adsabs/AstroCLIMB huggingface dataset:
Paper: Probing the Density Fine Structuring of the Solar Corona with Comet Lovejoy
Authors: Giuseppe Nisticò, Gaetano Zimbardo, Silvia Perri, Valery M. Nakariakov, Timothy J. Duckenfield, Miloslav Druckmüller
Figure caption: Left: graph showing the trend of τ slow (left axis, solid line) and τ  D  (right axis, dotted line) as a function of the electron density n  e . The solid blue line for τ slow is computed with a speed U = 500 km s−1 (the comet’s speed), while the dotted one for τ  D  with v ran = 150 km s−1 (the thermal speed). The two data points as green triangles refer to the slits S4 and S5. Right: electron density in logarithmic units as a function of the radial distance in R ⊙. The data points as green triangles refer to slits S4 and S5. The data points are compared with a hydrostatic density model for different values of the density at the solar surface n 0 and with T = 1.5 MK.
Paper: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Figure caption: Predictions of the best CNN model B_014567 on the test set. The CNN prediction corresponds to the sigmoid output of the last layer. (left) Distribution of the CNN predictions for the N = 91,456 stable simulations of the test data set. The CNN-predicted values lie close to zero, meaning that it predicts these simulations to be stable. (right) CNN predictions for the N = 88,289 unstable simulations of the test data set as a function of the true breakup time. The CNN predicts values close to one, i.e., an unstable simulation. The darker blue dots correspond to the average over 100 equally spaced bins ranging from 5 × 105 to 108 P 1: the shorter-lived the simulation, the more confident the network.
Paper: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Figure caption: Disruption time distribution for TSUNAMI unstable simulations and comparison with the Mushkin & Katz (2020) disruption time estimates. The y-axis is logarithmic: our unstable data set (in orange) is very biased toward short-lived simulations. The blue histogram displays the distribution of disruption time estimates following Mushkin & Katz (2020), where the predictions below 5 × 105 P 1 have been removed (382,183 − 295,746 = 86,437). The contingency table shows the comparison over all simulations: the approximation of Mushkin & Katz (2020) overestimates instability.
Paper: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Figure caption: ROC curves of the 12 configurations under study. Not providing the eccentricities e 1 and e 2 to the CNN (channels 0 and 1) leads to much poorer performances. The (right) panel is a closer look at the upper left corner of the (left) panel. CNN architecture B performs as well as architecture A, although it has fewer trainable parameters. The mutual eccentricity i mut (channel 4) and the arguments at periapsis ω 1 and ω 2 (channels 5 and 6) appear to bear additional meaningful information.
Paper: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Figure caption: Simulated data-set and training/test split. The simulations that did not last longer than 5 × 105 P 1 (0.5% of our final time) have been discarded. The remaining simulations have been divided into two classes: stable and unstable. Stable simulations did reach the maximum final time t f = 108 P 1 without disruption, while unstable simulations did not. In each class, we selected 150,000 simulations for training, the rest were kept for testing.
Paper: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Figure caption: Instance of all available input channels. The left panel corresponds to a stable simulation in our data set, and the right panel shows an unstable simulation that undergoes disruption after 5.1 × 107 P 1. Note the scale on the y-axis for channels 2 and 3 (semimajor axes): there, the magnitudes greatly vary in the data set as they are not normalized. Channels 2 and 3 are not presented to the network; we use channels 7 and 8 instead.
Paper: Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks
Authors: Florian Lalande, Alessandro Alberto Trani
Figure caption: Initial configuration and input parameters for the hierarchical triple systems simulated with TSUNAMI. The masses are fixed (in blue) for all simulations, while all other parameters are sampled (in red) to explore the initial parameter space. The outer semimajor axis a 2 is sampled such that we expect medium-lived and long-lived systems, while avoiding obviously stable configurations.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: The CME reconstruction using the GCS model on 2021 January 21 at multiple times projected over WISPR-I images (cropped to the CME region). We keep all model parameters unchanged, except for height. Deformation becomes evident as time progresses, and the model becomes unable to reconstruct the CME observations.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: Comparison of the CME reconstruction (crosses) with the point-feature kinematics (squares and error bars) on WISPR-O FOV. The model used for reconstruction is the “croissant” CME model that does not assume any deformation. As the CME deformation is obvious, we fit the model only to the northern and southern leading edges, ignoring the central portion. As the reconstruction agrees well for the northern and southern leading edges, the difference between the reconstruction and the point-feature kinematics in the central portion indicates the deformation of the CME in the central latitudinal range.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: We calculate the position and speed of three different CME front portions on WISPR-O observations: the farthest points in the northern and southern parts of the CME front (red and blue plus signs), and the latitude where the front looks closer to the Sun (green plus signs). As we repeat the front identification by eye six times for each portion to estimate errors in the kinematics, we have six plus signs for each color.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: Radial speed and density map at 20 solar radii calculated using the CORHEL MAS MAST model. The red, green, and blue horizontal lines indicate the latitude of the three points in the CME front for which we calculated the speed. We projected in black the CME reconstruction before the deformation.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: The CME reconstruction at 2021 January 21 03:30 UT. The left panel has the CME projection in the solar equatorial plane, resembling a polar view. The right panel shows the plane formed by the solar rotation axis and Earth’s direction, corresponding to a view from the solar equator in quadrature with Earth. The larger blue plus sign represents PSP position at 03:30, and the smaller ones indicate the position in the following hours. The red and green lines indicate the directions to STEREO-A and Earth.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: Estimation of the CME ToA error that deformation causes. We define deformation as the differences between the actual CME front position in the lowest point in its dimple (r 2[t]; green squares in Figure 12) with the front position without any deformation (green crosses in Figure 12). The deformation increases over time and is higher for slow events (black line).
