adsabs/AstroCLIMB huggingface dataset:
Paper: Cosmic-Ray North–South Anisotropy: Rigidity Spectrum and Solar Cycle Variations Observed by Ground-based Muon Detectors
Authors: M. Kozai, Y. Hayashi, K. Fujii, K. Munakata, C. Kato, N. Miyashita, A. Kadokura, R. Kataoka, S. Miyake, M. L. Duldig, J. E. Humble, K. Iwai
Figure caption: Result of the spectrum estimation for various combinations of the hyperparameters, b                      ﹩[{\mathrm{log}}_{10}﹩(GV)] and σ  G  [%], in the Gaussian process. The coefficient of determination, R 2, of the mean spectrum Θ for the observed values ﹩{\eta }_{l}^{{TA}}﹩'s in the sample year 2015 is displayed as a function of b for each σ  G .
Paper: Cosmic-Ray North–South Anisotropy: Rigidity Spectrum and Solar Cycle Variations Observed by Ground-based Muon Detectors
Authors: M. Kozai, Y. Hayashi, K. Fujii, K. Munakata, C. Kato, N. Miyashita, A. Kadokura, R. Kataoka, S. Miyake, M. L. Duldig, J. E. Humble, K. Iwai
Figure caption: Rigidity spectrum of the parallel mean free path length [au] of GCRs for 2009 (solar activity minimum) and 2015 (maximum). The gray shaded area is the “Palmer consensus” region, mainly by SEP observations.
Paper: Cosmic-Ray North–South Anisotropy: Rigidity Spectrum and Solar Cycle Variations Observed by Ground-based Muon Detectors
Authors: M. Kozai, Y. Hayashi, K. Fujii, K. Munakata, C. Kato, N. Miyashita, A. Kadokura, R. Kataoka, S. Miyake, M. L. Duldig, J. E. Humble, K. Iwai
Figure caption: The black bold line is a differential response to the NS anisotropy for vertical channels of muon detectors at Husafell, Iceland (virtual detector), and Syowa Station. Other solid lines are repeats of those in Figure 2.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Transmission spectra of Saturn-like planets (M P = 0.3M jup, R 10bar = R jup, where R 10bar is the planetary radius at P = 10 bar) with diamond hazes around a Sun-like star. The different colored lines show the spectra for different values of the column-integrated seed production rate. The gray dashed line denotes the spectrum with a clear atmosphere. The colored shaded regions denote the contribution of absorption by diamond hazes, defined as the arising difference if we remove the absorption opacity of diamond hazes from total (absorption+scattering) diamond haze opacity. The right panel zooms in the wavelength range of 3–4 μm, where the hydrogenated diamonds produce a spectral feature at 3.53 μm, as indicated by the black dashed line. The spectra for F haze = 10−11–10−9 g cm−2 s−1 have been shifted for the sake of clarity. In this figure, I used the atmospheric TP and chemical profiles for T eq = 1000 K, [M/H] = 1.0, and C/O = 0.5. This figure adopts the optical constants of hydrogenated diamonds (A. P. Jones & N. Ysard 2022). The spectra with diamond hazes containing impurities are shown in Appendix A. Note that this figure vertically shifts each spectrum by adding constant offsets for clarification purpose.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Vertical distributions of haze mass mixing ratio (left), mass flux (middle), and volume mixing ratio of CH3 (right). Each colored line shows the profile for different equilibrium temperature. Solid and dashed lines show the profiles of CVD diamonds and soot, respectively. The solid and dashed green lines in the middle panel denote the column-integrated production rate of diamond and soot seeds. The haze mass flux should converge to the column-integrated rate if diamond and soot growth/decomposition do not occur, as seen in the diamond mass flux for T eq = 400 K. Atmospheric compositions are set to [M/H] = 1.0 and C/O = 0.5 in this figure.