adsabs/AstroCLIMB huggingface dataset:
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Comparison of variations in HARPS-N ΔRV (upper panel) and in ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ (lower panel), in red, with those in average unsigned magnetic fluxes of the strong field ((fB)spot+(fB)plage + (fB)NE) (in green), and that including the weak IN field (in black) covering 609 days over 2015–2018. Note that (fB)s are normalized with their maximum values to enable a comparison of the relative variations.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Time evolution of relative areas, f/f all, and average unsigned magnetic fluxes, (fB)/(fB)all, over the 4 yr period of 2015 January–2018 December.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Relation between area fill factors (f  m , left panel) or total magnetic fluxes (right panel) of different magnetic features and their disk-averaged unsigned field strengths (∣B  m ∣).
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Sample images of SDO observables, from the 2015 January 1: radial magnetic fields from SDO/HMI LOS magnetogram (top left panel) and limb-darkening-corrected continuum intensity (top right panel), SDO/AIA intensities at 1600 Å  (middle left panel) and at 1700 Å  (middle right panel). The color bar for HMI continuum intensity and AIA images saturate at a value well below the maximum value. The lower panels show thresholded images for the same date: the left panel shows sunspots and all the rest of the magnetic fields based on the thresholding criteria of Haywood et al. (2016), while the right panel shows the non-sunspot fields separated into plage, network, and weak IN based on our new thresholding criteria explained in Section 3.1.
Paper: Non-universality of the Turbulent Spectra at Sub-ion Scales in the Solar Wind: Dispersive Effects versus the Doppler Shift
Authors: Fouad Sahraoui, Shiyong Huang
Figure caption: Distribution of spectral slopes in the inertial range and sub-ion scales of the solar wind (a and b) and magnetosheath (c and d) turbulence obtained from the Cluster spacecraft data. Figure adapted from Sahraoui et al. (2013) and Huang et al. (2014).
Paper: Non-universality of the Turbulent Spectra at Sub-ion Scales in the Solar Wind: Dispersive Effects versus the Doppler Shift
Authors: Fouad Sahraoui, Shiyong Huang
Figure caption: The ratio ω plas/ k · V  (color bar) is shown as a function of the propagation angle and the flow speed in the inertial range (a) and the dispersive range (b). The spectral slopes in the inertial range (c) and dispersive range (d) are shown as a function of the ratio ∣ω plas/ k  ·  V ∣. The red (blue) color indicates positive (negative) values of ω plas/ k  ·  V .
Paper: Non-universality of the Turbulent Spectra at Sub-ion Scales in the Solar Wind: Dispersive Effects versus the Doppler Shift
Authors: Fouad Sahraoui, Shiyong Huang
Figure caption: The initial wavenumber spectrum (a) and its transformation in the spacecraft frame for different propagation angles and plasma flow velocities (b).
Paper: Non-universality of the Turbulent Spectra at Sub-ion Scales in the Solar Wind: Dispersive Effects versus the Doppler Shift
Authors: Fouad Sahraoui, Shiyong Huang
Figure caption: Variability in the spectral slopes (color bar) as function of the propagation angle and the flow speed in the inertial range (a) and the dispersive range (b). The corresponding histograms are shown in (c) and (d), respectively.
Paper: Non-universality of the Turbulent Spectra at Sub-ion Scales in the Solar Wind: Dispersive Effects versus the Doppler Shift
Authors: Fouad Sahraoui, Shiyong Huang
Figure caption: Numerical solutions of Equation (2) for quasi-parallel (solid blue) and quasi-perpendicular (dashed blue) propagation compared to the linear dispersion relations (slow, Alfvén or intermediate, and fast modes) computed using the two-fluid theory.
