adsabs/AstroCLIMB huggingface dataset:
Paper: The Comprehensive Archive of Substellar and Planetary Accretion Rates
Authors: S. K. Betti, K. B. Follette, K. Ward-Duong, A. E. Peck, Y. Aoyama, J. Bary, B. Dacus, S. Edwards, G.-D. Marleau, K. Mohamed, J. Palmo, C. Plunkett, C. Robinson, H. Wang
Figure caption: The CASPAR ﹩\dot{M}\mbox{--}M﹩ relation for stars (black circles), BDs (cyan diamonds), and PMCs (magenta squares). The gray dashed lines show accretion rates derived for the same object, and the downward arrows show accretion-rate upper limits. The black line and shaded region shows our best linear fit, ﹩\mathrm{log}\dot{M}=2.02\mathrm{log}M-8.02﹩, and 1σ dispersion, σ = 0.85 dex (﹩\dot{M}﹩ in M ⊙ yr−1 and M in M ⊙), to all accretion rates in CASPAR.
Paper: The Comprehensive Archive of Substellar and Planetary Accretion Rates
Authors: S. K. Betti, K. B. Follette, K. Ward-Duong, A. E. Peck, Y. Aoyama, J. Bary, B. Dacus, S. Edwards, G.-D. Marleau, K. Mohamed, J. Palmo, C. Plunkett, C. Robinson, H. Wang
Figure caption: Difference in accretion rates derived from excess continuum (top) and Hα 10% width (bottom) compared to Hα luminosity as a function of mass for objects with simultaneous Hα luminosity and either excess continuum or Hα 10% width. As mass decreases, we find an offset in accretion rate between these quantities. Accretion rates derived from these simultaneous observations should not physically vary, thereby underscoring systematic methodological differences in calculating accretion rates.
Paper: The Comprehensive Archive of Substellar and Planetary Accretion Rates
Authors: S. K. Betti, K. B. Follette, K. Ward-Duong, A. E. Peck, Y. Aoyama, J. Bary, B. Dacus, S. Edwards, G.-D. Marleau, K. Mohamed, J. Palmo, C. Plunkett, C. Robinson, H. Wang
Figure caption: Residuals between reference and CASPAR accretion rates as a function of CASPAR accretion rate for different accretion diagnostics. 88% of all rederived values changed by less than 0.5 dex, with the line luminosity accretion diagnostic showing the most change in calculated accretion rate. Right: histograms of difference between literature and rederived accretion rates for each tracer. The thin gray lines indicate the FWHM. Colors as in Figure 5.
Paper: The Comprehensive Archive of Substellar and Planetary Accretion Rates
Authors: S. K. Betti, K. B. Follette, K. Ward-Duong, A. E. Peck, Y. Aoyama, J. Bary, B. Dacus, S. Edwards, G.-D. Marleau, K. Mohamed, J. Palmo, C. Plunkett, C. Robinson, H. Wang
Figure caption: CASPAR accretion rate vs. age colored by mass. The accretion rates have been scaled by the ﹩\dot{M}\mbox{--}M﹩ relation to M ∼ 0.7 M ⊙, following Hartmann et al. (2016). The black line and shaded region show the best linear fit and 1σ scatter.
Paper: The Eclipse-mapping Null Space: Comparing Theoretical Predictions with Observed Maps
Authors: Ryan C. Challener, Emily Rauscher
Figure caption: Comparison between the uncertainties on the WASP-18b eclipse map from Coulombe et al. (2023; blue) and a fit that is allowed to explore the null space (red). Colored regions, from dark to light, denote the 68.27%, 95.45%, and 99.73% confidence regions of each fit. As expected, adding the null-space component to the fit increases the uncertainty, but, due to the positivity constraint, the uncertainties are kept finite. Top: equatorial slice of the maps. Bottom: ﹩\cos (\mathrm{latitude})﹩-weighted brightness map. The null space primarily introduces latitudinal variations that inflate the uncertainties at individual locations on the map, but the effect on the average longitudinal structure is minimal.
Paper: The Eclipse-mapping Null Space: Comparing Theoretical Predictions with Observed Maps
Authors: Ryan C. Challener, Emily Rauscher
Figure caption: Comparison of the true planet brightness map, the measurable modes of the true map, the retrieved map from a fit to our best-case precision synthetic data, and the retrieved map from a fit to our extreme-precision synthetic data. The black bars cover regions of the planet that are not visible during the observation. The vertical dotted lines bound the longitudes of the planet that are scanned during both ingress and egress. Within the dashed lines are longitudes scanned by ingress, egress, or both. Outside the dashed lines, all information comes from only the phase-curve variation.
