adsabs/AstroCLIMB huggingface dataset:
Paper: Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample
Authors: Aashay Pai, Michael R. Blanton, John Moustakas
Figure caption: Similar to Figure 3, showing where broad-line AGN lie in the space of W2 variability. The gray points are all of the MaNGA galaxies. The red and black crosses are broad-line AGN identified in the broad-line catalog of Y. Fu et al. (2023). The red crosses are the broad-line AGN that we identify as variable in the mid-infrared and the black are the broad-line AGN that we do not identify as variable.
Paper: Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample
Authors: Aashay Pai, Michael R. Blanton, John Moustakas
Figure caption: Similar to Figure 13, for plate-IFUs 12483–9102 and 8619–12702. Plate-IFU 12483–9102 observed the center of NGC 4395, which is a well-known low-mass AGN-hosting dwarf galaxy. The image and MaNGA data cube only cover the central region of the whole galaxy, which subtends a large solid angle on the sky.
Paper: Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample
Authors: Aashay Pai, Michael R. Blanton, John Moustakas
Figure caption: W1 − W2 (AB) colors of MaNGA galaxies vs. their specific star formation rate determined from stellar population analysis. The red points represent our mid-IR variable objects.
Paper: Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample
Authors: Aashay Pai, Michael R. Blanton, John Moustakas
Figure caption: W1 − W2 (AB) colors vs. ﹩{\mathrm{log}}_{10}\mathrm{Var}({\rm{W}}2)﹩ of our sample. The red points represent our mid-IR variable objects.
Paper: Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample
Authors: Aashay Pai, Michael R. Blanton, John Moustakas
Figure caption: Similar to Figure 3, showing where U/LIRGs lie in the space of W2 variability.
Paper: Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample
Authors: Aashay Pai, Michael R. Blanton, John Moustakas
Figure caption: Similar to Figure 13, for plate-IFUs 8717–1902 and 9186–9101.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Distribution of differences (observed minus predicted) in the derived physical quantities for the used fitting codes. Each histogram shows the difference in inferred quantities (age, metallicity, E(B − V), σ *) between the real and predicted spectra. The first row in green colors shows the results for the fitting code pPXF, and the second row shows the results for FIREFLY. Different morphological categories as marked before.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Histogram of ﹩\overline{{\rm{\Delta }}}﹩, MSE, and χ 2/N (with N the number of flux points) of all galaxies in the test set split into different morphologies. When taking the uncertainty of the observed spectra into account, all three groups perform equally well. The morphological categories “elliptical,” “spiral,” and “uncertain” were determined by the citizen science project Galaxy Zoo by C. J. Lintott et al. (2008) and C. Lintott et al. (2011). Galaxies were labeled as “uncertain” if their images were not clearly voted on as spiral or elliptical. Note that this does not say much about their spectral classification as, for instance, a post-merger starburst galaxy could belong to this group.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Comparison between the spectroscopic redshift from the observed spectra (z spec) and the prediction from our generative AI for the galaxies (z predict) in the test set. Instead of single data points, the overall kernel density estimate is shown for a clearer point of view. The gray dotted lines mark the regions with catastrophic outliers with ∣Δz/(1 + z spec)∣ > 0.05. The black dotted line shows the one-to-one relation.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Overview of different predicted spectra (orange) and their associated smoothed observed spectra (blue) in the rest frame from the test set. Gray-shaded areas depict the corresponding 1σ error bars of the smoothed observed spectra. The galaxies show different morphologies (according to the Galaxy Zoo classification: uncertain, elliptical, uncertain, spiral) as visible in the corresponding SDSS tricolor images from the inner three bands on the right. These jpeg images are available on the Skyserver and only serve as an illustration for the reader; the algorithm uses five-band images. The first example is a possible merger; the third example is classified as a LINER by J. Brinchmann et al. (2004), and the galaxy at the bottom is best described as a star-bursting galaxy. The galaxy names read (from top to bottom): SDSS J205600.32−053137.8, SDSS J150109.86+472039.6, SDSS J105728.22+065954.4, SDSS J025019.52−070223.4.