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Atmospheric TP profiles (left panel) and brightness temperature spectrum (right panel) of Saturn-like planets with T eq = 1000 K and surface gravity of g = 10 m s−2 around a generic M-dwarf represented by GJ 1214 (T eff = 3250 K). The different colored lines show the profiles for different values of the column-integrated haze production rate. I have used the optical constants of hydrogenated diamonds (A. P. Jones & N. Ysard 2022). The gray dashed lines denote the profile of clear atmospheres, while the reddish colored dashed–dotted lines in the right panel show the TP profiles with soot optical constants (P. Lavvas & T. Koskinen 2017) for reference.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: 2D diagram of haze compositions and mass flux. Left column shows the volume fraction of CVD diamond ﹩{{ \mathcal F }}_{\mathrm{dia}}﹩ in atmospheric column at P < 1 mbar as a function of atmospheric C/O ratio and equilibrium temperature. The right column shows the mass flux of total (soot+diamond) haze particles measured at 1 mbar. From top to bottom, each row shows the diagram for atmospheric metallicity of [M/H] = 1.0, 2.0, and 3.0, respectively.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Geometric albedo spectrum with photochemical hazes. Different colored lines in the left panel show the albedo spectra for various values of the column-integrated haze production rate, where I have adopted the optical constants of hydrogenated diamonds. The right panel shows the albedo spectra for various choices of representative haze optical constants. All albedo spectra are calculated for the atmospheric profile of T eq = 1000 K, [M/H] = 1.0, and C/O = 0.5. The gray dashed lines show the albedo spectrum for a clear atmosphere for reference.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Same as Figure 5, but varying atmospheric metallicity (top row) or C/O ratio (bottom row). The equilibrium temperature is fixed to T eq = 1000 K. I set the atmospheric C/O and metallicity to be C/O = 0.5 and [M/H] = 1.0 as fiducial values when the parameter is not varied.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Real (top) and imaginary (middle) parts of refractive indices for several materials that potentially represent exoplanetary hazes. The bottom panel shows the single scattering albedo of a particle with a radius of 0.03 μm for each substance, as computed by the Mie theory code PyMieScatt (B. J. Sumlin et al. 2018). Refractive indices are taken from E. D. Palik (1985) for pure diamond, A. P. Jones & N. Ysard (2022) for hydrogenated diamonds, H. Mutschke et al. (2004) for meteoric diamonds, B. N. Khare et al. (1984) for Titan tholin, P. Lavvas & T. Koskinen (2017) for soot, and C. He et al. (2024) for exoplanetary tholin.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Vertical distributions of haze mass mixing ratio for different values of particle eddy diffusion coefficient (top) and column-integrated seed production rate (bottom). The solid and dashed lines show the vertical profiles of CVD diamonds and soot, respectively.
Paper: Photochemical Hazes in Exoplanetary Skies with Diamonds: Microphysical Modeling of Haze Composition Evolution via Chemical Vapor Deposition
Authors: Kazumasa Ohno
Figure caption: Same as Figure 4, but for T eq = 1400 K, [M/H] = 1.0, and C/O = 1.3. The right panel shows the volume mixing ratios of CO, CH4, CH3, and C2H2 at each pressure level instead of mean particle size. This carbon-rich atmosphere with hot temperature leads to soot-dominated hazes at P ≳ 10−4 bar.
Paper: Unveiling the Emission Mechanisms of Blazar PKS 1510-089. I. Multiwavelength Variability
Authors: Alfredo Amador-Portes, Abigail García-Pérez, Vahram Chavushyan, Victor M. Patiño-Álvarez
Figure caption: Cross-correlation plots for the Hβ emission line and λ5100 Å continuum light curves, categorized by disk dominance (DD) and jet dominance (JD) regimes as detailed in Section 2.1. Top row: jet dominance light curves. Bottom row: disk dominance (DD) light curves. Columns: left column: Interpolation Method (ICCF). Middle column: discrete cross-correlation function (DCCF). Right column: Z-transformed discrete correlation function (ZDCF). Gray lines depict significance levels at 90%, 95%, and 99%.
