adsabs/AstroCLIMB huggingface dataset:
Paper: Astrometry and Photometry for ≈1000 L, T, and Y Dwarfs from the UKIRT Hemisphere Survey
Authors: Adam C. Schneider, Jeffrey A. Munn, Frederick J. Vrba, Justice Bruursema, Scott E. Dahm, Stephen J. Williams, Michael C. Liu, Bryan N. Dorland
Figure caption: A comparison of proper motions derived from UHS positions in this work to Gaia DR3 (top row), CatWISE 2020 (middle row), and Pan-STARRS derived proper motions in Best et al. (2018; bottom row). The left-hand panels show histograms of the difference between μ  α  measurements in combined σ, with dashed-black lines indicating 3σ differences between measurements. The middle panels show similar histograms for μ  δ . The right-hand panels compare the uncertainty values of the total proper motions for objects, with red symbols indicating smaller uncertainties in either Gaia DR3, CatWISE 2020, or Best et al. (2018) compared to UHS proper motions, and blue symbols indicating smaller uncertainties for UHS proper motions. These are separated by a solid diagonal line indicating 1-to-1 values.
Paper: Astrometry and Photometry for ≈1000 L, T, and Y Dwarfs from the UKIRT Hemisphere Survey
Authors: Adam C. Schneider, Jeffrey A. Munn, Frederick J. Vrba, Justice Bruursema, Scott E. Dahm, Stephen J. Williams, Michael C. Liu, Bryan N. Dorland
Figure caption: Absolute UHS J- and K-band magnitudes as a function of spectral type for L, T, and Y dwarfs observed as part of the UKIRT Hemisphere Survey. The symbol sizes represent distances, with larger symbols corresponding to closer distances. Colors correspond to spectral type. This figure does not include known close binaries (spectroscopic or resolved), young objects, subdwarfs, objects with uncertain types, objects designated “red,” “blue,” or “pec,” or objects with a parallax S/N < 10.
Paper: Astrometry and Photometry for ≈1000 L, T, and Y Dwarfs from the UKIRT Hemisphere Survey
Authors: Adam C. Schneider, Jeffrey A. Munn, Frederick J. Vrba, Justice Bruursema, Scott E. Dahm, Stephen J. Williams, Michael C. Liu, Bryan N. Dorland
Figure caption: Histogram of spectral types for the sample of known L, T, and Y dwarfs in the UHS catalog. Note the logarithmic y-scale. The orange histogram shows the subset of objects from the sample that have newly measured proper motions in this work. The entire sample contains 966 L, T, and Y dwarfs, of which 782 have newly measured proper motions using UHS data.
Paper: Astrometry and Photometry for ≈1000 L, T, and Y Dwarfs from the UKIRT Hemisphere Survey
Authors: Adam C. Schneider, Jeffrey A. Munn, Frederick J. Vrba, Justice Bruursema, Scott E. Dahm, Stephen J. Williams, Michael C. Liu, Bryan N. Dorland
Figure caption: UHS colors (J − K, y − J, J − W2, and K − W2) as a function of spectral type. Median colors for integer types are shown as blue diamonds with errorbars showing the 16 and 84 percentile intervals and values presented in Table 4.
Paper: Astrometry and Photometry for ≈1000 L, T, and Y Dwarfs from the UKIRT Hemisphere Survey
Authors: Adam C. Schneider, Jeffrey A. Munn, Frederick J. Vrba, Justice Bruursema, Scott E. Dahm, Stephen J. Williams, Michael C. Liu, Bryan N. Dorland
Figure caption: The near-infrared spectrum of 2MASS J10271549+5445175 compared to the L2 VL-G object 2MASSW J0045214+163445. Each spectrum is normalized to the J-band peak between 1.27 and 1.29 μm.
