adsabs/AstroCLIMB huggingface dataset:
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: Top panel: First- (black) and second-order (gray) RGS spectra of the M82 outflow from Obs 1. A flux offset is applied to the O2 spectrum for visual clarity. Emission lines labeled in red are included in our analysis. Bottom panels: Observed emission-line profiles (black points) and best-fit models (red curves) for O VIII (O1; upper panels), Ne X (O2; bottom left), and Mg XII (O2; bottom right). The models are shown for the best-fit σ  v  or 95% upper limit for each line (σ  v  = 1160 km s−1 for O VIII, σ  v  = 550 km s−1 for Ne X, and σ  v  = 370 km s−1 for Mg XII). The blue curves show the best-fit models with no velocity broadening (σ  v  = 0 km s−1). The presence of velocity broadening is clear in the O VIII 0.65 keV line profiles. The systemic redshift of M82 is indicated by the dotted purple lines. The O VIII line profile peak is shifted away from the systemic velocity by the surface brightness distribution at these energies.
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: The velocity dispersion, σ  v , of the soft X-ray wind from M82 is broad at O VIII (﹩{\sigma }_{v}={1160}_{-90}^{+100}﹩ km s−1) and is narrower in the hotter, more spatially compact gas traced by Ne X (﹩{\sigma }_{v}={550}_{-150}^{+130}﹩ km s−1) and Mg XII (σ  v  < 370 km s−1). PDFs for σ  v  measured from Mg XII, Ne X, and O VIII are indicated in cyan, purple, and red, respectively. The top four panels show measurements from Obs 1—Obs 4; Obs 1, 2, and 4 are combined via a weighted product (﹩{w}_{i}=\sqrt{{t}_{\mathrm{clean},i}}﹩) in the bottom panel (see the text for a discussion of Obs 3). The dotted lines indicate 95% upper limits, the dashed lines mark 50th percentile values, and the shaded regions indicate the 68% confidence intervals.
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: Projections of the events in the RGS1 (left) and RGS2 (right) modules in the λ–cross-dispersion angle plane. The inactive RGS2 chip leaves a black gap at the right. The color bar and scale are the same in each image, and each image has been smoothed with a Gaussian kernel with σ = 3 pixels to enhance faint structure. Each panel shows events between 18 and 22 Å in the dispersion direction and between −150″ and +150″ in the cross-dispersion direction. The top panels show events with detected energies between 0.5 and 0.7 keV. The oxygen “image” of the M82 nebula is clearly visible. The bottom panels show events between 0.1 and 2 keV and reveal bad columns (dark or bright columns). No bad columns or other artifacts can explain the double-peaked structure of the Obs 3 O VIII 0.65 keV line. The ULX spectrum appears as a narrow horizontal band near zero cross dispersion in the bottom panels.
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: The RGS LSF for key emission lines in first (top) and second order (bottom) for Obs 1. The LSF for point sources (black) is compared to the LSF for the spatially extended wind in M82 (purple), produced by convolving the point-source LSF with the spatial profile at the energy of the emission line of interest. There is significant instrumental line broadening due to the extended nature of the source, particularly on the blue wing of the O VIII line.
Paper: Evidence for a Fast Soft X-Ray Wind in M82 from XMM-RGS
Authors: Erin Boettcher, Edmund Hodges-Kluck
Figure caption: LSFs created for the Obs 3 spectrum at O VIII 0.65 keV, with a position angle of 138° and an extraction aperture of ﹩1^{\prime} ﹩. The LSF created from Chandra data is shown in black, while LSFs created from the Obs 1 and Obs 3 MOS images are shown in red and blue, respectively. The similarity between the LSFs demonstrates that the double-peaked feature in the Obs 3 O VIII line cannot be explained by a feature present in the Obs 3 image and also that one need not construct an LSF for each observation from the MOS images for that observation.