Paper: Unveiling the Emission Mechanisms of Blazar PKS 1510-089. I. Multiwavelength Variability
Authors: Alfredo Amador-Portes, Abigail García-Pérez, Vahram Chavushyan, Victor M. Patiño-Álvarez
Figure caption: Spectroscopic light curves for the disk dominance regime. (a) Hβ emission line flux, and (b) the λ5100 Å continuum flux. The λ5100 Å continuum data points added from the V-band observations (Section 2.2) are excluded from this light curve, focusing solely on spectroscopic measurements in the disk dominance regime.
Paper: Unveiling the Emission Mechanisms of Blazar PKS 1510-089. I. Multiwavelength Variability
Authors: Alfredo Amador-Portes, Abigail García-Pérez, Vahram Chavushyan, Victor M. Patiño-Álvarez
Figure caption: Photometric light curves. (a) γ-ray data from Fermi/LAT (0.1–300 GeV), (b) X-ray data from Swift/XRT (0.3–10 keV), (c) V band from SO and SMARTS, (d) J band from SMARTS, (e) 1 mm data from SMA, (f) Radio data at 15 GHz from OVRO, and (g) Optical polarization degree from SO and (h) Optical polarization angle from SO.
Paper: Unveiling the Emission Mechanisms of Blazar PKS 1510-089. I. Multiwavelength Variability
Authors: Alfredo Amador-Portes, Abigail García-Pérez, Vahram Chavushyan, Victor M. Patiño-Álvarez
Figure caption: Radio light curves. (a) 1 mm, (b) 15 GHz, and (c) spectral index between 15 GHz and 1 mm (230 GHz). In panel (c), green points indicate estimations through interpolation, while black squares denote available quasi-simultaneous epochs. The horizontal dotted–dashed line represents α = 0.
Paper: Unveiling the Emission Mechanisms of Blazar PKS 1510-089. I. Multiwavelength Variability
Authors: Alfredo Amador-Portes, Abigail García-Pérez, Vahram Chavushyan, Victor M. Patiño-Álvarez
Figure caption: Relation between λ5100 Å and V-band flux for quasi-simultaneous observations at the SO. The blue dashed line represents the best linear fit, and the shaded area has its uncertainty at 1σ.
Paper: Unveiling the Emission Mechanisms of Blazar PKS 1510-089. I. Multiwavelength Variability
Authors: Alfredo Amador-Portes, Abigail García-Pérez, Vahram Chavushyan, Victor M. Patiño-Álvarez
Figure caption: Spectroscopic light curves. (a) Hβ emission line flux, (b) Hγ emission line flux, (c) λ5100 Å continuum flux, and (d) NTD parameter. The red dashed–dotted and blue dashed lines in panel (d) represent NTD = 1 and NTD = 2, respectively.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Projections (left) and measured RVs (right) of simulated spectra of a rotating star with a single spot. Left: the ﹩\langle {{\boldsymbol{I}}}_{\bullet },{\boldsymbol{f}}\rangle ﹩ (x-axis) and ﹩\langle {{\boldsymbol{I}}}_{\circlearrowleft },{\boldsymbol{f}}\rangle ﹩ (y-axis) for each simulation arranged by spot radius (horizontally) and spot latitude (vertically). Dotted gray lines outline the spot radius/latitude grid. Each spectrum was projected onto the same vectors so that their relative values are significant. In this space, a rotating spot forms a coherent loop that varies in shape and size as a function of latitude and spot size. Right: measured RVs due to the spot where each time series has been artificially shifted to match the same grid layout as the left-hand plot. The size of the projected loop scales with the amplitude of the measured RV signal.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: RV corrections for simulations with an SNR of 500 of a single spot rotating in and out of view. Each time series spans exactly one stellar rotation. For scale, each row is accompanied by blue shading that represents 10 cm s−1. Left: measured RVs (black) and corrected RVs (orange) artificially shifted into a grid of spot radius (horizontally) and spot latitude (vertically). Each row is separated by 5 m s−1. Center: corrected RVs (orange) where each row is now separated by only 1 m s−1. Center and Right: corrected RVs (green) where no noise has been added.