Paper: Astrometry and Photometry for ≈1000 L, T, and Y Dwarfs from the UKIRT Hemisphere Survey
Authors: Adam C. Schneider, Jeffrey A. Munn, Frederick J. Vrba, Justice Bruursema, Scott E. Dahm, Stephen J. Williams, Michael C. Liu, Bryan N. Dorland
Figure caption: Objects with new (or updated) sdL classifications. Each spectrum is normalized to the J-band peak between 1.27 and 1.29 μm, and is offset by integer numbers for clarity. Spectral standards are plotted as orange or blue with types labeled in the figure.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A contour plot of the percent error in T eff as a function of the percent errors in R * and F bol, derived from Equation (11). We see that the dependence on M * is much weaker, and with a typical ﹩{\sigma }_{{R}_{* }}﹩ of 3% and a systematic floor of  L * ∼ 2.4%, the resulting constraint on T eff can be ∼1.5%—well below the systematic floor from evolutionary models, SED models, or spectra. In the best cases, we can measure R * to ∼1.7% and T eff uncertainties of just under 1%.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A contour plot of the percent error in R * as a function of the percent errors in  ρ * and M *, derived in Equation (9). We see that the dependence on M * is much weaker, and with a typical ﹩{\sigma }_{\,{\rho }_{* }}﹩ of 10%, the resulting constraint on R * can be well below the systematic floor from evolutionary models. In the best cases, we can measure  ρ * to ∼1%, resulting in R * uncertainties of ∼1.7%—almost entirely dominated by the systematic floor in M *.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A contour plot of the percent error in M * as a function of the percent errors in  ρ * and R *, derived from Equation (7). We see that the dependence on ﹩{\sigma }_{{R}_{* }}﹩ is strong, and with a typical ﹩{\sigma }_{\,{\rho }_{* }}﹩ of 10%, the resulting constraint on M * is not particularly informative. Even with no error in  ρ *, the systematic floor in R * of ∼4.2% implies an error of 13% in stellar mass, which is much larger than the ∼5% presumed for masses derived from evolutionary models.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A plot of the level of systematic error in Gaia as a function of distance. The black line shows a 30 μas systematic uncertainty in Gaia DR3 estimated by Lindegren et al. (2021), which dominates the luminosity systematics at ∼600 and ∼1400 pc when the R * uncertainties are at the systematic limits of 1.7% and 4.2%, respectively, marked as red dashed lines.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A plot of ﹩{\sigma }_{{R}_{* }}﹩ as a function of ﹩{\sigma }_{\,{\rho }_{* }}﹩, assuming ﹩{\sigma }_{{M}_{* }}=5 \% ﹩. The break-even point, where ﹩{\sigma }_{{R}_{* }}=4.2 \% ﹩ from Tayar et al. (2022), is shown as a vertical red dashed line and corresponds to ﹩{\sigma }_{\,{\rho }_{* }}\sim 11.5 \% ﹩.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A contour plot of the error in ﹩\mathrm{log}{g}_{* }﹩ as a function of the percent errors in  ρ * and R *, derived from Equation (19). A typical spectroscopic constraint is 0.1 dex. For precise values of  ρ *, we can do more than an order of magnitude better.
Paper: Beating Stellar Systematic Error Floors Using Transit-based Densities
Authors: Jason D. Eastman, Hannah Diamond-Lowe, Jamie Tayar
Figure caption: A plot of ﹩{\sigma }_{{T}_{\mathrm{eff}}}﹩ as a function of ﹩{\sigma }_{\,{\rho }_{* }}﹩, assuming ﹩{\sigma }_{{M}_{* }}=5 \% ﹩. The break-even point, where ﹩{\sigma }_{{T}_{\mathrm{eff}}}=2.0 \% ﹩ from Tayar et al. (2022), is shown as a vertical red dashed line and corresponds to ﹩{\sigma }_{\,{\rho }_{* }}\sim 10.3 \% ﹩.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Histogram of pixel values for the data shown in Figure 1 (before scaling and normalization). While all diff images (right) show a bell-shaped distribution, the templ and search images show different behaviors regardless of the real or bogus label. In this image, the x range for each subplot extends to cover the full range of values in the distribution. While this display choice decreases one’s ability to discern details in the core of the distribution, it highlights the information on skewness and asymmetry. For example, panels (b) and (d) have similar pixel value distributions; however, panel (b) is bogus, and panel (d) is real. Similarly, panels (a) and (c), a real and a bogus transient, respectively, both show right-skewed distributions for tmpl and srch. The vertical lines show the μ ± 3σ interval for the srch and tmpl images. The diff images (right column) are standardized individually to a mean of zero and a standard deviation of 1. The srch and tmpl are instead scaled, setting the pixel contained inside the 3σ interval (vertical lines on the histograms) to the range 0–1. This allows one to retain negative values or also values above 1 while keeping the core of the distributions within a homogeneous range. In Appendix A.1, we provide more details about the data preprocessing and include additional plots showing the distribution of data before and after the preprocessing tasks.