Paper: Probing Cold-to-temperate Exoplanetary Atmospheres: The Role of Water Condensation on Surface Identification with JWST
Authors: Ziyu Huang, 子钰 黄, Xinting Yu, 馨婷 余, Shang-Min Tsai, Julianne I. Moses, Kazumasa Ohno, Joshua Krissansen-Totton, Xi Zhang, Jonathan J. Fortney
Figure caption: Simulated transmission spectrum of K2-18 b (top) and LHS 1140 b (bottom) A b = 0.3 cases with different surface pressure levels with water condensation. Solid lines are simulation results with the pressure of the bottom surface pressure at 1 and 1000 bars. (Top): Green dots are JWST observations reproduced from N. Madhusudhan et al. (2023b). Red dots are HST observations from B. Benneke et al. (2019b). Purple dots are Spitzer observations from B. Benneke et al. (2017). (Bottom): The red and purple dots are JWST observations reproduced from M. Damiano et al. (2024) and C. Cadieux et al. (2024a) and R. Doyon (2024), respectively. Green dots are HST observations from B. Edwards et al. (2020).
Paper: Probing Cold-to-temperate Exoplanetary Atmospheres: The Role of Water Condensation on Surface Identification with JWST
Authors: Ziyu Huang, 子钰 黄, Xinting Yu, 馨婷 余, Shang-Min Tsai, Julianne I. Moses, Kazumasa Ohno, Joshua Krissansen-Totton, Xi Zhang, Jonathan J. Fortney
Figure caption: Simulated VMR profiles for the main chemical species for K2-18 b with water condensation for the albedo cases that are not presented in the main text. The left and right columns are for the shallow- and deep-surface cases, respectively. (Top) A b = 0 (T eq = 278 K), (middle) A b = 0.3 (T eq = 254 K), (bottom) A b = 0.75 (T eq = 197 K).
Paper: Probing Cold-to-temperate Exoplanetary Atmospheres: The Role of Water Condensation on Surface Identification with JWST
Authors: Ziyu Huang, 子钰 黄, Xinting Yu, 馨婷 余, Shang-Min Tsai, Julianne I. Moses, Kazumasa Ohno, Joshua Krissansen-Totton, Xi Zhang, Jonathan J. Fortney
Figure caption: Simulated VMR profiles for the main chemical species for LHS 1140 b with water condensation for the albedo cases that are not presented in the main text. The left and right columns are for the shallow- and deep-surface cases, respectively. (Top) A b = 0 (T eq = 234 K), (middle) A b = 0.56 (T eq = 191 K), (bottom) A b = 0.75 (T eq = 165 K).
Paper: Characteristics and Source Regions of Slow Alfvénic Solar Wind Observed by Parker Solar Probe
Authors: Tamar Ervin, Kai Jaffarove, Samuel T. Badman, Jia Huang, Yeimy J. Rivera, Stuart D. Bale
Figure caption: Distributions of solar-wind speed binned by heliocentric distance. The dashed lines indicate different threshold percentages based on observations of wind speeds at 1 au (see Section 3.1) as applied to Parker. The red, purple, and blue lines signify the uppermost 20%, 35%, and 50% of the wind respectively where 20% is the 500 km s−1 cutoff at 1 au, 50% is the 400 km s−1 cutoff, and 35% falls in between. In our data stream identification, we use the red line, or the equivalent threshold to 500 km s−1 at 1 au from Parker to differentiate between slow and fast solar wind. The velocities associated with each dashed line are listed. Panel (a): all Parker solar-wind velocity data below 40 R ⊙ for E4–E14 with associated dashed lines. Panels (b) and (c): Parker solar-wind velocity data for radial bin (b) 10–15 R ⊙ and (c) 15–20 R ⊙. Panels (d)–(f): all Parker solar-wind velocity data for radial bins (d) 10–20 R ⊙, (e) 20–30 R ⊙ and (f) 30–40 R ⊙ with the associated thresholds.
Paper: Quasiperiodic γ-Ray Modulations in the Blazars PKS 2155-83 and PKS 2255-282
Authors: M. A. Hashad, Amr A. EL-Zant, Y. Abdou, H. M. Badran
Figure caption: The same as Figure 1, but for the object PKS 2255-282.
