adsabs/AstroCLIMB huggingface dataset:
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Numerical experiment to show the demodulated Stokes Q/I spectra of SE204-4 with two different thresholds for the polarimetric demodulation by RPOS5 method mentioned in the text. Top panel: the Q/I spectra with a threshold 1/2I  c , the local continuum intensity in each of the dual beams. Bottom panel: the Q/I spectra with a threshold of 1/2I  c  + f × e × rms. rms is the root of the mean square of the noise fluctuation measured in the dark regions in the top and bottom rows without spectral lines for each frame shown in Figure 2, e the natural number, and f an adjustable factor, assigned to be 1.2 here. It is easily noted that the polarimetric background noise caused by the fluctuations is greatly reduced as the threshold increases, while more and more continuum intensity points are excluded.
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Six sets of Stokes I and their corresponding Q/I profiles of the green coronal line. Five of them drawn from the demodulated Q/I image of SE206-6 shown in Figure 11, the other one in the top-left panel shows Stokes I and Q/I profiles as dashed–dotted curves demodulated before the binning as a comparison.
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Four sets of Stokes I and their corresponding Q/I profiles drawn from SE204-4 with the wavelength range of 516.5–518.9 nm. This group shows the complexity in Q/I profile shapes of the magnesium triplet as well as other lines.
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Two sets of Stokes I and their corresponding Q/I profiles of P17 (left panel) and P18 (middle panel) drawn from SE206-6 without binning, depicted in the wavelength range of 516.5–518.9 nm, and the profiles after spatial binning of these two points are depicted in the right panel.
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Stokes I and their corresponding Q/I images of SE206-6 demodulated after binning over three pixels along the dispersion, which seems a contraction along the dispersion of that shown in Figure 7. The binning along the dispersion aims at promoting the polarimetric sensitivity.
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Five sets of Stokes I and their corresponding Q/I profiles drawn from the demodulated Q/I image of SE205-2 shown in Figure 7. They are produced again, respectively, from the 4th, the 9th, 14th, 19th, and 24th points.
Paper: Complexity of the Upper Solar Atmosphere Revealed from Spectropolarimetry during a Solar Eclipse
Authors: Z. Q. Qu, L. Chang, G. T. Dun, Z. Xu, X. M. Cheng, L. H. Deng, X. Y. Zhang, Y. H. Jin
Figure caption: Five sets of Stokes I and their corresponding Q/I profiles drawn from the demodulated Q/I image of SE204-6 shown in Figure 7. From top to bottom: the 4th point P4; the 9th point, P9; the 14th point, P14; the 19th point P19; and the 24th point P24.
Paper: Millisecond Pulsars in Dense Star Clusters: Evolution, Scaling Relations, and the Galactic-center Gamma-Ray Excess
Authors: Claire S. Ye, Giacomo Fragione
Figure caption: Gamma-ray luminosity per cluster mass as a function of the cluster mass. We show gamma-ray luminosities from multiple time steps, within 2 Gyr until either the time of cluster disruption or Hubble time. We only show model clusters that survived to the present day or dissolved after 7 Gyr, which is about the age of the known youngest globular clusters in the Milky Way (Dotter et al. 2010; Forbes & Bridges 2010; Dotter et al. 2011; VandenBerg et al. 2013; Kruijssen et al. 2019, and references therein).
Paper: Millisecond Pulsars in Dense Star Clusters: Evolution, Scaling Relations, and the Galactic-center Gamma-Ray Excess
Authors: Claire S. Ye, Giacomo Fragione
Figure caption: Numbers of MSPs for models with different initial numbers of stars N and virial radii R  v . All models have metallicity Z = 0.002 and Galactocentric distance R  g  = 8 kpc. From left to right the panels show the numbers of MSPs at 0.1, 1.0, 5.0, and 11.5 Gyr, respectively.
Paper: Millisecond Pulsars in Dense Star Clusters: Evolution, Scaling Relations, and the Galactic-center Gamma-Ray Excess
Authors: Claire S. Ye, Giacomo Fragione
Figure caption: Top panel: the average number of MSPs for models with different initial numbers of stars, N, as a function of the cluster age. The solid curves represent the model averages, while the black dashed curves show the fits from Equation (3). Bottom panel: the markers and the black dashed lines show the polynomial parameters and their fits from Equation (3), respectively.
