adsabs/AstroCLIMB huggingface dataset:
Paper: Mechanical Properties of Dust, Pebbles, and Planetesimals Based on Johnson–Kendall–Roberts, Griffith, and Weibull Theories
Authors: Hiroshi Kimura, ヒロシ キムラ, Takaya Okamoto, Takayuki Hirai, Fumi Yoshida, Peng K. Hong, Koji Wada, Tomoko Arai, Eiichiro Kokubo, Yuki Yoshida, Toshihiko Kadono, Sota Arakawa, Hiroki Senshu, Masanori Kobayashi, Ko Ishibashi, Manabu Yamada, Hiroshi Akitaya, Osamu Okudaira, Takafumi Matsui
Figure caption: The ratios E*(1 − ν2)/E and R*/(2γ) for the Lennard–Jones 12-6 potential with ν ≪ 1 as a function of volume-filling factor ϕ.
Paper: Mechanical Properties of Dust, Pebbles, and Planetesimals Based on Johnson–Kendall–Roberts, Griffith, and Weibull Theories
Authors: Hiroshi Kimura, ヒロシ キムラ, Takaya Okamoto, Takayuki Hirai, Fumi Yoshida, Peng K. Hong, Koji Wada, Tomoko Arai, Eiichiro Kokubo, Yuki Yoshida, Toshihiko Kadono, Sota Arakawa, Hiroki Senshu, Masanori Kobayashi, Ko Ishibashi, Manabu Yamada, Hiroshi Akitaya, Osamu Okudaira, Takafumi Matsui
Figure caption: The fracture toughness KIc experimentally (open circles) and numerically (crosses) determined for amorphous silica (left) and water ice (right) as a function of the volume-filling factor ϕ in comparison to our formula (solid curves) given in Equation (8).
Paper: Mechanical Properties of Dust, Pebbles, and Planetesimals Based on Johnson–Kendall–Roberts, Griffith, and Weibull Theories
Authors: Hiroshi Kimura, ヒロシ キムラ, Takaya Okamoto, Takayuki Hirai, Fumi Yoshida, Peng K. Hong, Koji Wada, Tomoko Arai, Eiichiro Kokubo, Yuki Yoshida, Toshihiko Kadono, Sota Arakawa, Hiroki Senshu, Masanori Kobayashi, Ko Ishibashi, Manabu Yamada, Hiroshi Akitaya, Osamu Okudaira, Takafumi Matsui
Figure caption: The fracture energy R* experimentally (open circles) and numerically (crosses) determined for amorphous silica (left) and water ice (right) as a function of the volume-filling factor ϕ in comparison to our formula (solid curves) given in Equation (7).
Paper: Mechanical Properties of Dust, Pebbles, and Planetesimals Based on Johnson–Kendall–Roberts, Griffith, and Weibull Theories
Authors: Hiroshi Kimura, ヒロシ キムラ, Takaya Okamoto, Takayuki Hirai, Fumi Yoshida, Peng K. Hong, Koji Wada, Tomoko Arai, Eiichiro Kokubo, Yuki Yoshida, Toshihiko Kadono, Sota Arakawa, Hiroki Senshu, Masanori Kobayashi, Ko Ishibashi, Manabu Yamada, Hiroshi Akitaya, Osamu Okudaira, Takafumi Matsui
Figure caption: A comparison between κ (open circles) in Equation (14) and κem in H. Kimura et al. (2020b; open triangles) along the left axis and the ratio of κem/κ (filled squares) along the right axis as a function of the Weibull modulus m.