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: Determination of a CME-point position (r 2[t], ϕ 2, σ 2) using the spacecraft coordinates (r 1[t], ϕ 1[t], σ 1[t]) and extracting the angles β and γ from the images.
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: Position of the CME front at three different point in its front: northern and southern leading edges (red and blue, respectively) and the central valley (green).
Paper: Coronal Mass Ejection Deformation at 0.1 au Observed by WISPR
Authors: Carlos R. Braga, Angelos Vourlidas, Paulett C. Liewer, Phillip Hess, Guillermo Stenborg, Pete Riley
Figure caption: Radial speed and density map at 30 R ⊙ calculated using the CORHEL MAS MAST model. The red, green, and blue horizontal lines indicate the latitude of the three points in the CME front for which we calculated the speed. We projected in black the CME reconstruction before the deformation.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Confusion matrix of the test sample that summarizes the prediction results of 16 variable star subtypes. For this task, the Multiple-input Neural Network was used with lightcurve images and periods as inputs. The individual deviation values from the K-fold cross-validation are shown with red color below the prediction values. For better readability the main variable star classes are marked with orange boxes.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Performance comparison of the convolutional neural network (CNN; left column) and Multiple-Input Neural Network (right column) showing the neural networks’ accuracy and loss evolution during the training and validation phases. The first row shows the training and validation performance with the six main types, while the bottom row shows the neural network’s performance with 16 variable star subtypes. Due to the additional numerical data, the Multiple-input Neural Network outperforms the CNN. At the end of the training phase it reaches much better accuracy and performs more reliable variable star subtype classification. Note that the x-scale is different in the subpanels.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Test result of the pulsating variable stars from the LMC field, based on their period–Wesenheit index relation. The training and the test was based on phase-folded light curves and the given star’s period and Wesenheit index. The neural network was trained on 16 variable star subtypes. For better readability we grouped them with one color for each main type and only the pulsating variable stars are shown in this figure. The crosses without a border are from the training and validation; the correct classifications are shown with black borders. Those misclassified stars that are plotted with red borders remained in their main group. The triangles with red edges are “true” misclassifications, which are found mostly on the borders of different pulsating variable star types.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Confusion matrix of the classification result of the test sample of six main variable star classes. Lightcurve images and the periods were used as inputs for the multiple-input neural network.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Schematic view of the architecture of the Multiple-input Neural Network built in this study. In this case two inputs are present, the first one is the data set of phase-folded lightcurve images, the second one is the numerical data of the periods associated with the given stars. This figure shows the neural network used to classify stars into main variable star classes. When we classified stars into subclasses, the number of the neurons in the last dense layer and those dense layers that are right before the concat layer are changed to 16.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: The architecture of our extended Multiple-input Neural Network, where the inputs are the following: phase-folded images, the period, and Wesenheit index of the variable stars. We used this architecture to classify stars observed in the LMC into 16 different variable star subclasses.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Confusion matrix of the testing phase, which shows the performance of our Multiple-input Neural Network using OGLE-III data only from the LMC field. The inputs were phase-folded light curves and the given star’s period and Wesenheit index. For better readability the main variable star groups are marked with orange boxes.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: Features learned by the convolution neural network (part of the MINN), visualized by images of the latent spaces of the convolutional layers.
Paper: Variable Star Classification with a Multiple-input Neural Network
Authors: T. Szklenár, A. Bódi, D. Tarczay-Nehéz, K. Vida, Gy. Mező, R. Szabó
Figure caption: The light curves of the highlighted misclassified stars from Figure 12.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: Average spectral lines of 12CO (blue), 13CO (green), and C18O (red) extracted from the six identified filaments. The dashed lines with the corresponding colors are the Gaussian fitting results. The vertical dotted lines indicate the fitted central velocity. The average v LSR of the filaments are obtained from 13CO.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The blue curves represent the mean radial column density profiles of the 13CO emission. The yellow curves indicate the ±1σ dispersion. The red dashed curves represent the Gaussian fitting results. The peak position in each profile is regarded as the center of the profile. The widths (deconvolved FWHMs) are marked in the upper right corner of each panel.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The 13CO channel maps from −41 to −32 km s−1 by a step of 1 km s−1. Only pixels with at least three channels above 3σ are kept for integration. The colored dotted lines indicate the filament skeletons identified by visual inspection step by step. The names are marked out beside each filament. The last panel shows the intensity image integrated within [−41, −32] km s−1 that overlaps with the visual inspection filaments (colored solid lines).
