adsabs/AstroCLIMB huggingface dataset:
Paper: Characterizing the Star Cluster Populations in Stephan’s Quintet Using HST and JWST Observations
Authors: P. Aromal, S. C. Gallagher, K. Fedotov, N. Bastian, U. Lisenfeld, J. C. Charlton, P. N. Appleton, J. Braine, K. E. Johnson, P. Tzanavaris, B. H. C. Emonts, A. Togi, C. K. Xu, P. Guillard, L. Barcos-Muñoz, L. J. Smith, I. S. Konstantopoulos
Figure caption: Left: The JWST NIRcam false-color image (red square) is shown with the boxes inside representing the field of view for HST B438-V606-I814 bands (orange) and HST U336-V547 bands (blue). The five galaxies in the field are marked including the four galaxies in the group, namely NGC 7317, 7318a, 7318b, and 7319, and the foreground galaxy NGC 7320.
Paper: Characterizing the Star Cluster Populations in Stephan’s Quintet Using HST and JWST Observations
Authors: P. Aromal, S. C. Gallagher, K. Fedotov, N. Bastian, U. Lisenfeld, J. C. Charlton, P. N. Appleton, J. Braine, K. E. Johnson, P. Tzanavaris, B. H. C. Emonts, A. Togi, C. K. Xu, P. Guillard, L. Barcos-Muñoz, L. J. Smith, I. S. Konstantopoulos
Figure caption: Spatial distribution of SCCs in different AV bins (color coded with cluster age). The different regions of interest are shown here in white, similar to Figure 8.
Paper: Characterizing the Star Cluster Populations in Stephan’s Quintet Using HST and JWST Observations
Authors: P. Aromal, S. C. Gallagher, K. Fedotov, N. Bastian, U. Lisenfeld, J. C. Charlton, P. N. Appleton, J. Braine, K. E. Johnson, P. Tzanavaris, B. H. C. Emonts, A. Togi, C. K. Xu, P. Guillard, L. Barcos-Muñoz, L. J. Smith, I. S. Konstantopoulos
Figure caption: Histogram of best-fit metallicity values after keeping it as a free parameter for the older clusters (τ > 108 yr).
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: Flowchart describing the steps and sources used to simulate lensed and unlensed images in this training sample. To simulate unlensed images: for ﹩0\mathop{.}\limits^{^{\prime\prime} }5\lt {\theta }_{{\rm{E}}}\lt 1\mathop{.}\limits^{^{\prime\prime} }5﹩, we turn off source light and set θE = 0; for ﹩{\theta }_{{\rm{E}}}\lt 0\mathop{.}\limits^{^{\prime\prime} }50﹩, we still set θE = 0, but keep the source light on (see Section 3.1.2).
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: Trained Model 1a (HST-long) tested on lenses with brighter arcs (top two rows) and non-lenses (bottom two rows), from HST GO-15923, with exposure times between 4800 and 7800 s. The model predicted probability is shown for each image.
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: Distributions of zl, zs, and θE for the three Einstein radius ranges.
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 simulated lensed systems with ﹩0\mathop{.}\limits^{^{\prime\prime} }5\lt {\theta }_{{\rm{E}}}\lt 1\mathop{.}\limits^{^{\prime\prime} }5﹩ (Model 1 (HST-long/short)). Noise is added, as described in Section 3.1.5 at the level of Model 1a (HST-long; Table 3).
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: Validation and training loss and area under the ROC curve (AUC) over 360 epochs for Model 1a (HST-long). The validation AUC for the validation set reaches a maximum of 0.9978. We also indicate the best validation AUC and loss. The curves are boxcar smoothed with a window size of 3.
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: AUC and loss curves for the model trained on small Einstein radius systems (﹩0\mathop{.}\limits^{\unicode{x02033}}15\lt {\theta }_{{\rm{E}}}\lt 0\mathop{.}\limits^{\unicode{x02033}}50﹩) with ﹩{t}_{\exp }=1000﹩ s (Model 2b (JWST-short)).
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: Trained Model 1a (HST-long) tested on lenses with fainter arcs (top row) and non-lenses (bottom row), from HST GO-10886, with exposure time between 1500 and 2000 s. The probability the model predicted is shown for each image.
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: AUC and loss curves for the model trained on small Einstein radius systems (﹩0\mathop{.}\limits^{^{\prime\prime} }15\lt {\theta }_{{\rm{E}}}\lt 0\mathop{.}\limits^{^{\prime\prime} }50﹩) with ﹩{t}_{\exp }=\rm{10,000}﹩ s (Model 2a (JWST-long)).
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: Precision and recall for Model 1a (HST-long) as a function of probability threshold, with selected values shown in Table 6 below.
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: A relatively straightforward image for lens detection by the U-Net. The quantity “Distance” at the top left of the figure indicates the separation between the predicted lens location and the true lens location in pixels. The probability heat map (bottom left) shows that the U-Net only focuses on one single pixel at (58, 63)—for the horizontal and vertical directions, respectively—giving a high detection probability of 0.725 for that pixel and much lower probabilities elsewhere. The zoomed-in panels (bottom row, middle and right) for the prediction and ground truth are identical because the prediction and the ground truth coincide.