Paper: Millisecond Pulsars in Dense Star Clusters: Evolution, Scaling Relations, and the Galactic-center Gamma-Ray Excess
Authors: Claire S. Ye, Giacomo Fragione
Figure caption: Similar to Figure 3, but only for models with a Galactocentric distance R  g  = 2 kpc.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: Top panel shows the bar strength evolution with time; middle panel shows the buckling strength evolution with time (here the plots are shifted by arbitrary value for clear comparison) and bottom panel shows evolution of the boxy/peanut strength of bars in all the spinning models.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption:  σ  z /σ  R  evolution with time clearly shows that after first jump the tendency of having second jumps increases with increasing spin case. S075 model clearly show a second jump indicating second buckling, which can be clearly seen in buckling strength (middle panel of Figure 3). S100 model does not show an appreciable jump but it shows second buckling.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: Angular momentum gained by halos within 20 kpc cylindrical region for all the spinning halo models. Faster-spinning halos have larger angular momentum in the triggering phase of bar while trend reverses during secular evolution.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: Evolution of the bar pattern speed for all the spinning halo models are hereby shown.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: Left panel shows the fitted isophotes with increasing semimajor axis along with density map of face-on disk; middle panel shows the ellipticity of all the isophotes with increasing semimajor axis; right panel shows the position angle of the isophotes with increasing semimajor axis. Here the solid lines correspond to a max and dashed lines correspond to a min, which is denoting the length of bars with maximum ellipticity and minimum ellipticity values such that position angle of isophotes change within 10°.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: Figure shows density projection maps at end of simulations (t = 9.78 Gyr) for all the increasing prograde halo spin models; namely XY, XZ, and YZ density maps. XY density map clearly shows that bar changes its shape from square to farfalle as the halo spin increases. Bottom right map corresponds to counterrotating spinning model.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: This figure shows time evolution of bar strength for the high-spin models (Spin100). This clearly shows dark matter halo having all prograde orbits within 80 kpc radius region suppresses the bar strength in secular evolution phase as seen in previous studies (Long et al. 2014; Collier et al. 2018).
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: This plots shows the face-on line profile parallel to the bar major axis at three lines: at the center of bar (solid line), y = 4 kpc line (dashed line), and y = −4 kpc line (dotted line). It shows clearly that as the spin of halo increases, X-shape feature in face-on bar becomes more prominent while the counterrotating spin model does not show this feature.
Paper: Effects of Inner Halo Angular Momentum on the Peanut/X Shapes of Bars
Authors: Sandeep Kumar Kataria, Juntai Shen
Figure caption: This figure shows the variation of prograde fraction with radius for all our models.
Paper: Modeling Gamma-Ray Burst Afterglow Observations with an Off-axis Jet Emission
Authors: N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia, M. G. Dainotti, S. Dichiara, P. Veres, R. L. Becerra, A. C. Caligula do E. S. Pedreira
Figure caption: Synchrotron light curves generated by the deceleration of the off-axis jet for k = 1.5 (left) and 2 (right). Panels from top to bottom correspond to the radio (1.6 GHz), optical (R), and X-ray (1 keV) bands, respectively. The parameters E = 1051 erg, ε B = 10−4, ε e = 10−1, Γ = 300, p = 2.2, ζ  e  = 1.0, and d  z  = 6.6 Gpc are used.
Paper: Modeling Gamma-Ray Burst Afterglow Observations with an Off-axis Jet Emission
Authors: N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia, M. G. Dainotti, S. Dichiara, P. Veres, R. L. Becerra, A. C. Caligula do E. S. Pedreira
Figure caption: Synchrotron light curves generated by the deceleration of the off-axis jet for k = 0 (left) and 1 (right). Panels from top to bottom correspond to the radio (1.6 GHz), optical (R), and X-ray (1 keV) bands, respectively. The parameters E = 1051 erg, ε B = 10−4, ε e = 10−1, Γ = 300, p = 2.2, ζ  e  = 1.0, and d  z  = 6.6 Gpc are used.