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Final mass–metallicity relationship for the predicted SDSS spectra. The result for FIREFLY can be found in the upper panel and for pPXF in the lower one. Error bars in the metallicity indicate the 1σ uncertainty derived by the corresponding bootstrapping procedure. The stellar mass estimates come from the SDSS photometry alone (C. Maraston et al. 2009). The dashed red lines show the galaxy lookback evolution models from R.-P. Kudritzki et al. (2021a, 2021b). The colors indicate the value of Dn4000 for each galaxy. Low values (in yellow) point toward young galaxies, and high values (dark blue) toward older stellar populations. SDSS galaxies mostly occupy the space of old and metal-rich populations at low redshift; we also find this in the test set.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Comparison of Lick indices measured using the observed spectra and the predicted spectra. Spiral galaxies were excluded. The color coding of the density plots indicates how many galaxies reside at a specific (x, y) position and is kept frozen in each row for comparability. Pearson correlation coefficients ρ are printed in each subfigure. Generally, the coefficient's value ranges from +1 (perfect positive correlation) to −1 (perfect negative correlation), with 0 indicating no correlation.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Another four predicted created spectra (orange) and their corresponding full-spectrum fits with a population synthesis code. A FIREFLY result is shown in the first two rows. Regions with potential emission lines (not necessarily visible in each spectrum) are masked out for the fit and displayed here on a gray background. The third and fourth spectra are fitted with pPXF with emission and absorption fitted in parallel. The images on the right show again the corresponding SDSS tricolor images for illustration; the algorithm uses five-band images.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Visualization of photometric images of different galaxies in the training set, which serves as the input for the ML algorithm. The corresponding galaxy name can be found at the top of the image and the depicted band is written on each image. We chose to visualize diverse objects to better emphasize the variety of galaxy images in the training set.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Visualization of our pipeline. We use a conditional diffusion model to generate candidate low-resolution spectra for the given image and select the best-matching candidates with a contrastive network. Then, we use a conditional diffusion model to upsample the candidate spectra to full-resolution candidates and select the final predicted spectrum with the second contrastive network.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: BPT diagram for the real spectra (left) and the predicted spectra (right). Pure star-forming and possible AGNs are split in both cases by the empirical relation found by G. Kauffmann et al. (2003; dashed gray). The additional solid gray line from L. J. Kewley et al. (2001) is drawn as an additional guideline. Galaxies in the region between the two lines (often labeled as composites) probably contain AGN but are still likely to be dominated by the contribution of star formation.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Bimodality of the galaxy population in the test sample. All shown quantities are retrieved exclusively with artificial spectra and photometry. No observed SDSS spectra from the test set were used. The color coding in each subplot indicates again how many galaxies reside at a specific (x, y) position; darker means more galaxies. Additionally, kernel density estimates are overdrawn to highlight the bimodal behavior in the mean stellar age, extinction, velocity dispersion, extinction, and color.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Visualization of the degeneracy for both codes. The x-axis (y-axis) depicts the difference between the metallicity (log age) from the observed and predicted spectrum. In other words, an error in [Z] means ﹩{\left[Z\right]}_{\mathrm{obs}}-{\left[Z\right]}_{\mathrm{pred}}﹩. The color coding shows the error in the reddening. Contours are again drawn with a kernel estimate representing a 2D histogram analog. Whereas FIREFLY incorporates an expected age–metallicity degeneracy, pPXF shifted the problem more toward a blurring in age-reddening.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Generated spectra (in orange) for the object SDSS J091940.30+144450.4 based on input images with a resolution of 64 × 64 (in five bands), 16 × 16 (in three bands), 4 × 4 (in five bands), 1 × 1 (in one band only). The smoothed observed spectrum is drawn in each subplot in dark blue, and the 1σ error bars in gray. All spectra are shifted to the rest frame, and the MSE is written in each subfigure for a better overview. Results are visibly better with higher-resolution images and more color information available. Given only the information in the g band causes the generative AI to spuriously predict overall wrong shapes (wrong SEDs) and often strong emission lines.