adsabs/AstroCLIMB huggingface dataset:
Paper: Multiwavelength JWST Observations of (3200) Phaethon Show a Dehydrated Object with an Aqueously Altered Origin
Authors: Cristina A. Thomas, Andrew S. Rivkin, Ian Wong, Matthew M. Knight, Sean E. Marshall, Christopher W. Haberle, Aidan Madden-Watson, Joshua P. Emery, Annika Gustafsson, Stefanie N. Milam, Heidi B. Hammel, Ellen S. Howell, Ronald J. Vervack
Figure caption: The mid-infrared spectra of B-types Phaethon and Bennu are distinctly different from each other with key features at different wavelengths and distinct spectral structure. Both spectra are also different from unheated (and heated) samples of Murchison. For clarity, Phaethon is scaled by a factor of 0.6, and all spectra are offset from Phaethon in emissivity.
Paper: Multiwavelength JWST Observations of (3200) Phaethon Show a Dehydrated Object with an Aqueously Altered Origin
Authors: Cristina A. Thomas, Andrew S. Rivkin, Ian Wong, Matthew M. Knight, Sean E. Marshall, Christopher W. Haberle, Aidan Madden-Watson, Joshua P. Emery, Annika Gustafsson, Stefanie N. Milam, Heidi B. Hammel, Ellen S. Howell, Ronald J. Vervack
Figure caption: Left: VNIR spectra from RELAB show that the observed spectral slope of CM chondrite Murchison decreases significantly with increased temperature. Right: The VNIR spectrum of Phaethon has a much bluer spectral slope than Bennu and other B-type near-Earth objects.
Paper: Multiwavelength JWST Observations of (3200) Phaethon Show a Dehydrated Object with an Aqueously Altered Origin
Authors: Cristina A. Thomas, Andrew S. Rivkin, Ian Wong, Matthew M. Knight, Sean E. Marshall, Christopher W. Haberle, Aidan Madden-Watson, Joshua P. Emery, Annika Gustafsson, Stefanie N. Milam, Heidi B. Hammel, Ellen S. Howell, Ronald J. Vervack
Figure caption: The emissivity spectra from JWST MIRI and Spitzer IRS are largely consistent with each other. The JWST data show higher signal to noise, which enables a clearer view of diagnostic spectral features at ∼19–20.2 μm and ∼15–17 μm.
Paper: Effects of the 2018 Martian Global Dust Storm on Boundary Positions in the Induced Magnetosphere
Authors: Catherine E. Regan, Andrew J. Coates, Mark Lester, Anne Wellbrock, Geraint H. Jones, Beatriz Sánchez-Cano, Philippe Garnier, Louisa J. Preston, František Němec, Václav Linzmayer, Rudy A. Frahm, Mats Holmström
Figure caption: Dayside crossings from 2018 April 1 to November 30: (a) fitted subsolar distances (Equation (2)) for the bow shock (red) and IMB (blue), (b) distance between the Sun and Mars, and (c) fitted normalized subsolar distances taking into account the Sun–Mars distance in arbitrary units. Distances are measured from the center of Mars. Storm phases are overplotted as vertical lines where (i) dust lifting begins, (ii) the storm becomes global, (iii) the decay phase begins, and (iv) surface conditions return to normal (Table 1).
Paper: Effects of the 2018 Martian Global Dust Storm on Boundary Positions in the Induced Magnetosphere
Authors: Catherine E. Regan, Andrew J. Coates, Mark Lester, Anne Wellbrock, Geraint H. Jones, Beatriz Sánchez-Cano, Philippe Garnier, Louisa J. Preston, František Němec, Václav Linzmayer, Rudy A. Frahm, Mats Holmström
Figure caption: Dayside crossings from 2018 April 1 to November 30 for terminator distances (Equation (1)). Features are the same as described for Figure 3.
Paper: Effects of the 2018 Martian Global Dust Storm on Boundary Positions in the Induced Magnetosphere
Authors: Catherine E. Regan, Andrew J. Coates, Mark Lester, Anne Wellbrock, Geraint H. Jones, Beatriz Sánchez-Cano, Philippe Garnier, Louisa J. Preston, František Němec, Václav Linzmayer, Rudy A. Frahm, Mats Holmström
Figure caption: (a) MAVEN crossings of the induced magnetospheric boundary (IMB) and the bow shock (BS) on 2018 July 14. (i)–(ii) Electron energy, density, and temperature from SWEA; (iii)–(iv) ion energy, density, and temperature from SWIA; (v) magnetic field components and magnitude from MAG; (vi) MAVEN altitude over Mars in Mars radii. (b) Terminator and subsurface distances to boundaries from Mars in Mars Solar Orbital coordinates.