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Saliency map for the four transients in Figure 1 for the DIA-based model. On the left, in gray, the original diff, srch, and tmpl images are plotted in their natural flux scale (before normalization). On the right is the saliency map for the combined image. The intensity of a pixel’s color on the white-to-green scale indicates the pixel’s relative importance; the maps are normalized to 1 individually, such that dark green corresponds to a high saliency score, with 1 corresponding to the most important pixel in the image triplet. With the side-by-side organization of the input data, these maps enable a visual understanding of the importance of each element of the combined image in the RB classification. We note how in some cases (panel (a)), the decision is largely based on the tmpl, rather than the diff image, and in some cases, all three image elements contribute similarly to the decision (panel (b)). This figure is discussed in more detail in Section 4.4.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Confusion matrix for our testing data, a set composed of 10,078 objects labeled as real and 9922 as bogus. The squares of the matrix, from the top left in the clockwise direction, indicate TP (label=0, prediction=0), FN (label=0, prediction=1), TN (label=1, prediction=1), and FP (label=1, prediction=0). We note that here, somewhat unusually, zero corresponds to real and positive and 1 to bogus and negative, as we chose to remain consistent with the original labeling of the data presented in Goldstein et al. (2015). On the left, the confusion matrix of our DIA-based model shows that of the 10,078 transients labeled as real, 9571 (i.e., 95%) were correctly classified, and the rate for the TN objects is even higher at 97%. On the right, the confusion matrix for the noDIA model shows that of the 10,078 transients labeled as real, 9163 (i.e., 91%) were predicted correctly, and the TN rate is 92%.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Confusion matrix reporting the proportion of transients for which the highest concentration of important pixels is found in the diff, srch, or tmpl portion of the input image for the DIA-based model results (left) and noDIA model (right). Left: DIA-based. For 80% (7683) of the 9571 transients correctly classified as real, the classification principally relied on the diff image (I diff > 1/3); 18% (1758) relied on the srch, and 1% (130) relied on the tmpl. For incorrect real classifications, 88% of the images relied principally on diff, 10% on srch, and 2% on tmpl. For incorrect bogus classifications, 79% of the images relied principally on diff, 17% on srch, and 4% on tmpl. For correct bogus classifications, 66% of the images relied principally on diff, 13% on srch, and 21% on tmpl. Right: noDIA. Of the 9163 transients classified correctly as real, for 66% of them, the classification relied principally on the tmpl image. For the incorrect real classifications, 61% of the cases were principally based on tmpl. For the incorrect bogus classifications, 71% of the cases were principally based on tmpl. For correct bogus classifications, 72% of the cases were principally based on tmpl.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Architecture of the neural networks used in this project to classify real and bogus transients. Left: DIA-based model that uses image triplets as input (diff, tmpl, srch). The input layer is 51 × 153 (see left panels of Figure 3); a convolution layer (5 × 5) learns 16 filters; max pooling (2 × 2) and dropout; convolution (5 × 5) learns 32 filters; maximum pooling (2 × 2) and dropout; convolution (5 × 5) learns 64 filters; maximum pooling (2 × 2) and dropout; flatten layer, Dense (32) and the output is a Dense (2)-class layer. Right: noDIA model that uses the tmpl and srch images only. The input layer is 51 × 102 (see right panels of Figure 3); a convolution layer (7 × 7) learns one filter; maximum pooling (2 × 2); convolution (3 × 3) learns 16 filters; maximum pooling (2 × 2) and dropout; convolution (3 × 3) learns 32 filters; maximum pooling (2 × 2) and dropout; flatten layer, Dense (32) and the output was a Dense (2)-class layer. The illustrations were made using NN-SVG tool by LeNail (2019).