Paper: Modeling Gamma-Ray Burst Afterglow Observations with an Off-axis Jet Emission
Authors: N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia, M. G. Dainotti, S. Dichiara, P. Veres, R. L. Becerra, A. C. Caligula do E. S. Pedreira
Figure caption: Comparison of numerical simulations (solid lines) and a theoretical approach (dashed lines) of the Doppler factor (δ D), the jet’s opening angle θ j, the bulk Lorentz factor (Γj), and the deceleration radius (r), all of them evolving in a constant circumburst medium. Variables observed off- and on-axis are in black and gray, respectively. The parameters E = 1054 erg, n = 1.0 cm−3, Δθ = 5.°72, and Γ0 = 300 are used.
Paper: Modeling Gamma-Ray Burst Afterglow Observations with an Off-axis Jet Emission
Authors: N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia, M. G. Dainotti, S. Dichiara, P. Veres, R. L. Becerra, A. C. Caligula do E. S. Pedreira
Figure caption: Corner plot of the parameters derived from fitting the multiwavelength light curves of GRB 080503 with a synchrotron off-axis model, together with median values (green lines) and 1σ credible intervals (dashed lines). The MCMC summary statistics for all parameters are listed in Table 5. A set of normal distributions is made for the priors.
Paper: Modeling Gamma-Ray Burst Afterglow Observations with an Off-axis Jet Emission
Authors: N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia, M. G. Dainotti, S. Dichiara, P. Veres, R. L. Becerra, A. C. Caligula do E. S. Pedreira
Figure caption: The X-ray and optical light curves of GRB 080503 with the best-fit curve of synchrotron afterglow model. The synchrotron light curves are shown at 1 keV (gray), the R band (red), and the g band (green). Data points are taken from Perley et al. (2009).
Paper: Modeling Gamma-Ray Burst Afterglow Observations with an Off-axis Jet Emission
Authors: N. Fraija, A. Galvan-Gamez, B. Betancourt Kamenetskaia, M. G. Dainotti, S. Dichiara, P. Veres, R. L. Becerra, A. C. Caligula do E. S. Pedreira
Figure caption: Same as Figure 4 but for GRB 140903A.
Paper: An Atmospheric Retrieval of the Brown Dwarf Gliese 229B
Authors: Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales, Daniella Bardalez-Gagliuffi, Johanna M. Vos, Marina Gemma, Niall Whiteford, Josefine Gaarn
Figure caption: Best Fit Model: Cloudless, Allard Alkalies Figures on the top line show (a) retrieved temperature-pressure profile and (b) contribution function for the best fit model. Our maximum likelihood retrieved profile is shown in black compared to the cloudless Sonora Solar model profiles (green and blue) at similar temperature to our retrieved T eff . Dashed lines show condensation curves for possible cloud species. In the bottom panel (c) we show retrieved uniform-with-altitude mixing abundances as compared to thermo-chemical equilibrium grid abundances for supersolar C/O (1.0 relative to 0.55 of Asplund et al. 2009) and Solar metallicity. In both figures (a) and (c), we show the approximate location of the photosphere as determined by our contribution function with the inclusion of a light gray panel.(The data used to create this figure are available.)
Paper: An Atmospheric Retrieval of the Brown Dwarf Gliese 229B
Authors: Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales, Daniella Bardalez-Gagliuffi, Johanna M. Vos, Marina Gemma, Niall Whiteford, Josefine Gaarn
Figure caption: Best Fit Model with a Mass Constraint (a) Retrieved temperature-pressure profile for the mass constrained cloudless model with Allard alkalies as compared against Sonora model profiles (blue and green) and the retrieved profile from our best fit model (purple). Again, we show the approximate location of the photosphere as determined by our contribution function with the inclusion of a light gray panel. Figures (b) and (c) show the maximum likelihood retrieved spectrum for the mass constrained model in yellow compared against observed data (black) and the maximum likelihood retrieved spectrum from the best fit model (purple). In red, we show flux uncertainty on this retrieved spectrum.
