adsabs/AstroCLIMB huggingface dataset:
Paper: A Deep, High-angular-resolution 3D Dust Map of the Southern Galactic Plane
Authors: Catherine Zucker, Andrew K. Saydjari, Joshua S. Speagle, 佳士 沈, Edward F. Schlafly, Gregory M. Green, Robert Benjamin, Joshua Peek, Gordian Edenhofer, Alyssa Goodman, Michael A. Kuhn, Douglas P. Finkbeiner
Figure caption: Normalized probability density functions of the distribution of the minimum reliable distance (left), maximum reliable distance (middle) and reddening at the maximum reliable distance (right), computed over the 51 million pixels in the DECaPS footprint.
Paper: The Electron Temperature Distribution and the High Ionization Just behind the Shock in the Cygnus Loop
Authors: Masahiro Ichihashi, Aya Bamba, Dai Tateishi, Kouichi Hagino, Satoru Katsuda, Hiroyuki Uchida, Hiromasa Suzuki, Ryo Yamazaki, Yutaka Ohira
Figure caption: The spectrum and the best-fit model of the background region. The NXB spectrum made by mosback is already subtracted. The red, orange, and blue lines show the model of the Cygnus supper bubble, active galactic nuclei, and NXB fluorescent lines, respectively.
Paper: The Electron Temperature Distribution and the High Ionization Just behind the Shock in the Cygnus Loop
Authors: Masahiro Ichihashi, Aya Bamba, Dai Tateishi, Kouichi Hagino, Satoru Katsuda, Hiroyuki Uchida, Hiromasa Suzuki, Ryo Yamazaki, Yutaka Ohira
Figure caption: The spectrum and best-fit model of outer shell region. Cyan, gray, and blue lines show the source model, source background, and NXB fluorescent lines, respectively.
Paper: Shedding Light on the Solar Dynamo Using Data-driven Bayesian Parameter Inference
Authors: Simone Ulzega, Jürg Beer, Antonio Ferriz-Mas, Simon Dirmeier, Carlo Albert
Figure caption: Comparison of the FFT magnitude spectra of the data (dark red lines) and 15 simulated data sets (gray lines), obtained using the posterior parameter sets with the smallest SABC distances. The two panels show the spectra of the observed SN data set (a) and of the 14C-based SN reconstruction (b). It is evident that the spectrum of the 14C data, spanning an entire millennium, exhibits a significantly lower signal-to-noise ratio compared to the much cleaner spectrum obtained from direct sunspot observations. The red squares show the FFT components used in the SABC inference. The k = 0 component is off-scale and therefore not shown here. Note that our choice of summary statistics allows us to select the prominent 11 yr peak in both data sets (the dashed vertical line) and to disregard low-frequency features that the model may not be able to adequately reproduce. Notably, among these low-frequency features not used in the inference process is the well-known Gleissberg cycle (≈87 yr), particularly pronounced in the observed SN spectrum of (a) at FFT component 3 (blue circle).
Paper: Shedding Light on the Solar Dynamo Using Data-driven Bayesian Parameter Inference
Authors: Simone Ulzega, Jürg Beer, Antonio Ferriz-Mas, Simon Dirmeier, Carlo Albert
Figure caption: Contour plots representing the posterior distributions for the parameters τ, T, and ﹩{ \mathcal N }﹩. The first and second rows show the SABC posteriors based on the observed SN record and on the 14C-based SN reconstruction of I. G. Usoskin et al. (2021), respectively, as described in Section 2.1. The third row shows the results obtained with the sequential neural density estimator APT, as described in Section 2.2. Both the parameters τ and T are expressed in units of years, while the dynamo number ﹩{ \mathcal N }﹩ is dimensionless.
Paper: Shedding Light on the Solar Dynamo Using Data-driven Bayesian Parameter Inference
Authors: Simone Ulzega, Jürg Beer, Antonio Ferriz-Mas, Simon Dirmeier, Carlo Albert
Figure caption: Bifurcation diagram showing the field amplitudes B2 as a function of the dynamo number ﹩{ \mathcal N }﹩. This diagram is generated using a deterministic version of the model of Equation (1) with σ = 0 and the other parameters set to values corresponding to the highest posterior density: τ = 2.8, T = 3.7, and ﹩{B}_{{\rm{\max }}}=5.0﹩. The parameters τ and T are in years, ﹩{B}_{{\rm{\max }}}﹩ has the same arbitrary units as B(t), and ﹩{ \mathcal N }﹩ is dimensionless.
