM θF = 7﹩\mathop{.}\limits^{{\rm{^{\prime} }}{\rm{^{\prime} }}}﹩2, while the right panel shows the W2-band image (46.2 s integration) with θF = 7﹩\mathop{.}\limits^{{\rm{^{\prime} }}{\rm{^{\prime} }}}﹩7. Heliocentric, WISE-centric distances, and phase angle were Rh = 1.20 au, Δ = 0.40 au, and α = 49﹩\mathop{.}\limits^{\circ }﹩8, respectively. The cardinal directions (north and east), the direction of the negative heliocentric velocity vector (−V), and the antisolar direction (−⊙) are marked. A 40″ scale bar is also shown.
Paper: Comet 289P/Blanpain: Near-perihelion Activity and the Phoenicids
Authors: Toshihiro Kasuga
Figure caption: Normalized surface brightness profiles of 289P (blue circles) and a field star (black circles), from median-combined W1 image in Visit B (MJD 58860: UT 2020 January 11–12, outbound; Figure 2, left panel). The FWHM of 289P is θF = 6﹩\mathop{.}\limits^{{\rm{^{\prime} }}{\rm{^{\prime} }}}﹩6. Linear distances > 40″ are precluded (vertical dashed line) due to the nonuniform background, preventing further profile analysis.
Paper: Comet 289P/Blanpain: Near-perihelion Activity and the Phoenicids
Authors: Toshihiro Kasuga
Figure caption: Same as Figure 3, but from the W2 band from Figure 2, right panel. The FWHM of 289P is θF = 6﹩\mathop{.}\limits^{{\rm{^{\prime} }}{\rm{^{\prime} }}}﹩9.
Paper: Keck and Gemini Characterization of Hayabusa2# Rendezvous Target 1998 KY26
Authors: Bryce T. Bolin, Christoffer Fremling, Matthew Belyakov, Jin Beniyama, Marco Delbo, Robert Jedicke, Ian Wong, Laura-May Abron, Keith S. Noll, Andrew W. Stephens
Figure caption: Deep g- and R-band imaging of 1998 KY26 taken with Keck/LRIS on 2024 June 8. Panel (A): a median combination stack of 4 x 180 s g filter LRIS images of 1998 KY26. An arrow indicating a width of 15″ is shown for scale. The cardinal, solar, and orbital motion directions are indicated. Panel (B): a median combination stack of 4 x 180 s R filter images of 1998 KY26. Panel (C): a combined stack of all g + R filter images of 1998 KY26. The pixel scale is 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩27. Streaks and points are due to incompletely removed background star trails in the image stacks. Panel (D): the azimuthally averaged surface brightness profile of the combined 4 × 180 g + R filter stack plotted as yellow data points with error bars. The error bars are smaller than the marker for the first five data points. A nearby comparison star’s azimuthally averaged surface brightness profile is overplotted as a piecewise between pixel orange line.
Paper: Keck and Gemini Characterization of Hayabusa2# Rendezvous Target 1998 KY26
Authors: Bryce T. Bolin, Christoffer Fremling, Matthew Belyakov, Jin Beniyama, Marco Delbo, Robert Jedicke, Ian Wong, Laura-May Abron, Keith S. Noll, Andrew W. Stephens
Figure caption: Visible spectra of 1998 KY26 taken with Gemini N/GMOS on 2024 June 9. The Gemini N/GMOS spectrum of 1998 KY26 is plotted in red, covering 550–920 nm and normalized to unity at 550 nm (indicated by the light blue cross). The normalized reflectivity from the Keck/LRIS g and R filter (converted to r, K. Jordi et al. 2006) observations are included in green and orange, respectively. The gaps in the spectrum at 640, 780, and 850 nm are due to incomplete removal of telluric features. The data are rebinned by a factor of 70 using an error-weighted mean. The spectral ranges of K-, C-, Xe-, and D-type asteroids from F. E. DeMeo et al. (2009) are overplotted in purple, gold, aqua, and orange.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Top: portion of the APOGEE spectrum (black) and inferred Lux spectra for a random star (2M00011399+8408446) from our high-SNR field RGB-test set. The meaning of the cyan and navy spectra shown in the top panel is the same as in Figure 3. Other rows: derivatives of the spectrum with respect to ﹩{T}_{{\rm{eff}}},\mathrm{log}\,g﹩, [Fe/H], and [Mg/Fe], computed using Equations (12) and (13). Highlighted with colored bands in the bottom two rows are the main atomic Fe I (orange) and Mg I (green) lines in this portion of the spectrum. If one focuses on the bottom two rows, the ∂[X/Fe]/∂f derivative shows large values around the atomic lines for that particular element. This result illustrates how the Lux model is learning the correct portions of the spectrum for a corresponding label (see text in Section 4.4 for further details). We note that some of the bad sky subtraction features that have been seen before in this region of the APOGEE spectra are visible (e.g., 15343 Å, K. A. McKinnon et al. 2024).
