adsabs/AstroCLIMB huggingface dataset:
Paper: Dust Production Rates in Jupiter-family Comets: A Two Year Study with ATLAS Photometry
Authors: A. Fraser Gillan, Alan Fitzsimmons, Larry Denneau, Robert J. Siverd, Ken W. Smith, John L. Tonry, David R. Young
Figure caption: JFC maximum activity as a function of heliocentric distance is shown. The 							minimum A(0°)f ρ for the detection of the coma is 							displayed by the green dashed line, assuming m(o) = 18.5. The water–ice 							sublimation line is shown in cyan.
Paper: Dust Production Rates in Jupiter-family Comets: A Two Year Study with ATLAS Photometry
Authors: A. Fraser Gillan, Alan Fitzsimmons, Larry Denneau, Robert J. Siverd, Ken W. Smith, John L. Tonry, David R. Young
Figure caption:  A(0°)f ρ projected to 2 au vs. perihelion 							distance for each JFC. The JFCs projected A(0°)f ρ at 2 au are shown in orange and the 							projected A(0°)f ρ at 2 au postperihelion is shown in 							green. The red dashed line links the observations of the same JFC 							preperihelion to postperihelion.
Paper: Dust Production Rates in Jupiter-family Comets: A Two Year Study with ATLAS Photometry
Authors: A. Fraser Gillan, Alan Fitzsimmons, Larry Denneau, Robert J. Siverd, Ken W. Smith, John L. Tonry, David R. Young
Figure caption:  A(0°)f ρ for comet 6P/d’Arrest during 							preperihelion dates when it exhibited constant values within the 							measurement uncertainties. Plots (b) and (c) show the same for 							156P/Russell–LINEAR and 254P/McNaught, respectively. The orange and cyan 							points in the plots represent the o-band 							and c-band filters.
Paper: Dust Production Rates in Jupiter-family Comets: A Two Year Study with ATLAS Photometry
Authors: A. Fraser Gillan, Alan Fitzsimmons, Larry Denneau, Robert J. Siverd, Ken W. Smith, John L. Tonry, David R. Young
Figure caption: Every JFC reaching perihelion in 2020 and 2021 visible to ATLAS. Blue 							stars mark the perihelion of each comet, coral/pink represents the 							preperihelion R  h  range coverage by ATLAS, and black represents the postperihelion 								R  h  range coverage by ATLAS.
Paper: Dust Production Rates in Jupiter-family Comets: A Two Year Study with ATLAS Photometry
Authors: A. Fraser Gillan, Alan Fitzsimmons, Larry Denneau, Robert J. Siverd, Ken W. Smith, John L. Tonry, David R. Young
Figure caption: The top panel (a) shows the activity index, n, for the JFC subset preperihelion. Plot (b) shows 								n for JFCs postperihelion/postmaximum. 							Binned to Δn = 3.
Paper: Interpolation and Synthesis of Sparse Samples in Exoplanet Atmospheric Modeling
Authors: Jacob Haqq-Misra, Eric T. Wolf, Thomas J. Fauchez, Ravi K. Kopparapu
Figure caption: The left panel shows a uniformly distributed sparse sample of points calculated by the ExoCAM model for the SAMOSA exoMIP (Haqq-Misra et al. 2022), with two examples of spatial separation vectors,  h , drawn from point 3 at the base to points 7 and 12. The right panel shows the variogram from this sparse sample of ExoCAM calculations, which is calculated by assessing all pairs of points in the sample, with gray bars showing the experimental variogram as a histogram of dissimilarities, γ*, vs. spatial separation, ∣ h ∣, and a black line showing the best-fit theoretical variogram. The trend of increasing dissimilarity with increasing spatial separation is a typical pattern.
