adsabs/AstroCLIMB huggingface dataset:
Paper: Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog
Authors: Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Figure caption: Dual AGN candidates (ranks 1 to −1) that required X-ray imaging for selection (see Section 4.1.5). Shown is a breakdown of dual AGN selection strategies used in concert with (left) or independently of (right) X-ray imaging, where the bars of the histograms are normalized to the total number of candidates that required X-ray imaging as a part of their selection function.
Paper: Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog
Authors: Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Figure caption: Top and bottom panels: the distributions of angular separation, projected separation (rp, in kiloparsecs), and redshift for objects within the Big MAC DR1. Angular separation (units of arcseconds) is given on the x-axis, and physical, projected separation (units of kiloparsecs) is given on the y-axis. The data are color coded according to the redshift, given on the logarithmic auxiliary color map. DR1 systems without known angular separations are not shown in these panels. Due to the large range of predicted angular separations for DR1 systems (particularly for those candidates with predicted rp < 0.1 kpc), this figure is split into two panels at 0.1 kpc. Top panel: DR1 systems with physical separations rp ≥ 0.1 kpc (angular separation range of ∼0﹩\mathop{.}\limits^{\unicode{x02033}}﹩06–300″). Inverted, open triangles denote systems with only upper limits on the angular separation (for example, dual AGN candidates selected within a single spectroscopic fiber based on double-peaked emission lines). Bottom panel: DR1 systems with physical separations rp < 0.1 kpc (angular separation range of ∼10−7″ to 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩3).
Paper: Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog
Authors: Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Figure caption: The distribution of redshift and projected, physical separations of objects in the Big MAC DR1. Redshift is given on the x-axis, and the projected, physical separation (in units of kiloparsecs) is given on the y-axis. To aid the eye of the reader, the data are additionally color coded according to the redshifts, with the logarithmic color map provided on the auxiliary axis. This plot, while similar to the information provided in Figure 25, is provided to complement a similar plot shown in Y.-C. Chen et al. (2022; see their Figure 1). As in Figure 25, open inverted triangles denote systems with only angular separation upper limits (the bands of tightly spaced inverted triangles correspond to dual AGN candidates selected from 3″ and 2″ diameter spectroscopic fibers, and hence have only upper limits for their separations). Systems showing projected separations >0.03 kpc should be considered dual AGN/SMBH candidates, while systems with separations <0.03 kpc should be considered binary AGN/SMBH candidates, as per the definitions outlined in Section 2.
Paper: Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog
Authors: Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Figure caption: Shown is a breakdown of the selection methods used to identify multi-AGNs in the literature that are listed in this catalog as confirmed (confidence rank = 1). This chart shows the aggregate percent usage of all multi-AGN selection techniques (normalized by the number of confirmed—rank 1—multi-AGNs in the catalog) and corresponds to Section 4.4. Note, these selection methods are not all necessarily mutually exclusive.
Paper: Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog
Authors: Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Figure caption: Shown is a breakdown of the confirmation methods used to identify dual AGNs in the literature. This chart shows the aggregate percent usage of all dual AGN confirmation techniques (normalized by the number of confirmed dual AGNs—or confidence rank 1 candidates—in the catalog) and corresponds to Section 4.4. Note, these confirmation methods are not all necessarily mutually exclusive.