Paper: Shedding Light on the Solar Dynamo Using Data-driven Bayesian Parameter Inference
Authors: Simone Ulzega, Jürg Beer, Antonio Ferriz-Mas, Simon Dirmeier, Carlo Albert
Figure caption: Prediction for solar cycle 24 using the model of Equation (1) and the SABC parameter calibration based on cycles 1 to 23. Darker red points correspond to cycle 24. Our prediction is represented by the mean (green solid line) of 700 model runs, as described in the text, and a confidence interval given by the 5 and 95 percentiles (shaded area).
Paper: Shedding Light on the Solar Dynamo Using Data-driven Bayesian Parameter Inference
Authors: Simone Ulzega, Jürg Beer, Antonio Ferriz-Mas, Simon Dirmeier, Carlo Albert
Figure caption: Marginal posterior distributions for the parameters τ, T, ﹩{ \mathcal N }﹩, and σ. The latter is dimensionless. More details are given in the caption of Figure 2.
Paper: Shedding Light on the Solar Dynamo Using Data-driven Bayesian Parameter Inference
Authors: Simone Ulzega, Jürg Beer, Antonio Ferriz-Mas, Simon Dirmeier, Carlo Albert
Figure caption: Prediction for solar cycle 25 using the model of Equation (1) and the SABC parameter calibration from this work. All full cycles 1 to 24 were used in the SABC inference (cycles 23 and 24 are shown here in light gray). The darker red points correspond to the monthly SN values already measured within cycle 25, which were not used in the inference. Our prediction is represented by the mean (green solid line) of 700 model runs, as described in the text, and a confidence interval given by the 5 and 95 percentiles (shaded area).
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Redshift-deprojected, bias-weighted mean CIB intensities (dIν/dz) b over 0 < z < 4 for the 11 Planck, Herschel, and IRAS bands, corresponding to ﹩{\overline{w}}_{{\rm{IR}}}/({b}_{{\rm{R}}}\,{\overline{w}}_{{\rm{DM}}})﹩ in Equation (3). Light blue and yellow data points show raw measurements before and after redshift rebinning, both dominated by the CIB but also including the thermal SZ effect, more visible as decrements at 100 and 143 GHz at z < 1. Black data points show the fiducial CIB result with two minor corrections: (1) SZ removal based on C20 and (2) a CC for deviations from the ﹩\nu \,{I}_{\nu }=\mathrm{const}.﹩ in-band spectral shape assumed for the maps. Black (yellow) lines show the best-fit ensemble cosmic dust model without (with) SZ distortion. These redshift distributions can serve as baseline inputs for future studies using 2D far-infrared maps as 3D matter tracers.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Tomographic CIB spectrum in the rest frame, expressed as the bias-weighted CIB emissivity ϵν b over 0 < z < 4. Data points are derived from the observed frame (dIν/dz) b in Figure 2 (black data points) via a simple z-dependent unit conversion and 1 + z frequency shifts, leading to dense spectral sampling. The overall amplitude increases from z = 0 to z = 2–3 before declining at z > 3 (occurring at a higher redshift than the peak cosmic star formation since b(z) is monotonically increasing). Solid lines, colored by redshift, show our best-fit ensemble cosmic dust SEDs from the same posterior as Figure 2 but sliced along different dimensions. The model is flexible enough for the best fits to smoothly interpolate the data. The peak frequency, and thus dust temperature, increases slightly toward higher redshifts. This tomographic CIB spectrum forms the foundation for a census of cosmic dust and star formation.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Observer-frame CIB monopole spectrum. The red line and band show the posterior median and 68% range from our CIB tomography. The black dashed line represents the z < 4 contribution resolved directly via cross correlations, with its ratio to the total shown in the top panel. The cyan line and band show the CIB plus the SZ effect of the CMB due to hot gas in the LSS. Data points represent constraints from the literature, among which the N. Odegard et al. (2019) points up to 545 GHz are used in our MCMC fitting (so our monopole estimate is not an independent measurement). Our high-frequency posterior suggests that the FIRAS monopole cleaned with H I (gray squares) is the most robust among the three foreground-cleaning methods in D. J. Fixsen et al. (1998).
