adsabs/AstroCLIMB huggingface dataset:
Paper: The CHILES Continuum and Polarization Survey. I. Survey Design and Noise Characterization
Authors: Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D.J. Pisano, Emmanuel Momjian, Chris Hales
Figure caption: Distribution functions of observed P(D), Gaussian noise, Lévy distribution, and modeled P(D) are shown with red solid, black-dashed, black-dashed–dotted, and blue solid lines, respectively.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: Mass–radius diagram for LP 791-18 d (square) and c (triangle), with updated measurements from this work (filled markers from the damped eccentricity TTV results, open markers for the free eccentricity TTV results) compared to the mass constraints from P23 (gray markers). Filled circles represent all small (<3 R⊕) planets orbiting M dwarfs (T* < 3900 K) with masses and radii measured to better than 3σ, based on the NASA Exoplanet Archive list of confirmed planets as of 2024 November. The predicted equilibrium temperatures of the planets are indicated by the point color. For comparison, we also plot Earth-like water-rich mass–radius curves (for 10, 30, and 50% water mass fractions) from A. Aguichine et al. (2021) and pure iron, Earth-like, and rocky iso-density curves from L. Zeng et al. (2016).
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: Distributions for the eclipse-timing offsets of LP 791-18 d, using 104 draws from the period, e, and ω distributions of the planets in the damped (red) and nondamped (blue) free eccentricity TTV retrievals.
Paper: Updated Mass, Eccentricity, and Tidal Heating Constraints for the Earth-sized Planet LP 791-18 d
Authors: Michael Greklek-McKeon, Heather A. Knutson, W. Garrett Levine, Renyu Hu, Morgan Saidel, Jonathan Gomez Barrientos, Konstantin Batygin, Björn Benneke
Figure caption: Corner plot of posteriors for TTV model parameters for LP 791-18 d and c from the damped eccentricity (red) and nondamped (blue) versions of the TTV fit, made with the corner package (D. Foreman-Mackey 2016). Fit parameters included planet-to-star mass ratios but we have converted these distributions into units of Earth masses for ease of reference. Columns labels are displayed for the undamped fit results.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Search for the optimal size of the training set. The figure shows the magnitude and a moving average of the magnitude of a weighted loss function (Equation (2)) for the neural network predictions and true values for different sizes of training data sets. We found that the best training size is around 10,000 examples. Both curves display fluctuations due to stochastic training effects, but beyond 10,000 examples typical loss values plateau and show no significant improvement. The Bayesian loss remains flat because its predictions are fixed and independent of training size.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Residuals between Bayesian predictions and true values plotted against u magnitude (top), Mr (middle), and differences in [Fe/H] (bottom). The full red line represents the median residuals, the dashed red line represents the standard deviation of the residuals, and the density of residuals is shown on a color scale.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the Bayesian algorithm for Mr. The color code in the left panel shows the median of Mr in each pixel, that in the middle panel shows the median residuals ΔMr (Bayesian – ground truth), and that in the right panel shows the standard deviation of the residuals.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the Bayesian algorithm for Ar. The color code in the left panel shows the median of Ar in each pixel, that in the middle panel shows the median residuals ΔAr (Bayesian – ground truth), and that in the right panel shows the standard deviation of the residuals.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Comparison between Bayesian estimates and ground truth for Mr, Ar, and [Fe/H] using simulated data. The dashed red line indicates the 1:1 correlation. The data set includes 434,865 stars with a minimum magnitude SNR of 5 in all bands. The color scale represents the density of points.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the Bayesian algorithm for [Fe/H]. The color code in the left panel shows the median of [Fe/H] in each pixel, that in the middle panel shows the median residuals Δ[Fe/H] (Bayesian – ground truth), and that in the right panel shows the standard deviation of the residuals.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Comparison between NN and Bayesian estimates for Mr, Ar, and [Fe/H] using simulated data. The dashed red line indicates the 1:1 correlation. The data set includes 434,865 stars with a minimum magnitude SNR of 5 in all bands. The color scale represents the density of points.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for Mr. Analogous to Figure 3 except the residuals are defined as (NN – Bayesian).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Histograms of residuals between Bayesian estimates and ground truth for Mr (left), Ar (middle), and [Fe/H] (right). The red curves show a normal distribution (N(0, 1)) for comparison.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Residuals between NN predictions and Bayes estimates plotted against u magnitude (top), Mr (middle), and differences in [Fe/H] (bottom). The full red line represents the median residuals, the dashed red line represents the standard deviation of the residuals, and the density of residuals is shown on a color scale. Limits of the y-axis are halved when compared to Figure 6.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Histograms of residuals between NN and Bayesian estimates for Mr (left), Ar (middle), and [Fe/H] (right). The red curves show a normal distribution (N(0, 1)) for comparison.