Paper: Super-size Me: The Big Multi-AGN Catalog (The Big MAC) Data Release 1: The Source Catalog
Authors: Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Figure caption: Recoiling AGN/SMBH candidates (ranks 1 to −1) that required optical imaging for selection (see Section 4.3.4). Shown is a breakdown of recoiling AGN/SMBH selection strategies used in concert with optical imaging, where the bars of the histograms are normalized to the total number of candidates that required optical imaging as a part of their selection function. None of these objects were selected independently of optical imaging using other techniques.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: All-sky distribution of the ERQs discovered in the present article, along with previously discovered ERQs, in an Aitoff projection. The red and green points represent the GRQs and NGERQs from the present study. The orange and gray points correspond to previously identified GRQs and NGERQs.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: Flowchart for identifying and selecting new GRQs and NGERQs from TGSS-ADR1. More details about the selection criteria are presented in Section 2.2.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: Scatter plot with marginal histograms represents the variation of radio power (log P150 [W Hz-1]) with integrated spectral index (﹩{\alpha }_{150}^{1400}﹩). The previous GRQ sample refined by M. Mahato et al. (2022) is included in the plot for comparison. For uniformity, we calculate the radio power of GRQs using TGSS-ADR1 flux density at 150 MHz.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: The figure indicates the variation of the projected linear size with the radio power of GRQs and NGERQs of the present work. Previous GRQ sample refined by A. Kuźmicz & M. Jamrozy (2021) and M. Mahato et al. (2022), and the NGERQ sample from A. Kuźmicz & M. Jamrozy (2021) are included in the plot for comparison.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: Scatter plot with marginal histograms shows the variation of the integrated spectral index (﹩{\alpha }_{150}^{1400}﹩) with redshift (z). Previous GRQ samples refined by M. Mahato et al. (2022) are included in the plot for comparison.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: Scatter plot with marginal histograms shows the variation of radio power at 150 MHz (log P150 [W Hz−1]) with redshift (z). The previous GRQ sample refined by M. Mahato et al. (2022) is included in the plot for comparison.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: Histograms indicate the distribution of the different asymmetry parameters, such as the arm-length ratio parameter (﹩{ \mathcal Q }﹩), flux density asymmetry ratio (﹩{ \mathcal S }﹩), fractional separation difference (﹩{ \mathcal X }﹩), and the ambient density asymmetry ratio (fρ) of GRQs and NGERQs from the ERQ sample studied here.
Paper: Unveiling New Giant Radio Quasars from the TGSS Sky and Their Large-scale Environment
Authors: Souvik Manik, Netai Bhukta, Sabyasachi Pal, Sushanta K. Mondal
Figure caption: The figure shows an ﹩{ \mathcal S }-{ \mathcal Q }﹩ (flux density asymmetry vs. arm-length ratio) diagnostic scatter plot for the total ERQ sample, including new GRQs and NGERQs as well as previous samples (A. Kuźmicz & M. Jamrozy 2021; M. Mahato et al. 2022) studied here. Red points represent all the GRQs, and green points indicate all the NGERQs.
Paper: A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Authors: Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
Figure caption: Ratios between SFRs determined by other tracers (SFRANN, ﹩{\mathrm{SFR}}_{{\rm{D}}n4000}﹩, SFR[O II], SFRHα, SFRSED, and SFRMIR) and SFRFIR. The circles and stars represent SFRFIR determined by IR fluxes observed by AKARI and Herschel, respectively. Each panel illustrates the classifications among galaxies, such as star-forming, composite, Seyfert, and LINER, respectively. The color scales represent the Dn(4000) values.
Paper: A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Authors: Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
Figure caption: Top panels: stellar masses of the selected sample as a function of redshift. Bottom panels: fraction of the u-band light captured by the SDSS fiber, defined as the difference in magnitude, fSDSS = Ufiber − Utotal, shown as a function of redshift. The black squares show the median stellar mass values of each redshift bin Δz = 0.05. Each panel corresponds to a different galaxy classification: star-forming, composite, Seyfert, and LINER, respectively.
Paper: A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Authors: Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
Figure caption: Ratios between SFRs determined by other tracers (﹩{\mathrm{SFR}}_{{\rm{D}}n4000}﹩, SFR[O II], SFRHα, SFRSED, and SFRMIR) and SFRANN. SFRANN determined with σANN < 0.3. Each panel illustrates the classifications among galaxies, such as star-forming, composite, Seyfert, and LINER, respectively. The color scales represent the Dn(4000) values.
Paper: A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Authors: Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
Figure caption: AGN fraction estimations obtained using the Markov Chain Monte Carlo rainbow Python package. The color scale represents the estimated fractions based on large potential reference points of pure star-forming galaxies (magenta) and AGNs (red).
Paper: A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Authors: Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
Figure caption: BPT diagram of the selected sample, ∼113,000 galaxies (z < 0.3) in this work. The classification of galaxies into star-forming, composite, Seyfert, and LINER categories is based on G. Kauffmann et al. (2003) (dashed line), L. J. Kewley et al. (2006) (solid line), and K. Schawinski et al. (2007) (dotted line), respectively.
Paper: A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Authors: Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
Figure caption: Comparison between [O III] luminosity (top panel) and SFR[O II] (bottom panel) with and without correction for AGN fraction (fAGN). We only show sources where 0 < fAGN < 1.
