adsabs/AstroCLIMB huggingface dataset:
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: The blue histogram shows small Einstein radius lenses, with Mhalo < 1011M⊙ and ﹩{\theta }_{{\rm{E}}}\lt 0\mathop{.}\limits^{^{\prime\prime} }05﹩, correctly classified by the ResNet (with threshold 0.96; see Section 3.2.2.4). The orange histogram shows the number of lenses with a U-Net detection threshold above 0.5, and the green histogram shows the number of lenses with a detection threshold above 0.72. For both thresholds, every image has a correct lens detection. In fact, for Mhalo < 1011M⊙ and ﹩{\theta }_{{\rm{E}}}\lt 0\mathop{.}\limits^{^{\prime\prime} }05﹩, with either thresholds, the pixel-level precision remains at 100%, albeit with low statistics. Note that in this figure, we simply show counts in the vertical axis (and not in terms of per square degree).
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: Detection probability vs. Einstein radius at U-Net training epoch 110, for the Einstein radius range of ﹩0\mathop{.}\limits^{^{\prime\prime} }02\lt {\theta }_{{\rm{E}}}\lt 0\mathop{.}\limits^{^{\prime\prime} }15﹩ (as shown on the x-axis) and all halo mass values. For an image with a lens, the pixel with the maximum probability represents the U-Net’s prediction, with correct and incorrect detections (green and blue dots, respectively) defined in Section 4.2.1. Non-lenses do not have an Einstein radius, but are displayed with an Einstein radius of 0 here (red points). For approximately 25% (1072 lenses) of the predictions with a probability over 0.5 in the validation set (orange dashed line), the model achieves a 94.2% pixel-level precision. At a higher threshold, for 7.4% (296 lenses) of the predictions with a probability over 0.72 (purple dashed line), the model achieves a 99.7% pixel-level precision (only one false positive: the single red dot above the purpose dashed line).
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: Hexbin plot of the distance between the ground truth and U-Net prediction vs. the brightest pixel to ground truth flux ratio. The top panel (entire validation set) includes predictions on all images in the validation set, the middle panel (only ResNet) includes predictions on all images that have a ResNet classification threshold above 0.96, and the bottom panel (RUN pipeline) includes predictions on images that have a ResNet classification threshold above 0.96 and U-Net detection threshold above 0.72. At all stages of the RUN pipeline, the U-Net correctly detects (below the red dotted line of 1.4 pixels) numerous lenses that have a brightest pixel to ground truth flux ratio >1. Even after applying the U-Net detection threshold of 0.72 (transitioning from the middle to bottom panel), there are still several lenses with such a flux ratio being >1, all of which the U-Net detects correctly. This demonstrates that the U-Net does not just predict the pixel with a local maximum brightness near the center of the image, but rather shows that it appears to “understand” the features of a lens.
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: The left half of this figure shows four examples of simulated systems where a 1%–95% pixel scaling allows for clearly identifying the lens. The right half of this image shows two examples of simulated systems where the lens cannot be clearly identified with a 1% to 95% pixel scaling, along with optimal scaling to clearly reveal the lensed arcs. The top-right system shows a barely noticeable small gap at the lower-left corner of the image, hinting at the lensing effect, while the bottom-right system shows no clues.
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: Examples of lenses that are correctly classified and detected by the ResNet and U-Net models, respectively. All images have a ResNet classification probability above 0.96 and U-Net detection probability above 0.72, where the RUN pipeline achieves a pixel-level precision of 1.0 (see Figure 26). Orange crosshairs represent the location of the correctly detected lens. Out of these 30 examples, there are numerous complex lens systems in which the U-Net identifies the lens correctly, supporting the fact that the U-Net is not just predicting a local maximum near the source.
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: Loss and accuracy vs. training epochs. The loss curve drastically drops in the first 25 epochs. It appears that it may still be gradually decreasing afterward, as the accuracy continues to slowly improve. In our experiments, beyond approximately the 100th Epoch, the validation accuracy tends to plateau, even when trained up to 400 Epochs. Due to the nature of the detection problem, where the model predicts only one pixel in the entire image, the validation accuracy is quite volatile compared to the training accuracy.
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: Two new lens candidates found in this work. The first is at α =  09:36:03.00, δ =  09:14:03.12 (HST-144.0125+9.2342), and the second is at α =  12:02:00.41, δ =  47:42:16.92 (HST-180.5017+47.7047). Note that our naming convention uses the decimal coordinates of the lens center. The second candidate, on the right, was first identified as a lens by the ResNet while humans had not initially recognized it as one.