Paper: Constraint on the Physical Origin of Gamma-Ray Burst Prompt Emission via Its Nondetected Diffuse Neutrino Emission
Authors: Yang-Dong-Jun Ou, Hou-Jun Lü, Jia-Ming Chen, Ben-Yang Zhu, En-Wei Liang
Figure caption: Predicted flux of diffuse neutrino for the benchmark parameters of εp/εe = 10 and Γ = 300 by assuming that all GRBs are originated from signal model with different mean redshift. Left: changed mean redshift of short GRBs as z = 0.3, 0.5, and 1.0 for fixed z = 2.15 of long GRBs. Middle: changed mean redshift of long GRBs as z = 1.0, 2.0, and 3.0 for fixed z = 0.5 of short GRBs. Right: simultaneously increasing the redshift of long GRBs (z = 1.0, 2.0, and 3.0) and short GRBs (z = 0.25, 0.5, and 1.0).
Paper: Constraint on the Physical Origin of Gamma-Ray Burst Prompt Emission via Its Nondetected Diffuse Neutrino Emission
Authors: Yang-Dong-Jun Ou, Hou-Jun Lü, Jia-Ming Chen, Ben-Yang Zhu, En-Wei Liang
Figure caption: Constraint on the fraction of GRB prompt emission models by adopting the method of summing up the individual GRB contributions. Left: the constraint results for the varying value of Γ = 300 and 500 with fixed εp/εe = 10. Right: the constraint results for the varying value of εp/εe = 3, 5, and 10 with fixed Γ = 300.
Paper: Constraint on the Physical Origin of Gamma-Ray Burst Prompt Emission via Its Nondetected Diffuse Neutrino Emission
Authors: Yang-Dong-Jun Ou, Hou-Jun Lü, Jia-Ming Chen, Ben-Yang Zhu, En-Wei Liang
Figure caption: Constraint on the fraction of GRB prompt emission models by adopting the method of assumed luminosity functions of GRB. Left: the constraint results for the varying value of Γ =  300 and 500 with fixed εp/εe = 10. Right: the constraint results for the varying value of εp/εe = 5 and 10 with fixed Γ = 300.
Paper: Constraint on the Physical Origin of Gamma-Ray Burst Prompt Emission via Its Nondetected Diffuse Neutrino Emission
Authors: Yang-Dong-Jun Ou, Hou-Jun Lü, Jia-Ming Chen, Ben-Yang Zhu, En-Wei Liang
Figure caption: Predicted flux of diffuse neutrino for the benchmark parameters of εp/εe = 10 and Γ = 300 by assuming that all GRBs are originated from the signal model (e.g., the dissipative photosphere model, the internal shock model, and the ICMART model). The bands for the diffuse neutrino flux are displayed for εp/εe = 3–10 and Γ = 100–500. The gray line with arrows is the upper limit flux of diffuse neutrino. Left: results using the method of summing up the individual GRB contributions. Right: results using the method of assumed luminosity functions of the GRB.
Paper: The Formation and Evolution of Dust in the Colliding-wind Binary Apep Revealed by JWST
Authors: Yinuo Han, Ryan M. T. White, Joseph R. Callingham, Ryan M. Lau, Benjamin J. S. Pope, Noel D. Richardson, Peter G. Tuthill
Figure caption: Color image of Apep created from JWST/MIRI observations with the F770W, F1500W, and F2550W filters. North is oriented toward the top of the page. A color image of the inner dust shell observed with VLT/VISIR in the 2024 epoch is shown in the inset, where tick marks are separated by 5″. A distance of 2.4 kpc is assumed for the linear displacement axes.
Paper: The Formation and Evolution of Dust in the Colliding-wind Binary Apep Revealed by JWST
Authors: Yinuo Han, Ryan M. T. White, Joseph R. Callingham, Ryan M. Lau, Benjamin J. S. Pope, Noel D. Richardson, Peter G. Tuthill
Figure caption: The difference image of the inner shell of Apep observed by VISIR with the B11.7 filter between 2016 and 2024. Over-emission in 2024 compared to 2016 is shown in purple and under-emission is shown in orange. The difference image suggests a consistent and significant expansion of the dust structure, which we measured to be 90 ± 4 mas yr−1.
