adsabs/AstroCLIMB huggingface dataset:
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC X grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for X grains. Type X grains plot in the silicon three-isotope diagram (b) at negative δ 30Si and δ 29Si values, usually below the lower left border of the area for mainstream grains (M4). However, as there is some overlap with the region for mainstream grains, that part of the X grain region is shown in a lighter color, indicating the lower probability for grains that plot in that region to be consistent with X grains. Type X grains are usually subdivided into X0, X1, and X2 grains. Dashed lines in (b) indicate the borders between those subtypes. Inferred 26Al/27Al ratios are usually above 0.01, but this is not used as a strict limit as some grains could be affected by 27Al contamination lowering their 26Al/27Al ratios, hence the lighter colored area in (c). Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC N grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for N grains. Type N grains are characterized by having 12C/13C ratios (a) below 13.5, with a reduced probability up to a ratio of 25 like AB grains, and inferred 26Al/27Al ratios above 0.01. In the silicon three-isotope plot (b), N grains plot below and to the right of the band for mainstream grains and are limited by δ 29Si < +200‰ and δ 30Si > 0. Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC AB grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for AB grains. Type AB grains are characterized by having 12C/13C ratios (a) below 13.5, but grains with ratios up to 25 (lighter colored area) could still be classified as AB grains, if nitrogen or inferred aluminum isotope ratios exclude other types. In the silicon three-isotope plot (b), they populate the same region as mainstream grains but plot along a line (AB) with a slightly steeper slope than the mainstream line (M0). Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC Y grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for Y grains. Type Y grains are characterized by having 12C/13C ratios (a) above 100. In the silicon three-isotope diagram (b), they plot in the same areas as mainstream and Z grains combined. The dashed, blue line in (b) is a weighted regression line through the Y grain data but is poorly defined (MSWD = 13.923). Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC Z grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for Z grains. Type Z grains have the same ranges for carbon, nitrogen, and inferred aluminum isotopes (a), (c) as mainstream grains. In the silicon three-isotope diagram (b), they plot in a region that is below and to the right of the band for mainstream grains with −200‰ < δ 29Si < +200‰ and δ 30Si > 0. Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC C grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for C grains. Type C grains have the same ranges for carbon, nitrogen, and inferred aluminum isotopes (a), (c) as X grains. In the silicon three-isotope diagram (b) they plot in the upper right of the diagram at δ 29Si > +200‰ and δ 30Si > +200‰. Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Histogram showing the distribution of 12C/13C ratios for mainstream, AB, and Y grains.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Histogram showing the distribution of Δ30Si for mainstream, D, and Z grains.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of individual presolar SiC D grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Colored areas show the ranges allowed for D grains. Type D grains have the same ranges for carbon, nitrogen, and inferred aluminum isotopes (a), (c) as X grains. In the silicon three-isotope diagram (b) they plot in the upper left of the band for mainstream grains and at δ 29Si > 0 and δ 30Si < +200‰. Further details are given in the main text and the Appendix.