Paper: ML-driven Strong Lens Discoveries: Down to θE﹩\,\sim \,0\mathop{.}\limits^{\unicode{x02033}}03﹩ and Mhalo < 1011M⊙
Authors: Ethan Silver, R. Wang, Xiaosheng Huang, Adam S. Bolton, Christopher J. Storfer, S. Banka
Figure caption: We show the forecast numbers for lenses with M < 1011M⊙ that are correctly classified by the ResNet Model 3 (JWST-small) at a 0.96 threshold (with zero false positives; see bottom-right panel in Figure 21) and are then correctly detected by the U-Net at a 0.72 threshold (again with zero false positives). Note that the forecast numbers from Method A contribute minimally to this category, as expected.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Measured emission surfaces (top panel) and brightness temperatures (bottom panel) for HD 163296 binned in annuli. The solid lines show the estimated parametric behaviors for the J = 2–1 lines obtained by C. J. Law et al. (2021) for reference. The gray line in the lower panel shows Tb = 20 K, below which each emission line was eliminated from the analysis.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Radial profiles of Tmid, γ, and az for HD 1623926. The black line shows the median of the posteriors, the dark-blue shaded region shows the 68% confidence interval, and the light-blue shaded region shows the 95% confidence interval. The scatter reflects both measurement uncertainties and degeneracies in the model fit. The sharp change in the median values exterior to 2﹩\mathop{.}\limits^{^{\prime\prime} }﹩is due to the 13CO J = 1–0 emission being discarded in these annuli, not because of any discontinuity in the physical structure.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Results of the annulus-by-annulus (top panels) and 2D (bottom panels) temperature inference methods, using the pixels extracted with disksurf from the J = 2–1 image cubes and corrected to the τ = 2/3 surfaces. The black lines show the emission surface of each line. Panels are the same as in Figure 3.
Paper: The He I D3 Line as a Proxy for Magnetic Activity Using EXPRES Solar Observations
Authors: Momo Ellwarth, Joe Llama, Lily L. Zhao, John M. Brewer, Andrew Szymkowiak, Rachael M. Roettenbacher, James T. Sikora, Alex S. Polanski, Steven H. Saar
Figure caption: Top: EXPRES master spectrum (pink) and one single observation (black) of the He D3 line. Bottom: ratio of this wavelength range obtained by dividing the master spectrum by the single observation. The He D3 index is obtained by integrating the gray-shaded area under these ratios.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Results of the vertical temperature inference using the J = 2–1, J = 4–3, and J = 6–5 emission lines. The panels are the same as in Figure 3.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Median values of the temperature posteriors (top panel) and standard deviation (bottom panel) for HD 163296. The black lines show the measured emission surface in each annulus.
Paper: The He I D3 Line as a Proxy for Magnetic Activity Using EXPRES Solar Observations
Authors: Momo Ellwarth, Joe Llama, Lily L. Zhao, John M. Brewer, Andrew Szymkowiak, Rachael M. Roettenbacher, James T. Sikora, Alex S. Polanski, Steven H. Saar
Figure caption: Averaged EXPRES spectrum over the year 2024. Purple: observed Spectrum with telluric lines. Black: telluric-corrected spectrum. The gray area shows the wavelength range of interest.
Paper: The He I D3 Line as a Proxy for Magnetic Activity Using EXPRES Solar Observations
Authors: Momo Ellwarth, Joe Llama, Lily L. Zhao, John M. Brewer, Andrew Szymkowiak, Rachael M. Roettenbacher, James T. Sikora, Alex S. Polanski, Steven H. Saar
Figure caption: EXPRES spectra of the He D3 line, averaged by year from 2021 to 2025. The red lines mark the laboratory wavelengths of the He D3 line (Table 1). The gray area marks the wavelength range used to calculate the line index.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Left: our GJ 1214 b-like model atmosphere pressure–temperature profile (solid, lavender) computed with PICASO for a 100× solar metallicity composition and the condensation curve of KCl (dashed, maroon) computed with Virga. Center: the eddy diffusion coefficient, Kzz, evolution of our model atmosphere with pressure, following B. Charnay et al. (2015a). Right: the mean mass mixing ratio (MMR) of condensed KCl particles as produced by Virga with the model parameters, which is the same for all runs described in our case study, regardless of whether computed for spherical or aggregate particles.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Top: mean particle size (in terms of equivalent-mass compact spheres, ragg,m) for fixed monomer sizes of rmon = 0.01, 0.1, and 1 μm as a function of pressure for our model GJ 1214 b atmosphere. Bottom: the same as top, but for fixed Nmon = 100, 1000, and 10,000 monomers. The thin dashed–dotted red lines in both rows denotes the Df = 2.0 compression radius taken from Equation (18) of K. Ohno et al. (2020), beyond which such particles are unlikely to persist (see the text for more details).