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Horizontally stacked data for the examples shown in Figure 1. On the left are the composite images used as input to the DIA-based CNN model: the composite follows the order, from left to right, diff, srch, tmpl. On the right are the composite images used for the noDIA model, composed of srch and tmpl. Each image element was scaled or normalized following the description given in Section 3 and Figure 2 before combining them into a single image. Above each composite are the unique transient ID, original label, and prediction made by our model. The four transients were classified correctly by both DIA-based and noDIA models. Purple indicates negative and green indicates positive pixel values.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Loss (top) and accuracy (bottom) for both models presented in this work. Details of the loss function, early stop and saving strategies, and optimization method are available in Appendix B. Purple lines correspond to the validation data (20% or 20,000 images) and green to the training data (80% or 80,000 images). Left: results for our DIA-based model (Figure 4). Right: results for the noDIA model (Figure 4). Both models show convergence at a >90% accuracy with no major overfitting. This figure is discussed in detail in Section 5.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: The ROC curve for the 20,000 images used for testing. The blue line is for the DIA-based model, and the AUC is 0.992. The orange line is for the noDIA model, and the AUC is 0.973. This figure is discussed in Section 5.
Paper: What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction
Authors: Tatiana Acero-Cuellar, Federica Bianco, Gregory Dobler, Masao Sako, Helen Qu
Figure caption: Same as Figure 8 but for the noDIA model. This figure is discussed in more detail in Section 4.4.
Paper: Characterizing a World Within the Hot-Neptune Desert: Transit Observations of LTT 9779 b with the Hubble Space Telescope/WFC3
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Andrew Allan, Patrick Behr, Simone R. Hagey, Michael D. Himes, Sushuang Ma, Keivan G. Stassun, Luis Thomas, Alexandra Thompson, Aaron Boley, Luke Booth, Jeroen Bouwman, Kevin France, Nataliea Lowson, Annabella Meech, Caprice L. Phillips, Aline A. Vidotto, Kai Hou Yip, Michelle Bieger, Amélie Gressier, Estelle Janin, Ing-Guey Jiang, Pietro Leonardi, Subhajit Sarkar, Nour Skaf, Jake Taylor, Ming Yang, Derek Ward-Thompson
Figure caption: Search for the 1.083 μm helium triplet. While it is expected that the planet is undergoing mass loss, little evidence could be found via this tracer with the data collected with HST WFC3 G102. Top: the higher-resolution HST WFC3 G102 spectrum extracted in this work. The bins are spaced by 0.1 Å but have a bandwidth of 3 nm and, therefore, are overlapping. The location of the peak of the helium triplet signature is indicated by the purple dashed line. The best-fit atmospheric model from Section 3.1 is also shown (red) for context, as are a set of bins which are not overlapping (green). Middle: expected signal of the helium triplet derived from simulations of atmospheric escape driven by photoevaporation (A. Allan et al. 2023, submitted). The line is formed of three segments (dotted, dashed, and dotted–dashed) which together produce the final signal (solid). The expected triplet signature is shown for two He/H ratios. The HST WFC3 G102 data are also shown, with no excess absorption seen in these bins. Bottom: our simulated helium signature when convolved with the bins extracted. As the spectral bins are overlapping, the expected signal in these bins from the triplet appears as a plateau. The size of the signal (∼20 ppm) is much smaller than the uncertainties on each data point (∼135 ppm), even in the 10/90 He/H case. Thus we conclude that the data are not sensitive enough to detect the expected triplet signature.
Paper: Characterizing a World Within the Hot-Neptune Desert: Transit Observations of LTT 9779 b with the Hubble Space Telescope/WFC3
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Andrew Allan, Patrick Behr, Simone R. Hagey, Michael D. Himes, Sushuang Ma, Keivan G. Stassun, Luis Thomas, Alexandra Thompson, Aaron Boley, Luke Booth, Jeroen Bouwman, Kevin France, Nataliea Lowson, Annabella Meech, Caprice L. Phillips, Aline A. Vidotto, Kai Hou Yip, Michelle Bieger, Amélie Gressier, Estelle Janin, Ing-Guey Jiang, Pietro Leonardi, Subhajit Sarkar, Nour Skaf, Jake Taylor, Ming Yang, Derek Ward-Thompson
Figure caption: Plots from our ephemeris fitting for LTT 9779 b. Top: observed minus calculated (O − C) plots for all transit and eclipse midtimes. The reference epoch is set to be halfway between the first and last transit used in this work. Traces from all three models, linear (gray), decay (red), and precession (turquoise), are also shown. Middle: O − C plots once the Spitzer transits have been discarded. In doing so, all evidence for nonlinear ephemerides is also removed. Bottom: fit to the literature radial velocity data. For brevity, we only show the best-fit model to this data when considering a constant period and when the Spitzer transit midtimes have been discarded, which we adopt as our favored model. However, these data are used in all our fits.