Paper: The Serpent Eating Its Own Tail: Dust Destruction in the Apep Colliding Wind Nebula
Authors: Ryan M. T. White, Benjamin J. S. Pope, Peter G. Tuthill, Yinuo Han, Shashank Dholakia, Ryan M. Lau, Joseph R. Callingham, Noel D. Richardson
Figure caption: Using the four VLT epochs plus the 25.5 μm JWST image, we found parameters for our geometric model that faithfully reproduce the observed geometry; the numerical values of each parameter are shown in Table 1. The top left panel shows the model geometry at ϕ2024, and all other panels show the difference imaging of Fmodel − Fobs adjusted for the respective orbital phases; note that the JWST panel (bottom right) is over a larger angular scale. Although the exact flux often does not entirely cancel out in the difference imaging, we note the nebula ridge positions—which encode all of the fit parameters—align very well. We have an animation available online ✎ that shows the evolution of a shell with our parameters over one orbital period.
Paper: The Formation and Evolution of Dust in the Colliding-wind Binary Apep Revealed by JWST
Authors: Yinuo Han, Ryan M. T. White, Joseph R. Callingham, Ryan M. Lau, Benjamin J. S. Pope, Noel D. Richardson, Peter G. Tuthill
Figure caption: Top: MIRI F770W image of Apep with the azimuthal range of shell radius measured in this study indicated by dashed lines, and the direction orthogonal to the line of nodes and its uncertainty range (R. M. T. White et al. 2025) indicated by dotted lines. Middle: F1500W image with points indicating examples of shell location detections. Bottom: F2550W image labeled with the E1, E2, and E3 features and apertures used to extract photometry.
Paper: The Serpent Eating Its Own Tail: Dust Destruction in the Apep Colliding Wind Nebula
Authors: Ryan M. T. White, Benjamin J. S. Pope, Peter G. Tuthill, Yinuo Han, Shashank Dholakia, Ryan M. Lau, Joseph R. Callingham, Noel D. Richardson
Figure caption: Panel (a) shows the false-color composite image of the Apep nebula made by combining data from the F770W, F1500W, and F2550W filters. Three concentric shells of dust are clearly seen, with faint evidence of a fourth at the edge of frame. The W-R + W-R central engine and the tertiary O supergiant lie in the center of frame, while the bright star in the lower left is not associated with the Apep system. The processing procedure is described in Appendix A.1. In panel (b) we highlight the concentric shell structure with a gray skeleton, where the colored ridges indicate regions of interest: the position of the cavity across concentric shells (see Figure 3), the relative positions of the southeast ridge coinciding with the southeast tail over time are key for the new eccentricity value, and the southern bar aids in the period determination. Panel (c) shows our geometric fit to the three visible shells of Apep, where the alignment of the modeled to the observed shells is shown in Figure 2.
Paper: The Formation and Evolution of Dust in the Colliding-wind Binary Apep Revealed by JWST
Authors: Yinuo Han, Ryan M. T. White, Joseph R. Callingham, Ryan M. Lau, Benjamin J. S. Pope, Noel D. Richardson, Peter G. Tuthill
Figure caption: The spatially resolved SED of Apep constructed from a range of shell orders and observing dates. The SED models fitted individually to each feature at each epoch (Section 5.2) are shown with dotted lines, whereas the SED models assuming the best-fit power-law temperature profile (Equation 4) are shown with dashed lines. The top, middle, and bottom rows correspond to the E1, E2, and E3 features, respectively.
Paper: The Formation and Evolution of Dust in the Colliding-wind Binary Apep Revealed by JWST
Authors: Yinuo Han, Ryan M. T. White, Joseph R. Callingham, Ryan M. Lau, Benjamin J. S. Pope, Noel D. Richardson, Peter G. Tuthill
Figure caption: The radial temperature profile of dust in Apep measured from its spatially resolved SED. The thicker error bars are derived by fitting SED models to individual features. The thinner error bars show the adjusted uncertainties upon enforcing the condition that the measurements sample the same temperature profile. The best-fit power-law profile with a power-law index of −0.31 is shown, along with the blackbody equilibrium temperature, an amorphous carbon equilibrium temperature profile based on RADMC-3D simulations and an amorphous carbon approximation with a power-law index of −0.38 for comparison.