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Measured RVs (top) and spectral residuals (bottom) for examples of the three different test cases. Results are shown for the case of a single spot (left), multiple spots (center), and spot snapshots (right), i.e., where every spectrum is a completely independent realization of spots. Top: measured RVs from the simulated spectra. The top of each subplot shows the observed stellar surface associated with five example phases across the simulated observations. Bottom: zoomed-in residuals of the simulated spectra relative to the quiet, i.e., spotless, spectra for the five example phases in the corresponding color. The zoom-in is centered on the H-alpha line, which is known to be magnetically sensitive. Residuals are artificially offset vertically, with later phases appearing higher in the plot.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Surface maps and measured RVs (top) as well as projections into ﹩{{\boldsymbol{I}}}_{\bullet }﹩ and ﹩{{\boldsymbol{I}}}_{\circlearrowleft }﹩ space for the three different test cases. Points are colored by stellar rotation phase, which allows for the visible stellar surface and measured RV to be mapped to location in projection space. The projection axes have the same limits across the bottom row of subplots, meaning projections are directly comparable across the bottom row of subplots.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Top: changing Gaussian profiles. From left to right, the Gaussian profiles change in center, depth, width, and symmetry. To enact a change in symmetry, a bi-Gaussian profile is used. The dotted, green Gaussian, the same across all four plots, is the reference Gaussian profile used to construct the ﹩{{\boldsymbol{I}}}_{\beta }﹩ against which all other Gaussian profiles are projected. Bottom: normalized ﹩\langle {{\boldsymbol{I}}}_{\beta },{\boldsymbol{f}}\rangle ﹩ of different Gaussian profiles with an SNR of 1000. The projection of the example profiles shown in the top row with no noise added is shown in the corresponding color in the bottom row.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Projections onto vectors constructed to be sensitive to visible spot fraction, spot Doppler distortion, and pure Doppler shifts. Left/Center: normalized projections (y-axis) of simulated spectra of a rotating star with a single spot projected onto either ﹩{{\boldsymbol{I}}}_{\bullet }﹩ (left) or ﹩{{\boldsymbol{I}}}_{\circlearrowleft }﹩ (center) as a function of the stellar rotation phase (x-axis). Images of the visible stellar surface as a function of phase are inset along the top of each subplot. Right: normalized projections of a spectrum with an injected Doppler shift (x-axis) onto the constructed Doppler vector.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Observed spectra of the “quiet” (i.e., unspotted) solar surface (L. Wallace et al. 1998) in orange and observed spectra of the umbra of a sunspot (L. Wallace et al. 2005) in blue. Top: the full, optical range of the observed spectrum. Variations in the quiet and spot spectrum can be seen throughout the wavelength range. Bottom: Zoom-ins on smaller-wavelength ranges of the spectra. Even in regions where it appears the spectra are similar (see the first two subplots, with blue and yellow axes), there are significant deviations. The rightmost plot (red axes) highlights a wavelength region that exhibits large deviations.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Projections onto ﹩{{\boldsymbol{I}}}_{\beta }﹩ of PHOENIX models with different T eff (left) and log g (right) values. Black points show the projection for simulations for which the spectra were degraded to an SNR of 1000. Green vertical lines indicate the T eff/log g of the reference simulation used to construct ﹩{{\boldsymbol{I}}}_{\beta }﹩.
Paper: A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain
Authors: Lily L. Zhao, Megan Bedell, David W. Hogg, Rodrigo Luger
Figure caption: Projections (left) onto spot fraction/Doppler distortion vectors (i.e., ﹩{{\boldsymbol{I}}}_{\bullet }﹩ and ﹩{{\boldsymbol{I}}}_{\circlearrowleft }﹩ space) and the associated measured RVs (right) for six simulations of a stellar surface with multiple spots. The color of the projections on the left correspond to the RV time series on the right.