Paper: Characteristics and Source Regions of Slow Alfvénic Solar Wind Observed by Parker Solar Probe
Authors: Tamar Ervin, Kai Jaffarove, Samuel T. Badman, Jia Huang, Yeimy J. Rivera, Stuart D. Bale
Figure caption: All usable E4–E14 data (see Section 3.1) is compiled into normalized histograms of cross helicity (σ  C ) against the residual energy (σ  R ) to showcase the identification method. The criteria for categorization of “high” Alfvénicity and the radially dependent velocity thresholding method are described in Section 3.1. Panels (a), (b), and (c) plot the designated data for SSW, SASW, and FSW respectively.
Paper: Quasiperiodic γ-Ray Modulations in the Blazars PKS 2155-83 and PKS 2255-282
Authors: M. A. Hashad, Amr A. EL-Zant, Y. Abdou, H. M. Badran
Figure caption: The GLSPs and WWZs of PKS 2155-83 (left panel) and PKS 2255-282 (right panel) γ-ray 1 m binned LCs. The GLSPs are represented in black lines and the WWZs are represented in blue lines. The green, red, and magenta lines represent the 2σ, 3σ, and 4σ confidence levels, respectively, of the GLSPs of 105 simulated LCs utilizing Emmanoulopoulos’ method (D. Emmanoulopoulos et al. 2013).
Paper: Quasiperiodic γ-Ray Modulations in the Blazars PKS 2155-83 and PKS 2255-282
Authors: M. A. Hashad, Amr A. EL-Zant, Y. Abdou, H. M. Badran
Figure caption: Autocorrelation function for PKS 2155-83 (left panel) and PKS 2255-282 (right panel) 1 m binned LCs. The blue lines represent the smoothed correlation, using a Savitzky–Golay filter. The green, red, indigo, and brown lines represent the local 1σ, 2σ, 3σ, and 4σ chances of observing the autocorrelation levels indicated by the corresponding lines. These were estimated through simulations of 105 LCs, utilizing Emmanoulopoulos’ method (D. Emmanoulopoulos et al. 2013).
Paper: Quasiperiodic γ-Ray Modulations in the Blazars PKS 2155-83 and PKS 2255-282
Authors: M. A. Hashad, Amr A. EL-Zant, Y. Abdou, H. M. Badran
Figure caption: REDFIT periodicity analysis for the 1 m binned LCs of PKS 2155-83 (left panel) and PKS 2255-282 (right right). The bias-corrected power spectrum is represented by the black line. Spectrum of the theoretical red noise (red line), 95% confidence level (blue dashed line), and 99% confidence level (orange dashed line) were estimated by fitting the data with the AR1 process.
Paper: Quasiperiodic γ-Ray Modulations in the Blazars PKS 2155-83 and PKS 2255-282
Authors: M. A. Hashad, Amr A. EL-Zant, Y. Abdou, H. M. Badran
Figure caption: DCDFT of the 1 m (blue line) and 2 m (red line) binned LCs of PKS 2155-83 (left panel) and PKS 2255-282 (right panel).
Paper: Quasiperiodic γ-Ray Modulations in the Blazars PKS 2155-83 and PKS 2255-282
Authors: M. A. Hashad, Amr A. EL-Zant, Y. Abdou, H. M. Badran
Figure caption: Two-dimensional contour plots of the WWZ power for the 1 m binned LCs of PKS 2155-83 (left panel) and PKS 2255-282 (right panel).
Paper: Heavy Ion Escape at Mars during the Disappearing Solar Wind Event in 2022 December
Authors: Han-Wen Shen, Jasper S. Halekas, James P. McFadden, Jacob R. Gruesbeck, Neesha R. Schnepf
Figure caption: Variations in the flux of escaping ions for different ion species, escape channels, and energy levels. In each panel, escaping ion fluxes observed by the STATIC instrument are sorted according to their respective MAVEN orbits. The median value and interquartile range of fluxes in each orbit are indicated by a blue diamond symbol and vertical line, respectively. The DSW period is marked between the two pink vertical dashed lines.
