adsabs/AstroCLIMB huggingface dataset:
Paper: On Error Estimates for Hapke Photometric Model Parameters from Disk-integrated Phase Curves of Airless Planetary Bodies
Authors: Paul Helfenstein, Anne J. Verbiscer
Figure caption: Left: model phase-curve plots used to determine the relation between macroscopic roughness ﹩(\bar{\theta })﹩ and opposition amplitude behavior (see also Figure 4). The upper right corner of each plot gives the value of ﹩\bar{\theta }﹩ used to generate the curves. All model parameters except ﹩\bar{\theta }﹩ were fixed at ϖo = 0.01, B0SH = B0CB = b = 0, c = 1.0, hSH = 0.522, hCB = 0.0001. All magnitudes have been normalized to zero at α = 0°. In each case, a least-squares fit to the linear portion of the phase curve was obtained to model the straight lines against which the opposition effect can be distinguished. The best-fit values of the phase coefficient βfit and the intercept mo are given in Table 6 of the main text. Right: model phase-curve plots for angular widths reported in Table 6 and presented in Figure 4, shown here on a disk-integrated reflectance scale. For each plot, the amplitude of the opposition effect was measured at α = 0°, and the phase angle at half of the amplitude was measured to estimate αhm. These were then used to obtain corresponding values of B0SH, hSH and B0CB, hCB reported in Table 6.
Paper: On Error Estimates for Hapke Photometric Model Parameters from Disk-integrated Phase Curves of Airless Planetary Bodies
Authors: Paul Helfenstein, Anne J. Verbiscer
Figure caption: Histogram analysis plot pairs for all bodies. In each panel, the discrete histogram is at left and the corresponding cumulative histogram is at right. The plots for 2002 KX14 is used in Figure 9. See Section 5.1.5 for discussion.
Paper: On Error Estimates for Hapke Photometric Model Parameters from Disk-integrated Phase Curves of Airless Planetary Bodies
Authors: Paul Helfenstein, Anne J. Verbiscer
Figure caption: Solar phase curve of 2002 KX14 (solid curve) from our nominal fit of the B. Hapke (2012b) model (Table 4) to merged Earth-based and LORRI observations (filled circles with relative-scale error bars). Note that phase angles are plotted on a logarithmic scale. Dashed curves show the predicted error envelope from Hapke fits to the observations that were offset by their corresponding absolute calibration and body diameter uncertainty (Table C1). Offset data points have been omitted for clarity.
Paper: On Error Estimates for Hapke Photometric Model Parameters from Disk-integrated Phase Curves of Airless Planetary Bodies
Authors: Paul Helfenstein, Anne J. Verbiscer
Figure caption: Phase curves modeled from best-fit Hapke parameters (Table 4) shown with the observations and goodness-of-fit error bars (Table C3). The dashed error envelopes are predicted model phase curves obtained from fits to the observation data that have been offset by the instrumental absolute calibration uncertainties (Table 1), errors predicted from body diameter uncertainties (Table C1), and, additionally, with the magnitude offsets that were used to tie-point the reflectance scale of one instrument to best match the reference instrument. Tie-pointing the phase-curve components was practical with only some of the targets: Triton, Pluto, Makemake, and the Haumea system. See example Figure 10 and its discussion.
Paper: Effects of Ultraviolet Radiation on Sub-Neptune Exoplanet Hazes through Laboratory Experiments
Authors: Lori Huseby, Sarah E. Moran, Neil Pearson, Tiffany Kataria, Chao He, Cara Pesciotta, Sarah M. Hörst, Pierre Haenecour, Travis Barman, Vishnu Reddy, Nikole K. Lewis, Véronique Vuitton
Figure caption: Enlarged spectrum between 3500 and 1100 cm−1 (2.5–9 μm) of the 5% CH4 (top) and 5% CO (bottom) atmosphere haze samples as a function of wavelength in transmittance with major spectral features labeled.
Paper: Effects of Ultraviolet Radiation on Sub-Neptune Exoplanet Hazes through Laboratory Experiments
Authors: Lori Huseby, Sarah E. Moran, Neil Pearson, Tiffany Kataria, Chao He, Cara Pesciotta, Sarah M. Hörst, Pierre Haenecour, Travis Barman, Vishnu Reddy, Nikole K. Lewis, Véronique Vuitton
Figure caption: Streamlined schematic of the experimental setup, simulated atmospheric compositions and conditions, UV bombardment process, measurements, and experimental outcomes. Two laboratory hazes were produced (the initial conditions varying only in the minor carbon source) in the PHAZER chamber (C. He et al. 2017) by exposing the gas mixture at room temperature to an AC plasma source. After the hazes were produced, each half of the films were exposed to UV radiation under two different filters. The transmittance and reflectance spectra pre- and post-UV irradiation of both samples were measured using a Fourier transform IR (FTIR) spectrometer to quantify molecular changes and destruction during the irradiation process.