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The H2 column density map estimated from the 13CO emission. The dotted black lines and the red dashed lines represent the filament skeletons that are extracted by the DisPerSE algorithm and visual inspection, respectively. The color bar is in units of 1021 cm−2.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: Upper panel: The LV map of 12CO toward the region of 148° ≤ l ≤ 158° and −50 ≤ v ≤ 10 km s−1. The latitude is integrated from 1.°85 to 3.°5. The integrated noise level (σ) is about 0.051 K deg. The contours are set from 10σ to peak value (9.2 K deg) by a step of 10%. The horizontal white dashed line separates the velocity intervals into the Local Arm and the Perseus/Outer Arm (see Dame et al. 2001; Reid et al. 2019). The red rectangle indicates the region focused on in this work. Lower panel: A zoomed-in map of the red rectangle with a velocity range of [−45, −25] km s−1 and a longitude range of [153.°6, 156.°5].
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The three-color image of 12CO (blue), 13CO (green), and C18O(red) in the velocity interval of [−42.5, −30.0] km s−1. For 12CO and 13CO, pixels with emission above 3σ with at least three continuous channels are used for integration. The pixels of C18O are obtained by the DBSCAN algorithm (see Section 3.1). The blue, green, and red contours are the 3σ, 3σ, and 1σ boundaries for 12CO, 13CO, and C18O, respectively. The yellow squares and the red plusses represent the locations of H II regions and IRAS sources, respectively.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The H2 column density maps derived from 12CO (upper panel) and 13CO (bottom panel) in the velocity interval of [−42.5, −30] km s−1. The contours are 0.4 (3σ noise level), 1, 2, 3, 4, 5, 10, and 15 × 1021 cm−2. The color bar is in units of 1021 cm−2.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: Distance probability density function of position No. 1 (see Table 2), using the Bayesian distance calculator (Reid et al. 2019).
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The distribution of the excitation temperature calculated from the peak value of the main-beam temperature of 12CO in the velocity interval of [−42.5, −30.0] km s−1.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The background is the composite three-color image of three WISE bands (22 μm in red, 4.6 μm in green, and 3.4 μm in blue) toward the zoomed-in region of G154.346+02.606. The white contours show the 13CO intensities integrated in the velocity interval of [−39.5, −33.5] km s−1, which are set from 10σ to the peak value in this region by a 10% average step. The green box is centered at (l, b) ∼ (154.°347, 2.°609) with a size of 0.°1 × 0.°1. The green contour indicates the region with intensity greater than 12.65 K km s−1, or roughly ﹩{N}_{{{\rm{H}}}_{2}}\gtrsim 6.1\times {10}^{21}\,{\mathrm{cm}}^{-2}﹩. The magenta, yellow, and green plus signs represent the candidate YSOs of Class I, Class II, and the flat type, respectively. The red circles with plus signs represent the candidate massive YSOs. The yellow circles, cyan diamond, red triangle, and orange ellipses represent the 13CO clumps, the IRAS sources, the H2O maser, and the 350 μm clumps found by BGPS, respectively.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: Same as Figure 19 but for the region of SH2-211. The white contours show the 13CO intensities integrated in the velocity interval of [−39.5, −33.5] km s−1, which are set from 5σ to the peak value in this region by a 10% average step. The green box is centered at (l, b) ∼ (154.°646, 2.°438) with a size of 0.°07 × 0.°07. The red dashed arrow line indicates the PV slice path (width ∼2 pixels). The IR bubble is indicated by a magenta circle with a center of (l, b) ∼ (154.°653, 2.°432) and a radius of 0.°025.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: Same as Figure 19 but for the region of SH2-212. The white contours show the 13CO intensities integrated in the velocity interval of [−39.5, −33.5] km s−1, which are set from 10σ to the peak value in this region by a 10% average step. The green box is centered at (l, b) ∼ (155.°350, 2.°620) with a size of 0.°1 × 0.°1.
Paper: CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50
Authors: Weihua Guo, Xuepeng Chen, Jiancheng Feng, Li Sun, Shiyu Zhang, Chen Wang, Yang Su, Yan Sun, Qingzeng Yan, Shaobo Zhang, Xin Zhou, MiaoMiao Zhang, Min Fang, Ji Yang
Figure caption: The PV diagram of 12CO (gray scale and blue contours) and 13CO (green contours) along the PV slice path in Figure 20. The color bar is in units of kelvins. The contours are set from 3σ to the peak emission by an average step of 10%. The magenta ellipse is centered at −38 km s−1 with a velocity radius of 1.5 km s−1 and a position radius of 0.°025, corresponding to the magenta circle in Figure 20.