Paper: Characterizing Pulsars Detected in the Rapid ASKAP Continuum Survey
Authors: Akash Anumarlapudi, Anna Ehlke, Megan L. Jones, David L. Kaplan, Dougal Dobie, Emil Lenc, James K. Leung, Tara Murphy, Joshua Pritchard, Adam J. Stewart, Rahul Sengar, Craig Anderson, Julie Banfield, George Heald, Aidan W. Hotan, David McConnell, Vanessa A. Moss, Wasim Raja, Matthew T. Whiting
Figure caption: Correlation plots looking for any intrinsic correlation between the pseudo-luminosity and the pulsar’s spin parameters, characteristic age, the surface dipole magnetic field strength, and other combinations of these parameters of interest (see Section 4.1). The distance to the pulsar is estimated using electron density models—the blue scatter plot shows the estimates using Yao et al. (2017) and the orange scatter plot shows the same using Cordes & Lazio (2002). A clear distinction between normal pulsars and recycled pulsars can be seen; especially in the spin parameter(s) space. No strong correlations between the pseudo-luminosity and any of the pulsar parameters are clearly seen. The bottom right plot shows the correlation between the radio luminosity (see Section 4.1) and the spindown luminosity. The black dashed line corresponds to 0.01% of spindown luminosity.
Paper: Characterizing Pulsars Detected in the Rapid ASKAP Continuum Survey
Authors: Akash Anumarlapudi, Anna Ehlke, Megan L. Jones, David L. Kaplan, Dougal Dobie, Emil Lenc, James K. Leung, Tara Murphy, Joshua Pritchard, Adam J. Stewart, Rahul Sengar, Craig Anderson, Julie Banfield, George Heald, Aidan W. Hotan, David McConnell, Vanessa A. Moss, Wasim Raja, Matthew T. Whiting
Figure caption: Comparison of the predicted flux densities at 150,  200, and 3000 MHz using the spectrum estimated with RACS and PSRCAT data, with the measured flux densities from other imaging surveys. Shown are the residuals (in logarithmic space) or the ratio between the observed fluxes and the modeled fluxes (in linear space); hence, any residual structure corresponds to the deviation from a single power law. We observe that in many of the pulsars, a single power law estimated from the ATNF and RACS data (see Figure 7 and Section 3.4) greatly overestimates the flux at these frequencies (while underestimating it in a few), hinting at the evidence for low- and high-frequency deviations from a power-law model. The color scheme represents the extent of the deviation from a power law (violet/yellow corresponds to maximum deficit/excess). These individual pulsar residuals are adjusted to 0 at 888 MHz.
Paper: Characterizing Pulsars Detected in the Rapid ASKAP Continuum Survey
Authors: Akash Anumarlapudi, Anna Ehlke, Megan L. Jones, David L. Kaplan, Dougal Dobie, Emil Lenc, James K. Leung, Tara Murphy, Joshua Pritchard, Adam J. Stewart, Rahul Sengar, Craig Anderson, Julie Banfield, George Heald, Aidan W. Hotan, David McConnell, Vanessa A. Moss, Wasim Raja, Matthew T. Whiting
Figure caption: The spectra of the 14 pulsars that have flux density measurements available at 150, 200, 400, 888, 1400, and 3000 MHz. The spectra were modeled using a simple power law, a quadratic power law, and a broken power law. In many cases (e.g., J0452−1759), we see that the spectrum prefers a quadratic/broken power law over a simple power law, indicating that the deviation from a simple power law is quite common.
Paper: Characterizing Pulsars Detected in the Rapid ASKAP Continuum Survey
Authors: Akash Anumarlapudi, Anna Ehlke, Megan L. Jones, David L. Kaplan, Dougal Dobie, Emil Lenc, James K. Leung, Tara Murphy, Joshua Pritchard, Adam J. Stewart, Rahul Sengar, Craig Anderson, Julie Banfield, George Heald, Aidan W. Hotan, David McConnell, Vanessa A. Moss, Wasim Raja, Matthew T. Whiting
Figure caption: Residuals for the sample of 35 pulsars that have flux measurements at five frequencies (150, 400, 888, 1400, and 3000 MHz). The top panel shows the residuals when the data are fit using a pure power law, and the bottom panel shows the residuals when the data are fit using a quadratic power law. In both cases, the residuals are normalized using the measurement uncertainties.
