adsabs/AstroCLIMB huggingface dataset:
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Trajectories of 10 randomly chosen footpoints in the bottom photospheric driving plane. The color bar shows the evolution of time, with time = 0 denoting the time when we start our analysis (∼350 minutes after the start of the simulation), which corresponds to a time when the system has already reached a statistical steady state. The zoomed-in trajectory of the footpoint indicated by the dashed box on the left is displayed in the right plot. The trajectories clearly show the rotational and translational aspects of our photospheric driving.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Sketch showing the method of determining δ–a quantity to identify if a field line has undergone reconnection.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: (Left) Random field lines and (right) ±2 × 10−3 Amp m−2 iso-surfaces of vertical current density in the multistrand simulation. The elongated current sheets are formed at the boundaries of misaligned quasi-homogeneous flux strands produced by driving at both ends.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Top panel: evolution of the reconnection jump (δ) in gray and the coronal-integrated viscous heating rate in blue for a randomly chosen footpoint. The δ are normalized with the grid resolution (δgrid). Bottom panel: normalized δ (in gray) and the corresponding heights of the maximum viscous heating rate (in blue) for the chosen field line in the chosen duration marked by the dashed box in the top panel. The gray dashed line marks δcut = 2 grid cells, while the blue horizontal lines indicate the coronal boundaries. The two orange bars show the durations of two nanoflares as defined in the main text using twindow = 10 s.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Top panel: the coronal-integrated viscous heating rate and reconnection jumps normalized by the grid resolution. The blue circles are nanoflares identified from Method A. Middle panel: zoom-in of the reconnection jumps. The collective nature of the jumps demonstrates multiple reconnections while the field line crosses the current sheet diffusion region. The durations of the nanoflares are shown with orange bars for twindow = 10 s. Bottom panel: corresponding zoom-in of the viscous heating rate. Method A captures only a part of the energy released by the reconnections because the defined nanoflare duration fails to cover the entire heating event.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Evolution of the coronal-integrated viscous heating rate for a randomly chosen field line over the entire 6000 s tracking period. Blue circles, green stars, and red circles indicate the times when nanoflares are identified from Methods A, B, and C, respectively, with twindow = 10 s. The corresponding durations are indicated by orange, green, and purple bars. The nanoflare identified by the black arrow near 4000 s is an example of how Method B can sometimes measure extreme durations (∼2500 s in this case.)
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Nanoflare energies and durations from Method A with δcut=2 grid cells and twindow=5, 10, and 20 s (top to bottom). The left column shows the 2D histograms of nanoflare energies and durations. The middle and right columns show the energy distributions obtained by collapsing the histograms in the vertical direction. Double power-law fits and log-normal fits are indicated in the middle and right columns, respectively.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: 2D histogram of δ jumps vs. the location of the maximum viscous heating rate along a field line obtained from 3136 footpoints tracked over 6000 s.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: A comparison plot showing nanoflares identified from Methods A, B, and C. The orange, green, and purple bars are the nanoflare durations from Methods A, B, and C, respectively, with twindow = 10 s. The zoomed-in box in the bottom panel shows the presence of short duration nanoflares not visible in the original panel. The blue circles are nanoflares from Methods A and B, while nanoflares from Method C are marked by the red circles.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: A comparison plot showing nanoflares identified from Methods A and B. The orange and green bars are the nanoflare durations from Methods A and B, respectively. The blue circles are the nanoflares identified from both the methods. twindow used in this case is 10 s.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Table summarizing all three methods introduced in this paper to define a nanoflare and its duration and energy.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: An example when the delay between consecutive nanoflares can be as long as 4000 s for Methods A and B.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Histograms illustrating the relative fraction of nanoflare energy compared to the total energy from viscous shock heating per field line. The histograms indicate that nanoflares identified by Method A contribute at least 30% of the total energy per field line, whereas those from Methods B and C contribute at least 40% and 50%, respectively.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Cumulative viscous shock heating vs. percentage of contributing grid cells. At this instant (t  = 1500 s), less than 3% of the grid cells account for 50% of the total heating. This behavior is consistent at all times once the system achieves a statistical steady state.