Paper: The Presolar Grain Database. I. Silicon Carbide
Authors: Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore
Figure caption: Carbon, nitrogen, silicon, and inferred aluminum isotope ratios of all individual presolar SiC grains from the PGD. Horizontal and vertical solid lines show solar and terrestrial values for comparison. Panels (a)–(c) show 14N/15N, inferred 26Al/27Al, and Δ30Si, respectively, plotted vs. 12C/13C. Panels (d)–(f) show δ 30Si vs. δ 30Si at various scales, zooming into the central region where >96% of all grains plot. The red line labeled M0 in (c) and (f) refers to the mainstream weighted regression line.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: t-SNE-based two-dimensional projection of real and synthetic proton flux time series data for ∼30, ∼60, and ∼100 MeV SEP and non-SEP events.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Input time series before and after using data augmentation methods with (a) Gaussian noise, (b) SMOTE, and (c) ADASYN. Original and augmented time series are shown in red and green, respectively. The time series shown in yellow shows the k-nearest neighbor.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Solar flare images captured by SDO of AIA 304, accessible from https://helioviewer.org/. The 5 hr time series proton flux data P6, P5, and P4 channels are shown for ∼100, ∼60, and ∼30 MeV and a non-SEP event.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Comparison of the data augmentation models with the specific classifier used for evaluation.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Data collection, augmentation, and classification pipeline.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Evaluation of SEP and non-SEP prediction of ROCKET, SHAPELET, and TSF. The box plots show the distribution of k-fold cross-validation in terms of accuracy and F1-score before and after applying Gaussian data augmentation techniques. The line plots show TSS and HSS2 before and after applying Gaussian data augmentation techniques. [1/0] represents the results on the balanced data set without using DA, and the rest represent the results on data augmentation partition according to their corresponding SEP [Real/Synthetic] ratio.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Evaluation of SEP and non-SEP prediction of ROCKET, SHAPELET, and TSF. The box plots show the distribution of k-fold cross-validation in terms of accuracy and F1-score before and after applying ADASYN data augmentation techniques. The line plots show TSS and HSS2 before and after applying ADASYN data augmentation techniques. [1/0] represents the results on the balanced data set without using DA, and the rest represent the results on data augmentation partition according to their corresponding SEP [Real/Synthetic] ratio.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Evaluation of SEP and non-SEP prediction of ROCKET, SHAPELET, and TSF. The box plots show the distribution of k-fold cross-validation in terms of accuracy and F1-score before and after applying SMOTE data augmentation techniques. The line plots show TSS and HSS2 before and after applying SMOTE data augmentation techniques. [1/0] represents the results on the balanced data set without using DA, and the rest represent the results on data augmentation partition according to their corresponding SEP [Real/Synthetic] ratio.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Important features extracted by the SHAPELET classifier for 100, 60, and 30 MeV binary classification using a 5 hr observation window size. The first row shows the SEP class, and the bottom row represents the non-SEP (NSEP) class.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Evaluation of different strategies of multivariate time series data for four-class classification with NSEP and 100, 60, and 30 MeV labels. Results are demonstrated before and after using data augmentation techniques.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Comparison between univariate and multivariate time series models for SEP/NSEP binary classification in 100, 60, and 30 MeV, separately. Multivariate time series models include three strategies: concatenation, aggregation, and statistics.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Accuracy of binary TSF classification before and after using SMOTE data augmentation.
Paper: Improving Solar Energetic Particle Event Prediction through Multivariate Time Series Data Augmentation
Authors: Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
Figure caption: Hierarchical framework diagram.
Paper: Maximum Aligned Directional Derivative (MADD) Technique for Planar Structure Analysis in Space
Authors: Y. Y. Liu, H. S. Fu, J. B. Cao, C. X. Du, W. Z. Zhang
Figure caption: Test results of the MADD application to a discontinuity modeled of linear magnetic field transition. (a)–(c) Three snapshots of the discontinuity (see the white-dashed lines) passing a spacecraft tetrahedron (see the hexagrams); (d) the simulated observation of SC-1; (e), (f) ξ, DA, and SR calculated from the simulated four spacecraft measurements; (g), (h) the azimuthal angle ϕ (the angle between the +X direction and the projection of the normal vector on the XY plane) and polar angle θ (the angle between the normal vector and +Z direction) of the discontinuity normal vector calculated from the model and by the MADD, timing, and MVA methods; (i) the discontinuity motion speed along the normal direction; (j), (k) the magnetic field transition observed by SC-1 during the crossing. In panel (e), the dotted and solid curves correspond to the original and filtered magnetic field data from four spacecraft.
Paper: Maximum Aligned Directional Derivative (MADD) Technique for Planar Structure Analysis in Space
Authors: Y. Y. Liu, H. S. Fu, J. B. Cao, C. X. Du, W. Z. Zhang
Figure caption: Test results of identifying planar magnetic fields in simulation data by DA and SR. (a), (b) The distributions of DA and SR in the Y–Z cross section at ﹩X=\tfrac{\pi }{2}﹩ in the simulation box; three-dimensional topologies of the magnetic field in (c) the whole simulation box and (e), (f) three sub-boxes shown in panel (c). The magenta block in panel (c) indicates the area where DA < 10°.