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: Even though the prediction (red crosshairs, barely visible) is one pixel away from the ground truth (white crosshairs), the detection is considered to be correct (see Section 4.2.1), as the model would still identify the relevant location in the image for human inspection and follow-up observations. This situation occurs in 2.25% (45) of the lenses in the validation set.
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: Precision–recall curve for the ResNet and U-Net pipeline, evaluated on the SIS and truncated SIS simulations. Colored shapes represent U-Net probability thresholds that are multiples of 0.1 (leftmost is 0.9), corresponding to the curve of the same color. Across most probability thresholds, the pipeline performs slightly better on the truncated SIS simulation, suggesting that the U-Net model is not dependent on distortions that are far away from the lens when identifying the location 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: Histogram of the VELA galaxies used in the training (blue bars) and validation (orange bars) data sets for the k-fold 0th split. VELA images of the galaxies at z = 1 with a randomly chosen angle are shown above the histogram.
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: A challenging lens that the U-Net detects correctly. There are many objects around the lens that make it difficult to detect the correct location of the lens. The U-Net nevertheless correctly predicts the lens location. This challenging example provides promising evidence that the model is not simply predicting the brightest pixel: it clearly singles out the lens, compared with the many local maxima, as is clear from the heat map.
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: Precision and recall for Model 3 (JWST-small) as a function of probability threshold. The solid lines show the performance on the original validation set with SIE lenses, and the dotted lines show the performance on the validation set with truncated SIS lenses.
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: Trained Model 1b (HST-short) tested on single-exposure lenses (top two rows) and non-lenses (bottom two rows). From HST GO-10174, with exposure time of 420 s. The probability the model predicted is shown for each image.
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: Example of the lensing effect of a lens with Einstein radius ﹩{\theta }_{{\rm{E}}}=0\mathop{.}\limits^{^{\prime\prime} }032﹩, shown using a standard SIS and a truncated SIS lens model.
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: A lens with an Einstein radius of ﹩{\theta }_{{\rm{E}}}=0\mathop{.}\limits^{^{\prime\prime} }03﹩. Even though the lens is not the brightest pixel in the image and has a very small Einstein radius, the U-Net is able to confidently detect the lens with a 0.7243 detection probability.
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: While training each k-fold model for Figure 41, we noticed that sometimes the validation loss has significantly less fluctuation than is typical and worse performance, even when trained using the same hyperparameters and data set (compare the top-middle and top-right loss charts). This result is not specific to k-fold 1, as we also observed a similar instance for k-fold 3 (not shown in the figure). Since learning rate is constant across all of these models, we suspect that the loss function calculation may be subject to high volatility when predicting for only one pixel in the image, but requires more exploration of the hyperparameter space to confirm. From our findings, we recommend performing a thorough search of the hyperparameter space, including learning rate, such that one can find a set of hyperparameters that delivers more consistent training results from one training session to the next. Due to the stochastic nature of neural network training, we also recommend training multiple models with the same hyperparameters and selecting the best one. While we did perform hyperparameter tuning for the U-Net model, a fuller exploration of the hyperparameter space beyond the scope of this paper.
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 AUC performance of each k-fold model evaluated on its corresponding validation set compared to the baseline model’s performance (from Section 3.2.2.2) for the U-Net model. The horizontal axis indicates the validation set used. Note that the AUC is calculated using the pixel-level precision–recall curve, rather than the traditional PRC curve (see Section 4.1.3).
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: A lens that is correctly classified by the ResNet (with a probability higher than the chosen threshold of 0.96), but incorrectly detected by the U-Net (of course, with a probability lower than the chosen threshold of 0.72). Since an environmental galaxy in the image is much brighter than the lens and very close to it, with a separation of only 2.8 pixels (see the “Distance” quantity at the top left), a local maximum does not occur at the location of the lens, but at the center of the environmental galaxy. Generally, a local maximum is a necessary but not sufficient condition for detecting a lens (see the discussion in Section 4.3). We have verified that the U-Net model is indeed confused by the environmental galaxy: without the environmental galaxy, the U-Net predicts the correct coordinate with a probability of 0.576. However, one expects this kind of coincidence—the lens and the environmental galaxy virtually overlap—to be very rare.
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: A lens that is correctly classified by the ResNet (with a probability higher than the chosen threshold of 0.96), but incorrectly detected by the U-Net (again, with a probability lower than the chosen threshold of 0.72). In this case, the model’s prediction (red crosshairs) is far away from the ground truth (white crosshairs), with a separation (“Distance”) of 47.2 pixels. Most of these cases occur when there is a bright environmental galaxy paired with a dim lens in a complex system.