Paper: The Eclipse-mapping Null Space: Comparing Theoretical Predictions with Observed Maps
Authors: Ryan C. Challener, Emily Rauscher
Figure caption: Uncertainties (68.27%, 95.45%, and 99.73% quantiles) of fits to the synthetic WASP-18b extreme-precision eclipse observation with (red) and without (blue) null-space components in the model. The GCM ground-truth and observable components are overplotted. The model without the null space captures the large-scale brightness variations, but the uncertainties are greatly underestimated and do not encompass the ground truth. The model with the null space has significantly increased uncertainties that encompass the truth across much of the dayside, where the observation is sensitive to 2D spatial information.
Paper: The Eclipse-mapping Null Space: Comparing Theoretical Predictions with Observed Maps
Authors: Ryan C. Challener, Emily Rauscher
Figure caption: Hot-spot longitudes of the true and observable maps of WASP-18b compared against the retrieved hot-spot longitudes and phase-curve offset. Vertical lines denote the hot-spot longitudes and phase-curve offset. Note the difference in hot-spot longitudes between the true and observable maps. In black are the histograms of retrieved hot-spot longitudes for the best-case precision and extreme-precision cases showing evidence of a bias in the measured hot-spot location compared to both the true location and the location we expect to observe.
Paper: The Eclipse-mapping Null Space: Comparing Theoretical Predictions with Observed Maps
Authors: Ryan C. Challener, Emily Rauscher
Figure caption: Same as Figure 10 but for the synthetic extreme-precision eclipse observation of HD 209458b. When the null components are included in the model exploration, the model uncertainties encompass the observable modes of the GCM and the unprocessed GCM.
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Distribution of T eff and absolute magnitude for a selection of stellar samples. The T eff values are obtained from the LAMOST pipeline (LASP), while the absolute magnitudes are sourced from the Gaia database. In the graph, the green dots represent stellar samples, encompassing all the data points. The blue crosses indicate the red giants identified by the CBAMResNets classifier, while the orange dots correspond to the red giants in LA_VAC. Additionally, the orange dashed box represents the fitted regions of red giants based on the LA_VAC data. The right and bottom panels of the graph display histograms showcasing the distribution of absolute magnitude and T eff, respectively. The green, orange, and blue curves represent the counts of stellar samples, the counts of red giants in the LA_VAC sample, and the counts of stars with identified red giants, respectively.
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Recall rate of red giants and the precision of non-red giants in the g-band magnitude (differential counts) by Kernel density estimation. The first panel shows the histogram of the distribution of the number of red giants when the interval size of magnitude in the g band is 0.1 mag, and the red solid line indicates the KDE of the number of red giants. The second panel shows the percentage of red giants when the interval size in the g band is 0.1 mag. The third and the fourth panels compare the recall rate of red giants and the precision of non-red giants using CBAMResNets (purple solid line), VGG16 (green dashed line), and TPC (blue dashed line), and the 1σ confidence bands are estimated using bootstrap-sampling methods (Kim & Brunner 2017).
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Recall rate of red giants and the precision of non-red giants in the u-band magnitude (differential counts) by Kernel density estimation. Parameter settings and the meaning of each panel are the same as those in Figure 5.
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Recall rate of red giants and the precision of non-red giants in the g-band magnitude (integrated counts) by Kernel density estimation. The first and second panels compare the recall rate of red giants and the precision of non-red giants for CBAMResNets, TPC, and VGG16. The 1σ error bars are computed following the method from Paterno (2004) to avoid the unphysical errors of binomial or Poisson statistics (Kim & Brunner 2017).
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: The (v − g) and (g − i) profiles from red giant candidates, color coded by LAMOST [Fe/H]. The different colored lines are the (v − g) and (g − i) sequences of stars predicted in the Dartmouth Stellar Evolution Database (Dotter et al. 2008).
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Recall rate of red giants and the precision of non-red giants in the u-band magnitude (integrated counts) by Kernel density estimation. Parameter settings and the meaning of each panel are the same as those in Figure 6.
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: CBAMResNets network structure, where the residual module (a) is the module (a) introduced in Figure 2 and the residual module (b) is the module (b) introduced in Figure 2.
