adsabs/AstroCLIMB huggingface dataset:
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: The 2D power spectrum of the 21 cm signal emulated with the small-scale GAN with the mean (dashed line) and 2σ scatter (shallow shaded region). We show the results with different values of reionization parameters (in different colors). Each set of reionization parameter is calculated with 6400 clips of size (2,64,64) from the raw image. For comparison, we show the results of test set images, with the mean (solid line) and 2σ scatter (thick shaded region). From top to bottom, we show the auto-PS of the 21 cm field Tb, the cross-PS between Tb and the matter overdensity field δm, and the auto-PS of δm, respectively, at three representative redshifts of the center slice (from left to right) z = 7.933, 9.384, and 11.221. The lower subpanel in each panel shows the relative error between the GAN-emulated PS and the test set, εrel. For visualization, the 10% error level is indicated with the gray dotted–dashed line, while neglecting the data points at which the statistics for the test set are nearly zero. Note that the case of ﹩({{\mathrm{log}}}_{10}\zeta =2.05,{{\mathrm{log}}}_{10}{T}_{{\rm{vir}}}=4.5)﹩ (green) is completely ionized at z = 7.933, so the 2D PS is not shown in the top left and middle left panels.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: The 2σ scatter of the ST coefficient S2(j1, l1, j2, l2) of the 21 cm signal emulated with the large-scale GAN (dashed line for the mean and error bar for 2σ scatter) and that of the simulation test set images (solid line for the mean and shaded region for 2σ scatter) over 1024 image samples at three representative redshifts of the center slice (from left to right) z = 7.933, 9.384, and 11.221, respectively. We show the results with different values of reionization parameters (in different colors and rows). The indices (j1, j2) take the value of (0, 3), (0, 6), and (3, 6).
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: Visualization of the emulated images using the large-scale GAN (top), in comparison with the simulated images using 21cmFAST (bottom). Here we show four different realizations with the same parameters ﹩({{\mathrm{log}}}_{10}\zeta ,{{\mathrm{log}}}_{10}{T}_{{\rm{vir}}})=(1.7,5.0)﹩. Each realization was computed using a different latent vector (for GAN) or initial condition (for simulation). In each panel, we show the 21 cm brightness temperature (Tb) field (the upper half) and the matter overdensity (δm) field (the lower half). The LoS is along the x-axis.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: FSD for the small-scale GAN as a function of the number of training simulations (blue solid line). For comparison, the gray dashed line shows the FSD calculated using small-scale ST coefficients (j = 0, 2, 4) for our trained large-scale GAN, while the gray dotted line shows the FSD calculated using large-scale ST coefficients (j = 2, 4, 6) for the large-scale GAN.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: Same as Figure 7 but for the large-scale GAN and a choice of the index (j1, j2) taking the value of (0, 3), (0, 6), and (3, 6). Each set of reionization parameter is calculated with 1024 clips of size (2, 256, 128) from the raw image.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: Same as Figure 6 but for the large-scale GAN and a choice of the index j = 0, 3, 6. Each set of reionization parameter is calculated with 1,024 clips of size (2, 256, 128) from the raw image.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: The standard deviation of the 21 cm brightness temperature map for each pixel over 1024 image samples of the large-scale GAN (top), in comparison with the simulated images using 21cmFAST (bottom).
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: Same as Figure 4 but for the large-scale GAN. Each set of reionization parameters is calculated with 1,024 image samples.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: Same as Figure 5 but for the large-scale GAN. Each set of reionization parameter is calculated with 1,024 clips of size (2, 256, 128) from the raw image.
Paper: Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network
Authors: Kangning Diao, Yi Mao
Figure caption: Same as Figure 3 but for the large-scale GAN.
