adsabs/AstroCLIMB huggingface dataset:
Paper: Learning to Estimate Photospheric Vector Fields from Line-of-sight Magnetograms
Authors: David Fouhey, KD Leka
Figure caption: Temporal trends, using Blos from either SDO/HMI or NSO/GONG . Following R. E. L. Higgins et al. (2021, 2022), we plot disk-averaged flux statistics since a start time of 2016 February 1, 00:36:00 TAI. We factor each time series into long-term trends (shown in left two panels) and short-term trends, to separately handle trends and the known oscillations in SDO/HMI’s magnetic field data. For reference, we plot the HMIP result with a black line (or HMIP downsampled to NSO/GONG ). The left two panels show plots of disk-averaged total flux ∣B∣ and signed BR as a function of time. The plots coalign all methods to HMIP to facilitate comparison, reporting the additive correction in the legend. For both ∣B∣ and BR , the proposed method follows HMIP far better and requires less correction. The right two panels show the residual high-frequency signal after detrending. HMIP’s oscillation is clearly present. We report results only on confident pixels (conf_disambig ≥ 60); results are similar using all pixels.
Paper: Learning to Estimate Photospheric Vector Fields from Line-of-sight Magnetograms
Authors: David Fouhey, KD Leka
Figure caption: Comparison of SSLOS with data from the U. Hawai‘i Imaging Vector Magnetograph for NOAA AR 10486 on 2003 October 29, a few hours before the X10 flare (same format as Figure 9). Only the detail around the strongly sheared PILs is shown, with continuum image from each of the sources; for SSLOS, every vector is plotted, for the imaging vector magnetograph (IVM), every 3rd vector is shown. While SSLOS does match the IVM BR well, and there is good indication of complex field structure, there is less shear overall in the SSLOS horizontal field; in some areas, it is in the incorrect direction.
Paper: Learning to Estimate Photospheric Vector Fields from Line-of-sight Magnetograms
Authors: David Fouhey, KD Leka
Figure caption: Comparison of SSLOS with data from the Hinode Spectropolarimeter, 2007 April 28 targeting NOAA AR 10953. The image is continuum intensity from the respective sources, the same format as Figure 9. The Hinode/SP vectors are plotted every 8th pixel, and the BR and μ contours are as labeled. SSLOS output is substantially smoother, since its inference is on an 8×  coarser grid from GONG; thus, detailed structures visible in Hinode/SP are unresolved. Nonetheless, SSLOS recovers the overall structure well.
Paper: Learning to Estimate Photospheric Vector Fields from Line-of-sight Magnetograms
Authors: David Fouhey, KD Leka
Figure caption: The distribution of BR as a function of viewing angle μ. Like its target HMIP data, SSLOS output (a) shows no (or at least, little) center-to-limb bias in active regions and (b) shows a clear center-to-limb bias over all pixels. The CTL bias is driven by the large numbers of low-polarization signal pixels. We show two options: (1) the standard method and (2) the results obtained when the bin correction δn is randomized rather than predicted. Both closely track the HMIP results; randomization leads to a slight decrease in accuracy but mitigates banding artifacts in the output. Panels are identically color mapped, so comparison within a panel is possible.