Paper: Variability of Known Exoplanet Host Stars Observed by TESS
Authors: Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper, Teo Močnik, Paul A. Dalba
Figure caption: Variability analysis results for four known exoplanet hosts (from top to bottom): HD 110082, HD 112640, WASP-33, and WASP-8. Left: the normalized TESS lightcurve with a red sinusoidal fit line, TIC ID, and TESS magnitude. Gray points indicate data that has been removed from the analysis due to being flagged as a transit event or >5σ outlier. Center: the L-S periodogram with the strongest one or two periodicities indicated by a gray vertical line. When available from the TOI catalog (Guerrero et al. 2021), the associated TOI number, orbital period, and transit depth are listed. Right: the phase-folded lightcurve, with a red curve indicating the sinusoidal fit. he gray points show all data included in the analysis, and the black points show the binned data. The best-fit period of variability and flux amplitude are also listed.
Paper: Variability of Known Exoplanet Host Stars Observed by TESS
Authors: Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper, Teo Močnik, Paul A. Dalba
Figure caption: A graph comparing the variability period of the host star to the reported orbital period of the planet. The line represents a one-to-one match. The closer a point lies to this line, the closer the variability period and the orbital period match in value. Note that orbital period uncertainties are included in the plot, but are too small to be visible on this scale.
Paper: Variability of Known Exoplanet Host Stars Observed by TESS
Authors: Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper, Teo Močnik, Paul A. Dalba
Figure caption: Left: a scatter plot that represents the mass–radius relationship of known planets around variable stars. The points are colored by their photometric variability amplitude in ppm. Points in gray represent the overall population of confirmed exoplanets from the NASA Exoplanet Archive as of 2022 May 20. Right: a scatter plot of exoplanet radii and their orbital periods for exoplanets around variable host stars (colored) and the overall population of confirmed exoplanets (gray).
Paper: Variability of Known Exoplanet Host Stars Observed by TESS
Authors: Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper, Teo Močnik, Paul A. Dalba
Figure caption: The variable stars with known exoplanets colored by the detection method of their planets. The starred points are the locations of the unique cases KELT-9 (A), PDS 70 (B), and HD 106270 (C).
Paper: Variability of Known Exoplanet Host Stars Observed by TESS
Authors: Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper, Teo Močnik, Paul A. Dalba
Figure caption: Top: a histogram of the known host population’s effective temperature, separated by spectral type. O- and B-type stars are not present within this sample, so they are not represented within this graph. Bottom: a histogram of the known host population’s stellar variability amplitude in log10, separated by spectral type.
Paper: Variability of Known Exoplanet Host Stars Observed by TESS
Authors: Emilie R. Simpson, Tara Fetherolf, Stephen R. Kane, Joshua Pepper, Teo Močnik, Paul A. Dalba
Figure caption: Top: the initial population of variable stars (Fetherolf et al. 2022) colored by their measured photometric variability amplitude in parts per million (ppm). Bottom: the final population of variable known exoplanet host stars.
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Accuracy of prediction in the CBAMResNets network for images of different sizes.
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: Network structure of two residual modules of ResNets34, where (a) is a residual module with a shortcut connection and (b) is a downsampled residual module with a 1 × 1 convolution operation added to (a).
Paper: Red Giants Search Method Based on Convolutional Neural Networks
Authors: Hao Yuan, Meng Liu, Zechao Yang, Yude Bu, Zhenping Yi, Xiaoming Kong, Tianmin Wu, Ran Zhang
Figure caption: CBAM network structure.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: SFHs measured with a 50 pc exaction radius. The red lines show the SFH when the background populations were simultaneously fit. The fit without the background populations is shown in blue for those younger than 50 Myr and green for those older. The top panel shows the most recent 150 Myr of the SFHs, and the bottom panel zooms in on the first 55 Myr, highlighting the young populations measured. The black dotted box in the top panel indicates the limits of the bottom panel plot.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: CMD of the stars within 50 pc of SN 2017eaw shown as yellow circles with the background populations shown in gray scale. Overplotted are isochrones between 10 and 200 Myr with blue (red) representing younger (older) ages. The progenitor star is highlighted in the top right corner of the figure and is shown as a star. The red arrow shows the impact of reddening on our photometry.