Paper: Effects of Ultraviolet Radiation on Sub-Neptune Exoplanet Hazes through Laboratory Experiments
Authors: Lori Huseby, Sarah E. Moran, Neil Pearson, Tiffany Kataria, Chao He, Cara Pesciotta, Sarah M. Hörst, Pierre Haenecour, Travis Barman, Vishnu Reddy, Nikole K. Lewis, Véronique Vuitton
Figure caption: Optical reflected light mosaic images (Keyence digital microscope, University of Arizona) of the post-UV bombardment water-world hazes deposited onto MgF2 substrate disks. The image labeled blank is of a clean MgF2 substrate disk. The image labeled 5% CH4-derived haze sample is more yellow in color than the image labeled 5% CO-derived haze sample, indicating the differing compositions and haze-forming efficiencies of the samples.
Paper: Effects of Ultraviolet Radiation on Sub-Neptune Exoplanet Hazes through Laboratory Experiments
Authors: Lori Huseby, Sarah E. Moran, Neil Pearson, Tiffany Kataria, Chao He, Cara Pesciotta, Sarah M. Hörst, Pierre Haenecour, Travis Barman, Vishnu Reddy, Nikole K. Lewis, Véronique Vuitton
Figure caption: Transmittance spectra of our 5% CH4 atmosphere haze sample (top) and 5% CO atmosphere haze sample (bottom) in the visible to mid-IR wavelength region (26,000–1100 cm−1, 0.4–9 μm). The black line represents the samples before the irradiation process began, and the teal and red lines represent the postirradiation spectrum using the 350 nm and 228 nm filters, respectively.
Paper: A Limit on the Mass of the Taurid Resonant Swarm at Sub-100 m Sizes
Authors: Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
Figure caption: The expected on-sky motion of simulated main-belt asteroids and Taurid stream members within the images taken. The on-sky motions of all detected moving objects are superimposed. The rates of on-sky motion that pass our moving-object detection filter (that is, on-sky rates of motion less than 150″ per hour) are indicated by the green circular area. The moving objects falling below the dashed line are our Taurid candidates (indicated in purple). These eight, which include three near the boundary together with five with larger negative rates of motion in decl., are discussed in Section 3.1.
Paper: A Limit on the Mass of the Taurid Resonant Swarm at Sub-100 m Sizes
Authors: Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
Figure caption: An example of modeled on-sky locations and motions of the TS used to plan observations. Each panel shows a portion of the sky, and the Taurid radiant is located near the center of the plot. Observations were chosen to cover a portion of the sky extending from the center to the lower right. The upper-left panel shows the location of simulated Taurids colored by their apparent magnitude along with an arrow indicating their direction of motion. The lower-left panel shows the mean apparent magnitude per bin; the upper-right panel shows the on-sky number density; and the lower-right panel indicates the mean binned on-sky motion. See the text for further details.
Paper: A Limit on the Mass of the Taurid Resonant Swarm at Sub-100 m Sizes
Authors: Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
Figure caption: The locations of all object detections on the sky. Our four slightly overlapping CFHT MegaCam fields are aligned along the expected direction of motion of Taurids. The eight candidate TS objects discussed in Section 3.1 are superimposed, as are the three additional outliers in on-sky motion. Arrows are directed along the directions of motion. The gaps between detections correspond to the gaps between the chips of the MegaCam detector.
Paper: A Limit on the Mass of the Taurid Resonant Swarm at Sub-100 m Sizes
Authors: Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
Figure caption: The geometry of the Earth relative to the Taurid stream at one instant during the survey. The white frustum indicates schematically the volume of space sampled by the observations. The color of the Taurid particles indicates their apparent magnitude as seen from Earth.
Paper: A Limit on the Mass of the Taurid Resonant Swarm at Sub-100 m Sizes
Authors: Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
Figure caption: The distribution of apparent magnitudes of detected moving objects.
Paper: The Composition and Stratigraphy of the Chang’e-6 Landing Mare
Authors: Pei Ma, Zhiyong Xiao, Yichen Wang, Yunhua Wu, Yizhen Ma, Fanglu Luo, Rui Xu, Qingyun Deng
Figure caption: The ejecta thickness distribution of the Chaffee S crater.