Paper: Photon Ring Polarimetry with Next-generation Black Hole Imaging. I. M87*
Authors: Aditya Tamar, Daniel C. M. Palumbo
Figure caption: Example of Stokes V transition in GRMHD as motivation for the geometric ring model. Top left: Stokes V snapshot from a MAD GRMHD simulation with a * = −0.5, a viewing inclination of 17°, and electron heating parameter R h = 160. Top middle: time-averaged Stokes V image from the same model. Top right: geometric model of two concentric rings of equal radius but different Gaussian thicknesses. Bottom row: corresponding real part of the interferometric quotient ﹩\breve{v}\equiv \tilde{V}/\tilde{I}﹩. The transition from direct image dominated (red) to photon ring dominated (blue) occurs at a radius in the (u,v) plane dependent on model parameters such as ring thickness and relative brightness; in this example, the GRMHD model’s photon ring causes a transition to negative ﹩\breve{v}﹩ on shorter baselines than this example geometric model.
Paper: Photon Ring Polarimetry with Next-generation Black Hole Imaging. I. M87*
Authors: Aditya Tamar, Daniel C. M. Palumbo
Figure caption: The behavior of total intensity (on log scale), LP, and CP as a function of baseline length ρ, measured in units of Giga λ (Gλ). Here ﹩{\bar{m}}_{01}=\breve{{m}_{01}}{e}^{-2i\theta }﹩ and arg(β 2,1) = −arg(β 2,0) = 45°, with the latter condition arising from Equation (13). For all the plots, it is assumed that σ 0 = 5 μas and σ 0/σ 1 = 15. For the LP and CP plots, it is assumed that ﹩{ \mathcal F }=5﹩.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Detected AGN fraction as a function of M ⋆, σ  v , SFR, and sSFR. The gray hexagons show the expectation value based on the best-fit model accounting for dependence on M ⋆, with error bars showing the expected 1σ distribution around the expectation value. The black circles show the observations. The model, which only accounts for a dependence on M ⋆, explains all of the dependencies of detected fractions as a function of other properties.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 10, with identical observations but a uniform Eddington ratio distribution model. This model does a poor job quantitatively explaining any dependence, though the qualitative trends are similar to those observed.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 10, with identical observations but an Eddington ratio distribution model that depends only on σ  v . This model explains many qualitative dependencies, but not as quantitatively well as the M ⋆ model.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 5, using the Uniform model.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 10, with identical observations but an Eddington ratio distribution model that depends only on sSFR. This model does a poor job quantitatively explaining any dependence except for that with sSFR.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 5, using the σ  v  model.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 10, with identical observations but an Eddington ratio distribution model that depends only on SFR. This model does a poor job quantitatively explaining any dependence except for that with SFR.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 5, using the sSFR model.
Paper: Radio Active Galactic Nuclei Activity in Low Redshift Galaxies Is Not Directly Related to Star Formation Rates
Authors: Arjun Suresh, Michael R. Blanton
Figure caption: Similar to Figure 5, using the SFR model.
Paper: Making the Invisible Visible: Magnetic Fields in Accretion Flows Revealed by X-Ray Polarization
Authors: Samuel Barnier, Chris Done
Figure caption: (a) The color map and contours shows the Faraday rotation for a depth τ LoS = 1 over the entire disk given a large-scale azimuthal magnetic field B  ϕ  = 107 G. The arrows show the direction of the magnetic field. The orange line shows the direction of the line of sight, fixed at an inclination angle of 30°. (b) Cross section of the accretion flow taken at the azimuth marked in green line in (a). The color map of (b) shows the Faraday rotation for escaping photons emitted at different depths inside the flow. (c) 3D view of the magnetic field structure and of the line-of-sight direction. (d) Surface luminosity weighted distribution of the Stokes parameter of all regions of the disk shown in (a).
Paper: Making the Invisible Visible: Magnetic Fields in Accretion Flows Revealed by X-Ray Polarization
Authors: Samuel Barnier, Chris Done
Figure caption: Faraday rotation as a function of the optical depth of the line of sight and strength of the magnetic field component parallel to the line of sight for different energies of the IXPE bandpass.