Paper: Stellar Characterization, Magnesium Abundances, and Chromospheric Activity Analysis of Stars with Confirmed Exoplanets from the K2 Mission
Authors: V. Loaiza-Tacuri, Diogo Souto, F. Quispe-Huaynasi, Katia Cunha, S. Daflon, Ellen Costa-Almeida, V. V. Smith, Luan Ghezzi
Figure caption: Iron abundance distributions (top-left panel) are shown for the K2 host star sample from this study in comparison with the metallicity distributions for the CKS sample from L. Ghezzi et al. (2021) and red giants from the APOGEE survey. The median uncertainties in the iron abundances for each sample are shown on the left. In the second row panel, we show the CDFs of metallicities of host stars of single planets (red) vs. multiple planets (blue), small planets (black) vs. large planets (orange), and single small (purple) vs. single large (green). In the third and bottom panels, we show the same cases as for metallicity, but now for [Mg/H] and [Mg/Fe], respectively. The [Mg/H] distribution is noticeably shifted toward higher values for stars hosting large planets, but this is not shown for [Mg/Fe]. The shaded regions around each CDF correspond to the 95% confidence intervals calculated via bootstrap.
Paper: Stellar Characterization, Magnesium Abundances, and Chromospheric Activity Analysis of Stars with Confirmed Exoplanets from the K2 Mission
Authors: V. Loaiza-Tacuri, Diogo Souto, F. Quispe-Huaynasi, Katia Cunha, S. Daflon, Ellen Costa-Almeida, V. V. Smith, Luan Ghezzi
Figure caption: Comparison between the stellar radii determined in this work using the Stefan–Boltzmann law and using the isochrone method implemented in PARAM and isochrones (left panel). Comparisons of the stellar radii and masses in this study with those determined through asteroseismology by H16 (middle and right panels).
Paper: Stellar Characterization, Magnesium Abundances, and Chromospheric Activity Analysis of Stars with Confirmed Exoplanets from the K2 Mission
Authors: V. Loaiza-Tacuri, Diogo Souto, F. Quispe-Huaynasi, Katia Cunha, S. Daflon, Ellen Costa-Almeida, V. V. Smith, Luan Ghezzi
Figure caption: [Mg/Fe] ratio as a function of [Fe/H] derived in this work (blue circles) compared with the APOGEE data (gray symbols) is shown in the left panel. The dashed line represents the separation between the low-alpha and high-alpha sequences (thin and thick disks according to the division from D. H. Weinberg et al. (2022). There is a larger fraction of single-detected small planets compared to single-detected large planets in the low-alpha sequence (3.6 times more small planets) when compared to the high-alpha sequence (1.5 times more small planets).
Paper: Stellar Characterization, Magnesium Abundances, and Chromospheric Activity Analysis of Stars with Confirmed Exoplanets from the K2 Mission
Authors: V. Loaiza-Tacuri, Diogo Souto, F. Quispe-Huaynasi, Katia Cunha, S. Daflon, Ellen Costa-Almeida, V. V. Smith, Luan Ghezzi
Figure caption: Kiel diagram of the sample stars. Gray dashed lines show solar-metallicity evolutionary tracks for 4.6 and 10 Gyr from the Yonsei-Yale models. The solar proxy result is indicated by a black star.
Paper: Stellar Characterization, Magnesium Abundances, and Chromospheric Activity Analysis of Stars with Confirmed Exoplanets from the K2 Mission
Authors: V. Loaiza-Tacuri, Diogo Souto, F. Quispe-Huaynasi, Katia Cunha, S. Daflon, Ellen Costa-Almeida, V. V. Smith, Luan Ghezzi
Figure caption: Toomre diagram colored by metallicity. The red dashed line marks the typical velocity boundary of the thin disk (70 km s−1), while the region between the red and gray dashed lines (180 km s−1) corresponds to the expected kinematic range of the thick disk.