Paper: The Composition and Stratigraphy of the Chang’e-6 Landing Mare
Authors: Pei Ma, Zhiyong Xiao, Yichen Wang, Yunhua Wu, Yizhen Ma, Fanglu Luo, Rui Xu, Qingyun Deng
Figure caption: Examples of fresh craters used in this work, and boulders are visible in/around fresh craters. The base maps for (a), (b), and (c) are LROC NAC images M153909651R, M1326948834R, and M1464260114R, respectively.
Paper: ORIGIN: Laser Desorption Ionization Mass Spectrometry of Nucleobases for In Situ Space Exploration
Authors: Nikita J. Boeren, Peter Keresztes Schmidt, Marek Tulej, Peter Wurz, Andreas Riedo
Figure caption: Laser pulse energy scan of adenine at a concentration of 14 pmol mm–2 with applied pulse energies of 3.0, 3.9, 4.9, 5.9, and 7.1 μJ. Mass spectra are presented with an offset on the y-axis. At 4.9 μJ, a peak at m/z 56 becomes visible, which can be assigned to 56Fe from the stainless steel sample holder. This means that at this pulse energy the ablation regime is already starting, which can also be observed from the 23Na and 39K signals. Fragmentation of adenine is occurring as well, but it is not as severe as expected.
Paper: ORIGIN: Laser Desorption Ionization Mass Spectrometry of Nucleobases for In Situ Space Exploration
Authors: Nikita J. Boeren, Peter Keresztes Schmidt, Marek Tulej, Peter Wurz, Andreas Riedo
Figure caption: Laser pulse energy scan of adenine at a concentration of 7 pmol mm–2 with applied pulse energies of 1.0, 1.5, 2.0, and 2.5 μJ. Mass spectra are presented with an offset on the y-axis. By increasing the pulse energy, higher signals for adenine are observed at m/z 135 and 136, but also the background signals are increased. A pulse energy of 1.5–2.0 μJ was selected as optimal for the other measurements.
Paper: ORIGIN: Laser Desorption Ionization Mass Spectrometry of Nucleobases for In Situ Space Exploration
Authors: Nikita J. Boeren, Peter Keresztes Schmidt, Marek Tulej, Peter Wurz, Andreas Riedo
Figure caption: Mass spectra of three blank measurements of the stainless steel sample holder (top), MilliQ water (middle), and acetonitrile (bottom). Note that the solvents were evaporated prior to measurement, and these blank measurements are aimed at assessing potential residue left behind by the solvents.
Paper: ORIGIN: Laser Desorption Ionization Mass Spectrometry of Nucleobases for In Situ Space Exploration
Authors: Nikita J. Boeren, Peter Keresztes Schmidt, Marek Tulej, Peter Wurz, Andreas Riedo
Figure caption: Detector voltage scan of the peak area of m/z 135 of adenine against the voltage difference on the MCP detector in V. A factor 1.5 signal increase is observed between the 50 V steps. The measurements were conducted on a 5 μM adenine sample (∼700 fmol mm–2) on 40 positions with a laser pulse energy of 1.5 μJ.
Paper: Cross Validation of Albedo Determination for 1627 Ivar from Three Different Techniques
Authors: Elena Selmi, M. Devogèle, J. R. Masiero, N. Vega Santiago, E. L. Wright, M. Ferrais, E. Fernández-Valenzuela, G. Borisov, Ph. Bendjoya, J.-P. Rivet, L. Abe, D. Vernet, A. Cellino
Figure caption: Corner plots and progression of the MCMC routine fitting for H, G1, and G2 (left) and fitted phase curve of Ivar (right), with the data extracted from the SSCAT.
Paper: ORIGIN: Laser Desorption Ionization Mass Spectrometry of Nucleobases for In Situ Space Exploration
Authors: Nikita J. Boeren, Peter Keresztes Schmidt, Marek Tulej, Peter Wurz, Andreas Riedo
Figure caption: Mass spectra of thymine (T) and uracil (U) from Figure 1 with the complete measured m/z range with concentrations of 141 and 707 pmol mm−2, respectively. Peaks are annotated with their corresponding m/z value.