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Effective CIB bias evolution. The red line and shaded region show the posterior median and 68% range, constrained empirically by combining clustering-based tomography with FIRAS-Planck-based monopole intensities in Figure 7. Colored dashed lines represent literature constraints. The effective thermal SZ bias (dashed–dotted line) from the halo model in C20 provides a theoretical upper limit to the CIB bias. Thin gray lines indicate dark matter halo bias from J. L. Tinker et al. (2010), with the effective light-weighted mean dark matter halo mass for the CIB shown in the upper panel.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Redshift distribution dN / dz (left) and bias factor bR (right) for the reference SDSS galaxies and QSOs. Their precise spectroscopic redshifts make them high-fidelity 3D tracers of LSS, enabling our CIB deprojection via cross correlations. The bR shown here is measured over scales of 0.5–10 Mpc h−1 across the full SDSS footprint. In practice, our CIB tomography uses multiple versions of the full-sample bR(z), calculated based on the angular scales and footprint (with spatial weighting) applied for each frequency band. The dotted line shows a smooth function measured in Y. Shen et al. (2013), which we use at z > 2.5 where our measurements become noisy. These bR(z) are divided out from the raw galaxy-CIB cross-correlation amplitudes to isolate the tomographic CIB-only signal that will be shown in Figure 2.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Marginalized 1D posteriors and pairwise 2D covariances for the 15 parameters in our fiducial warm-cold ensemble cosmic dust SED model fit to the tomographic CIB measurements. The physical role of the parameters can be organized into dust mass, temperature, opacity, and bias, and only three parameters are prior-driven (log Σd and 2 dof for the bias). We include very cold CGM/IGM dust in a separate MCMC run, with posteriors for the two additional parameters shown in the upper right. Since this extra cold component is not significantly detected, the posteriors for the remaining 15 parameters in this run remain consistent with the fiducial results shown in the main block.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Covariance matrix for our tomographic CIB measurements of 11 bands × 16 redshift bins, shown in a normalized, dimensionless form. The same correlation coefficients apply to both the (dIν/dz) b data vector in Figure 2 and ϵν b in Figure 5.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Comparison of our bias-corrected dIν/dz (black) with that from S. J. Schmidt et al. (2015) (pink) for the four Planck bands measured in the latter. Our results, taken from Figure 2 with bias removed using Figure 8, have much higher S/N. S. J. Schmidt et al. (2015) likely overestimate CIB amplitudes at z < 2 due to the inclusion of small-scale clustering within and around the Planck beams.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Cosmic dust density Ωdust history. The black solid line and gray band show the posterior median and 68% range from our CIB tomography, with black data points representing our per-redshift fits. The black dotted line marks an absolute upper limit (95%) if temperature bimodality is allowed for additional very cold dust (Figure 9). Various literature measurements and lower limits are shown as labeled, with green open symbols indicating those from far-infrared galaxy surveys. Most estimates, including far-infrared emission and optical reddening ones, are rescaled to assume the same reference opacity κ850 = 0.77 cm2 g−1. All errors shown, including ours, are statistical only. We find Ωdust increased rapidly in the first few Gyr, peaked at z = 1–1.5, and declined at z < 1. Note that our overall normalization is empirically constrained by the CIB monopole, and the uncertainty shown already includes that from the bias evolution (Figure 8).