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Residuals between NN predictions and true values plotted against u magnitude (top), Mr (middle), and differences in [Fe/H] (bottom). The full red line represents the median residuals, the dashed red line represents the standard deviation of the residuals, and the density of residuals is shown on a color scale.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Comparison between NN and ground truth estimates for Mr, Ar, and [Fe/H] using simulated data. The dashed red line indicates the ideal 1:1 correlation. The data set includes 434,865 stars with a minimum magnitude’s SNR of 5 in all bands.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for Ar. Analogous to Figure 4 except the residuals are defined as (NN – Bayesian).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for [Fe/H]. Analogous to Figure 5 except the residuals are defined as (NN – Bayesian).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for Mr. Analogous to Figure 3 except the residuals are defined as (NN – ground truth).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for Ar. Analogous to Figure 4 except the residuals are defined as (NN – ground truth).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Performance of the NN algorithm for [Fe/H]. Analogous to Figure 5 except the residuals are defined as (NN – ground truth).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Demonstration of the median [Fe/H] as a function of assumed u − g errors, using Gaussian distributions of u − g, for three different u − g colors: 0.89 (circles), 0.99 (squares), and 1.09 (triangles). Lines represent the true values of [Fe/H], open symbols correspond to medians of a uniform [Fe/H] distribution (as shown in Figure 28, while closed symbols represent the case where [Fe/H] is composed of two concatenated Gaussian distributions of equal size, centered at [Fe/H] = −1.5 and [Fe/H] = −0.5, with standard deviations of 0.3 dex and 0.2 dex, respectively, as shown in Figure 29.
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Uniform toy model illustrating the bias in photometric metallicity estimation caused by significant errors in u − g color. The panel shows approximately 40,000 small dots, generated according to the process outlined in the text. The dashed line represents Equation (A1). The large symbols represent the median [Fe/H] values within each bin of u − g color. The figure is generated by sampling [Fe/H] from a uniform distribution before calculating Equation (A1).
Paper: Accelerating Stellar Photometric Distance Estimates with Neural Networks
Authors: Karlo Mrakovčić, Željko Ivezić, Lovro Palaversa
Figure caption: Binormal toy model illustrating the bias in photometric metallicity estimation caused by significant errors in u − g color. Analogous to Figure 28 except it uses a distribution made of two equal-size concatenated samples from the Gaussian distributions centered on [Fe/H] = −1.5 and [Fe/H] = −0.5, and with σ equal to 0.3 dex and 0.2 dex, respectively.
Paper: The True Stellar Obliquity of a Sub-Saturn Planet from the Tierras Observatory and the Keck Planet Finder
Authors: Patrick Tamburo, Samuel W. Yee, Juliana García-Mejía, Gudmundur Stefánsson, David Charbonneau, Allyson Bieryla, Andrew W. Howard, Howard Isaacson, Benjamin J. Fulton, Aaron Householder
Figure caption: Photometric data used for our initial rotation period search. Each row shows a different data set. Note the different x and y scales for the light curves. LS periodograms of each data set are shown in blue to the right of their respective light curves, with the window function of the data shown in orange. The FAP = 0.01 level is indicated with a dashed green line. We also show a zoom-in on the periodograms from 5 to 40 days. A significant, narrow peak is detected in the Tierras data at 23.5 days.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Left: the larger black point marks the location of an example test-set star overlaid on the full training set (color coded by [Fe/H]) shown in a Kiel diagram (upper left) and in the [C/N]–age relation (lower left). In the [C/N]–age plot, a red error bar indicates the model’s age estimate and uncertainty for the star. Right: age posterior for the example star. The black curve represents the full posterior computed with the input uncertainties, with the solid orange line indicating the maximum likelihood and dashed lines showing 1σ error bars. The red dashed line indicates the asteroseismic age, while the blue histogram displays the age distribution of training-set stars that are nearby in input parameter space. Additional example stars from the test set are shown in Appendix C.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Parameter space coverage of stars with a training space density above 3 × 109 (color coded by density) compared to the full DR19 data set (black points). These are scatterplots with each stars training density used for color coding. Regions with higher densities are better sampled in the training data, yielding more reliable age estimates. Upper panel: Kiel diagram. Lower panel: [Fe/H] vs. surface gravity (﹩\mathrm{log}\,g﹩). The sharp banded features seen in the black points are artifacts from the ASPCAP grid edges. This figure is repeated in Appendix B.1 with different training space density thresholds.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Upper panel: 2D probability distribution of [C/Fe] and [N/Fe] for the star depicted in Figure 2. The red dot marks the maximum likelihood, and the red line indicates the 1σ boundary. The orange point and error bar shows the star’s measured [C/Fe] and [N/Fe] and associated uncertainties. The diagonals show lines of constant [C/N]. Lower panel: a similar plot for a different star with a higher [C/Fe] than the model predicts. Both plots exhibit slight banding features: these are most likely artifacts of the normalizing flow model overfitting to local noise or small-scale features.