Paper: Multifluid Theory of Electrostatic Ion Cyclotron Waves in Partially Ionized Plasmas
Authors: Wenlong Huang, Yu Liu, Pengcheng Yu, Jiuhou Lei
Figure caption: The normalized mode frequency and damping rate as a function of k ρ  s  with ﹩{\hat{\nu }}_{{in}}=0.4﹩, δ  n  = 0.01, and τ = 0.01 ((a) and (b)); ﹩{\hat{\nu }}_{{in}}=4﹩, δ  n  = 0.01, and τ = 0.01 ((c) and (d)); ﹩{\hat{\nu }}_{{in}}=4﹩, δ  n  = 0.01, and τ = 0.1 ((e) and (f)). In this figure, the red and blue solid curves are numerical results of hEIC and lEIC calculated from Equation (55); the red and blue dotted curves represent the corresponding solutions with T  n  = 0; the black dashed–dotted curves are calculated from Equation (56); and the black dotted vertical lines are ﹩k{\rho }_{s}=\left({\delta }_{n}\right./\left.2\sqrt{\tau }\right){\hat{\nu }}_{{in}}﹩. In panels (a), (c), and (e), the red and blue dotted–dashed horizontal lines are ﹩{\hat{\omega }}_{r}=1﹩ and ﹩{\hat{\omega }}_{r}={\hat{\omega }}_{{ci}}^{\mathrm{eff}}﹩, respectively. The red dashed and dotted–dashed curves in panels (b), (d), and (f) satisfy ﹩-\hat{\gamma }=(1+{\delta }_{n}){\hat{\nu }}_{{in}}﹩ and ﹩-\hat{\gamma }={\hat{\nu }}_{{in}}/2﹩, respectively.
Paper: Heavy Ion Escape at Mars during the Disappearing Solar Wind Event in 2022 December
Authors: Han-Wen Shen, Jasper S. Halekas, James P. McFadden, Jacob R. Gruesbeck, Neesha R. Schnepf
Figure caption: SWIA ion flux and density observations and MAVEN orbits in MSE coordinates. (a) and (b) show the ion energy spectra and density measurements by SWIA. The color bar at the top indicates region identifications, with the solar wind and magnetosheath regions represented in orange and green, respectively. The densities in these two regions shown in (b) are highlighted in blue. The two pink vertical dashed lines indicate the DSW period. (c) displays the orbit-averaged magnetic fields (denoted by diamond symbols) and their standard deviations (denoted by vertical lines) in the upstream solar wind and magnetosheath. (d) shows the orbit-averaged electric field strengths in the magnetosheath, along with their standard deviations. (e)–(h) present MAVEN orbits in MSE coordinates, with the left-hand side illustrating the normal periods and the right-hand side illustrating the DSW period.
Paper: Heavy Ion Escape at Mars during the Disappearing Solar Wind Event in 2022 December
Authors: Han-Wen Shen, Jasper S. Halekas, James P. McFadden, Jacob R. Gruesbeck, Neesha R. Schnepf
Figure caption: Variations in the density of escaping ions for different ion species, escape channels, and energy levels. The format is the same as that of Figure 2, but the flux is replaced by the density.
Paper: Heavy Ion Escape at Mars during the Disappearing Solar Wind Event in 2022 December
Authors: Han-Wen Shen, Jasper S. Halekas, James P. McFadden, Jacob R. Gruesbeck, Neesha R. Schnepf
Figure caption: Variations in the speed of escaping ions for different ion species, escape channels, and energy levels. The format is the same as that of Figure 2, but the flux is replaced by the speed.
Paper: Heavy Ion Escape at Mars during the Disappearing Solar Wind Event in 2022 December
Authors: Han-Wen Shen, Jasper S. Halekas, James P. McFadden, Jacob R. Gruesbeck, Neesha R. Schnepf
Figure caption: Variations in the flux of planetward ions within the tail region for different ion species and energy levels. The fluxes observed by the STATIC instrument are sorted according to their respective MAVEN orbits. In each orbit, the median value and interquartile range of fluxes are indicated by a blue diamond symbol and vertical line. The DSW period is marked between the two pink vertical dashed lines.