Paper: Maximum Aligned Directional Derivative (MADD) Technique for Planar Structure Analysis in Space
Authors: Y. Y. Liu, H. S. Fu, J. B. Cao, C. X. Du, W. Z. Zhang
Figure caption: Test results of the MADD application to a discontinuity modeled of circular magnetic field transition, exhibited in the same format as Figures 3(d)–(k).
Paper: Maximum Aligned Directional Derivative (MADD) Technique for Planar Structure Analysis in Space
Authors: Y. Y. Liu, H. S. Fu, J. B. Cao, C. X. Du, W. Z. Zhang
Figure caption: Numerical test results of the stability of the MADD method in estimating the normal direction of a perturbed magnetic field gradient matrices ﹩{\rm{\nabla }}{\boldsymbol{B}}^{\prime} ﹩, including four scatterplots of two parameters among the DDP, DA, SR, and the method error of MADD.
Paper: Studying the Spheromak Rotation in Data-constrained Coronal Mass Ejection Modeling with EUHFORIA and Assessing Its Effect on the B  z  Prediction
Authors: Ranadeep Sarkar, Jens Pomoell, Emilia Kilpua, Eleanna Asvestari, Nicolas Wijsen, Anwesha Maharana, Stefaan Poedts
Figure caption: The HEEQ B  z  component of the spheromak associated with the LD (panel (a)) and HD (panel (c)) runs is shown on the equatorial plane. The colored dots drawn on the equatorial plane are the locations of the virtual spacecraft placed at 1 au. Each consecutive spacecraft is longitudinally separated by 5°, where the red/purple dot corresponds to ±15° longitude in HEEQ and the central dot in green denotes the location of Earth. The red dashed contour encloses the domain with positive B  z  inside the spheromak. The temporal profiles of magnetic field vectors inside the spheromak as it passes through the respective virtual spacecraft for the LD and HD runs are shown in panels (b) and (d). The abovementioned modeled magnetic field profiles (﹩{{B}_{x}}^{m}﹩, ﹩{{B}_{y}}^{m}﹩, and ﹩{{B}_{z}}^{m}﹩) are plotted with a color that is the same as that of the associated virtual spacecraft shown in panels (a) and (c). The black solid curves are the observed in situ magnetic field (﹩{{B}_{x}}^{\mathrm{obs}}﹩, ﹩{{B}_{y}}^{\mathrm{obs}}﹩, and ﹩{{B}_{z}}^{\mathrm{obs}}﹩). In panel (b), the modeled magnetic profiles obtained at the virtual spacecraft during the whole spheromak crossing are plotted within the observed MC boundaries indicated by the red vertical lines. In panel (d), only the modeled magnetic profiles obtained at the virtual spacecraft during the passage of the positive B  z  domain (the region enclosed by the red dashed contour in panel (c)) of the spheromak are plotted within the observed MC boundaries to avoid the double flux signature inside the spheromak while assessing the model results with observations.
Paper: Studying the Spheromak Rotation in Data-constrained Coronal Mass Ejection Modeling with EUHFORIA and Assessing Its Effect on the B  z  Prediction
Authors: Ranadeep Sarkar, Jens Pomoell, Emilia Kilpua, Eleanna Asvestari, Nicolas Wijsen, Anwesha Maharana, Stefaan Poedts
Figure caption: The poloidal (a) and toroidal (d) field strength in a cross-sectional plane perpendicular to the magnetic axis of an analytical linear force-free spheromak. The poloidal field is denoted by B  h , which lies in the plane of the image, and the toroidal field is denoted by B  z , which is perpendicular to the plane of the image. The black streamlines mark the poloidal field direction. The red dashed contours in panel (a) enclose the region where the strength of B  z  is higher than 0.6 times the maximum B  z  on the cross-sectional plane shown in the figure. The two white dots in panel (d) mark the two locations where the magnetic axis passes through the plane. The magnetic field of the spheromak on the HEEQ equatorial plane, during one of its initial ((b) and (e)) and later ((c) and (f)) phases of heliospheric propagation as obtained from Run7.