Paper: Evidence of Time-dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event
Authors: Xiaohang Chen, Lulu Zhao, Joe Giacalone, Nishtha Sachdeva, Igor V. Sokolov, Gábor Tóth, Christina M. S. Cohen, David Lario, Fan Guo, Athanasios Kouloumvakos, Tamas I. Gombosi, Zhenguang Huang, Ward B. Manchester, Bart van der Holst, Weihao Liu, David J. McComas, Matthew E. Hill, George C. Ho
Figure caption: (a) Simulated spectrogram of proton intensities observed near the PSP orbit at 15 R⊙. The green shaded region marks the interval between the CME eruption and the time PSP first becomes magnetically connected to the shock. The red and blue shaded regions indicate the durations of proton acceleration at the shock and transport to the observer, respectively. The red curve tracks the maximum proton energy achievable at the shock as it expands outward. The blue curve outlines the spectrogram limits imposed by the magnetic connection, acceleration, and transport processes. Double-headed arrows indicate the respective timescales (ΔtC, ΔtA, and ΔtT) for 1 MeV protons. (b) Comparison of simulated (solid lines) time–intensity profiles and PSP’s observations (cross symbols) for a selection of energy channels.
Paper: Evidence of Time-dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event
Authors: Xiaohang Chen, Lulu Zhao, Joe Giacalone, Nishtha Sachdeva, Igor V. Sokolov, Gábor Tóth, Christina M. S. Cohen, David Lario, Fan Guo, Athanasios Kouloumvakos, Tamas I. Gombosi, Zhenguang Huang, Ward B. Manchester, Bart van der Holst, Weihao Liu, David J. McComas, Matthew E. Hill, George C. Ho
Figure caption: Overview of the SolO observations from 2022 September 5 00:00 UT to September 7 10:00 UT. (a) Magnetic field magnitude and components in the RTN coordinate system, with the heliocentric distance of SolO shown along the top x-axis in solar radii. (b) Integrated energy spectrograms for ion intensities from EPD, including sunward HET, sunward EPT, and STEP. (e) Energy spectrograms for EPD plotted against inverse velocity. The vertical dotted line indicates the CME eruption time. The gray dashed lines represent the fitted velocity dispersion.
Paper: Evidence of Time-dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event
Authors: Xiaohang Chen, Lulu Zhao, Joe Giacalone, Nishtha Sachdeva, Igor V. Sokolov, Gábor Tóth, Christina M. S. Cohen, David Lario, Fan Guo, Athanasios Kouloumvakos, Tamas I. Gombosi, Zhenguang Huang, Ward B. Manchester, Bart van der Holst, Weihao Liu, David J. McComas, Matthew E. Hill, George C. Ho
Figure caption: (a) Radial photospheric magnetic field BR from the ADAPT-GONG magnetogram for Carrington rotation (CR) 2261. The magnetic field saturation value is set to 60 G to enhance the visibility of active region AR 13088. (b) Steady-state solar wind total velocity in the Z = 0 plane of the heliocentric inertial coordinate system at 16:00 UT on 2022 September 5. (c) Configuration of the CME embedded within the background solar wind solution (shown in the ion temperature Ti) at 17:30 UT, when the shock front is approximately crossing the PSP. The blue, red, and green dots denote the positions of PSP, SolO, and Earth, respectively. Solid lines represent the magnetic field lines connected to each spacecraft and Earth.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Galaxy image and model subtraction results for VCC 0407 using traditional methods. The left image shows the original ﹩{g}^{{\prime} }﹩-band image of VCC 0407, an early-type dwarf galaxy in the Virgo cluster with ﹩{M}_{{g}^{{\prime} }}=-16.82﹩ mag. The size of this image is 512 × 512 pixels, corresponding to ﹩95\mathop{.}\limits{^{\prime\prime} }7\times 95\mathop{.}\limits{^{\prime\prime} }7﹩ in observed units. The center image shows the residual image produced by a ring median filter with an inner radius of 11 pixels (2﹩\mathop{.}\limits{^{\prime\prime} }﹩057) and an outer radius of 16 pixels (2﹩\mathop{.}\limits{^{\prime\prime} }﹩992). The right image shows the residual image produced by ellipse subtraction. The ring median filter is unable to subtract the bright galaxy center, while the ellipse fitting struggles to fit the spiral structures in this galaxy.