Paper: An Atmospheric Retrieval of the Brown Dwarf Gliese 229B
Authors: Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales, Daniella Bardalez-Gagliuffi, Johanna M. Vos, Marina Gemma, Niall Whiteford, Josefine Gaarn
Figure caption: Best Fit Model: Cloudless, Allard Alkalies On top (a) and (b) show the median and maximum likelihood retrieved spectra as compared to observed data for the best model. In red, we show flux uncertainty on this retrieved spectrum. In the bottom panel (c) and (d), we show the maximum likelihood retrieved spectrum and observed spectrum as compared to Sonora grid models of Solar metallicity and C/O at 750, 850, 950 K (blue and green).(The data used to create this figure are available.)
Paper: An Atmospheric Retrieval of the Brown Dwarf Gliese 229B
Authors: Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales, Daniella Bardalez-Gagliuffi, Johanna M. Vos, Marina Gemma, Niall Whiteford, Josefine Gaarn
Figure caption: Distanced-calibrated SED for Gl 229B using the spectra listed in Table 2 and photometry listed in Table 1. Spectrum calibration was done using the techniques of Filippazzo et al. (2015), updated with a system parallax measurement from Gaia Collaboration et al. (2021). Pink lines show the wavelength coverage of each photometric band and photometric points are each plotted at their respective band center. Underlying red shows the flux uncertainty which was calculated using SNR = 10.(The data used to create this figure are available.)
Paper: An Atmospheric Retrieval of the Brown Dwarf Gliese 229B
Authors: Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales, Daniella Bardalez-Gagliuffi, Johanna M. Vos, Marina Gemma, Niall Whiteford, Josefine Gaarn
Figure caption: Best Fit Model: Cloudless, Allard Alkalies Posterior probability distributions for retrieved gas abundances, mass, radius and derived quantities of the best fit cloudless model with Allard alkalies. Far right diagonal plots show marginalized posteriors for each parameter along with 2D parameter correlation histograms. Dashed lines show the median retrieved value (reported value) along with 1σ confidence intervals. Gas abundances are in units of dex.
Paper: An Atmospheric Retrieval of the Brown Dwarf Gliese 229B
Authors: Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales, Daniella Bardalez-Gagliuffi, Johanna M. Vos, Marina Gemma, Niall Whiteford, Josefine Gaarn
Figure caption: Best Fit Model with a Mass Constraint Posterior probability distributions for retrieved gas abundances and derived fundamental parameters of the mass constrained model shown in blue, as compared to probability distributions from the best fit model (green). As before, marginalized posteriors are plotted on the main diagonal with 2D correlations between each parameters shown. Gas abundances are given in units of dex.
Paper: ALMA Observations of the HD 110058 Debris Disk
Authors: Antonio S. Hales, Sebastián Marino, Patrick D. Sheehan, Silvio Ulloa, Sebastián Pérez, Luca Matrà, Quentin Kral, Mark Wyatt, William Dent, John Carpenter
Figure caption: The 13CO(3–2) channel maps for HD 110058, best-fit disk model (middle), and residuals (bottom). Contour levels are the same as in Figure 14. The rms per channel is 1.53 mJy beam−1.
Paper: ALMA Observations of the HD 110058 Debris Disk
Authors: Antonio S. Hales, Sebastián Marino, Patrick D. Sheehan, Silvio Ulloa, Sebastián Pérez, Luca Matrà, Quentin Kral, Mark Wyatt, William Dent, John Carpenter
Figure caption: The 13CO(2–1) channel maps for HD 110058, best-fit disk model (middle), and residuals (bottom). Contour levels are the same as in Figure 14. The rms per channel is 0.73 mJy beam−1.
Paper: Redshift-evolutionary X-Ray and UV Luminosity Relation of Quasars from Gaussian Copula
Authors: Bao Wang, Yang Liu, Zunli Yuan, Nan Liang, Hongwei Yu, Puxun Wu
Figure caption: One-dimensional likelihood distributions and two-dimensional contours at 1σ and 2σ CLs; α, β, and γ are the coefficients of the luminosity relation and δ is the intrinsic dispersion. Left panel shows the result from the ΛCDM with Ω m0 = 0.3, and right panel shows the result from the ΛCDM with Ω m0 = 0.4. Blue and orange contours represent the three-dimensional and standard relations, respectively.