Paper: Stellar Characterization, Magnesium Abundances, and Chromospheric Activity Analysis of Stars with Confirmed Exoplanets from the K2 Mission
Authors: V. Loaiza-Tacuri, Diogo Souto, F. Quispe-Huaynasi, Katia Cunha, S. Daflon, Ellen Costa-Almeida, V. V. Smith, Luan Ghezzi
Figure caption: Distribution of planetary radii of our exoplanets sample, derived using stellar radii estimated from the Stefan–Boltzmann equation and the isochrones method using the PARAM and isochrones packages. The vertical line highlights the radius gap region.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Example of distributions of upper-hybrid waves and density fluctuations in the radio source, at initial time t = 0, for ωc/ωp = 0.1. (a) Density fluctuations’ spectrum ﹩\left|{\rho }_{{\boldsymbol{k}}}\right|=| \delta {n}_{{\boldsymbol{k}}}/{n}_{0}| ﹩ in the map (kxλD, kyλD); δnk is the Fourier component of δn. (b) Corresponding spatial distribution ρ(x, y) = δn(x, y)/n0 in the map (x/λD, y/λD),  with ΔN = 0.05. (c) Electric field energy spectrum ﹩{\left|{{\boldsymbol{E}}}_{{k}}\right|}^{2}﹩ in the map (kxλD, kyλD), which mimics the wave energy distribution generated by an electron beam directed along the x-axis. All variables are normalized. The lengths of the simulation plane are Lx = 15,000λD and Ly = 8000λD, with Nx = 4096 and Ny = 2048 grid points.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Time variations of the electric (a) and magnetic (b) wave energies η(t) and μ(t), respectively, in an unmagnetized plasma (ωc = 0),  for ΔN = 0.03 and four values of cL (see the legend in (a)). The insets show, in logarithmic scales, the variations of the corresponding radiation rates ﹩\dot{\eta }﹩ and ﹩\dot{\mu }﹩ as a function of 1/cL, which exhibit scaling indices of σ ≃ 2.14 (a) and σ ≃ 1.78 (b) (red lines), respectively, to be compared with the index p = 2 (blue lines); the values of ﹩\dot{\eta }﹩ and ﹩\dot{\mu }﹩ provided by the simulations are indicated by black diamonds. The superimposed thick yellow lines represent the linear interpolations of η(t) and μ(t) at large times, which provide the radiation rates. All variables are normalized.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Energy spectra of the modes ﹩{ \mathcal O }﹩ (﹩{W}_{{\boldsymbol{k}}}=|{{\boldsymbol{E}}}_{{\boldsymbol{k}}}^{2}|+|{{\boldsymbol{B}}}_{{\boldsymbol{k}}}^{2}|﹩, left column), ﹩{ \mathcal X }﹩ (﹩|{E}_{k}^{2}|﹩, middle column) and ﹩{ \mathcal Z }﹩ (﹩|{{\boldsymbol{E}}}_{{\boldsymbol{k}}}^{2}|﹩, right column), in the map (kxλD, kyλD), at times ωpt = 100 (upper row), 200 (middle row), and 400 (bottom row), for ωc/ωp = 0.15, ΔN = 0.03, and cL = 30. The circles represent the conditions ﹩{k}^{2}{\lambda }_{D}^{2}=2{\rm{\Delta }}N/{c}_{L}^{2}﹩ (red) and ﹩{k}^{2}{\lambda }_{D}^{2}={\rm{\Delta }}N/{c}_{L}^{2}﹩ (green), where k is the electromagnetic wavenumber modulus. Scales are linear. All variables are normalized.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Electromagnetic ﹩{ \mathcal O }﹩-mode in an unmagnetized plasma. (a) Variations of the radiation rate ﹩\dot{\eta }﹩ as a function of 1/cL, for different ΔN, corresponding to the scaling indices σ listed in the legend. (b) Variations of ﹩\dot{\eta }{({c}_{L}/70)}^{2}﹩ as a function of ΔN, for different cL (see the legend). The initial density fluctuations and upper-hybrid wave turbulence spectra are anisotropic and isotropic, respectively. (a) Logarithmic scales; (b) linear scales. All variables are normalized.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Wave and density turbulence at ωpt ≃ 7600. (a) Spatial distribution of the upper-hybrid wave energy ﹩{\left|{\boldsymbol{E}}\right|}^{2}﹩ in the map (x/λD, y/λD); the red square delimits the plasma region where a zoom is shown in (d). (b) Spatial distribution of δn(x, y)/n0 in the map (x/λD, y/λD); the red square is the same as in (a). (c) Upper-hybrid wave spectrum ﹩{\left|{{\boldsymbol{E}}}_{{k}}\right|}^{2}﹩ in the map (kxλD, kyλD). (d) Zoom of the plasma domain delimited by a red square in (a); the isocontours with vanishing ion density perturbation δn(x, y) = 0 are represented by red lines. (e) Profile along y, at fixed x, of the upper-hybrid wave energy ﹩{\left|{\boldsymbol{E}}\right|}^{2}﹩. All variables are in arbitrary units. Initial conditions are shown in Figure 1.