Paper: Interpolation and Synthesis of Sparse Samples in Exoplanet Atmospheric Modeling
Authors: Jacob Haqq-Misra, Eric T. Wolf, Thomas J. Fauchez, Ravi K. Kopparapu
Figure caption: The left two panels show the distribution of surface average temperature for the ExoCAM (top) and ExoPlaSim (bottom) model calculations of SAMOSA case 4 (with 1200 W m−2 and 2.34 bar). The right panel shows the theoretical variogram curves for both models. The warmer substellar point is one contributing factor to the ExoCAM variogram showing greater dissimilarities at all spatial separations when compared to ExoPlaSim.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: An overview of the inputs for each step in our processing pipeline. (a) A region in the HiRISE image of ESP_018707_2205 is shown at the native resolution (0.3 m pixel−1), with a blue box highlighting the window size for our classifier network. (b) A 256 × 256 pixel window is used as input for our spatial classifier algorithm; however, the window is at 1/16 the original resolution (1.2 m pixel−1). A grid of 8 × 8 squares is displayed, showing how that image gets tiled and preprocessed using parts of a JPEG encoder, which involves the discrete cosine transform (panel (d)). (c) A single 8 × 8 tile flattened into a 1D array, which is used for the DCT transform (panel (d)). (e) A block of Fourier coefficients is rearranged into a data cube and used as input for our Fourier classifier. Reducing the image size and ultimately the channel size after the first conv. layer significantly shortens the network’s processing time compared to the spatial image input.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: A distribution of regions that contain brain terrain displaying latitude on the left and local time on the right. The confirmed detections were manually checked after our algorithms flagged an image, whereas the candidate detections have not been manually vetted. The latitude distribution has a peak around 40N and another peak around 40S. The preferential distribution in latitude suggests that whatever process formed brain terrain also ties to certain climate conditions. Our brain terrain detections and training data span a range of local times, suggesting that the algorithm is robust against a range of illumination conditions.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: A map of candidate regions that contain brain terrain plotted over elevation data. Our survey vetted 58,209 HiRISE images down to 201 confirmations and 1141 candidates. The orange points are images flagged by the classifier and segmentation algorithm, while the green points were once orange but have been manually vetted by our team afterward. The elevation data are blended from the Mars Orbiter Laser Altimeter (MOLA), an instrument aboard NASA's Mars Global Surveyor spacecraft (MGS), and the High-Resolution Stereo Camera (HRSC), an instrument aboard the European Space Agency's Mars Express (MEX) spacecraft.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: A comparison of outputs between our final classifier and segmentation network overlaid on the image ESP_016287_2205. Left: an output from our classifier corresponding to detections from the brain terrain class. The pixelated output corresponds to the field of view for the classifier (∼64 × 64 m region at 1/16 resolution). Right: a segmentation mask at the native image resolution from the U-Net algorithm for the brain terrain class. The brain terrain is highlighted in green in both images.
Paper: Mapping “Brain Terrain” Regions on Mars Using Deep Learning
Authors: Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alexander M. Morgan
Figure caption: A plot comparing the training behavior for the networks used in our survey. The classifier leverages data in the Fourier domain from a spatial image at 1/16 the original resolution, whereas the U-Net algorithm uses spatial data at the native resolution and a MobileNetV3 architecture. We found that training the U-Net in pieces yielded better testing performance.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: The TI estimates for asteroid (152671) 1998 HL3 generated with the ASTERIA model. The top panel shows the full distribution obtained for our nominal rotation period solution. The bottom panel shows how the results change with the rotation period, highlighting the importance of reliable determination of this parameter. The median values of Γ and their corresponding lower and upper 1σ uncertainties are shown. The vertical gray area in the bottom panel indicates the result for the nominal period solution. See text for additional details.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: TI vs. diameter values for objects with available TI estimates. Our sample of TI estimates for 38 asteroids (black triangles) is shown along with the thermophysically derived literature values (orange circles) (see MacLennan & Emery 2021; Hung et al. 2022, and references therein). Note that numbers from the literature include multiple instances of the same asteroid. Additionally, our two previous estimates for asteroids 2011 PT (Fenucci et al. 2021) and 2016 GE1 (Fenucci et al. 2023a) are shown as red squares and indicated in the plot.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Posterior distributions for key decision tree biosignature species (modern Earth). (a) Posterior distributions for the H2O abundance derived from simulated S/N 10 observations at various combinations of wavelength coverage. The “All three” combination encompasses the 0.89 μm bandpass, the 0.75 μm bandpass, and the 1.65 μm bandpass (gray filled). The singular observational bandpasses include the 0.75 μm bandpass (red), the 0.89 μm bandpass (blue hatched), and the 1.65 μm bandpass (yellow). The paired observations include the 0.75 μm and 0.89 μm bandpass pair (purple), the 0.75 μm and 1.65 μm bandpass pair (orange), and the 0.89 μm and 1.65 μm bandpass pair (green hatched). For CH4, O2, and CO2, the black vertical dashed lines indicate the surface values given by the input atmospheric model. The input value for H2O (also indicated by a vertical dashed line) is taken to be the column average mixing ratio. (b) The same as (a), but for the CH4 abundance. The gray vertical dashed line represents a modeled estimate for an Archean-Earth-like abundance of CH4 (3.5 × 10−2) from Robinson & Reinhard (2020) for comparison to the modern value. (c) The same as (a) and (b), but for the O2 abundance. (d) The same as (a), (b), and (c), but for the CO2 abundance.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Posterior distributions for key decision tree species (Archean Earth pathway). (a) Marginal posterior distributions for the H2O abundance derived from the simulated S/N 10 observations covering two singular bandpasses—the 0.89 μm bandpass and the 1.65 μm bandpass—and a combination of the two. The posterior for the 0.89 μm bandpass is illustrated in the blue hatched distribution, the 1.65 μm posterior is the yellow distribution, and the combination of the two is the green filled distribution. For CH4 and CO2, the black vertical dashed lines indicate the surface values given by the input atmospheric model. The input value for H2O (also indicated by a vertical dashed line) is taken to be the column average mixing ratio. (b) The same as (a), but for the CH4 abundance. (c) The same as (a) and (b), but for the CO2 abundance. An additional retrieved posterior is included for a simulated S/N 20 observation with the 1.65 μm singular bandpass (purple filled).