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Updated version of Figure 6 with the larger test set. It shows the comparison between the spectroscopic redshift from the observed spectra (z spec) and the prediction from our generative AI for the galaxies (z predict). Instead of single data points, the overall kernel density estimate is shown for a clearer point of view. The gray dotted lines mark the regions with catastrophic outliers with ∣Δz/(1 + z spec)∣ > 0.05. The black dotted line shows the one-to-one relation.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Updated version of Figures 5 and 8 with the larger test set. Top: histograms of ﹩\overline{{\rm{\Delta }}}﹩, MSE, and χ 2/N (with N the number of flux points) of the 14,000 galaxies split into the different morphologies. Bottom: distribution of differences in the derived physical quantities for pPXF between the real and predicted spectra.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Distribution of the differences between the physical quantity derived from the observed and the predicted spectrum. Left: redshift prediction, right: velocity dispersion. The dark blue color means that only one galaxy is visible in the image, and the orange distribution means that two or more galaxies are clearly visible.
Paper: Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Authors: Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman, Pablo Márquez-Neila
Figure caption: Illustration of the r band of the spiral galaxy SDSS J143419.97−011332.1 using varying resolutions. This galaxy is part of the training set.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Summary plot detailing the features of the magnetotail crossing by MMS on 2017 July 6: (a) Locations where either 11 (blue) or 12 (green) slow-mode shock conditions were satisfied; ((b), (c)) Electron and ion energy–time spectrogram; (d) ion number density (HPCA values green and FPI values in black); (e) ion velocity in GSM coordinate; (f) ion velocity magnitude (black) and the parallel component of velocity to the magnetic field (green); (g) magnetic field in GSM coordinate; (h) magnetic field magnitude; (i) plasma pressure (P p) (HPCA values green and FPI values in black) and magnetic pressure (Pb) (blue), and (j) Plasma beta (HPCA values green and FPI values in black). The solid black line at 22:04:10 UT shows one of the upstream locations of the magnetotail slow-mode shock, and the dashed black line at 22:00:05 UT indicates one of the downstream locations of the same shock.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Summary plot detailing the features of the magnetotail crossing by MMS on 2019 September 9: (a) Locations where either 11 (blue) or 12 (green) slow-mode shock conditions were satisfied; (b, c) Electron and ion energy–time spectrogram; (d) ion number density (HPCA values green and FPI values in black); (e) ion velocity in GSM coordinate; (f) ion velocity magnitude (black) and the parallel component of velocity to the magnetic field (green); (g) magnetic field in GSM coordinate; (h) magnetic field magnitude; (i) plasma pressure (Pp; HPCA values green and FPI values in black) and magnetic pressure (Pb; blue), and (j) Plasma beta (HPCA values green and FPI values in black). The solid black line at 07:07:11 UT shows one of the upstream locations of the magnetotail slow-mode shock, and the dashed black line at 07:04:47 UT indicates one of the downstream locations of the same shock. The red lines mark the locations which are chosen to plot ion distribution functions in Figure 4.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: The ion distribution functions are plotted for the different locations shown by orange lines in Figure 3. (a) corresponds to orange line 1 (in Figure 3), where an isotropic ion population is present, (b) corresponds to orange line 2, where an almost isotropic population is present, (c) corresponds to orange line 3, where a high-energy beam population and cold ions are present, (d) corresponds to orange line 4 in the lobe region where cold ions are present.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Orbital coverage plots of 51 crossings by MMS in GSM coordinate from 2017 July 6 to 2021 September 1, (a) x–y plane, (b) x–z plane, (c) y–z plane; Re is Earth radii. The gray lines show the orbit of MMS, and the squares represent the position of the magnetotail reconnection crossings. Blue solid squares represent the Earthward flow slow-mode shock events, and the red solid squares show the tailward flow slow-shock events. The remaining crossings, which had Earthward flows, are shown by blue hollow squares, and the ones with tailward flows are represented by red hollow squares.