Paper: Redshift-evolutionary X-Ray and UV Luminosity Relation of Quasars from Gaussian Copula
Authors: Bao Wang, Yang Liu, Zunli Yuan, Nan Liang, Hongwei Yu, Puxun Wu
Figure caption: One-dimensional likelihood distributions and two-dimensional contours at 1σ and 2σ CLs from quasar data with the simultaneous fitting method; α, β, and γ are the coefficients of the luminosity relation, δ is the intrinsic dispersion, and Ω m0 is the present dimensionless matter density parameter.
Paper: Redshift-evolutionary X-Ray and UV Luminosity Relation of Quasars from Gaussian Copula
Authors: Bao Wang, Yang Liu, Zunli Yuan, Nan Liang, Hongwei Yu, Puxun Wu
Figure caption: The comparison of the relations of ﹩\mathrm{log}({L}_{X})﹩-﹩\mathrm{log}({L}_{{UV}})﹩ and ﹩\mathrm{log}(L{{\prime} }_{X})﹩-﹩\mathrm{log}({L}_{{UV}})﹩ with quasar data points, where ﹩\mathrm{log}({L}_{X})^{\prime} =\mathrm{log}({L}_{X})-\alpha \mathrm{ln}(z+5)﹩. The solid lines are luminosity relations calibrated by using the ΛCDM model with Ω m0 = 0.3 from the low-redshift quasar data.
Paper: Redshift-evolutionary X-Ray and UV Luminosity Relation of Quasars from Gaussian Copula
Authors: Bao Wang, Yang Liu, Zunli Yuan, Nan Liang, Hongwei Yu, Puxun Wu
Figure caption: Hubble diagrams of quasars calibrated by low-redshift quasar data, which denotes the relation between the distance modulus μ and redshift z. The orange dots and the blue dots are derived from the standard relation and the three-dimensional and redshift-evolutionary one, respectively. The dashed red line corresponds to z = 2.
Paper: Redshift-evolutionary X-Ray and UV Luminosity Relation of Quasars from Gaussian Copula
Authors: Bao Wang, Yang Liu, Zunli Yuan, Nan Liang, Hongwei Yu, Puxun Wu
Figure caption: Constraint on Ω m0 of the ΛCDM model from the high-redshift quasar data. The vertical axis represents the probability density. The left and right panels represent the results from the ΛCDM model with Ωm0 = 0.3 and 0.4, respectively.
Paper: Charting Galactic Accelerations with Stellar Streams and Machine Learning
Authors: Jacob Nibauer, Vasily Belokurov, Miles Cranmer, Jeremy Goodman, Shirley Ho
Figure caption: Illustration of our method and the underlying workflow. We plot an on-sky projection of a mock stellar stream in the bottom left panel (A). The arrows in this panel indicate the projected velocity vectors of stars along the stream, where the n th star has velocity  v   n . An ordering is then assigned to stars that populate the stream, as illustrated in the top left panel (B). Stars are color coded by the phase angle γ, which increases monotonically along the stream from the trailing to the leading tail. The positions and velocities of stars along the stream are then fed to a neural network (C), which parameterizes a mean track,  x   θ (γ), through the position of stream stars along with a track speed. Both the stream track and track speed are parameterized in terms of the γ parameter. These fits can be differentiated and combined through Equation (6) to estimate the Cartesian components of the acceleration vector along the stream directly. An intermediate model for the galactic potential never needs to be specified. During training of the neural network, we encourage the condition that the tangent vector to the stream track falls roughly parallel to the local trajectory of stream stars. We also encourage a smooth acceleration field, by penalizing neural-network parameters that give rise to large local changes in accelerations along the stream track. An example output is illustrated in the bottom right panel (D), where each Cartesian component of the stellar stream is “unwrapped” along the γ-axis and color coded by the inferred accelerations.