Paper: Characterizing Pulsars Detected in the Rapid ASKAP Continuum Survey
Authors: Akash Anumarlapudi, Anna Ehlke, Megan L. Jones, David L. Kaplan, Dougal Dobie, Emil Lenc, James K. Leung, Tara Murphy, Joshua Pritchard, Adam J. Stewart, Rahul Sengar, Craig Anderson, Julie Banfield, George Heald, Aidan W. Hotan, David McConnell, Vanessa A. Moss, Wasim Raja, Matthew T. Whiting
Figure caption: 3′ image cutouts of Stokes I and Stokes V intensity maps depicting three different cases: combined Stokes I and V detection (PSR J0711−6830), Stokes I detection with a marginal Stokes V detection (PSR J0528+2200), and a purely Stokes I detection (PSR J0509+0856). Shown in the red ellipse is the detected RACS source, and shown in the crosshairs is the pulsar’s position, as reported in the ATNF catalog.
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: The spin-axis flip diagram. Each panel has specific initial parameters m 1 and e 2: (a) m 1 = 0.2 M ⊕, e 2 = 0.2; (b) m 1 = 0.2 M ⊕, e 2 = 0.4; (c) m 1 = 1 M ⊕, e 2 = 0.2; (d) m 1 = 1 M ⊕, e 2 = 0.4. The color bar represents the range of maximum values of θ  p  over 100 Myr, where those segments in blue denote cases without a spin-axis flip. The flipping regions are filled with yellow green and orange.
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: The relationship between t eq and the timescale ratio r  t . Panel (a): m 1 = 0.2 M ⊕, e 2 = 0.2. Panel (b): m 1 = 0.2 M ⊕, e 2 = 0.4. Panel (c): m 1 = 1 M ⊕, e 2 = 0.2. Panel (d): m 1 = 1 M ⊕, e 2 = 0.4. In each plot, the initial semimajor axis of the secondary star ranges from 2 au to 20 au. Points plotted with different markers are the numerical simulation results. The solid lines are the estimated relationships fitted by the maximum likelihood method.
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: Distribution of the maximum obliquity ﹩{\theta }_{\max }﹩ in the dimension of timescale ratio r  t . The semimajor axis a 2 ranges from 2 to 20 au. Regions with or without a flip are also filled with pink or green, respectively, with 90° as the horizontal boundaries.
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: The equilibrium timescale map in the parameter space of the semimajor axis. In panels (a) and (b), the planetary mass m 1 = 0.2 M ⊕, and e 2 = 0.2 and 0.4, and in panels (c) and (d), the planetary mass changes to m 1 = 1 M ⊕, and e 2 is the same as those in panels (a) and (b).
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: The obliquity evolution for τ Boo Ab of an orbital inclination i 1 = 45°.
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: The obliquity evolution for HD 42936 b of an orbital inclination i 1 = 140°, 150°, 160°.
Paper: Evolution of Planetary Obliquity: The Eccentric Kozai–Lidov Mechanism Coupled with Tide
Authors: Xiumin Huang, Jianghui Ji, Shangfei Liu, Ruobing Dong, Su Wang
Figure caption: The obliquity evolution for HD 42936 b of an orbital inclination i 1 = 10°, 20°, 30°.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: GLS periodograms of feature-specific fill factors f  m .
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: GLS periodograms of feature-specific average unsigned magnetic fluxes, (fB) m .
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Correlations between ΔRV and the fill factors (f  m , upper panel), and average unsigned magnetic flux ((fB) m , lower panel) of the different magnetic features, as indicated in axis labels. The Spearman correlation coefficients are given in each panel.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Correlations of HARPS-N ΔRVs with the five main parameters characterizing solar magnetic contributions: total fill factor, disk-averaged UV intensities at 1600 and 1700 Å, average unsigned magnetic field, and chromospheric Ca II K flux index (left to right). Spearman correlation coefficients are given in each panel.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Correlations of the average unsigned magnetic flux of the different magnetic features with the average UV intensities at the wavelengths 1600 and 1700 Å, and the chromospheric flux index (top to bottom). Spearman correlation coefficients are given in each panel.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Correlations of fill factors of the different magnetic features with the average UV intensities at the wavelengths 1600 and 1700 Å, and the chromospheric Ca II K flux index (top to bottom). Spearman correlation coefficients are given in each panel.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Comparison of GLS periodograms of photospheric TSI and ΔRV with those of chromospheric ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ and 〈I 1600〉.