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Comparison of portions of the spectrum for two stars (see legend) in the high-SNR field RGB-test sample with similar ﹩{T}_{{\rm{eff}}},\mathrm{log}\,g﹩, and [Fe/H] but different individual chemical abundance ratios. The stellar spectra shown in each panel are the Lux model spectra for those stars. To determine these spectra, we use the spectral fluxes for each star to infer the latent representations (zn), and then impute spectral fluxes (using Equation (2)). For the case of [C/Fe] and [N/Fe], as these elements are determined from the CH and CN molecular lines, we also constrain the comparison to two stars with similar [N/Fe] abundance when examining [C/Fe], and two stars with similar [C/Fe] when examining [N/Fe]. For all element abundance ratios examined, stars with a higher [X/Fe] abundance present deeper absorption lines when compared to their doppelganger star with lower [X/Fe] at the location of individual atomic/molecular lines. These results illustrate how Lux is able to accurately determine spectra for doppelganger stars (i.e., approximately the same ﹩{T}_{{\rm{eff}}},\mathrm{log}\,g﹩, and [Fe/H]) with different individual abundances, and thus accurately identifies the spectral features associated with a given stellar label.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Validation results for RGB stars at lower SNR (low-SNR field RGB-test). Here, we have chosen an SNR range that is expected for the SDSS-V Galactic Genesis survey (J. A. Kollmeier et al. 2017). As in Figure 7, we show the mean ASPCAP uncertainty, bias, and RMSE values for each label in each of the first four panels. In the last two panels we also show the Lux labels for these stars in the Kiel and Tinsley–Wallerstein diagrams. The Lux labels are determined by optimizing the test set latent representations using each star’s spectral fluxes. Overall, the RMSE values obtained are reasonably low and the bias values are approximately equal to the average ASPCAP uncertainty, indicating that the Lux model is able to infer stellar labels at reasonable precision for lower-SNR stars. The full validation for all labels is shown in Figure 16 in Appendix B. See text in Section 4.6 for further details.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Validation for the high-SNR field RGB-test set stars using the 12 labels trained/tested on. Each panel shows the Lux-predicted label values plotted against the ASPCAP values. The Lux labels are determined by optimizing the test set latent representations using each star’s spectral fluxes. Each panel also lists the mean ASPCAP uncertainty for that stellar label (σASPCAP), the mean difference between the Lux model labels and the ASPCAP labels (bias), and the RMSE between the labels. For all labels, the bias is smaller than the average reported ASPCAP uncertainty, indicating that the Lux model is emulating the ASPCAP pipeline well. Similarly, the RMSE value is small across all labels, and for most labels is comparable to the average ASPCAP uncertainty; this indicates that the Lux model is able to robustly estimate stellar labels for high-SNR RGB stars. See text in Section 4.5 for further details.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Six GALAH chemical abundances for 1000 APOGEE giant stars (GALAH-APOGEE field giants-test set), determined using a Lux label transfer model trained on 4000 stars with APOGEE DR17 spectra and GALAH DR3 labels (GALAH-APOGEE field giants-train set). Here the Lux labels are inferred by optimizing the latent representations for test set stars using each star’s spectral fluxes. All these abundances, with the exception of [Na/Fe] and [Ce/Fe], cannot be determined in APOGEE’s ASPCAP but can be determined using our Lux label transfer model. However, while Lux is able to determine these stellar labels, care should be taken to treat these results as causal rather than a correlation with other label information (see text in Section 5 for further details). The horizontal streak of GALAH [Li/Fe] and [Eu/Fe] abundances are for stars that GALAH reported an NaN value and Lux set their abundance value equal to the median of the distribution (and its uncertainty inflated to 9999); this ensures these stars do not influence the training and testing.
Paper: Lux: A Generative, Multioutput, Latent-variable Model for Astronomical Data with Noisy Labels
Authors: Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
Figure caption: Kiel (top) and Tinsley–Wallerstein (bottom) diagrams for stars in the high-SNR field RGB-test sample. We show Lux labels (left), the labels computed using The Cannon (M. Ness et al. 2015) assuming a quadratic relationship in the labels (middle), and the labels from ASPCAP (right). The Lux labels are determined by optimizing the test set latent representations using each star’s spectral fluxes. The samples look qualitatively similar, highlighting that our model works well. However, small differences can be seen in the details; Lux labels appear tighter and show less scatter than The Cannon labels, which in turn appear tighter and show less scatter again than the ASPCAP labels. See text in Section 4.5 for further details.