Paper: Learning to Estimate Photospheric Vector Fields from Line-of-sight Magnetograms
Authors: David Fouhey, KD Leka
Figure caption: Temporal trends over all pixels. This figure follows Figure 14 in terms of overall trends: the proposed method tracks HMIP well and far better than μ-corrected Blos. The daily trends are slightly more concentrated, likely due to the far larger amount of data reducing variance. The difference in the HMIP is due to resampling the data to a smaller grid, which is done per-component, as opposed to by resampling the norm and angles. Accordingly, the average flux is reduced.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: Vertical asymmetry (left) and slope of the metallicity gradient (middle left) have weak correlations with the goodness-of-fit, χ2, normalized by θ, the total number of parameters, while total density (middle right), median age (right), and scale height moderately correlate with χ2. Here, r refers to the Pearson correlation coefficient between each quantity plotted on the horizontal axis and χ2 (see Figure F1 in Appendix F for r between all quantities). OTI can reasonably accommodate a range of vertically asymmetric distributions and a range of slopes, but results in a loss in accuracy for volumes where the total density is low, the median age is high, or the scale height is high. See Section 6 and Appendix G for further discussion.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: 2D histogram of stellar surface mass density in R–z for each volume. The shaded panel indicates the radial extent of each solar volume. In spite of the diversity of asymmetrical conditions present in the edge-on stellar surface mass density distributions, the OTI model az accuracy remains largely independent of this quantity. We note varying asymmetry about the midplane as a function of galactocentric radius for all volumes and plot the stellar mass normalized by bin area using bins of size 5 pc2. See Section 6 for further discussion.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: As in the rightmost panel of Figure 1 from A. M. Price-Whelan et al. (2025), but for [Fe/H] instead of [Mg/Fe], we plot the mean abundance as a function of a proxy for ﹩{z}_{{\rm{\max }}}﹩. Our sample is drawn from the original stellar sample, contained within the solar volumes described in Figure 1, with an additional cut on vz such that ∣vz∣ < 10 km s−1. Selecting for particles with low velocities enables us to keep only the stars that are near their apocenters, where they reach their highest vertical excursions. Across all solar-analog volumes, we note a nonzero, measurable gradient for the vertical metallicity profiles and include the slope of the best-fit line. OTI necessitates a measurable vertical metallicity gradient, as it provides the modeling with constraining power by being able to map distinct orbital paths to the vertical distribution. We compute a Pearson correlation coefficient between ∣m∣ and χ2 and report a value of -0.24, indicating a weak correlation. The negative sign indicates the two variables move in opposite directions: as the slope decreases, χ2 climbs, indicating a worse fit corresponds to a shallower gradient. However, in this study, all 16 solar volumes exhibit a gradient sufficient for OTI analysis.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: Vertical density profiles show distinct distributions for stars, gas, and dark matter across all volumes. Profiles are shown at R = 8 kpc for stars (colored solid lines), gas (colored dashed lines), dark matter (colored dotted lines), and all particles (black solid line). We compute the mass density by summing the mass within each solar volume (using bins of size 22 pc2) and normalizing by the cylindrical volume element. Although vertical asymmetry is present in certain volumes, it does not correlate with the accuracy of az estimates (see Figure 9 and Section 6 for further discussion) because torusimaging  (A. M. Price-Whelan et al. 2025) recenters the midplane according to the peak density within each volume.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: The OTI model accurately recovers the true vertical acceleration profile within 3σ for 15 out of 16 solar volumes (all but V14) and within 1σ for 12 out of 16 solar volumes (all but V2, V13, V14, and V16) in m12i. The black solid line represents FIRE-2 simulated galaxy m12i’s vertical acceleration profile, while the black dashed line represents the OTI model inference. The darker shaded region indicates the 1σ uncertainty, and the lighter shaded region shows the 3σ limit. We compute the uncertainty by MCMC sampling the posterior distribution, bootstrapping the FIRE-2 data, and combining the variances in quadrature to determine the total error. Vertical solid lines show the Jeans modeling az estimates with errors calculated from bootstrapping with replacement. We discuss several properties of the local disk that are correlated with a decrease in OTI accuracy in Section 6 and in Appendix G.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: The systematic transition in 〈[Fe/H]〉 values across z and vz within solar volumes provides strong constraints for OTI modeling. The 2D histogram shows 〈[Fe/H]〉 in vertical phase space for stars in the 16 solar volumes described in Figure 1. As in Figure 4, the range for bin sizes is 170–226 (pc km s−1) due to the scatter within volumes. Each volume reveals a smooth gradient from higher 〈[Fe/H]〉 values (orange) at lower ∣z∣ and ∣vz∣ to lower 〈[Fe/H]〉 values (purple) at higher ∣z∣ and ∣vz∣, generating the basis for the OTI model’s constraining power.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: The median (across all 16 volumes) normalized residual profile shows the OTI model achieves reasonable accuracy in recovering FIRE-2 az profiles. We also plot 1σ (light shaded region from the median) and 1.5 × MAD (darker shaded region). This provides an estimate of the model accuracy across the galactic disk, revealing a systematic offset relative to the midplane: OTI overpredicts above the midplane and underpredicts below the midplane. See Section 5.1 for further discussion.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: OTI estimates agree with FIRE-2 true values for total (stars, gas, and dark matter) surface mass density at z = 1.1 kpc within 3σ except for V14. Total surface mass density for FIRE (star symbols) and OTI (circle symbols) is shown with 1σ and 3σ error bars at ∣z∣ = 1.1 kpc. The gray-shaded panel indicates the range of possible MW solar values from the literature (Table 1 from D. Horta et al. 2024). For comparison, we include the OTI-inferred MW total surface mass density from D. Horta et al. (2024), which lies well within the range of values from our simulated solar volumes sample. See Section 5.2 for further discussion.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: The OTI model reliably captures the known FIRE-2 〈[Fe/H]〉 distribution with nonsystematic residuals. Left: 2D histogram of the 〈[Fe/H]〉 abundance from Volume 1 (V1) in m12i at the present day, in bins ranging from 170 to 226 (pc * km s−1), as in Figure 4 but for a single volume. Middle: OTI-optimized model. Right: residual plot of FIRE—OTI mean [Fe/H], normalized by the intrinsic scatter within each pixel. See Section 4.2 for further discussion and Appendix A for the vertical metallicity gradients within each volume.