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Biosignature abundance constraints broken down by observational bandpass combinations. (a) Chemical species abundance constraints for the 0.75 μm bandpass. The abundance constraints for O2 (purple hatched), H2O (purple filled), CO2 (red), O3 (gray), and CH4 (yellow filled) are all shown. (b) Abundance constraints for the same species, but for the singular 0.89 μm bandpass observation. (c) Constraints for the same species, but for the singular 1.65 μm bandpass observation. (d) Constraints for the same species, but for the 0.75 μm and 0.89 μm bandpass pair. (e) Constraints for the same species, but for the 0.75 μm and 1.65 μm bandpass pair. (f) Constraints for the same species, but for the 0.89 μm and 1.65 μm bandpass pair. (g) Constraints for the same species, but for “All three” bandpasses combined (i.e., 0.75 μm and 0.89 μm and 1.65 μm bandpasses).
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Simulated reflected light spectrum of modern Earth. Shown here is the reflected light spectrum of modern Earth with coronagraph wavelength channels indicated along the top for the UV (0.2–0.4 μm), the visible (0.4–1.0 μm), and the NIR (1.0–1.8 μm). Individual 20% bandpasses are indicated according to the decision tree strategy, with the 0.75 μm bandpass (0.68–0.81 μm) shown as the dark green shaded region, the 0.89 μm bandpass (0.81–0.97 μm) shown as the green shaded region, and the 1.65 μm bandpass (1.50–1.80 μm) shown as the red shaded region. The Rayleigh scattering slope and absorption features of key species are labeled with text, along with an error-bar scaling for an S/N of 10 relative to 0.68 μm. Note that atmospheric retrievals were performed on individual or a combination of these three bandpasses.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Modeled haze-free Archean Earth spectrum. Shown here is the planet-to-star flux ratio vs. wavelength and in black is the simulated spectrum for a haze-free Archean-Earth-like planet around the Sun. The spectral bandpasses from the Archean Earth pathway on the decision tree are shown, where the 0.89 μm bandpass is shaded in green and the 1.65 μm bandpass is shaded in red. The Rayleigh scattering slope and species absorption features are labeled with text. Also shown is an S/N 10 error-bar scaling indicative of the simulated observations for each singular bandpass and combination.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Posterior distributions for global surface pressure and global temperature. (a) Posterior distribution for global surface pressure of the planet derived from simulated S/N 10 observations and all of the decision tree combinations of wavelength coverage. For each parameter, the black vertical dashed lines indicate the surface values given by the input atmospheric model. (b) The same as (a), but for temperature. Note that the retrieved spread would be representative of temperatures at the surface and throughout the troposphere.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Posterior distributions for planetary surface albedo, planetary radius, and planetary mass. (a) Posterior distributions for the log of the planetary surface albedo (A s) derived from simulated S/N 10 observations and all of the decision tree combinations of wavelength coverage. The input values for each parameter are indicated by a black vertical dashed line. (b) The same as (a), but for the planetary radius (R p). (c) The same as (a) and (b), but for the planetary mass (M p).
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Posterior distributions for planetary surface albedo, planetary radius, and planetary mass (Archean Earth pathway). (a) Posterior distributions for the log of the planetary surface albedo (A s) derived from simulated S/N 10 observations and all of the decision tree combinations of wavelength coverage for the Archean Earth path. The input values for each parameter are indicated by a black vertical dashed line. (b) The same as (a), but for the planetary radius (R p). (c) The same as (a) and (b), but for the planetary mass (Mp).