Paper: Dynamics of Photospheric Magnetic Flux Distribution and Variations in Solar RVs: A Study Using HARPS-N Solar and SDO Observations
Authors: Anisha Sen, S. P. Rajaguru
Figure caption: Comparison of fill factors f IN, f NE, and total network (f ntwk = f IN + f NE) for 45 s cadence data (without binning), and 720 s cadence data sets (2 × 2, 4 × 4 binning and without binning). See Section 4.1 for a discussion and summary of these results.
Paper: The Role of Mass and Environment on Satellite Distributions around Milky Way Analogs in the ROMULUS25 Simulation
Authors: Jordan Van Nest, Ferah Munshi, Charlotte Christensen, Alyson M. Brooks, Michael Tremmel, Thomas R. Quinn
Figure caption: (a) Quenched fraction plotted against K-band magnitude for the M  K +Env. sample from ROMULUS25 compared to SAGA II (Mao et al. 2021) and ELVES (Carlsten et al. 2022) data. (b) The same sample from ROMULUS25 with addition criteria of requiring satellites to have μ eff,r  < 25 mag arcsecond−2. As a direct comparison, the SAGA and ELVES data plotted here only contain satellites with stellar masses above 108 M ⊙. Error bars represent the standard error within each bin. Approximate stellar mass values are taken from a linear fit between M  K  and log(M */M ⊙) for our Milky Way analogs. The Milky Way and M31 values are taken from ELVES, and also only consider satellites with stellar masses above 108 M ⊙. While all three samples show the quenched fractions increasing with host brightness, our ROMULUS25 sample exhibits slightly larger quenched fractions (particularly in the faintest bin) unless low surface brightness galaxies are removed. The SAGA and ELVES data are also in good agreement up to the brightest magnitude bin where the sample sizes are small.
Paper: The Role of Mass and Environment on Satellite Distributions around Milky Way Analogs in the ROMULUS25 Simulation
Authors: Jordan Van Nest, Ferah Munshi, Charlotte Christensen, Alyson M. Brooks, Michael Tremmel, Thomas R. Quinn
Figure caption: Quenched fraction plotted against the distance to the closest Milky Way halo or larger for the (a) M * and (b) M  K +Env. criteria. Analogs are binned by D MW+ and the quenched fractions are taken from an aggregate of all satellites in each bin. The upper-hemisphere points represent analogs from the sample with a Milky Way radius of 300 kpc, while lower-hemisphere points represent the sample using the simulated virial radius with vertical lines bridging the two. The colors of each point represent the number of hosted satellites with stellar mass greater than 108 M ⊙. The samples are separated into “Pairs” and “Isolated” by whether the closest Milky Way or larger halo is within 1 Mpc, and the means of each sample are denoted by the red diamonds. For reference, the Milky Way–M31 distance is plotted with a vertical dashed line. Subplots (c) and (d) require satellites to have μ eff,r  < 25 mag arcsecond−2. The pairs exhibit a slightly higher mean quenched fraction, and changing the satellite selection radius from 300 kpc to R vir typically takes the quenched fraction to an equivalent or lower value. Applying the surface brightness cut to our satellites strengthens the disparity between paired and isolated samples.
Paper: The Role of Mass and Environment on Satellite Distributions around Milky Way Analogs in the ROMULUS25 Simulation
Authors: Jordan Van Nest, Ferah Munshi, Charlotte Christensen, Alyson M. Brooks, Michael Tremmel, Thomas R. Quinn
Figure caption: The specific frequencies of the number of satellites hosted by Milky Way analogs normalized to their (a) stellar mass and (b) distance to a Milky Way–sized or larger halo, D MW+. The plots show the results for the M vir (black) and M * (red) with simulated R vir and SAGA II (blue) samples. Error bars represent the standard error within each bin (﹩\sigma /\sqrt{N}﹩). With the exception of some large outliers, the S  N,env values do not show statistically significant trends. However, the S  N,mass values show a clear positive trend.