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Parameter space coverage of the training data. Left: Kiel diagram of the training set color coded by [Fe/H]. Right: [C/N]–age relation of the training set color coded by [Fe/H].
Paper: StarFlow: Leveraging Normalizing Flows for Stellar Age Estimation in SDSS-V DR19
Authors: Alexander Stone-Martinez, Jon A. Holtzman, Yuxi(Lucy) Lu, Sten Hasselquist, Julie Imig, Emily J. Griffith, Earl P. Bellinger, Andrew K. Saydjari
Figure caption: Scatterplot comparing model-derived maximum-likelihood ages to asteroseismic ages for the test set. Points are color coded by iron abundance ([Fe/H]). The red line indicates the one-to-one relation where both ages agree.
Paper: No Influence of Passing Stars on Paleoclimate Reconstructions Over the Past 56 Million Years
Authors: Richard E. Zeebe, David M. Hernandez
Figure caption: Divergence times, τ’s, vs. impulse gradient (﹩I/{d}_{{\rm{\min }}}﹩) shown for runs with strong close encounters ﹩I/{d}_{{\rm{\min }}}\gt 200﹩ m s−1 pc−1 that occur within the time interval [−20 0] Myr. Note that flybys with ﹩I/{d}_{{\rm{\min }}}\ll 200﹩ m s−1 pc−1 are very frequent (multiple per run) and exhibit no relationship with τ at all (not shown, see text). Also note the logarithmic abscissa.
Paper: Accelerating Long-period Exoplanet Discovery by Combining Deep Learning and Citizen Science
Authors: Shreshth A. Malik, Nora L. Eisner, Ian R. Mason, Sofia Platymesi, Suzanne Aigrain, Stephen J. Roberts, Yarin Gal, Chris J. Lintott
Figure caption: Volunteer and PlaNet model predictions on example light curves in test sectors. (a) TIC 252939798, sector 60. An example of a cTOI found by PlaNet but not by the volunteer pipeline. (b) TIC 457351794 Sector 65. An example of a single-transit PHT cTOI found by both volunteers and the model. (c) TIC 284361752 Sector 59. An example of a PHT cTOI missed by PlaNet but found by the volunteer pipeline. This is a shallow transit event. (d) TIC 150031040, Sector 65. An example of a false positive (eclipsing binary) for both volunteers and the model.
Paper: Accelerating Long-period Exoplanet Discovery by Combining Deep Learning and Citizen Science
Authors: Shreshth A. Malik, Nora L. Eisner, Ian R. Mason, Sofia Platymesi, Suzanne Aigrain, Stephen J. Roberts, Yarin Gal, Chris J. Lintott
Figure caption: (a) Simplified schematic of the proposed PlaNet architecture featuring a series of Convolutional Blocks (Conv-Blocks) with residual connections (K. He et al. 2016), followed by two Dense-Blocks. (b) Ordering of layers in the Conv-Blocks. (c) Ordering of layers in the Dense-Blocks. See main text for details and Appendix B for hyperparameters.
Paper: Accelerating Long-period Exoplanet Discovery by Combining Deep Learning and Citizen Science
Authors: Shreshth A. Malik, Nora L. Eisner, Ian R. Mason, Sofia Platymesi, Suzanne Aigrain, Stephen J. Roberts, Yarin Gal, Chris J. Lintott
Figure caption: Candidates (cTOIs) found by PlaNet, originally found by other teams. See Table 4 for details.
Paper: Accelerating Long-period Exoplanet Discovery by Combining Deep Learning and Citizen Science
Authors: Shreshth A. Malik, Nora L. Eisner, Ian R. Mason, Sofia Platymesi, Suzanne Aigrain, Stephen J. Roberts, Yarin Gal, Chris J. Lintott
Figure caption: Fractional recovery of TOIs (left) and precision (right) of top-ranked predictions, averaged across test sectors. The shaded areas indicate the standard deviation across sectors. The optimal and random curves are averages across sectors. PlaNet significantly outperforms Volunteers in both precision and recall.
Paper: Accelerating Long-period Exoplanet Discovery by Combining Deep Learning and Citizen Science
Authors: Shreshth A. Malik, Nora L. Eisner, Ian R. Mason, Sofia Platymesi, Suzanne Aigrain, Stephen J. Roberts, Yarin Gal, Chris J. Lintott
Figure caption: Receiver operating characteristic for TOIs and PHT cTOIs. PlaNet outperforms Volunteers on both classes.