Paper: Signals of Bursts from the Very Early Universe
Authors: Leo Stodolsky, Joseph Silk
Figure caption: The emission energy–emission time plane for the free flight of neutrinos. Eem is the emission energy and x is the scaled emission time. The plot shows which regions can or cannot contribute to a given ﹩{E}_{\mathrm{cmb}}^{\nu }﹩ at scaled time x = 1. The inclined line rising to the right, x = sEem/GeV, separates the plane into the regions of “escape” (above the line) and “contained” (below the line). In the well-defined ﹩\bar{{E}^{\nu }}\,﹩ model, the potentially contributing regions would correspond to a vertical band at some ﹩{E}_{{\rm{em}}}=\bar{{E}^{\nu }}\,﹩ (not shown). For the spread spectrum model, where all energies up to a certain maximum ﹩{E}_{{\rm{\max }}}﹩ can occur, the region to the left of ﹩{E}_{{\rm{\max }}}﹩ potentially contributes. Points that can contribute to a given ﹩{E}_{\mathrm{cmb}}^{\nu }﹩ are found by following a curve down from the point where it starts at x = 1. The two curves are examples for particles with different energies at x = 1. The curve to the right, Curve I, is for a relatively higher energy and the curve to the left, Curve II, is for a lower energy. Curve I encounters the ﹩{E}_{{\rm{\max }}}﹩ limitation before encountering the “escape” limitation. For Curve II it is the other way around. These two possibilities lead to the different lower limits of integration in Equation (61).
Paper: Signals of Bursts from the Very Early Universe
Authors: Leo Stodolsky, Joseph Silk
Figure caption: Symbolic, in only two spatial dimensions and not to scale, representation of how bursts would appear on the last scattering surface (LSS) at tcmb. The large thick circle stands for the intersection of our backward light cone (BLC) with the LSS. Its radius is indicated by the arrow and its thickness is meant to represent the finite size of the observable region. The radius increases by 10−3 lt-yr in 1 Earth year. The larger of the smaller circles represent“escaping” neutrinos, which reach tcmb or later times. According to Equation (17), these are all of about the same size. The smallest circles represent “absorbed” bursts, whose radius is governed either by tfree, or the “diffusion distance.” Observations via photons are given symbolically by the intersection of the large circle with the burst areas. In 2D this is a line segment, but in the real 3D case the circles become spheres, so the intersection with the BLC becomes a disk. We have assumed that the bursts are spherically symmetric.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Azimuth-integrated angular distributions for logarithmic intensity ﹩{\mathrm{log}}_{10}(I)﹩ (left column) and Stokes parameter Q/I (right column) as a function of the zenith angle θz for photons emergent at different surface locales corresponding to four different magnetic colatitudes: θB = 0° (blue), θB = 30° (green), θB = 60° (red), and θB = 90° (orange). Results are obtained at fixed frequency ratios, ω/ωB = 0.01 (top row) and ω/ωB = 0.3 (bottom row), for different ﹩{{ \mathcal N }}_{{\rm{rec}}}﹩, as labeled. The injection distributions are shown with black-centered points that are color-coded as for the emergent histogram distributions. For ﹩\,{\mathrm{log}}_{10}(I)\,﹩ at left, these injections are displayed only for two magnetic colatitudes, while for  Q/I  (right), they are shown for all colatitudes. The two injection traces for intensity in the top-left panel overlap at θz ≳ 1°.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Two-dimensional angular distributions as a function of zenith and azimuthal angles, θz and ϕz, of Stokes parameters Q/I (left column), U/I (middle column), and ﹩{f}_{{\rm{OX}}}=\{| {{ \mathcal E }}_{{\rm{O}},{\rm{S}}}| -| {{ \mathcal E }}_{{\rm{X}},{\rm{S}}}| \}/\{| {{ \mathcal E }}_{{\rm{O}},{\rm{S}}}| +| {{ \mathcal E }}_{{\rm{X}},{\rm{S}}}| \}﹩ (right column) for ω/ωB = 0.01 (τeff = 200, panels (a)–(c)), ω/ωB = 0.1 (τeff = 30, panels (d)–(f)), ω/ωB = 0.3 (τeff = 20, panels (g)–(i)), ω/ωB = 0.5 (τeff = 10, panels (j)–(l)), and ω/ωB = 1.5 (τeff = 6, panels (m)–(o)) at θB = 60° for ﹩{{ \mathcal N }}_{{\rm{rec}}}=1{0}^{8}﹩ recorded photons.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Distributions of photons exiting the top of an atmospheric slab positioned at the magnetic pole (θB = 0°). These density maps are functions of the emerging zenith angle θz (x-axes) and the injected zenith angle θzi (panel (a)), logarithmic scattering numbers ﹩{\mathrm{log}}_{10}{n}_{{\rm{scat}}}﹩ (panel (b)), Stokes parameter Q (panel (c)), and Stokes parameter V (panel (d)). The maps are for ω/ωB = 0.03, appropriate for surface X-ray emission from a young pulsar. The color bar on the right of each panel indicates the number of photons in each bin, so that the sum of these over all pixels equals the total number of photons recorded: ﹩{{ \mathcal N }}_{{\rm{rec}}}=5\times 1{0}^{5}﹩. The pixel size is 1° for both  θz  and  θzi .