Paper: ORIGIN: Laser Desorption Ionization Mass Spectrometry of Nucleobases for In Situ Space Exploration
Authors: Nikita J. Boeren, Peter Keresztes Schmidt, Marek Tulej, Peter Wurz, Andreas Riedo
Figure caption: Optical microscopy images of sample cavities (Ø 3 mm) with adenine (left; ∼140 pmol mm−2), uracil (middle; ∼140 pmol mm−2), and an empty cavity (right).
Paper: Cross Validation of Albedo Determination for 1627 Ivar from Three Different Techniques
Authors: Elena Selmi, M. Devogèle, J. R. Masiero, N. Vega Santiago, E. L. Wright, M. Ferrais, E. Fernández-Valenzuela, G. Borisov, Ph. Bendjoya, J.-P. Rivet, L. Abe, D. Vernet, A. Cellino
Figure caption: Fitted phase curve using only the best ATLAS apparition.
Paper: Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
Authors: Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue
Figure caption: Unsupervised K-Means algorithm unit map of the central SPA study region. Data used in the classification algorithm include the band ratios (321/415 nm and 566/689 nm) from Hapke photometrically standardized data and TRI. See text for details regarding these data sets. Note that the masked areas in TRI (see Figure 2) are also masked in the output map, as these are considered no data and are excluded in the algorithm. A total of four units are identified: unit 0 (red), unit 1 (yellow), unit 2 (green), and unit 3 (blue). Unit 0 is clearly associated with and densest in areas of dark smooth maria. Units 1 and 2 cover most of the remaining areas but are not associated with any specific features. Unit 3 is related to edge effects near highly sloping transition areas. Cross-hatched circles 25 km in diameter represent training areas for the user-defined units of mare (cyan), cryptomare (dark blue), and cryptomare/light plains (pink) for the supervised MLC algorithm. See text for details on the definition of these three units. The area outlined in orange is shown in Figure 8 for comparison to the MLC unit map (Figure 7). Areas outlined in black and white are lunar maria mapped by D. A. Nelson et al. (2014) and cryptomaria mapped by J. L. Whitten & J. W. Head (2015a), respectively (see Figure 1).
Paper: Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
Authors: Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue
Figure caption: Supervised MLC algorithm unit map for the central SPA study region. The mapped units are based on the values from the Hapke photometrically standardized band ratios (321/415 nm and 566/689 nm) and TRI for select training areas of three user-defined units: mare (red), cryptomare (yellow), and cryptomare/light plains (cyan). See text for more details regarding these units. Note that the masked areas in TRI (see Figure 2) are also masked in the output map, as these are considered no data and are excluded in the algorithm. Consistent with the K-Means classification, mare is densest in areas of dark smooth maria seen in the LRO WAC basemap. However, much of the outer edges and interior portions of these dense areas are occupied by cryptomare, resulting in decreased mare coverage compared to the K-Means unit map. Cryptomare are present in various patches across the entire study region and agree well with previously mapped cryptomaria (outlined in white) by J. L. Whitten & J. W. Head (2015a). Areas outlined in black are mapped lunar maria by D. A. Nelson et al. (2014). The scattered amounts of present in some cryptomare patches suggest incomplete or uneven mantling by ejecta deposits or impact sheet materials. The area outlined in orange is shown in Figure 8 for comparison to the K-Means unit map.
Paper: Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
Authors: Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue
Figure caption: Comparison area from Figures 5 and 7 for the (a) unsupervised K-Means unit map and (b) supervised MLC unit map. Note the overall reduction in coverage from the dark smooth mare of unit 0 (red) in the K-Means map to the mare unit (pink) in the MLC map. Areas with dense continuous maria such as in “Mickey mare” are still present. However, in other locales, such as Nishina crater, much of the continuous mare has been mapped as cryptomare, resulting in scattered mare coverage. The large area previously mapped as mare (outlined in black) to the upper right by D. A. Nelson et al. (2014) is mapped mostly as cryptomare from the MLC algorithm, an indication that such areas may not consist primarily of well-exposed mare materials.
Paper: Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
Authors: Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue
Figure caption: Top: histogram of the 321/415 nm band ratio from Hapke photometrically standardized data for each MLC unit in the central SPA study region. Distinct regions within the histogram are apparent, with cryptomare/light plains (cyan) having the lowest mean values, followed by cryptomare (yellow) and mare (red) with progressively higher mean ratio values. Colored text shows the counts (n) and mean ± standard deviation for each unit. These are reflected in the statistical ranges shown in Table 2. Bottom: the scatterplot of 321/415 nm vs. 566/689 nm also shows the same distinct areas for the three MLC units: mare, cryptomare, and cryptomare/light plains.