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: Correlation of pre-SN helium-core mass with the age of a Type II progenitor systems. Black line is the unique correlation for our single-star models at Z = 0.008. The 2D probability density of binary progenitors (red density map) spreads to higher ages compared to single stars (for the same final helium-core mass). For SN 2017eaw, we show with gray the constraints between 4.4 and 6.0  M ⊙ helium-core mass from prior pre-SN imaging, light-curve modeling, and nebular spectra. The two possible population ages found in this work for the 50 pc region around SN 2017eaw, ﹩{16.8}_{-1.0}^{+3.2}﹩ (﹩{85.9}_{-6.5}^{+3.2}﹩) Myr, allowing for star formation up to 50 (150) Myr with no simultaneous fitting of a separate background component, are shown with blue (green) vertical shaded regions (first line of Table 2). The dotted, black line is the prior age estimate for 2017eaw from Williams et al. (2018) of ∼33 Myr without error bars.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: Probability of each evolutionary scenario as a function of measured population age, for progenitors of Type II SNe with final helium cores between ﹩{M}_{\mathrm{He},\mathrm{core}}=4.4\mbox{--}6.0\,{M}_{\odot }﹩, corresponding to the inferred core mass range of SN 2017eaw progenitor. The total probability of all binary scenarios (red line) is subdivided into mass gainers (yellow), MS mergers (pink), and reverse mergers (brown). The gray shaded region shows the probability of a single-star progenitors, as a complement of binary probability, and is concentrated to ages between 11 and 18.2 Myr. In white regions, no scenario can reproduce a progenitor with that age. The two possible population ages found in this work for the 50 pc region around SN 2017eaw, ﹩{16.8}_{-1.0}^{+3.2}﹩ (﹩{85.9}_{-6.5}^{+3.2}﹩) Myr, allowing for star formation up to 50 (150) Myr with no simultaneous fitting of a separate background component, are shown with blue (green) vertical shaded regions (first line of Table 2). The former, blue, falling on the high side of the single-star age regime, and the latter, green, is inconsistent even with the long delay-time of binary scenarios.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: Initial mass distribution of Type II SN progenitors, with final helium core, ﹩{M}_{\mathrm{He},\mathrm{core}}=4.4\mbox{--}6.0\,{M}_{\odot }﹩ and an age estimate between ﹩{16.8}_{-1.0}^{+3.2}﹩ Myr, similar to SN 2017eaw-like events. The normalized distribution of all binaries (red solid line) is subdivided into binary scenarios (mass gainers or MS mergers), and includes both lower and higher mass progenitors than the normalized distribution of single-star progenitors.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: Normalized distribution of the estimated ejecta mass of Type II SN progenitors, with final helium-core masses, ﹩{M}_{\mathrm{He},\mathrm{core}}=4.4\mbox{--}6.0\,{M}_{\odot }﹩ and an age estimate between ﹩{16.8}_{-1.0}^{+3.2}﹩ Myr, similar to SN 2017eaw-like events. We assume that a compact remnant mass of 1.4 M ⊙ and the rest of the final, pre-SN mass is ejected. The normalized distribution of all binaries (red solid line) is subdivided into binary scenarios (mass gainers or MS mergers), and includes a broader spread of progenitors' masses than the normalized distribution of single-star progenitors. We also add the best-fit inferred ejecta mass for SN2017eaw according to Goldberg & Bildsten (2020; vertical dashed magenta line).
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: Our F606W image of the region around SN 2017eaw. The white rings indicate our extraction regions with 50, 100, and 200 pc radii. The red circle indicates the young stellar cluster that seems to dominate our fits beyond 150 pc.
Paper: Considering the Single and Binary Origins of the Type IIP SN 2017eaw
Authors: K. Azalee Bostroem, Emmanouil Zapartas, Brad Koplitz, Benjamin F. Williams, Debby Tran, Andrew Dolphin
Figure caption: Same as Figure 4, for different metallicities. Lower metallicity shifts the distribution toward longer ages. For Z = 0.02, due to stronger stellar winds, there are no giant Type II progenitors with final core mass above ∼8.5 M ⊙.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to 0.20 mag) at fixed, but slightly larger smoothing (0.05 mag) than previously discussed and again for very large populations of 120,000 RGB stars. The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to 0.20 mag) at fixed, but slightly larger smoothing (0.05 mag) than previously discussed and for small populations of RGB stars (1240). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Magnified view of the idealized toy model of the RGB + AGB luminosity function, centered on the discontinuity in the RGB luminosity function at the TRGB. Solid yellow segments, from left to right, show the AGB LF, the discontinuity, and the RGB LF. The blue histogram is a binned version of straight lines used as digital input to the differencing (Sobel) kernel: [−1, 0, +1]. The digital output of the Sobel response-function is shown in red histogram form in the lower portion of the figure. The maximum of the Sobel filter marks the position of the discontinuity. See text for a step-by-step description of the tip detection.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to ±0.20 mag) at fixed smoothing (±0.01 mag) and extremely large populations of RGB stars (about 120,000). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to 0.20 mag) at fixed, but slightly larger smoothing (0.05 mag) than previously discussed and for moderately large populations of RGB stars (11,331). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.