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Time variations of the magnetic wave energy μ(t) in an unmagnetized plasma (ωc = 0), for ΔN = 0.03 and cL = 30, 40, 50, and 60. The dashed black and solid pink lines correspond to simulations performed with the time steps ωpΔt = 1 and ωpΔt = 5.4, respectively. The inset shows the radiation rates ﹩\dot{\mu }﹩ as a function of 1/cL (in logarithmic scales), estimated using the slopes of μ(t) at large times, for the four values of cL, and represented by blue stars (ωpΔt = 1) and pink diamonds (ωpΔt = 5.4). For comparison, the solid black line corresponds to the scaling index p = 2. All variables are normalized.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Electromagnetic ﹩{ \mathcal O }﹩-mode in an unmagnetized plasma. (a) Variations of the radiation rate ﹩\dot{\eta }﹩ as a function of 1/cL, for different ΔN, corresponding to the scaling indices σ listed in the legend. (b) Variations of ﹩\dot{\eta }{({c}_{L}/70)}^{2}﹩ as a function of ΔN, for different cL (see the legend). The initial density fluctuations and upper-hybrid wave turbulence spectra are both anisotropic. (a) Logarithmic scales; (b) linear scales. All variables are normalized.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Time variations of the electromagnetic wave energy η(t) in a weakly magnetized plasma, for ΔN = 0.03, ωc/ωp = 0.15, and five values of cL (see the legend in the left panel), for the ﹩{ \mathcal O }﹩-mode (left), the ﹩{ \mathcal X }﹩-mode (middle), and the ﹩{ \mathcal Z }﹩-mode (right). The superimposed thick yellow straight lines highlight the slopes of the linear growths at large times, providing the time-independent radiation rates ﹩\dot{\eta }﹩. All variables are normalized.
Paper: Linear Mode Conversion Theory of Radio Emission from Turbulent Solar Wind Plasmas
Authors: C. Krafft, A. S. Volokitin
Figure caption: Electromagnetic ﹩{ \mathcal O }﹩-mode in an unmagnetized plasma. (a) Variations of the radiation rate ﹩\dot{\eta }﹩ as a function of 1/cL, for different ΔN, corresponding to the scaling indices σ listed in the legend. (b) Variations of ﹩\dot{\eta }{({c}_{L}/70)}^{2}﹩ as a function of ΔN, for different cL (see the legend). The initial density fluctuations and upper-hybrid wave turbulence spectra are isotropic and anisotropic, respectively. (a) Logarithmic scales; (b) linear scales. All variables are normalized.
Paper: Detection of Kink Oscillations in Solar Coronal Loops by a Convolutional Neural Network–Long Short-term Memory Neural Network
Authors: Sergey A. Belov, Yu Zhong, Dmitrii Y. Kolotkov, Valery M. Nakariakov
Figure caption: (a) Definition of the 3D loop coordinate system in the loop plane (gray) with respect to heliographic coordinates. Here, α denotes the azimuth angle between the footpoint baseline and the heliographic east–west direction, and θ is the inclination angle between the loop plane and the local vertical to the solar surface. The hollow circles represent the boundary of the local coronal-loop cross section, which together form a semitorus. (b) Definition of the spherical coordinate system (λ, φ, RSun) and the local ENU coordinate system (u, v, w) of the loop plane with respect to the Heliocentric Earth Equatorial coordinates (x, y, z).
Paper: Detection of Kink Oscillations in Solar Coronal Loops by a Convolutional Neural Network–Long Short-term Memory Neural Network
Authors: Sergey A. Belov, Yu Zhong, Dmitrii Y. Kolotkov, Valery M. Nakariakov
Figure caption: Confusion matrices for the synthetic test data set (left panel), the real data set (middle panel), and the preprocessed real data set (right panel).