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Cosmic star formation history. Red band and data points show our posterior dust-obscured star formation rate density from CIB tomography, with the raw total infrared luminosity density labeled on the right. For the total star formation (black band and data points), we add the CIB to a compilation of unobscured UV contributions from literature luminosity function integrals without dust correction (light steel blue; see the text for references). The upper panel quantifies the obscured fraction fobs, given by the ratio of IR to total star formation, indicating a heavily dust-obscured star formation history. Literature measurements are overlaid as labeled, with all values converted to assume a G. Chabrier (2003) IMF. Our CIB tomography is robust against systematics common in galaxy surveys, such as faint-end incompleteness, photometric redshift errors, and large SED extrapolations, and has negligible cosmic variance, utilizing 1π sky coverage instead of pencil beam surveys. As with Ωdust, our star formation normalization is empirically set by the CIB monopole and its associated uncertainties. The resulting error at a given redshift is dominated by the detailed bias evolution (Figure 8), with photon noise already subdominant.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Summary of EBL monopole intensity νIν from UV to millimeter wave. Intensity mapping results are organized into two categories. The first is cross-correlation-based tomography, the method most reliable against foregrounds and capable of capturing, if present, physically diffuse light in the LSS. This includes the thin red band for our CIB result (68% range, same as Figure 7) and the blue band for the UV background tomography in Y.-K. Chiang et al. (2019). Y.-K. Chiang et al. (2019) also obtained a detection of cosmic Lyα at z = 1 and a limit at z = 0.3, which we show on the left panel with blue symbols, together with an upper limit of Lyα at z = 2.55 from R. A. C. Croft et al. (2018) in pink. The second category is intensity mapping using global intensity or projected auto-fluctuations with known foreground subtracted. This includes CIBER (S. Matsuura et al. 2017), New Horizons (M. Postman et al. 2024; J. Murthy et al. 2025), COBE FIRAS (D. J. Fixsen et al. 1998), and Planck (N. Odegard et al. 2019). A selected compilation of integrated galaxy light measurements (D. Elbaz et al. 1999; J. P. Gardner et al. 2000; P. Madau & L. Pozzetti 2000; G. G. Fazio et al. 2004; C. Papovich et al. 2004; S. Berta et al. 2010; M. Béthermin et al. 2012b) is marked in filled green triangles as lower limits following A. Cooray (2016) based on an earlier figure from H. Dole et al. (2006). We also show two synthesized models combining galaxy abundance, redshift distributions, and SEDs from V. Khaire & R. Srianand (2019) and A. Domínguez et al. (2011) as thick dashed lines.
Paper: Cosmic Infrared Background Tomography and a Census of Cosmic Dust and Star Formation
Authors: Yi-Kuan Chiang, Ryu Makiya, Brice Ménard
Figure caption: Dust temperature constraints from our CIB tomography. Left: full P(T) ∼ dMdust/dT distributions, with thick lines for posterior medians and thin lines for selected posterior samples at z = 0, 1, and 3. Right: evolution of the mass-weighted mean 〈T〉 (blue) and emissivity peak temperature Tpeak (red), indicating stronger interstellar dust heating at higher redshifts. Dashed lines in both panels represent a theoretical temperature floor set by the metagalactic radiation background. At a given epoch, the main lognormal power-law component could, in principle, capture all dust regardless of the local environment, e.g., ISM, CGM, or IGM, as long as P(T) is single modal. If the secondary cold peak at Tfloor is significant, as explored in Section 4.1.3, more dust mass could be allowed given the observed CIB, but only at z ∼ 0, where Tfloor is low and the temperature gap is large.
Paper: Spectral Evolution of Rotating Population III Stars
Authors: Jake B. Hassan, Rosalba Perna, Matteo Cantiello, Tyler M. Parsotan, Davide Lazzati, Nathan Walker
Figure caption: H-R diagrams showing evolutionary tracks from the beginning of hydrogen burning to the end of the calculation for models of Pop III stars with different initial masses and rotational velocities. Lines of constant radius are shown in gray. Circle symbols mark the position at which X = 0.3, while the end points of hydrogen burning and helium burning are marked with a square and cross, respectively.
Paper: Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
Authors: H. Wu, Y. Guo, R. Keppens, C. Xia, Y. Su, X. L. Kong, M. D. Ding
Figure caption: Snapshots showing the evolution of ((a)–(c)) the parallel current density, ((d)–(f)) the current density along the x-axis, and ((g)–(i)) the magnetic field lines at t = 3.6 t0, 4.0 t0, and 4.4 t0, respectively. The magnetic field lines are integrated around the 2D slice position.
Paper: Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
Authors: H. Wu, Y. Guo, R. Keppens, C. Xia, Y. Su, X. L. Kong, M. D. Ding
Figure caption: The MFR and current-sheet location defined using specific criteria at t = 4.4 t0. Panel (a) shows their relative positions within the 3D simulation box. Panel (b) projects them onto the XZ-plane at y = 0. Panel (c) projects them onto the YZ-plane at x = 0, colored by the current-sheet locator criteria and augmented with the reconnecting magnetic field lines. The current sheet is colored by the current density in panel (d).
Paper: Spectral Evolution of Rotating Population III Stars
Authors: Jake B. Hassan, Rosalba Perna, Matteo Cantiello, Tyler M. Parsotan, Davide Lazzati, Nathan Walker
Figure caption: Color–magnitude diagrams at different redshifts and rotational velocities. Results are shown for ZAMS (blue), IAMS (green) and TAMS (red). The models from Figure 1 are marked with unique symbols corresponding to their initial mass, which are labeled (in solar masses) on the ZAMS curve of the leftmost plots. The bands used are generally the optimal pair for all models at a given redshift, with the exception of massive RSGs at TAMS. Observability limits under various magnifications μ are shown with gray lines and labeled on the rightmost plots.