Paper: Evidence of Time-dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event
Authors: Xiaohang Chen, Lulu Zhao, Joe Giacalone, Nishtha Sachdeva, Igor V. Sokolov, Gábor Tóth, Christina M. S. Cohen, David Lario, Fan Guo, Athanasios Kouloumvakos, Tamas I. Gombosi, Zhenguang Huang, Ward B. Manchester, Bart van der Holst, Weihao Liu, David J. McComas, Matthew E. Hill, George C. Ho
Figure caption: The distribution of compression ratio (ρ2/ρ1) along the shock surfaces at (a) 16:20 UT and (b) 17:30 UT on 2022 September 5.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: An example pair of images in the training set. We use the injected galaxy image (left) as input and the clean model galaxy image (right) as targeted output. Each image is of size 512 × 512 pixels (95﹩\mathop{.}\limits^{\unicode{x02033}}﹩7 × 95﹩\mathop{.}\limits^{\unicode{x02033}}﹩7 in observed units). The left image is the injected galaxy image (or “noisy” image in training) produced by injecting GALFIT galaxy models into “blank sky” background images taken from the NGVS dataset. The sky background images are chosen so they do not contain bright stars or large galaxies. The right image is the clean model galaxy image (or “clean” image in training) generated using the GALFIT package. We bin the training data by apparent magnitudes. This model galaxy (﹩{g}^{{\prime} }=14.41﹩ mag) was used in the training set for galaxies with ﹩14\lt {g}^{{\prime} }\lt 15﹩ mag.
Paper: Evidence of Time-dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event
Authors: Xiaohang Chen, Lulu Zhao, Joe Giacalone, Nishtha Sachdeva, Igor V. Sokolov, Gábor Tóth, Christina M. S. Cohen, David Lario, Fan Guo, Athanasios Kouloumvakos, Tamas I. Gombosi, Zhenguang Huang, Ward B. Manchester, Bart van der Holst, Weihao Liu, David J. McComas, Matthew E. Hill, George C. Ho
Figure caption: (a) Solar wind velocity, (b) shock speed, (c) Alfvén Mach number, (d) shock normal angle, and (e) density compression ratio estimated at the COBPOINTs of PSP (blue) and SolO (red) as the shock expands outward from the Sun. The vertical dotted line marks the eruption time of the CME.