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Distributions of photons exiting the top of the slab as a function of the emerging zenith angle θz and: the injected zenith angle θzi (top row) and logarithmic scattering numbers ﹩{\mathrm{log}}_{10}{n}_{{\rm{scat}}}﹩ (bottom row) for ω/ωB = 0.03. Four columns correspond to four different field orientations, θB = 0°, 30°, 60°, 90°, corresponding to surface locales spanning the pole to the equator. The total number of photons recorded ﹩{{ \mathcal N }}_{{\rm{rec}}}﹩ and the value of the effective optical depth τeff for each θB are indicated in the bottom panel of each column. The color bar on the right of each panel indicates the number of photons in each bin.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Left image: 3D trajectories of four photons in the MAGTHOMSCATT simulation of an atmospheric slab of height h = τT/(neσT) at the magnetic pole (B∥n). The z-axis (zenith) is oriented along the direction of the normal vector n. The green, red, blue, and olive trajectories correspond to photons that scatter 1068475, 614756, 1299544, and 681861 times, respectively, before exiting through the upper boundary of the slab. Right image: projection of these trajectories onto the x–z plane. The arrows indicate the photon propagation directions after emerging from the upper boundary into the magnetosphere. The simulation was performed at a frequency ratio of ω/ωB = 0.03 and an effective optical depth of τeff = 6, as defined in Equation (8). At injection, the photons with red and green trajectories have ⊥-polarization mode, while those with blue and olive trajectories were injected in ∥ mode. Upon emergence, they are all in ∥-polarization mode.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: The phase space diagram of vertical position vs. scattering number: 68% (solid regions) and 99% (dashed regions) confidence intervals (CIs) of vertical positions z / h for ﹩{ \mathcal N }=1{0}^{5}﹩ photons recorded at the top (blue) and bottom (red-brown) of the slab as a function of the scattering number nscat. Six restrictive ranges of zenith angles were considered: θzi ∈ [0°, 5°] (panel (a)), θzi ∈ [10°, 15°] (panel (b)), θzi ∈ [20°, 25°] (panel (c)), θzi ∈ [40°, 45°] (panel (d)), θzi ∈ [60°, 65°] (panel (e)), and θzi ∈ [80°, 85°] (panel (f)). The simulations were performed at θB = 0° (i.e., at the magnetic pole), ω/ωB = 0.03, and τeff = 6.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Two-dimensional angular distributions as a function of zenith and azimuthal angles, θz and ϕz, of the Stokes parameter V/I for three selected frequency ratios: ω/ωB = 0.3 (top row), ω/ωB = 0.5 (middle row), and ω/ωB = 1.5 (bottom row). For each ω/ωB, three magnetic colatitudes, corresponding to θB = 0° (left column), θB = 60° (middle column), and θB = 90° (right column), are considered. The effective optical depth τeff values for the runs at the pole (θB = 0°) and equator (θB = 90°) are fixed at τeff = 6 for all frequencies, while τeff values for different ω/ωB in the mid-latitude case (θB = 60°) are as indicated in Figure 7. See also Table 3 in M. G. Baring et al. (2025) for the τeff values.
Paper: Monte Carlo Simulations of Polarized Radiative Transfer in Neutron Star Atmospheres
Authors: Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding, Joseph A. Barchas
Figure caption: Top panels: distributions of z/h of photons exiting the slab from the top at nscat = 10 (panel (a)), 103 (panel (b)), and 5 × 105 (panel (c)) for θzi ∈ [0°, 5°] (red), θzi ∈ [10°, 15°] (blue), and θzi ∈ [80°, 85°] (green). Bottom panel: median values of z/h for top photons injected in six θzi ranges. The simulations were performed at θB = 0°, ω/ωB = 0.03, and τeff = 6.