Paper: Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
Authors: Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue
Figure caption: Probability map from the MLC algorithm for the central SPA study region. Lower probabilities (≤91%) are red to orange, moderate probabilities (92%–95%) are yellow to light green, and higher probabilities (≥96%) are light green to dark green. Areas with extensive maria coverage generally have a mid-to-high level, or ≥92% probability of being assigned to the correct unit.
Paper: Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin
Authors: Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue
Figure caption: (a–d) Scatterplots of band ratios 321/415 nm (triangles) and 566/689 nm (circles) from Hapke photometrically standardized data vs. TRI for units 0 (red), 1 (yellow), 2 (green), and 3 (blue) from the K-Means unit map. No particular distinctions are observed among the four units. (e) The scatterplot of 321/415 nm vs. 566/689 nm similarly has no distinctions between the four units, all with significant data overlap.
Paper: The Viability of Glycine Fermentation in Titan’s Subsurface Ocean
Authors: Antonin Affholder, Peter M. Higgins, Charles S. Cockell, Catherine Neish, Krista M. Soderlund, Michael J. Malaska, Kendra K. Farnsworth, Rosaly M. C. Lopes, Conor A. Nixon, Mohit Melwani Daswani, Kelly E. Miller, Christophe Sotin
Figure caption: Glycine delivery specific estimates of steady-state standing biomass and productivity under the different compositional and temperature scenarios, in units of biotic kgC per kgGly delivered. Both panels use [Gly] = 1 mmolal to calculate viable parameter values retained and shown here, described in Section 3.3. (a) Glycine-specific standing biomass in Titan’s subsurface water ocean. (b) Glycine-specific biomass production after the microbial community has reached steady state with the environment and glycine delivery rate. Top panel: histogram of the number of viable parameter values in each of the compositional and temperature scenarios (see Figure 5). In both panels, black dots are individual calculations, boxes are drawn by excluding the outlier T. marianensis (for which the standing biomass is typically ~2 orders of magnitude more than the maximum value shown in panel (a)), as discussed in Section 4.2, then the whiskers span the 2.5th–97.5th percentile range, the top and bottom of the boxes delimit quartiles, and the center bar shows the median. As glycine-specific standing biomass and productivity depend only on organismic parameters and temperature, the shape of the box plots depends only on the distribution of viable organisms in a given composition and temperature.
Paper: The Viability of Glycine Fermentation in Titan’s Subsurface Ocean
Authors: Antonin Affholder, Peter M. Higgins, Charles S. Cockell, Catherine Neish, Krista M. Soderlund, Michael J. Malaska, Kendra K. Farnsworth, Rosaly M. C. Lopes, Conor A. Nixon, Mohit Melwani Daswani, Kelly E. Miller, Christophe Sotin
Figure caption: The number of Clostridia strains in our sample set that were viable as a function of glycine concentration in the different compositional endmember scenarios and at different temperatures.
Paper: The Viability of Glycine Fermentation in Titan’s Subsurface Ocean
Authors: Antonin Affholder, Peter M. Higgins, Charles S. Cockell, Catherine Neish, Krista M. Soderlund, Michael J. Malaska, Kendra K. Farnsworth, Rosaly M. C. Lopes, Conor A. Nixon, Mohit Melwani Daswani, Kelly E. Miller, Christophe Sotin
Figure caption: Estimated minimally required energy for population growth of sample Clostridia. (a) Minimally required affinity of all 40 sample Clostridia as a function of temperature. (b) Distribution of the estimated minimally required energy (Equation (14)) in the sample at temperatures 265 K (top left), 273 K (top right), and 285 K (bottom left). This corresponds to the distribution of values of the gray curves in panel (a) at select temperatures.
Paper: The Viability of Glycine Fermentation in Titan’s Subsurface Ocean
Authors: Antonin Affholder, Peter M. Higgins, Charles S. Cockell, Catherine Neish, Krista M. Soderlund, Michael J. Malaska, Kendra K. Farnsworth, Rosaly M. C. Lopes, Conor A. Nixon, Mohit Melwani Daswani, Kelly E. Miller, Christophe Sotin
Figure caption: Model-predicted growth rates using estimated parameter values (continuous black lines) against measured growth rates compiled in R. Corkrey et al. (2016, black circles). Dashed lines represent the least-squares regression of the cardinal temperature model with inflection, an empirical model designed to retrieve maximum, minimum, and optimal growth temperatures (see G. M. Grimaud et al. 2017, and references therein).