Paper: A Preinfall Magellanic Analog–Corona and Stream Formation in the HESTIA Cosmological Simulations
Authors: Robin Chisholm, Elena D'Onghia, Noam Libeskind, Scott Lucchini, Andrew J. Fox, Matthias Steinmetz
Figure caption: Dynamics of the Magellanic-analog system. (Upper-left panel) The distance between the LMC and SMC analog in our simulation as a function of dynamical time tdyn (as defined in Section 2.3) and redshift is shown as the solid black line. The same data from G. Besla et al. (2012), model 2 isolated without the influence of the Milky Way, and likewise from S. Lucchini et al. (2020) are shown as the solid blue and green lines, respectively. Note that the G. Besla et al. (2012) and S. Lucchini et al. (2020) curves correspond to isolated simulations, and the axis denoting redshift only applies to the curve of our Magellanic-analog dwarf pair. (Remaining panels) Stereoscopic projections of the tidally stripped gaseous stream colored by H I column density at (i) zero dynamical time tdyn = 0.0 Gyr, (ii) tdyn = +0.87 Gyr, and (iii) tdyn = +1.33 Gyr. NHI values were calculated from the frame of reference of an observer placed at (x, y, z) = (0, 0, +55kpc) above the plane of the LMC analog. Thus, the image shows the synthesized viewpoint of an observer facing downward (in the −z-direction) toward the Magellanic pair analog. The SMC and LMC analogs are shown as a black “+” and black diamond, respectively; the SMC analog orbits in the counterclockwise direction from this perspective.
Paper: A Preinfall Magellanic Analog–Corona and Stream Formation in the HESTIA Cosmological Simulations
Authors: Robin Chisholm, Elena D'Onghia, Noam Libeskind, Scott Lucchini, Andrew J. Fox, Matthias Steinmetz
Figure caption: Temperature profile of the coronae at zero dynamical time (z ∼ 0.1). (Left panel) Heat map of the radial distribution of the temperature of gas cells of the CGM of the LMC analog (darker blue corresponding to higher phase space density). The mass-weighted average temperature of strictly the warm phase of the CGM at each radial bin is shown as a solid blue line. The corresponding temperature profile for the slightly less-massive isolated LMC analog from S. Lucchini et al. (2024) is shown as a solid green line. The expected analytic profile for a virialized halo with a mass of our massive dwarf is shown as a solid black line. (Right panel) The temperature of coronae as defined in Section 2.2 as a function of halo mass. The solid points correspond to isolated halos (members of the Local Group analog or field dwarfs), and hollow points correspond to those within the virial radius of a larger host (satellites). Our massive dwarf is represented with a blue diamond, and the fiducial model of S. Lucchini et al. (2024) is represented as a green diamond. Vertical error bars denote the 1σ spread in the estimated temperature of the corona for a given dwarf. The virial temperature as a function of halo mass is shown with a dashed black line. The estimated LMC virial mass from globular clusters (L. L. Watkins et al. 2024) is denoted with the vertical gray band.
Paper: A Preinfall Magellanic Analog–Corona and Stream Formation in the HESTIA Cosmological Simulations
Authors: Robin Chisholm, Elena D'Onghia, Noam Libeskind, Scott Lucchini, Andrew J. Fox, Matthias Steinmetz
Figure caption: Edge-on projections of the simulated Magellanic-analog system at various snapshots, illustrating the evolution of the interacting dwarf galaxy pair.