Paper: Surface Waves at Switchback Boundaries in the Young Solar Wind from Parker Solar Probe Observations
Authors: Kyung-Eun Choi, Oleksiy V. Agapitov, Nina Bizien, Thierry Dudok de Wit, Lucas Colomban
Figure caption: An example of an SB recorded by PSP on 2018 November 1. Panels (a)–(f) show the overall structure of the SB, including its boundaries (the blue-shaded regions) and the main spike (the yellow-shaded region); (a)—pitch-angle distribution of suprathermal electrons (314.5 eV); (b)—magnetic field magnitude from MAG data; (c)—magnetic field components in the RTN coordinate system; (d)—velocity components in RTN, with dVR = VR − VR0, where VR0 represents the ambient solar-wind bulk velocity; (e)—angular difference of the magnetic field relative to the background solar wind; (f)—magnetic field perturbations from search-coil magnetometer data. Panels (g) and (h) provide a zoomed-in view of magnetic field components and angular differences at the trailing boundary near 07:48 UT, respectively. Panels (i) and (j) display wave activity within the time interval between the two dashed vertical lines in panels (g) and (h): (i)—magnetic field wave activity; (j)—electric field wave activity.
Paper: Surface Waves at Switchback Boundaries in the Young Solar Wind from Parker Solar Probe Observations
Authors: Kyung-Eun Choi, Oleksiy V. Agapitov, Nina Bizien, Thierry Dudok de Wit, Lucas Colomban
Figure caption: Wave activity at the trailing boundary of the SB in Figure 1. The first column presents the magnetic field variation (panel (a)) and wave properties. The power spectral density (PSD), wave propagation direction (k) relative to the normal vector (n) of the layer, and signed ellipticity are shown in panels (b)–(d), respectively. The black arrows in panels (c) and (d) mark intervals of enhanced wave activity where the propagation angle θkn exceeds 80°, consistent with the blue features in panel (c), and matching the frequency bands used for filtering in panels (e)–(h). The second column displays the magnetic field waveforms in the RTN frame, bandpass filtered around the frequencies indicated by the white dashed horizontal lines in panel (b): 3, 2.2, 1.5, and 1.75 Hz, respectively. The last column shows hodographs of the magnetic field during the intervals indicated by the thick horizontal bars in panels (e)–(h), revealing polarization characteristics—linear, elliptical, or circular—of the observed waves.
Paper: Surface Waves at Switchback Boundaries in the Young Solar Wind from Parker Solar Probe Observations
Authors: Kyung-Eun Choi, Oleksiy V. Agapitov, Nina Bizien, Thierry Dudok de Wit, Lucas Colomban
Figure caption: KHI growth rate and directional distribution of plasma parameters. The “+” symbols mark the magnetic fields (B1 and B2, black), their difference (﹩{\rm{\Delta }}{\boldsymbol{B}}={{\boldsymbol{B}}}_{1}-{{\boldsymbol{B}}}_{2}﹩, blue), and the velocity difference (﹩{\rm{\Delta }}{\boldsymbol{V}}={{\boldsymbol{V}}}_{1}-{{\boldsymbol{V}}}_{2}﹩, green). The gray curve outlines the plane defined by B1, B2, and ﹩{\rm{\Delta }}{\boldsymbol{B}}﹩. The background color map shows the KHI threshold (see Equation (2)) as a function of wavevector direction in spherical coordinates (ϕ, θ). Note that the threshold retains physical units; its numerical magnitude is not physically relevant, and only the sign matters. Star symbols in three colors indicate the wavevector directions of surface waves in Figure 2 at 1.5 (yellow), 2.2 (cyan), and 3 Hz (magenta), each showing both forward and backward propagation.
Paper: Surface Waves at Switchback Boundaries in the Young Solar Wind from Parker Solar Probe Observations
Authors: Kyung-Eun Choi, Oleksiy V. Agapitov, Nina Bizien, Thierry Dudok de Wit, Lucas Colomban
Figure caption: Summary of four additional surface wave events at SB boundaries. The first two events (panels (a)–(h)), observed at 15:10 UT on 2018 October 31 (O. V. Agapitov et al. 2023) and 02:22 UT on 2018 November 8 (W. M. Farrell et al. 2020), are located in unstable regions of the KH threshold test, while the latter two events (panels (i)–(p)), observed at 19:44 and 19:53 UT on 2018 October 31 (O. V. Agapitov et al. 2023), are in stable regions. Each column corresponds to one event and consists of (from top to bottom): the magnetic field components, the wavelet power spectral density (PSD), the hodograph in the minimum variance plane, and the directional dependence of the KHI threshold. In the PSD panels, contours indicate intervals that satisfy the linear surface wave, ∣ellipticity∣ < 0.5 and θk·n > 60°. Hodographs correspond to selected waves at 2, 1.4 (1.15 and 1.6), 1.2, and 1.75 Hz. Black arrows indicate the primary waves; light gray and gray arrows in the second event show additional components. Open and filled circles mark the hodograph’s start and end points. The corresponding wavevectors are shown in the KH threshold panels using matching star symbols, consistent with Figure 3.