Paper: Effects of the 2018 Martian Global Dust Storm on Boundary Positions in the Induced Magnetosphere
Authors: Catherine E. Regan, Andrew J. Coates, Mark Lester, Anne Wellbrock, Geraint H. Jones, Beatriz Sánchez-Cano, Philippe Garnier, Louisa J. Preston, František Němec, Václav Linzmayer, Rudy A. Frahm, Mats Holmström
Figure caption: Comparison of different boundary models listed in Table 2 and resultant rolling means of the bow shock (a) and induced magnetospheric boundary (b) where the mean terminator distance is calculated every 50 days since the start of the study period, with 25 day intervals. Error bars indicate the standard deviation of results.
Paper: Effects of the 2018 Martian Global Dust Storm on Boundary Positions in the Induced Magnetosphere
Authors: Catherine E. Regan, Andrew J. Coates, Mark Lester, Anne Wellbrock, Geraint H. Jones, Beatriz Sánchez-Cano, Philippe Garnier, Louisa J. Preston, František Němec, Václav Linzmayer, Rudy A. Frahm, Mats Holmström
Figure caption: Bow shock (BS) and IMB crossings from (a) MAVEN and (b) Mars Express from 2018 April 1 to November 30, in rotated MSO coordinates. Single orbits from 00:00 on May 1, August 1, and November 1 annotated in gray.
Paper: From Colors to Spectra and Back Again: First Near-IR Spectroscopic Survey of Neptunian Trojans
Authors: Larissa Markwardt, Hsing Wen Lin, 省文 林, Bryan J. Holler, David W. Gerdes, Fred C. Adams, Renu Malhotra, Kevin J. Napier
Figure caption: Results of a PCA to reduce the dimensionality of the full NIRSpec spectra of NTs, Centaurs, and TNOs. The TNOs have previously been grouped into three different spectral types (cliff, double-dip, and bowl) using an independent PCA (N. Pinilla-Alonso et al. 2025). The Centaur data are from J. Licandro et al. (2025).
Paper: From Colors to Spectra and Back Again: First Near-IR Spectroscopic Survey of Neptunian Trojans
Authors: Larissa Markwardt, Hsing Wen Lin, 省文 林, Bryan J. Holler, David W. Gerdes, Fred C. Adams, Renu Malhotra, Kevin J. Napier
Figure caption: JWST NIRSpec spectra, with arbitrary reflectance, of eight NTs sorted by measured near-IR slope (see Section 5) such that the bluest, 2006 RJ103, is at the top. The spectra are shifted for clarity. Optical photometry from the literature is also plotted as solid circles (see Section 4). The vertical lines, shaded areas, and dashed horizontal lines demark various molecular features evident in this wavelength region.
Paper: From Colors to Spectra and Back Again: First Near-IR Spectroscopic Survey of Neptunian Trojans
Authors: Larissa Markwardt, Hsing Wen Lin, 省文 林, Bryan J. Holler, David W. Gerdes, Fred C. Adams, Renu Malhotra, Kevin J. Napier
Figure caption: The spectra of NTs (colored) and their counterparts (gray and black), based on minimizing the sum of the squares of the residuals between the NT and TNO spectra.
Paper: A Multifrequency Global View of Callisto’s Thermal Properties from ALMA
Authors: Maria Camarca, Katherine de Kleer, Bryan Butler, Alexander Thelen, Cole Meyer, Alex B. Akins, Imke de Pater, Mark A. Gurwell
Figure caption: Left: calibrated ALMA images of Callisto. Right: residuals obtained by subtracting Lambertian disks from images on the left to highlight differences in Tb across the disk, with the unit being K for all panels. In both panels, the ellipse in the lower left corner represents the FWHM of the synthesized ALMA beam, which is the resolution element. The latitude–longitude grid is spaced at 30° increments. The images are all scaled such that the horizontal and vertical axes each span −3500 km to +3500 km on a side. Seven FITS images are in the .tar.gz package to provide the calibrated images shown in this figure.(The data used to create this figure are available in the online article.)(The data used to create this figure are available.)