Paper: Unveiling a New β Scaling of the Tearing Instability in Weakly Collisional Plasmas
Authors: Gabriel L. Ferreira-Santos, Grzegorz Kowal, Diego A. Falceta-Gonçalves
Figure caption: Comparison of maximum growth rates and wavenumbers for different polytropic closures in the nonideal gyrotropic MHD model. Left panel: the normalized maximum growth rate (﹩{\sigma }_{{\rm{\max }}}{\tau }_{A}﹩) as a function of plasma-β. Right panel: the wavenumber corresponding to the maximum growth rate (﹩{k}_{{\rm{\max }}}a﹩) as a function of plasma-β. The results are shown for a Lundquist number S = 104 and a magnetic Prandtl number Prm = 1. The lines represent three distinct polytropic closures: double-adiabatic (γ∥ = 3, γ⊥ = 2; blue), double-polytropic (γ∥ = 1/2, γ⊥ = 2; orange), and double-isothermal (γ∥ = 1, γ⊥ = 1; green). The figure demonstrates that the growth rate’s dependence on β emerges in nonisothermal models, with a notable suppression in high-β regimes, whereas the isothermal model remains independent of β, consistent with classical MHD theory. The black line indicates the reference slope ∼β−1/4 for the growth rate scaling at high β.
Paper: Unveiling a New β Scaling of the Tearing Instability in Weakly Collisional Plasmas
Authors: Gabriel L. Ferreira-Santos, Grzegorz Kowal, Diego A. Falceta-Gonçalves
Figure caption: Maximum growth rate (left) and the corresponding maximum wavenumber (right) of the tearing mode instability as functions of the equilibrium pressure anisotropy parameter Δβ0 for β = 1 and 10 (upper and lower rows, respectively) for different Lundquist numbers S = 104, 105, and 106, with Prm = 1. The points at Δβ0 = 0 represent ﹩{\sigma }_{{\rm{\max }}}{\tau }_{a}﹩ and ﹩{k}_{{\rm{\max }}}a﹩ obtained from the MHD approximation.
Paper: Unveiling a New β Scaling of the Tearing Instability in Weakly Collisional Plasmas
Authors: Gabriel L. Ferreira-Santos, Grzegorz Kowal, Diego A. Falceta-Gonçalves
Figure caption: Maximum growth rate (left) and the corresponding maximum wavenumber (right) of the tearing mode instability as functions of the equilibrium plasma-β parameter for Δβ = 0 for different Lundquist numbers S = 104, 105, and 106, with Prm = 1. For the plasma-β dependence, the analysis considers both the MHD framework (dashed) and the nonideal gyrotropic MHD model (solid).
Paper: TESS Subgiant and Lower-red-giant Asteroseismology in the Continuous Viewing Zones
Authors: Sophia Grusnis, Jamie Tayar, Diego Godoy-Rivera
Figure caption: Output charts from pySYD. Top: the power spectrum showing the area around νmax (shaded region). We see a power excess caused by stellar oscillations (gray vertical band). The black shows the original power spectrum given by pySYD based on our given light curve. The cyan shows a smoothed power spectrum and the pink shows a heavily smoothed power spectrum. The heavily smoothed power spectrum is used as an initial background fit to search for oscillations. We smoothed aggressively in the convective damping timescale to better see the star’s oscillations and this resulted in low power at lower frequencies. Bottom: the corrected power spectrum with all smoothing removed showing a definite power excess in the same region.
Paper: TESS Subgiant and Lower-red-giant Asteroseismology in the Continuous Viewing Zones
Authors: Sophia Grusnis, Jamie Tayar, Diego Godoy-Rivera
Figure caption: Linear relationship of νmax vs. Δν. Our unique star sample is shown in yellow compared to the samples that overlap with E. Hatt et al. (2023) in blue and C. J. Lindsay et al. (2024) in red. Average error bars for the sample are shown in black at the top of the figure.