Paper: Characterizing a World Within the Hot-Neptune Desert: Transit Observations of LTT 9779 b with the Hubble Space Telescope/WFC3
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Andrew Allan, Patrick Behr, Simone R. Hagey, Michael D. Himes, Sushuang Ma, Keivan G. Stassun, Luis Thomas, Alexandra Thompson, Aaron Boley, Luke Booth, Jeroen Bouwman, Kevin France, Nataliea Lowson, Annabella Meech, Caprice L. Phillips, Aline A. Vidotto, Kai Hou Yip, Michelle Bieger, Amélie Gressier, Estelle Janin, Ing-Guey Jiang, Pietro Leonardi, Subhajit Sarkar, Nour Skaf, Jake Taylor, Ming Yang, Derek Ward-Thompson
Figure caption: One source of orbital precession could be other planets within the system. We show the possible parameter space for the mass and orbital separation of a companion planet (LTT 9779 c) for three possible precession rates: the best-fit value from our model fit (black) and those of the two candidate solutions identified in Pearson (2019; blue and orange). The blue and orange dashed lines are the same as the latter but corrected for the expected precession from other effects calculated in Equation (14). The solid part of the black line represents the possible mass-separation solutions for a companion planet that would provide the best-fit apsidal precession rate from the timing data while remaining undetected in the radial velocity data (K < 5 m s−1) given the residuals of our fit (see Figure 11).
Paper: Characterizing a World Within the Hot-Neptune Desert: Transit Observations of LTT 9779 b with the Hubble Space Telescope/WFC3
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Andrew Allan, Patrick Behr, Simone R. Hagey, Michael D. Himes, Sushuang Ma, Keivan G. Stassun, Luis Thomas, Alexandra Thompson, Aaron Boley, Luke Booth, Jeroen Bouwman, Kevin France, Nataliea Lowson, Annabella Meech, Caprice L. Phillips, Aline A. Vidotto, Kai Hou Yip, Michelle Bieger, Amélie Gressier, Estelle Janin, Ing-Guey Jiang, Pietro Leonardi, Subhajit Sarkar, Nour Skaf, Jake Taylor, Ming Yang, Derek Ward-Thompson
Figure caption: The orbital period derivative in milliseconds per year expected for LTT 9779 b given different values of the modified stellar tidal quality factor ﹩{Q}_{* }^{{\prime} }﹩. The right-hand vertical axis shows the expected O − C signal over the current observational baseline and the gray shaded region represents the average timing uncertainty of the data. Three values of ﹩{Q}_{* }^{{\prime} }﹩ are highlighted: (1) derived with Equation (11) from the best-fit orbital decay rate when the Spitzer transit timings were included, (2) predicted with Equation (13) from the tidal forcing period of the system, and (3) the value for WASP-12 from Wong et al. (2022).
Paper: Characterizing a World Within the Hot-Neptune Desert: Transit Observations of LTT 9779 b with the Hubble Space Telescope/WFC3
Authors: Billy Edwards, Quentin Changeat, Angelos Tsiaras, Andrew Allan, Patrick Behr, Simone R. Hagey, Michael D. Himes, Sushuang Ma, Keivan G. Stassun, Luis Thomas, Alexandra Thompson, Aaron Boley, Luke Booth, Jeroen Bouwman, Kevin France, Nataliea Lowson, Annabella Meech, Caprice L. Phillips, Aline A. Vidotto, Kai Hou Yip, Michelle Bieger, Amélie Gressier, Estelle Janin, Ing-Guey Jiang, Pietro Leonardi, Subhajit Sarkar, Nour Skaf, Jake Taylor, Ming Yang, Derek Ward-Thompson
Figure caption: Changes to the retrieved abundances of H2O, CO2, and FeH when one of the other molecules is removed from the retrieval. In all three cases, the model without the molecule is not preferred to the full setup. Comparing the Bayesian evidence suggests the presence of H2O is preferred by 3.11σ while there is a 2.35σ preference for the inclusion of CO2 and a 2.12σ preference for FeH. All these retrievals were conducted with an isothermal temperature–pressure profile.