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Top: volume filling factor for fixed monomer sizes of rmon = 0.01, 0.1, and 1 μm as a function of pressure for our model GJ 1214 b atmosphere. Bottom: the same as top, but for fixed Nmon = 100, 1000, and 10,000 monomers. As in Figure 7, the rmon = 0.01, Df = 2 particles in the plot in the top left would likely compress to smaller values than those shown here in a real atmosphere. Consequently such particles would have a larger filling factor than shown.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Top: the number of particles as a function of pressure for fixed monomer sizes of rmon = 0.01, 0.1, and 1 μm for our model GJ 1214 b-like atmosphere. Bottom: the same as top, but for fixed Nmon = 100, 1000, and 10,000 monomers. The marker sizes are scaled by the equivalent-mass compact radius, ragg,m. As in Figure 7, the rmon = 0.01, Df = 2 particles in the plot in the top left would likely compress to smaller than the values here in a real atmosphere.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Top, center, bottom: simulated transmission spectra for aggregate KCl clouds in a 100× solar metallicity atmosphere on a GJ 1214 b-like planet using fixed rmon = 0.01, 0.1, and 1 μm sized monomers, respectively, for fractal dimensions of Df = 1.2, 1.6, 2.0, 2.4, and 2.8 along with spherical cloud particles. The change in opacity at visible–near-infrared wavelengths is highly dependent on the size of the monomer. For KCl clouds, lacier aggregates generally decrease the opacity overall, but this is sensitive to the exact location of the cloud deck.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Top, center, bottom: simulated transmission spectra for aggregate KCl clouds in a 100× solar metallicity atmosphere on a GJ 1214 b-like planet using fixed Nmon = 100, 1000, and 10,000 monomers, respectively, for fractal dimensions of Df = 1.2, 1.6, 2.0, 2.4, and 2.8 along with spherical cloud particles. Changing the number of monomers in this formulation hardly changes the spectra. For KCl clouds, lacier aggregates increase the opacity at longer wavelengths and decrease the opacity at shorter wavelengths compared to spheres.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Top: the cloud top pressure (i.e., where τ = 1) for fixed monomer sizes of rmon = 0.01, 0.1, and 1 μm as a function of pressure for our model GJ 1214 b-like atmosphere. Bottom: the same as top, but for fixed Nmon = 100, 1000, and 10,000 monomers.
Paper: Hemispheric Asymmetry of Coronal Mass Ejections with Different Angular Widths During Solar Cycles 23–25
Authors: Wei Zhou, Weihong Zhou, Wenhua Wang, Jie Cao, Tingting Xu, Miao Wan, Yuxia Liu, Shangxi Li, Xueliang Zhou, Peng E
Figure caption: Cumulative asymmetry curves of narrow, normal, and partial-halo CMEs in the northern hemisphere (yellow) and southern hemisphere (blue), covering SC23–SC25. Each row corresponds to a distinct CME type: the first row represents all CMEs, the second row represents narrow CMEs, the third row represents normal CMEs, and the fourth row represents partial-halo CMEs. Among these, the red solid lines denote the fitting curves for the curves corresponding to each solar cycle; a positive slope indicates the northern hemisphere as the dominant hemisphere, while a negative slope indicates the southern hemisphere as the dominant one. The vertical dashed lines mark the boundary between SC23 and SC24.
Paper: Hemispheric Asymmetry of Coronal Mass Ejections with Different Angular Widths During Solar Cycles 23–25
Authors: Wei Zhou, Weihong Zhou, Wenhua Wang, Jie Cao, Tingting Xu, Miao Wan, Yuxia Liu, Shangxi Li, Xueliang Zhou, Peng E
Figure caption: Cumulative number of CMEs in the northern (yellow) and southern (blue) hemispheres during SC23 (left column), SC24 (middle column), and SC25 (right column). From top to bottom, the rows correspond to all CMEs, narrow CMEs, normal CMEs, and partial-halo CMEs. This figure reveals the long-term temporal evolution and hemispheric dominance patterns for each CME type across different solar cycles.