Paper: TESS Subgiant and Lower-red-giant Asteroseismology in the Continuous Viewing Zones
Authors: Sophia Grusnis, Jamie Tayar, Diego Godoy-Rivera
Figure caption: Top: unedited time series for TIC 38602419. Before any normalizing, we can see the differences in each sector, including sectors 1–13 and 27–39. We show the totality of the two years of data that TESS provides, including the obvious discontinuities between sectors. Each sector is distinguished by a different color. Bottom: the final normalized light curve for TIC 38602419. The sector colors are consistent between the two plots.
Paper: TESS Subgiant and Lower-red-giant Asteroseismology in the Continuous Viewing Zones
Authors: Sophia Grusnis, Jamie Tayar, Diego Godoy-Rivera
Figure caption: Our νmax values (left) and Δν values (right) are consistent with previous work by E. Hatt et al. (2023; blue) and C. J. Lindsay et al. (2024; red). We show an overlapping subset of 66 stars out of our total 82 stars. We find an R2 correlation value to E. Hatt et al. (2023) of 0.9918 and to C. J. Lindsay et al. (2024) of 0.9979. We find average offsets of 2.9% in νmax and 3.7% in Δν, with no strong trends. Error bars are plotted for all points, but may be smaller than the markers.
Paper: TESS Subgiant and Lower-red-giant Asteroseismology in the Continuous Viewing Zones
Authors: Sophia Grusnis, Jamie Tayar, Diego Godoy-Rivera
Figure caption: Left: echelle diagram given by pySYD for TIC 25156036. With a Δν of 15.59 μHz we can see ridges for the l = 0, 1, and 2 modes. There is a small tilt to the ridges, meaning we need to do fine tuning in HeyEchelle. Right: echelle diagram given by HeyEchelle for this same star. With a Δν of 15.86 μHz we can see the ridges are lined up better than our original Δν estimate.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: Pearson correlation coefficient values for several aggregate statistics (per volume) considered in this work (from left to right or top to bottom): A, vertical asymmetry of the total (star, gas, and DM) distribution, m, slope of the 〈[Fe/H]〉 vertical gradient, Σ⊙, total density at a height of z = 1.1 kpc, median stellar age, b, scale height, and χ2, goodness-of-fit (Equation (10)). Section 6 describes how these quantities are obtained. For illustrative purposes, we overlay a black rectangle highlighting the properties that have the highest correlations with χ2. The properties most correlated with our goodness-of-fit parameter, χ2, are: median stellar age, scale height, and total density, in the order of most to least correlated. Conversely, the properties that correlate very weakly with χ2 are vertical asymmetry and the strength of the vertical metallicity gradient.
Paper: OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
Authors: Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Figure caption: Offset values in z and vz, obtained by fitting the OTI model to stellar data within a given solar-analog volume. Both z0 and vz,0 are free parameters, as described in Section 4.2, that we fit for because we do not know where the true center of a given distribution lies. The sign of the offset indicates whether the zero-point is shifted up or down to align with the typical vertical position of stars in that region, upward for volumes like V4–V14, where stars lie mostly above z = 0 kpc, and downward for volumes like V1–V3 and V15–V16, where stars lie mostly below z = 0 kpc. This recentering mitigates systematic uncertainty derived from the displacement of the stellar distribution from z = 0 kpc (as seen in the left panel of Figures 1, Figures B1, and B2) and bulk motions (bottom panel of Figure 2).
Paper: Feature Intensity Mapping: Polycyclic Aromatic Hydrocarbon Emission from All Galaxies Across Cosmic Time
Authors: Yun-Ting Cheng, 昀庭 鄭, Brandon S. Hensley, Thomas S.-Y. Lai, 劭愉 賴
Figure caption: PRIMA surface brightness sensitivity per spectral channel in a wavelength-dependent beam (5﹩\mathop{.}\limits^{\unicode{x02033}}﹩1/7﹩\mathop{.}\limits^{\unicode{x02033}}﹩6 to 10﹩\mathop{.}\limits^{\unicode{x02033}}﹩4/22﹩\mathop{.}\limits^{\unicode{x02033}}﹩9 from short to long wavelengths for PHI/FIRESS) with spectral resolution R = 10/100 for PHI/FIRESS. The brown and gray points represent the PHI and FIRESS sensitivity, respectively, assuming a fiducial survey of 1000 hr of integration covering an area of 1/0.1 deg2 for PHI/FIRESS.