Paper: Mitigating Eddington and Malmquist Biases in Latent-inclination Inference of the Tully–Fisher Relation
Authors: Hai Fu
Figure caption: MCMC-sampled posterior PDFs from the forward (blue) and the inverse (red) models for samples A, B, and C, respectively. For simplicity, only the two TF parameters are shown: slope (β) and intercept (γ). In all posterior plots, the contours in the joint PDFs enclose 68% and 95% of the volume, the highlighted regions in the marginalized PDFs show the credible intervals defined by the 16th and the 84th percentiles, and the black crosses indicate the truth. Biases appear whenever the data contains scattering in the model-assumed independent variable.
Paper: Mitigating Eddington and Malmquist Biases in Latent-inclination Inference of the Tully–Fisher Relation
Authors: Hai Fu
Figure caption: The distribution of mass vs. projected line width of the input data (a) is compared with the distributions of larger samples simulated using the best-fit parameters of the forward model (b) and the inverse model (d). The contours enclose bins containing a minimum of nine objects in panel (a). In this example, the input data are from sample A, so only the forward model inferred the correct TF parameters. However, both models fit the distribution of the data equally well according to the residuals in panels (c) and (e). This result shows that it is difficult to use a goodness-of-fit parameter such as the reduced χ2 to select the correct model.
Paper: Mitigating Eddington and Malmquist Biases in Latent-inclination Inference of the Tully–Fisher Relation
Authors: Hai Fu
Figure caption: MCMC-sampled posterior PDFs from the inverse model after iterative corrections of the general Eddington bias. Sample C is used as the input data. Iteration 0 is before any bias correction, so it is the same as the posterior of the inverse model in Figure 2(c). In subsequent iterations, ﹩{\{{\tilde{w}}_{k}\}}_{k=1}^{N}﹩ are bias corrected using Equation (51). The process converges after just a few iterations, but unbiased parameter inference is achieved only when the correct σm is specified.
Paper: Mitigating Eddington and Malmquist Biases in Latent-inclination Inference of the Tully–Fisher Relation
Authors: Hai Fu
Figure caption: Distributions of simulated sample C in baryonic mass vs. projected line width (left) and baryonic mass vs. redshift (right). The dashed line in the left panel shows the prescribed TFR, and the dashed curve in the right panel shows the mass limit as a function of redshift.
Paper: Searching for Continuous Gravitational Waves in the Parkes Pulsar Timing Array Data Release 3
Authors: Shi-Yi Zhao, Zu-Cheng Chen, Jacob Cardinal Tremblay, Boris Goncharov, Xing-Jiang Zhu, N. D. Ramesh Bhat, Małgorzata Curyło, Shi Dai, Valentina Di Marco, Hao Ding, George Hobbs, Agastya Kapur, Wenhua Ling, Tao Liu, Rami Mandow, Saurav Mishra, Daniel J. Reardon, Christopher J Russell, Ryan M. Shannon, Shuangqiang Wang, Lei Zhang, Andrew Zic
Figure caption: The posterior distributions of the CGW parameters show a clear peak at low frequency before taking the CRN into account (orange), which disappears after the inclusion of a CRN in the analysis (black). The green line is the prior distribution.
Paper: Searching for Continuous Gravitational Waves in the Parkes Pulsar Timing Array Data Release 3
Authors: Shi-Yi Zhao, Zu-Cheng Chen, Jacob Cardinal Tremblay, Boris Goncharov, Xing-Jiang Zhu, N. D. Ramesh Bhat, Małgorzata Curyło, Shi Dai, Valentina Di Marco, Hao Ding, George Hobbs, Agastya Kapur, Wenhua Ling, Tao Liu, Rami Mandow, Saurav Mishra, Daniel J. Reardon, Christopher J Russell, Ryan M. Shannon, Shuangqiang Wang, Lei Zhang, Andrew Zic
Figure caption: Posterior distributions of a power-law CRN parameter recovered using PPTA DR3 with (black) or without (orange) a CGW signal included in the analysis.