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: Melt fraction varies as a function of mantle temperature, so measuring the temperature of the lava as it erupts provides constraints on the state of Io’s interior. (a) Plot of melt fraction at the base of the crust as a function of temperature assuming Io has a chondritic composition similar to other bodies in the solar system (L. Keszthelyi et al. 2007). Constraining the temperature of the erupting lava to ±50 K would place useful constraints on the state of Io’s interior. However, lava cools rapidly, so isolating the temperature of the lava as it exits the ground is very challenging. IVO would rely on two different strategies to meet this challenge. First, large lava fountains are regularly observed erupting on Io, providing a direct view of lava as it first erupts. Second, lava tubes are known to exist on Io, so skylights—locations where the roof of the tube has collapsed—should also exist. If these can be imaged, they will provide a view of lava that has traveled away from the vent with minimal cooling. In both cases, in order to isolate the hottest subpixel region, color temperatures would be used. Color temperatures are obtained from ratios of the intensity of the incandescent glow as seen in different spectral bands. (b) Ratios of the red and near-infrared bandpasses for the NAC provide reliable temperature estimates across the full range of plausible lava temperatures. The plotted bandpasses are 1MU: 950–1100 nm; IR2: 850–950 nm; IR1: 800–850 nm; and RED: 650–800 nm. Using shorter wavelengths (e.g., green and orange) or narrow bandpasses allows identification of more exotic lava types that have been speculated to exist on Io (J. Kargel et al. 2003). The direct measurements of lava temperature would be tested with additional observations such as the silica content of the lava derived from thermal infrared spectroscopy (following the work of B. T. Greenhagen et al. 2010) and the mix of volcanic gases measured by the mass spectrometer (following the work of J. R. Spencer et al. 2000).
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: Electric conductivity of Io’s interior, based on the thermal profile and composition and electrical laboratory studies. Left panel: composition and thermal profile across Io. Middle panel: melt content and distribution in the silicate mantle considering different melt contents (5%, 20%, 30%) in the top part of the asthenosphere. Right panel: corresponding conductivity-depth profile. In the hypothetical melt-rich layer in the asthenosphere, dark blue, medium blue, and light blue data correspond to 5%, 20%, and 30% melt, respectively, with the width of the lines representing modeled electrical conductivities for each layer. Note that the high electrical conductivity values in the lower crust reflect the combination of the sharp increase in temperature at this depth and the composition of pure basalt, which has a conductivity higher than olivine (i.e., mantle material) at 1500°C. CMB = core–mantle boundary.
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: The expected quality of the global static gravity field for IVO-NF. The red/blue colored swath shows the range of plausible gravity fields of Io spanning the range of possible compensation states. The thick black line indicates the expected error spectrum from IVO-NF. Gravity signals above this line would be detectable with a signal-to-noise (SNR) greater than 1. The present gravity field of Io is only resolved to degree 2. IVO-NF would enable detection of regional-scale gravity anomalies and constrain the deeper interior structure of Io.
Paper: Formation of Ammonium Nitrate Particles in CO2-rich Atmospheres
Authors: Antonio Jiménez-Escobar, Angela Ciaravella, Alfonso Mangione, Cesare Cecchi-Pestellini, Francesco Piazzese, Roberto Candia, Gaspare Di Cicca, Salvatore Varisco
Figure caption: Sketch of the exo-atmosphere simulation chamber. A QMS and vacuum ultraviolet spectrometer are used for the gas diagnostics. The discharge system is used for simulating lightning. The ZnSe window hosted at the tip of the anode collects the particulate formed during the lightning.
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: Top: IVO Baseline tour. Bottom: IVO-NF Baseline tour example (Iteration-5). Trajectories are plotted over the Galileo–Voyager image mosaics produced at a spatial resolution of 1 km pixel−1 (D. A. Williams et al. 2011), with flybys labeled I1–I10 for IVO and I1–I20 for IVO-NF.
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: Improvement in GS (k2 uncertainty) and magnetic (induction inversion error) as a function of flyby number. The IVO Baseline mission has 10 flybys of Io, whereas the IVO-NF Baseline mission has 20 flybys, using the IVO-NF Iteration-5 tour. Estimates of the induction inversion error are unique for each tour and have yet to be completed for IVO-NF but are expected to be substantially better than the IVO Baseline over the course of the 20-flyby tour.
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: (a) Schematic of IVO-NF’s trajectory through a plume erupted from Pele Patera, with (b) the expected atmospheric profile above the volcano (J. I. Moses et al. 2002).