Paper: TrES-1 b: A Case Study in Detecting Secular Evolution of Exoplanet Orbits
Authors: Simone R. Hagey, Billy Edwards, Angelos Tsiaras, Aaron C. Boley, Anastasia Kokori, Norio Narita, Pedro V. Sada, Filip Walter, Robert T. Zellem, Napaporn A-thano, Kevin B. Alton, Miguel Ángel Álava Amat, Paul Benni, Emmanuel Besson, Patrick Brandebourg, Marc Bretton, Mauro Caló, Martin Valentine Crow, Jean-Christophe Dalouzy, Marc Deldem, Tõnis Eenmäe, Stephane Ferratfiat, Pere Guerra, Gary Vander Haagen, Ken Hose, Adrian Jones, Yves Jongen, Didier Laloum, Stefano Lora, Alessandro Marchini, Jacques Michelet, Matej Mihelčič, Johannes Mieglitz, Eric Miny, David Molina, Mario Morales Aimar, Raphael Nicollerat, Ivo Peretto, Manfred Raetz, François Regembal, Robert Roth, Lionel Rousselot, Mark Salisbury, Darryl Sergison, Anaël Wünsche, Jaroslav Trnka
Figure caption: Coordinate system used in the modeling by OrbDot. The positive y-axis points toward the observer, while the x- and z-axes define the sky plane. The planet’s orbit is shown in blue with the argument of pericenter (ωp) measured from the positive x-axis along the direction of motion. The transit and secondary eclipse points are labeled.
Paper: TrES-1 b: A Case Study in Detecting Secular Evolution of Exoplanet Orbits
Authors: Simone R. Hagey, Billy Edwards, Angelos Tsiaras, Aaron C. Boley, Anastasia Kokori, Norio Narita, Pedro V. Sada, Filip Walter, Robert T. Zellem, Napaporn A-thano, Kevin B. Alton, Miguel Ángel Álava Amat, Paul Benni, Emmanuel Besson, Patrick Brandebourg, Marc Bretton, Mauro Caló, Martin Valentine Crow, Jean-Christophe Dalouzy, Marc Deldem, Tõnis Eenmäe, Stephane Ferratfiat, Pere Guerra, Gary Vander Haagen, Ken Hose, Adrian Jones, Yves Jongen, Didier Laloum, Stefano Lora, Alessandro Marchini, Jacques Michelet, Matej Mihelčič, Johannes Mieglitz, Eric Miny, David Molina, Mario Morales Aimar, Raphael Nicollerat, Ivo Peretto, Manfred Raetz, François Regembal, Robert Roth, Lionel Rousselot, Mark Salisbury, Darryl Sergison, Anaël Wünsche, Jaroslav Trnka
Figure caption: RV data and best-fit orbital model for TrES-1 b. Top left: RV measurements over time, with data points shifted by the systemic RV and observation times shifted to the reference time, t0. Bottom left: residuals obtained by subtracting the best-fit RV model from the data. Right: phase-folded RV measurements plotted against mean anomaly, with 300 random samples from the model posterior shown in gray to represent model uncertainty. Data sources are color coded as indicated in the legend.
Paper: TrES-1 b: A Case Study in Detecting Secular Evolution of Exoplanet Orbits
Authors: Simone R. Hagey, Billy Edwards, Angelos Tsiaras, Aaron C. Boley, Anastasia Kokori, Norio Narita, Pedro V. Sada, Filip Walter, Robert T. Zellem, Napaporn A-thano, Kevin B. Alton, Miguel Ángel Álava Amat, Paul Benni, Emmanuel Besson, Patrick Brandebourg, Marc Bretton, Mauro Caló, Martin Valentine Crow, Jean-Christophe Dalouzy, Marc Deldem, Tõnis Eenmäe, Stephane Ferratfiat, Pere Guerra, Gary Vander Haagen, Ken Hose, Adrian Jones, Yves Jongen, Didier Laloum, Stefano Lora, Alessandro Marchini, Jacques Michelet, Matej Mihelčič, Johannes Mieglitz, Eric Miny, David Molina, Mario Morales Aimar, Raphael Nicollerat, Ivo Peretto, Manfred Raetz, François Regembal, Robert Roth, Lionel Rousselot, Mark Salisbury, Darryl Sergison, Anaël Wünsche, Jaroslav Trnka
Figure caption: Mass vs. orbital period for a hypothetical companion planet that is driving apsidal precession. The solid black line and surrounding gray shaded region correspond to the best-fit precession rate and 68% credible interval, ﹩\dot{\omega }=0.0005{8\,}_{-0.00009}^{+\,0.00016}﹩rad E−1. The dashed gray lines show additional precession rates for comparison (10−2, 10−3, and 10−4 rad E−1). The location of TrES-1 b is marked, dividing the plot into interior and exterior orbits. The green shaded area shows the region where the RV amplitude, Kc, is within the residuals of the TrES-1 b orbit fit (i.e., Kc ≪ 30, see Figure 3), which rules out the remaining parameter space (hatched).