Paper: Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
Authors: H. Wu, Y. Guo, R. Keppens, C. Xia, Y. Su, X. L. Kong, M. D. Ding
Figure caption: Distribution of O-type field-line sites within the current sheet ((a) and (b)) and the mini flux ropes around these reconnection points ((A)–(D)). Panel (b) enlarges the red box region in panel (a) to show the detailed distribution.
Paper: Spectral Evolution of Rotating Population III Stars
Authors: Jake B. Hassan, Rosalba Perna, Matteo Cantiello, Tyler M. Parsotan, Davide Lazzati, Nathan Walker
Figure caption: Comparison of spectral fluxes and evolutionary tracks for Pop III stars with different initial masses and rotational velocities. Spectral fluxes are shown at ZAMS (blue), IAMS (green), and TAMS (red), which are the points indicated in the H-R diagram of Figure 1. The corresponding blackbody models at the same effective temperatures and radii are shown with dashed curves. Photoionization breaks are indicated with gray dashed lines.
Paper: Spectral Evolution of Rotating Population III Stars
Authors: Jake B. Hassan, Rosalba Perna, Matteo Cantiello, Tyler M. Parsotan, Davide Lazzati, Nathan Walker
Figure caption: Color–magnitude diagrams for clusters of varying multiplicities. The plotted values for each cluster were obtained by summing the fluxes for the cluster’s individual stars, computing the total magnitude and color index for the cluster, and then averaging the cluster’s magnitude and color across 10,000 trials. The error bars indicate the 1σ dispersion. In contrast to Figure 4, the numbers beside the points denote the cluster’s multiplicity rather than its mass.
Paper: Spectral Evolution of Rotating Population III Stars
Authors: Jake B. Hassan, Rosalba Perna, Matteo Cantiello, Tyler M. Parsotan, Davide Lazzati, Nathan Walker
Figure caption: Color–magnitude diagrams in the optimal bands for massive stars near TAMS, at redshifts z = 3 and z = 7.
Paper: Spectral Evolution of Rotating Population III Stars
Authors: Jake B. Hassan, Rosalba Perna, Matteo Cantiello, Tyler M. Parsotan, Davide Lazzati, Nathan Walker
Figure caption: Change in surface rotational velocity in units of the critical velocity (left panel) and mass loss rate (right panel) from pre-MS to helium exhaustion, for a 50 M⊙ model with varying initial velocities.
Paper: Particle-in-cell Simulation of Current-driven Solar Type III Radiation and Whistler Waves in a Core–strahl Plasma: Relevance to PSP and Other Space Observations
Authors: Konrad Sauer, Kaijun Liu
Figure caption: Dispersion of the Langmuir wave and adjacent R and L electromagnetic modes for θ = 15° and two values of G: (a) G = 0.025, and (b) G = 0.1. In (c) the dispersion of the whistler wave is shown, which is nearly the same for both values of G. In case of oblique propagation, mode splitting takes place due to the coupling of the Langmuir and the left-handed electromagnetic wave (L), which cross each other; see the red dashed circles. With increasing G, the crossing point shifts to larger wavenumbers. For G = 0.1, the crossing point is at kc/ωe ∼ 0.3.
Paper: Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
Authors: H. Wu, Y. Guo, R. Keppens, C. Xia, Y. Su, X. L. Kong, M. D. Ding
Figure caption: Trajectory of high-energy electrons (≥25 keV) precipitating on the bottom boundary ((a) and (b)), their precipitation positions (c), and the magnetic field lines around the trajectory (d). Panel (b) enlarges the region within the dashed black box in panel (a), for clarity.
Paper: Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
Authors: H. Wu, Y. Guo, R. Keppens, C. Xia, Y. Su, X. L. Kong, M. D. Ding
Figure caption: (a) Trajectory of a single electron that eventually reaches the bottom boundary source position a. (b) Its kinetic energy and parallel energy change during the trajectory. Both the trajectory and energy are colored according to time. Note that the acceleration from the parallel electric field only happens when the electron is inside the current-sheet structure.