Paper: Evidence of Time-dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event
Authors: Xiaohang Chen, Lulu Zhao, Joe Giacalone, Nishtha Sachdeva, Igor V. Sokolov, Gábor Tóth, Christina M. S. Cohen, David Lario, Fan Guo, Athanasios Kouloumvakos, Tamas I. Gombosi, Zhenguang Huang, Ward B. Manchester, Bart van der Holst, Weihao Liu, David J. McComas, Matthew E. Hill, George C. Ho
Figure caption: Modeled SEP intensities at SolO. (a) Integrated energy spectrograms for ion intensities from EPD. (b) The simulated spectrograms at the SolO location. Vertical dashed lines denote the eruption time in both panels.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Test result comparisons on Virgo Cluster galaxies in apparent magnitude range 14–15. (a) VCC 0033, a smooth dwarf elliptical galaxy (﹩{M}_{{g}^{{\prime} }}=-16.23﹩). (b) VCC 1304, an edge-on galaxy (﹩{M}_{{g}^{{\prime} }}=-16.10﹩). (c) VCC 0407, a spiral galaxy with ﹩{M}_{{g}^{{\prime} }}=-16.83﹩. (d) VCC 1725, a star-forming galaxy (﹩{M}_{{g}^{{\prime} }}=-16.80﹩) with significant artifacts with ellipse model subtraction. Ellipse subtraction and the DAE perform similarly well on smooth galaxies, but the DAE yields cleaner residuals for edge-on, spiral, and star-forming galaxies.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: For each galaxy model subtraction method used, we extracted the integrated magnitude of objects in the residual image across different aperture sizes and present the comparison. The lower and upper error bars represent the 16th and 84th percentiles of the data points. The magnitude of objects obtained using different aperture sizes can vary. The plot in panel (a) shows that the magnitudes obtained by the denoising autoencoder are consistent with those obtained from ellipse fitting and are not dependent on aperture size. In contrast, in panel (b), the ring median filter subtracted images show oversubtraction for small aperture sizes and undersubtraction for large aperture sizes.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Aggregated recovery rate and mean magnitude error heat maps created from mock globular clusters injected into residual images for different galaxy morphologies. The left panel is the recovery rate from ellipse subtraction, the middle panel is the recovery rate from DAE, and the right panel shows the ratio between the two (top number in each bin). The bottom number in each bin represents the total number of objects injected in that bin. For all nonnucleated galaxies, particularly those with complex morphology, the recovery rate of faint GCs is markedly better in DAE-subtracted images.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Graphical illustration of our DAE model architecture. The encoder (left) compresses the input data into a lower-dimensional latent space vector with key features. The decoder (right) reconstructs the input data from this latent space into a target output, which shares some key features with input data but is not identical to the input data. In our DAE, the input data are a group of observed galaxy images (with the galaxy, foreground stars, and background objects), and the output data are the corresponding clean, galaxy-only images.
Paper: Ross 458 C: Gas Giant or Brown Dwarf?
Authors: William W. Meynardie, Michael R. Meyer, Ryan J. MacDonald, Per Calissendorff, Elijah Mullens, Gabriel Munoz Zarazua, Anuranj Roy, Hansica Ganta, Eileen C. Gonzales, Arthur Adams, Nikole Lewis, Yucian Hong, Jonathan Lunine
Figure caption: Summary figure of the fit to Ross 458 A using interpolated grids of PHOENIX models. Top panel: the median and 1σ and 2σ distributions of a sample of 100 spectra randomly sampled from the resulting parameter distribution, compared to the data. The 1σ and 2σ distributions are smaller than the width of the median line. Middle panel: the difference between the model and the data flux divided by the uncertainty in the data without considering model uncertainties parameterized by xtol and b. Bottom panels: probability density functions of each of the freely varying parameters.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Training loop of the denoising autoencoder. During training, the “noisy” images (galaxies injected into sky backgrounds) are put through the denoising autoencoder, producing output denoised images. The output images are then compared with the clean model galaxy image to calculate the loss by mean absolute error (MAE). Weights in the denoising autoencoder are updated by back propagation to minimize the loss.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Test result comparisons on Virgo Cluster galaxies in apparent magnitude range ﹩13\lt {g}^{{\prime} }\lt 14﹩ mag. (a) VCC 1949, a galaxy with weak spiral structure (﹩{M}_{{g}^{{\prime} }}=-17.43﹩). (b) VCC 0672, an edge-on galaxy (﹩{M}_{{g}^{{\prime} }}=-17.48﹩). (c) VCC 0575, a galaxy with a bulge (﹩{M}_{{g}^{{\prime} }}=-17.52﹩). The denoising autoencoder reduces the X-shaped residuals, but struggles to fit the central bulge, leaving a bright central region in the residual image.
Paper: Galaxy Model Subtraction with a Convolutional Denoising Autoencoder
Authors: Rongrong Liu, Eric W. Peng, Kaixiang Wang, Laura Ferrarese, Patrick Côté
Figure caption: Aggregated recovery rate and mean magnitude error heat maps created from globular clusters injected into residual images for star-forming galaxies.