Paper: Exploring Magnetic Fields in a Merging Galaxy: Comparing Polarization and Velocity Gradient in the Centaurus Galaxy
Authors: Quynh Lan Nguyen, Yue Hu, Alex Lazarian
Figure caption: The magnetic field of Centaurus mapped with VGT using the CO (2–1) emission line. The magnetic field segments are laid upon Centaurus’ CO emission intensity map.
Paper: Exploring Magnetic Fields in a Merging Galaxy: Comparing Polarization and Velocity Gradient in the Centaurus Galaxy
Authors: Quynh Lan Nguyen, Yue Hu, Alex Lazarian
Figure caption: Same as Figure 1, but the magnetic field is measured by HAWC+ polarization at 89 μm.
Paper: Exploring Magnetic Fields in a Merging Galaxy: Comparing Polarization and Velocity Gradient in the Centaurus Galaxy
Authors: Quynh Lan Nguyen, Yue Hu, Alex Lazarian
Figure caption: A 2D histogram of the dust polarization’s signal-to-noise ratio and AM between VGT and polarization.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption:  Vy  plots for the three beta cases of (a) ﹩\beta ﹩ = 0.2, (b) ﹩\beta ﹩ = ﹩0.01,﹩ and (c) ﹩\beta ﹩ = 0.001 in simulations. The box shows the location of the region that is used to perform the wavelet analysis for Figure 8.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Rankine–Hugoniot solutions in terms of square of upstream intermediate Mach number (﹩{{\boldsymbol{M}}}_{Iu}^{2}﹩) vs. square of downstream intermediate Mach number (﹩{{\boldsymbol{M}}}_{Id}^{2}﹩). Slow-mode shock (SS) solutions exist on the curve between 0 to 1, above which we get intermediate shock (IS) solutions. The rotational discontinuity solution lies at (1, 1). A green circle with error bars shows the observational values of ﹩{{\boldsymbol{M}}}_{Iu}^{2}﹩ and ﹩{{\boldsymbol{M}}}_{Id}^{2}﹩ for the reported slow-mode shock on 2017 July 6.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Distribution of total crossings (gray) vs. slow-mode shock events (green) showing the values of downstream (a) temperature anisotropy (﹩{{\boldsymbol{T}}}_{\parallel }/{{\boldsymbol{T}}}_{\perp }﹩) and (b) anisotropy parameter ﹩{\boldsymbol{\varepsilon }}=1-\left({{\boldsymbol{\beta }}}_{\parallel }-{{\boldsymbol{\beta }}}_{\perp }\right)/2.﹩.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: (a and b) Structure of Vy  and By  with respect to x-direction at y = −2 ﹩{\lambda }_{i};﹩ ((c) and (d)) continuous wavelet transform analysis of Vy  and By  fluctuations at y = −2 ﹩{\lambda }_{i}.﹩.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Expected Vy  plots from conservation of mass flux for the three beta cases of (a) ﹩\beta ﹩ = 0.2, (b) ﹩\beta ﹩ = 0.01, and (c) ﹩\beta ﹩ = 0.001 in simulations.
Paper: A Study of Slow-mode Shocks in the Near-Earth Magnetotail with MMS Observations and Hybrid Simulations
Authors: Nehpreet K. Walia, Kanako Seki, Takanobu Amano, Naritoshi Kitamura, Yoshifumi Saito, Tara Ahmadi, Daniel J. Gershman, Craig J. Pollock, Barbara L. Giles, Stephen A. Fuselier, Christopher T. Russell, James L. Burch
Figure caption: Histogram showing the number of point pairs satisfying all the 12 slow-mode shock (SS) conditions for all the slow-mode shock (SS) events.