Paper: Multifluid Theory of Electrostatic Ion Cyclotron Waves in Partially Ionized Plasmas
Authors: Wenlong Huang, Yu Liu, Pengcheng Yu, Jiuhou Lei
Figure caption: The normalized mode frequency (a) and damping rate (b) with l. The red circles, diamonds, crosses, and stars represent the normalized mode frequency and damping rate in hEIC with m = 0, m = 1, m = 2, and m = 3, respectively. Similarly, the blue ones correspond to ﹩{\hat{\omega }}_{r}﹩ and ﹩-\hat{\gamma }﹩ in lEIC with m = 0, m = 1, m = 2, and m = 3, respectively. In addition, the dashed and dotted–dashed horizontal lines in panel (a) are ﹩{\hat{\omega }}_{r}=1﹩ and ﹩{\hat{\omega }}_{r}={\hat{\omega }}_{{ci}}^{\mathrm{eff}}﹩, respectively.
Paper: Multifluid Theory of Electrostatic Ion Cyclotron Waves in Partially Ionized Plasmas
Authors: Wenlong Huang, Yu Liu, Pengcheng Yu, Jiuhou Lei
Figure caption: Dependence of (a) δ  n , (b) ﹩{\hat{\omega }}_{r}﹩ in hEIC, and (c) ﹩10{\hat{\omega }}_{r}﹩ in lEIC on ﹩{\hat{\nu }}_{{in}}﹩. The red circles, magenta diamonds, blue crosses, and black stars represent four different cases, i.e., case (1), case (2), case (3), and case (4), respectively. The parameters used here are m = 0 and l = 1.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Parameter space for peak absolute magnitude in each JWST NIRCam filter vs. peak time. Models using atomic data set HULLAC are in orange, data set ATOMIC in blue, and data set Autostructure in green. Larger size corresponds to larger mass models. A black outline indicates that the local thermalization efficiency prescription was used, while no boundary indicates global thermalization prescription. Circles, triangles, and squares indicate log10 X lan = −9, −4, and −2, respectively. Hatches inside the markers indicate a model with characteristic velocity of 0.3c, while those without indicate 0.1c. The effects of choice of atomic data set already becomes apparent in the simple observable of time of peak and peak magnitude and especially so at redder filters; for the same ejecta parameters, while the peak magnitude is approximately constant, the time at which the KN achieves its peak shifts by almost four days in the highest X lan models in the reddest filters.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Ratio of the global thermalization-prescription bolometric luminosity to the local thermalization-prescription bolometric luminosity curve as a function of time for each model (smoothed to remove numerical noise) with v  k  = 0.3c models in the upper panel and v  k  = 0.1c models in the lower panel, and lighter colors representing smaller masses. The predictions from global prescription models tend to differ from the local prescription predictions by ∼20%–50%. Excluding the least massive models limits the offset to up to ∼30%, regardless of atomic data set and X lan.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Same legend as Figure 4. Parameter space of LSST g − r, g − i, r − i, r − z, i − z, and z − y color at time of peak of the first filter. Due to the spacing of the grid, it is possible to see that each unique mass forms an arc of models in this parameter space, with more massive models pushing to higher peak magnitudes and higher X lan pushing models to redder colors and fainter peak magnitudes, creating an observable arc.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Spectral sequence of a M = 10−2 M ⊙, v = 0.1c, ﹩{\mathrm{log}}_{10}({X}_{\mathrm{lan}})﹩ = −2 model using atomic data set HULLAC (orange), ATOMIC (blue), and Autostructure (green) at t = 2, 3, 5, 10, and 15 days overlaid on top of the transmission curves of JWST photometric filters. At early times, the predictions from each atomic data set agree relatively well, but rapidly diverge (particularly in the IR) and result in vastly different colors. The δ t = 15 days spectral comparison, while likely in a regime where NLTE will affect the precise shape of features, particularly shows the uncertainty in the specific locations of lines between atomic data sets from the qualitative differences in shape, which should remain unaffected by NLTE effects.