Paper: Studying the Spheromak Rotation in Data-constrained Coronal Mass Ejection Modeling with EUHFORIA and Assessing Its Effect on the B  z  Prediction
Authors: Ranadeep Sarkar, Jens Pomoell, Emilia Kilpua, Eleanna Asvestari, Nicolas Wijsen, Anwesha Maharana, Stefaan Poedts
Figure caption: The B  z  component of the spheromak associated with the LD (panel (a)) and HD (panel (c)) runs is shown on the meridional plane. The colored dots drawn on the meridional plane are the locations of the virtual spacecraft placed at 1 au. Each consecutive spacecraft are latitude-wise separated by 5°, where the red/purple dot corresponds to ±15° latitude in HEEQ and the central dot in green denotes the location of Earth. The rest of the descriptions of this figure are same as those of Figure 10.
Paper: Studying the Spheromak Rotation in Data-constrained Coronal Mass Ejection Modeling with EUHFORIA and Assessing Its Effect on the B  z  Prediction
Authors: Ranadeep Sarkar, Jens Pomoell, Emilia Kilpua, Eleanna Asvestari, Nicolas Wijsen, Anwesha Maharana, Stefaan Poedts
Figure caption: Full-disk observation of the solar corona in EUV at 08:37 UT on 2013 April 11, as captured in the composite image constructed from the AIA 171 Å (red), 211 Å (green), and 193 Å (blue) passbands (a). The inset in panel (a) depicts the morphology of the posteruption arcade associated with the M6.5 class flare. The white light structure of the associated CME observed in the LASCO C2 coronagraph at 07:54 UT on 2013 April 11 is shown in panel (b).
Paper: Studying the Spheromak Rotation in Data-constrained Coronal Mass Ejection Modeling with EUHFORIA and Assessing Its Effect on the B  z  Prediction
Authors: Ranadeep Sarkar, Jens Pomoell, Emilia Kilpua, Eleanna Asvestari, Nicolas Wijsen, Anwesha Maharana, Stefaan Poedts
Figure caption: Visualization of the spheromak magnetic structure during its evolution in the heliosphere. The gray shaded sphere in the image represents the lower boundary of the simulation domain at 0.1 au. The dynamic heliosphere is depicted by the background colors of the image, which represent the solar wind speed on the equatorial plane. The bunch of twisted lines illustrates the three-dimensional magnetic flux rope structure of the spheromak propagating away from the Sun. The untwisted field lines emanating from the lower boundary depict the magnetic field topology of the Parker spiral.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: Same as for Figure 1, but for “B” maps, generated over roughly the second half of each year.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: IBEX-Hi ENA “A” flux maps in the spacecraft frame, generated over roughly the first half of each year.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: Survival probabilities for ENAs observed in IBEX's polar pixels in the northern (left) and southern (right) hemispheres. ESA steps (energies) are indicated by different number labels from the lowest energy (2) to the highest (6). The blue color indicates survival probabilities calculated from the new Porowski et al. (2022) model, and are compared to the prior model (red) used by McComas et al. (2020) in the last IBEX update.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: Averaged 2009–2022 ENA fluxes in the heliospheric reference frame. Averaging is done on a pixel-by-pixel basis and is weighted by the exposure times.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: Annual ram ENA maps in the spacecraft frame for 2009–2022, corrected for survival probability.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: Similar to Figure 10, but for anti-ram observations.
Paper: Fourteen Years of Energetic Neutral Atom Observations from IBEX
Authors: D. J. McComas, M. Alimaganbetov, L. J. Beesley, M. Bzowski, H. O. Funsten, P. H. Janzen, M. A. Kubiak, J. S. Rankin, D. B. Reisenfeld, N. A. Schwadron, J. R. Szalay
Figure caption: Same as for Figure 3, but for the B ENA maps.