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: A non-lens that is classified as a lens by the ResNet and U-Net model (false positive). We note that for this system, the predicted probabilities are below the chosen thresholds of 0.96 and 0.72 for ResNet and U-Net, respectively. There is a very bright environmental galaxy at the location of the prediction that caused the U-Net and even the ResNet to incorrectly predict a lens. The U-Net also outputs a detection probability of 0.494 for the environmental galaxy located at (53, 52).
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Example vertical temperature profiles following Equation (1) and the temperature profiles in E. Dartois et al. (2003) and C. P. Dullemond et al. (2020). The dashed gray lines show Tmid and Tatm (used only in the E. Dartois et al. 2003 and C. P. Dullemond et al. 2020 profiles).
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 AUC performance of each k-fold model evaluated on its corresponding validation set compared to the baseline model’s performance (from Section 3.2.2.2) for the ResNet model. The horizontal axis indicates the validation set used.
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 AUC performance of the model trained with only Sérsic environmental galaxies compared to the baseline model (from Section 3.2.2.2).
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: Magnetic activity indicators as a function of time. Cyan: unsigned magnetic flux calculated with SolAster. Magenta: daily He D3 index derived from EXPRES solar observations. The long-term gap starting in 2022 was caused by a broken fiber in the setup, while the large observation gap in 2023 was due to smoke near the telescope site.
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: SDO unsigned magnetic flux plotted against the S-index, Hα, and the He D3 index. Different symbols/colors denote data from individual years.
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: Obtained rotation period per year of observation using the Lomb–Scargle periodogram of the He D3 index time series.
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: Same as Figure 4 but zoomed in for the time range from 2024 April until the end of 2025 March (during the solar maximum).
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: SDO RVs plotted against the S-index, Hα, and the He D3 index. The different symbols/colors represent results of the different years.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between the simulation snapshot curves (set 2) from six nonoverlapping 60° wedges at different azimuthal angles (solid lines) at z = 0. The color bands show the corresponding statistical uncertainties in the curves. The dashed lines represent the rotation curves calculated directly from the snapshots’ recorded gravitational potential (see text for details). The scatter in the dashed line, while present, is not as large as the scatter in the solid lines, indicating asymmetries in both the potential and the stellar tracers. From left to right, the three plots show the results for different simulated galaxies at z ∼ 0 (snapshot 600): m12i, m12f, and m12m, respectively. For each galaxy, the upper panel shows the derived rotation curve as a function of radius from the galactic center based on stars in six different wedges. The solid and dashed lines in the lower panel show the scatter in the solid and dashed rotation curves, respectively.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Summary of all sources of systematic uncertainties in rotation curve measurements analyzed in this study. All uncertainties are relative to the curve derived from AGAMA (set 3). From top to bottom, the three panels show the results for different simulated galaxies at z = 0 (snapshot 600): m12i, m12f, and m12m, respectively. The systematic uncertainties from dynamical disequilibrium (dashed–dotted line) are moderate (<5%) in most cases (except for cases when there are recent merger events; see Section 4.4). The systematic uncertainties from the selection function (dashed line) are moderate for m12i and m12m but more significant (>5%) for m12f. The systematic uncertainties from axis asymmetry (dashed–double-dotted line) and higher-order correction (dotted line) are significant in most cases.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between the rotation curves from the synthetic Gaia survey (set 1; red circles) and the rotation curves from the star particles in the simulation at z = 0 (set 2; blue circles). The error bars show the corresponding statistical uncertainties in the curves. From left to right, the three plots show the results for different simulated galaxies at z ∼ 0 (snapshot 600): m12i, m12f, and m12m, respectively. The upper panel of each plot shows the derived rotation curve as a function of radius from the galactic center. The lower panel shows the absolute difference between the Ananke and snapshot curves. The error bars in the differences are calculated by adding the statistical uncertainties from the two curves in quadrature. The line style of the lower panel is matched to Figure 1.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Similar to Figure 2, but for comparison between the simulation snapshot curves (set 2; blue circles) and the AGAMA curves directly derived from the potential at z = 0 (set 3; black curve). From left to right, the three plots show the results for different simulated galaxies at z ∼ 0 (snapshot 600): m12i, m12f, and m12m, respectively. For each galaxy, the upper panel shows the derived rotation curve as a function of radius from the galactic center. The lower panel shows the difference between the snapshot and AGAMA curves.
Paper: Dual Perspectives on GX 17+2: A Simultaneous NICER and NuSTAR Study
Authors: Malu Sudha, Renee M. Ludlam, Jeroen Homan, Dacheng Lin, Benjamin Coughenour, Edward M. Cackett
Figure caption: Left: HID of GX 17+2 obtained using NuSTAR observations. Hard color is defined as the ratio of X-ray photon counts in the 8–20 keV and 5–8 keV energy bands. Intensity (counts s−1) used is from the 5–20 keV energy band. Colored polygons and differently styled markers are used to indicate the separate branches of the Z track selected for further analysis. Right: NuSTAR light curves in the 3–20 keV energy band on the bottom panel (binned to 128 s) marked with the same markers as used in the HID and NICER light curves in the 0.5–10 keV energy band binned to 128 s in the top panel marked with the star symbol in pink color.