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Same as Figure 3, except for each LSST filter u, g, r, i, z, and y. Similarly to the JWST filters, the reddest filters show the largest spread in days until peak among the different atomic data sets. As many of the LSST filters are blueward of JWST, this highlights the rapid decline of KNe at blue wavelengths as the majority of models achieve their peak in the u, g, and r filters within two days of merger.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Best-fitting light curve model for atomic data set HULLAC with bolometric luminosity points from M. W. Coughlin et al. (2018) and model bolometric luminosity in the inset. M. W. Coughlin et al. (2018) extrapolates the SED to redward of 2.5 μm by fitting a blackbody to photometric points, which tends to overestimate the true bolometric luminosities of the models presented in this work. The model tends to underproduce optical flux until z band, agree for J band, then overproduce H and Ks bands while having a similar bolometric luminosity. This is likely indicative of too much reprocessing of optical light into IR and demonstrates the difficulty of simplified 1D models fitting AT2017gfo.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Parameter space of the decay rate in each LSST filter vs. the magnitude at δ t = 2 days after peak of a given filter. Atomic data set Autostructure stands out as the slowest declining across all LSST filters, especially the bluest filters like u, g, and r.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Best-fitting light curve model for atomic data set ATOMIC. Similarly to Figure 9, ATOMIC overproduces H and Ks bands while underproducing optical emission, especially at δ t ≳ 5 days, though the bolometric luminosity curve is consistent.
Paper: Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
Authors: D. Brethauer, D. Kasen, R. Margutti, R. Chornock
Figure caption: Parameter space of time above half peak vs. the peak magnitude of that filter. Due to the slower decay rate in LSST filters of atomic data set Autostructure, those models last above half maximum luminosity by as much as a factor of 2 longer.
Paper: A Merger-driven Scenario for Clumpy Galaxy Formation in the Epoch of Reionization: Physical Properties of Clumps in the FirstLight Simulation
Authors: Yurina Nakazato, Daniel Ceverino, Naoki Yoshida
Figure caption: Three simulated clumpy galaxies at z = 9.00 (top; ID = FL977), z = 7.70 (middle; ID = FL957), and z = 7.20 (bottom; ID = FL958). Each column, from left to right, represents the averaged gas density, surface stellar mass density of a young (< 10 Myr) population, O III λ5007 line flux, surface mass density of an old (>100 Myr) stellar population and dark matter, and mass-weighted stellar age, respectively. Each panel has a side length of 10 proper kpc and the same size of projected depth.
Paper: Parametric Instability of Alfvén Waves and Wave Packets in Periodic and Open Systems
Authors: Maile Marriott, Anna Tenerani
Figure caption: The normalized growth rate as a function of ℓ/L is shown for three values of the box length with varying ℓ for an arc-polarized wave. For periodic boundary conditions (triangles): L = 20π (red), 14π (orange), and 2π (green). For the open boundary case (circles): L = 14π (neon pink) and L = 20π (neon blue). Results using a circularly polarized wave packet in periodic (triangles) and open (circles) boundaries are also shown in black. All cases show a nearly linear trend for the regime ℓ ≪ L. The dashed line corresponds to γ/ω 0 ∼ (ℓ/L) m  with m = 1. When ℓ ≲ 2π/k 0, the width becomes on the order of or smaller than the pump wave wavelength and the data shows some irregular curvature.
Paper: Parametric Instability of Alfvén Waves and Wave Packets in Periodic and Open Systems
Authors: Maile Marriott, Anna Tenerani
Figure caption: The eigenfunctions, normalized to the maximum amplitude of ρ, for k = 8 for pump waves of the type shown in Figure 4: Elsässer fields ﹩{z}_{\pm }^{\pm }﹩, ρ and u for the circularly (top row) and arc-polarized (second row) periodic wave train, and for the corresponding arc-polarized wave packet with periodic (third row) and open boundary (bottom row), with L = 20π, ℓ = 10.