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Posterior distributions for global surface pressure and global temperature. (a) Posterior distribution for global surface pressure of the planet derived from simulated S/N 10 observations and all of the decision tree combinations of wavelength coverage for the Archean Earth path. For each parameter, the black vertical dashed lines indicate the surface values given by the input atmospheric model. (b) The same as (a), but for temperature. Note that the retrieved spread would be representative of temperatures at the surface and throughout the troposphere.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Biosignature abundance constraints broken down by observational bandpass combinations (Archean Earth pathway). (a) Chemical species abundance constraints for the 0.89 μm bandpass. The abundance constraints for CH4 (yellow filled), H2O (purple filled), O2 (purple hatched), CO2 (red), and O3 (gray) are all shown. (b) The same as (a), but for the singular 1.65 μm bandpass. (c) The same as (a) and (b), but for the 0.89 μm and 1.65 μm bandpass combination. Note that oxygen-bearing species go unconstrained for all observational scenarios, given the anoxic environment of Archean Earth.
Paper: Retrievals Applied to a Decision Tree Framework Can Characterize Earthlike Exoplanet Analogs
Authors: Amber V. Young, Jaime Crouse, Giada Arney, Shawn Domagal-Goldman, Tyler D. Robinson, Sandra T. Bastelberger
Figure caption: Cartoon schematic of the decision tree framework. The modern Earth and Archean Earth paths that are highlighted in blue and orange, respectively, were selected for exploration in our retrieval simulations as a first look at how this framework could work in observations of exoplanets. In general, the purple, green, and red boxes represent wavelength regions in the UV, visible, and NIR channels, respectively. In practice, observations following the framework would be performed from the top to the bottom, where, at the base, observational targets could be categorized into the respective planetary scenarios. Ambiguous planetary scenarios are in dark gray, dead rocky cases are in red, modern-Earth-like cases are in blue, Proterozoic-Earth-like cases are in pink, and Archean-Earth-like cases are in orange.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: Distribution of asteroid Bennu’s TI Γ generated with the ASTERIA model. The figure shows the results for the nominal settings after considering the spatial variations in Bennu’s surface TI. See text for additional details. The median values of Γ and their corresponding lower and upper 1σ uncertainties are shown for each of the peaks.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: TI values derived from TPM listed in the literature compared to values derived from the ASTERIA model. The blue points are the results for 10 NEAs used for model validation purposes, while the black triangle shows the result for Bennu. The red line is the line of equality where the same results should appear.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: Coefficients k 1, k 2, and k 3 as a function of the scaled radius ﹩R^{\prime} ﹩.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: Data for two nights of photometric observations of asteroid (152671) 1998 HL3 and the corresponding light curve for the nominal period solution of P = 5.3 h, plotted with MPO Canopus.
Paper: ASTERIA—Asteroid Thermal Inertia Analyzer
Authors: Bojan Novaković, Marco Fenucci, Dušan Marčeta, Debora Pavela
Figure caption: A pdf of the obliquity distribution of the NEAs population.
Paper: Polarimetry of Didymos–Dimorphos: Unexpected Long-term Effects of the DART Impact
Authors: Zuri Gray, Stefano Bagnulo, Mikael Granvik, Alberto Cellino, Geraint H. Jones, Ludmilla Kolokolova, Fernando Moreno, Karri Muinonen, Olga Muñoz, Cyrielle Opitom, Antti Penttilä, Colin Snodgrass
Figure caption: The polarimetric growth curves of imaging polarimetric VLT data of Didymos–Dimorphos in BVRI filters, i.e., the polarization is measured in apertures of increasing size (pixel radii, where 0.″125 pixel−1). The color of each curve represents an observing epoch and phase angle, indicated on the right. The pixel scale varied from epoch to epoch due to the changing asteroid–observer distance (Δ). Thus, all curves have been scaled to the same distance scale (horizontal axis). The values reported in Table 1 and Figure 2 are those integrated in an aperture radius equivalent to 100 km (0.1 (103 km) on the x-axis). A number of the curves are spurious at larger radii due to the contamination of background stars: 2022 October 25 in the B filter, 2022 October 20 in the R filter, and 2022 September 28 in the I filter. These spurious signals have been left unshaded.
Paper: Polarimetry of Didymos–Dimorphos: Unexpected Long-term Effects of the DART Impact
Authors: Zuri Gray, Stefano Bagnulo, Mikael Granvik, Alberto Cellino, Geraint H. Jones, Ludmilla Kolokolova, Fernando Moreno, Karri Muinonen, Olga Muñoz, Cyrielle Opitom, Antti Penttilä, Colin Snodgrass
Figure caption: (a) The same polarimetric maps as in Figure 5, but with dynamic color scales. The scale of each panel has been set to P  Q  ± 2%, where P  Q  is the value measured from aperture polarimetry and is indicated in each panel. The grid indicates the photocentre of the asteroid system. (b) Quality checks of 2022 September 27a polarimetric maps. From left to right: P  Q  map with (−2, 10)% scale, dP  Q  map, N  Q  (Null) map, P  U  map, N  U  map. The latter three maps are plotted with absolute values. A color bar is given for each column.