Paper: The Role of Mass and Environment on Satellite Distributions around Milky Way Analogs in the ROMULUS25 Simulation
Authors: Jordan Van Nest, Ferah Munshi, Charlotte Christensen, Alyson M. Brooks, Michael Tremmel, Thomas R. Quinn
Figure caption: The specific frequencies of the quenched fraction of satellites hosted by Milky Way analogs normalized to their stellar mass. The plots show the results for the M vir (black) and M * (red) with simulated R vir and SAGA II (blue) samples. Subplot (b) applies the surface brightness cut to satellites (μ eff,r  < 25 mag arcsecond−2). Error bars represent the standard error within each bin. As with N sat, there is a notable positive trend with Milky Way analog mass. However, applying the surface brightness cut to our satellites effectively removes the trend with mass.
Paper: The Role of Mass and Environment on Satellite Distributions around Milky Way Analogs in the ROMULUS25 Simulation
Authors: Jordan Van Nest, Ferah Munshi, Charlotte Christensen, Alyson M. Brooks, Michael Tremmel, Thomas R. Quinn
Figure caption: Points show the average number of satellites hosted by Milky Way analogs binned by D MW+ for our (a) M vir with simulated R vir and (b) SAGA II comparison sample. Hosts whose D MW+ measurements are within 1 Mpc are considered to be in “pairs,” while the rest are considered “isolated,” and the means of these two subsamples are plotted as red diamonds. The histogram data shows the number of hosts in each bin, and the Milky Way–M31 distance is plotted as a dashed vertical line for reference. In both samples the isolated subset hosts fewer average satellites than the pairs, and this is driven predominantly by the most isolated bin.
Paper: Collapse of Rotating Massive Stars Leading to Black Hole Formation and Energetic Supernovae
Authors: Sho Fujibayashi, Yuichiro Sekiguchi, Masaru Shibata, Shinya Wanajo
Figure caption: Specific angular momentum (solid curves) and that for innermost stable circular orbits (dashed curves) along the equatorial direction as functions of enclosed mass for each model.
Paper: On the Effectiveness of the Planetary Infrared Excess (PIE) Technique to Retrieve the Parameters of Multiplanet Systems around M Dwarfs: A Case Study on the TRAPPIST-1 System
Authors: L. C. Mayorga, J. Lustig-Yaeger, K. B. Stevenson
Figure caption: The one-dimensional priors and posteriors for the retrieved semimajor axes for the best-fit models throughout our historical analysis. The posteriors for each planet are given in a different color with the prior denoted by a gray envelope. Solid tick marks show the true Agol et al. (2021) values, and the dotted lines mark the mean retrieved values. In “2015” and “2016,” we used uniform priors that extend beyond the plotted region for any unconfirmed planets; thus, only the outline (a flat gray line) appears in the figure. Alongside the year labels, we show the evidence.
Paper: On the Effectiveness of the Planetary Infrared Excess (PIE) Technique to Retrieve the Parameters of Multiplanet Systems around M Dwarfs: A Case Study on the TRAPPIST-1 System
Authors: L. C. Mayorga, J. Lustig-Yaeger, K. B. Stevenson
Figure caption: Best-fit system in each constraint scenario compared to the true TRAPPIST-1 system. The symbols are sized by the planet radius and colored by the albedo. Shaded bars indicate the error on the planet’s semimajor axis. Each modeling scenario is labeled with the source of the priors, the computed evidence, and for the Gillon et al. (2017) scenario we show the significance compared to the Agol et al. (2021) best-fit model. The posteriors do not improve over the priors in either case except for the upper limit on planet h’s semimajor axis (and subsequent orbital period), which has a more precise upper limit.
Paper: On the Effectiveness of the Planetary Infrared Excess (PIE) Technique to Retrieve the Parameters of Multiplanet Systems around M Dwarfs: A Case Study on the TRAPPIST-1 System
Authors: L. C. Mayorga, J. Lustig-Yaeger, K. B. Stevenson
Figure caption: Same as Figure 6 but for the equilibrium temperatures of the included planets for the best-fit models throughout our historical analysis. Unlike the prior reliant semimajor axes, the PIE technique can place informative constraints on the equilibrium temperatures of the planets.