Paper: Comparing NASA Discovery and New Frontiers Class Mission Concepts for the Io Volcano Observer
Authors: Christopher W. Hamilton, Alfred S. McEwen, Laszlo Keszthelyi, Lynn M. Carter, Ashley G. Davies, Katherine de Kleer, Kandis Lea Jessup, Xianzhe Jia, James T. Keane, Kathleen Mandt, Francis Nimmo, Chris Paranicas, Ryan S. Park, Jason E. Perry, Anne Pommier, Jani Radebaugh, Sarah S. Sutton, Audrey Vorburger, Peter Wurz, Cauê Borlina, Amanda F. Haapala, Daniella N. DellaGiustina, Brett W. Denevi, Sarah M. Hörst, Sascha Kempf, Krishan K. Khurana, Justin J. Likar, Adam Masters, Olivier Mousis, Anjani T. Polit, Aditya Bhushan, Michael Bland, Isamu Matsuyama, John Spencer
Figure caption: Volatile fluxes on Io, such as those illustrated here, are poorly understood. However, IVO’s INMS and other Objective C2 measurements (see Table 1) would provide important constraints for validating models used to determine rates for these processes as a necessary first step in understanding the long-term evolution of Io’s volatile inventory and mass exchange within the Jovian system. In this figure, processes are labeled in white font.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: Cumulative plots of detection efficiency of the MobileNet model trained using different input data sets. Upper: Trained on data with sources with m < 23. Center: m < 25. Lower: m < 27. The left side of each panel shows classification probability (p) vs. fraction (f) of sources with moving sources correctly classified. The vertical blue/navy/black lines show various classification probability thresholds; thresholds of 0.5, 0.9, and 0.99 are shown. The middle panels show the classification behavior near the 0.9 and 0.99 thresholds. The panels in the right column show the fraction of sources correctly classified as a function of the source’s brightness, also known as the completeness curve.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: Analysis of linear fitting approach on the C08132134 test set. (Left) Number of false-positive tracks as a function of the minimum length of the track (n) to accept a track as a detection candidate. The vertical lines and end points are at n = 3, n = 6, n = 11, and n = 22. Setting the minimum track length requirement to these values would result in 14,435, 1514, 387, and 98 candidates to vet, respectively. (Right) A completeness graph showing artificial TNO retrieval rates as a function of magnitude for various track length. “Found” objects did not go through the human inspection, and therefore the actual retrieval rate will be slightly lower. Figure 4 is based on each single-image pair classification result while the completeness shown here is for the final candidates after classification, and linear fitting.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: ROC curves from testing the C051020M25 classification model on image pairs with different magnitude limits. The x-axis is the false-positive rate (false alarms relative to the total number of negatives; less is better). The y-axis is the true-positive rate (positives correctly classified relative to the total number of positives; more is better). These ROC curves show the general performance of the classifier when applied with various p thresholds. The more skewed to the upper left, the better the classifier performance. The area under the curve (AUC) indicates the performance of the classification model (more is better). An ROC following the gray dashed line indicates random performance (AUC = 0.5). Our ROC curves have AUCs of 0.87, 0.82, and 0.74, depending on the maximum magnitude limits of 23, 24, and 25 in the test data set. “m_regr” denotes the magnitudes estimated by the regression model. The diamond markers indicate where the classification threshold, p, reaches 0.99.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: Analysis of scoring approach on the C08132134 test set. (Left) Number of false-positive subimage series as a function of the minimum score of the subimage series (n) to accept a subimage series as a detection candidate (with P thresholding previously applied; see text). The vertical lines are at s = 80 and s = 1000. Setting the minimum score requirement to these values resulted in 110 and 1 false-positive candidate(s), respectively. (Right) A completeness graph showing artificial TNO retrieval rates as a function of magnitude for two kinds of score requirements, along with the manual inspection requirement. Figure 4 is based on each single-image pair classification result while this completeness graph is to show to the final number of TNOs found after postprocessing.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: Cumulative plots of the model probability (p) vs. the fraction of images classified as containing a moving source (g). All image pairs used for this plot are negatives, i.e., they do not contain any artificial moving sources. Thus, g is the number of false positives compared to the total number of negative subimages. There is very little difference between the M23 and M25 false-positive rates. The M27 model, however, returns false positives at double the rate of the M23/M25 models when p = 0.5. For this reason, we select the M25 model as our preferred model.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: Blue dots are regression-predicted positions of a group of sources that the classification model gave p > 0.5 probability of being a moving source. Yellow crosses are the predicted locations where a moving source would be in each exposure, based on a maximum-likelihood-based linear fit to the regressor-based locations. The linear model provides an excellent match to this group of sources, which are then declared as moving-object candidates.
Paper: A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
Authors: Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton
Figure caption: Examples of subimages used as our inputs. The orange circle marks the location of the synthetic TNOs. From the left upper pair to the lower right pair, six pairs of positive–negative subimages are shown. For the magnitude 22, 23, and 24 cases, one can see the bright source in the positive subimage absent in the negative subimage taken at a different epoch. The magnitude 25, 26, and 27 sources are progressively more difficult to distinguish. The artificial sources range from magnitude 22 to 27.