Paper: Characterizing Nanoflare Energy and Frequency through Field Line Analysis
Authors: Shanwlee Sow Mondal, Lars K. S. Daldorff, James A. Klimchuk, Craig. D. Johnston
Figure caption: Histograms showing the number of nanoflares occurring per field in different energy ranges. The nanoflares in this example are defined using Method B.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: Comparison of the τ, reconstructed ﹩\widehat{K}﹩, and ﹩\widehat{K}\times \tau ﹩ signal power spectra from the EoR (AMBER) and low redshifts (Agora) alongside their effective noise curves at 0 < L < 3000. The orange signal curves are power spectra from three different AMBER models shown by the different shades in the legend. The black noise curves represent quadratic reconstructions of these fields, assuming white noise levels and beam for CMB-S4 temperature and polarization maps. The “S4+FG” noise curve represents the post-ILC noise forecast for CMB-S4, which includes CMB foreground power. The blue dashed curves are the power spectra of the three Agora simulations (discussed in Section 6), demonstrating that they contribute roughly the same amount to the total as the EoR.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: A demonstration of all the components used to forecast the signal-to-noise ratios of ﹩{C}_{L}^{KK}﹩, ﹩{C}_{L}^{\tau \tau }﹩, and ﹩{C}_{L}^{K\tau }﹩. (A) A plot of the lensed primary CMB temperature power spectrum, the fiducial kSZ power spectrum, and the four temperature noise curves for the different surveys used in this work. (B) The effective kSZ filters, ﹩{W}_{l}^{{\rm{kSZ}}}\equiv \sqrt{{C}_{l}^{TT,{\rm{kSZ}}}}/{C}_{l}^{TT,{\rm{t}}}﹩ used to reconstruct ﹩\widehat{K}﹩ in this work, demonstrating the sensitivities of the different survey configurations to the kSZ signal. (C) The filters on the CMB E-mode polarization, ﹩{W}_{l}^{{\tau }_{E}}\equiv {C}_{l}^{EE}/{C}_{l}^{EE,{\rm{t}}}﹩ for S4 and HD τ reconstruction calculations. (D) The filters on the CMB B-mode polarization, ﹩{W}_{l}^{{\tau }_{B}}\equiv 1/{C}_{l}^{BB,{\rm{t}}}﹩ for S4 and HD τ reconstruction calculations. Together these curves demonstrate the higher sensitivity that CMB-HD would have compared to CMB-S4. For kSZ science specifically, CMB-HD has much better sensitivity at much smaller scales than CMB-S4, even when foreground power is considered.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: A demonstration of the map-level effects of patchy screening and the kSZ effect. The left panel shows the primary, unmodified CMB. Panel (A) (center) demonstrates the screening that happens to the primary CMB when a bubble of electrons is simulated in the foreground, located inside the dashed green circle. Note that the CMB fluctuations, both temperature (colors) and polarization (black vectors), are “washed out” by the Thomson scattering. Panel (B) (right) demonstrates the shift in the CMB temperature fluctuations that happens when the bubble of electrons is given a positive line-of-sight velocity with respect to the Hubble flow. This leads to a colder (redder) CMB spot in temperature; the polarization is unaffected at this order in scattering.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: A three-panel display of 2° × 2° cutouts of the fiducial AMBER maps used in this work. Left: the reionization kSZ map showing the changes to CMB temperature that the kSZ effect makes. Center: the τ map showing the projected electron density fluctuations centered about ﹩\bar{\tau }=0.06﹩. We have added the low-z value of ﹩{\bar{\tau }}_{z\lt 5}=0.029﹩ to the AMBER map to include the optical depth from redshifts below those simulated by AMBER. Right: in purple, the ﹩\hat{K}(\hat{{\boldsymbol{n}}})﹩ map reconstructed using the kSZ map on the left and overlaid with colored τ contours from the center map to display the correlations between the two.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: Forecasts of the CMB-S4 error bars on different cross-power spectra between the τ and ﹩\widehat{K}﹩ fields corresponding to different EoR redshift durations. The solid, thick error bars represent the forecasts that only consider the CMB-S4 white noise level and beam, and the thin dashed error bars also contain the foreground power with the post-ILC noise level from CMB-S4. This demonstrates that residual foreground power has a significant impact on the ﹩{C}_{L}^{K\tau }﹩ SNR for CMB-S4.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: Forecasts of the CMB-HD error bars on different cross-power spectra between the τ and ﹩\widehat{K}﹩ fields corresponding to different EoR redshift durations. Foreground power is expected to have a much smaller impact on the size of these errors than for CMB-S4. Compared to CMB-S4 in Figure 6, both types of CMB-HD errors are much smaller and provide better SNR due to the reduced noise level, smaller beam, and more efficient delensing from CMB-HD.
Paper: Cross-correlating the Patchy Screening and Kinetic Sunyaev–Zel’dovich Effects as a New Probe of Reionization
Authors: Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
Figure caption: The dimensionless cross-correlation coefficient demonstrating the strength of the correlation between the fiducial ﹩\widehat{K}﹩ and τ AMBER fields up to L = 6000. This correlation peaks from 500 < L < 1000 at close to 50%. This is not a perfect correlation because the τ and ﹩\widehat{K}﹩ fields trace the electron density fields with different weights.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: Outflow velocities derived from our line fitting procedure against spectroscopic redshift. The various marker shapes represent the morphology groupings described in the text. Included as orange stars and blue squares are outflow candidates from Ca24 and Xu25, respectively. The two AGN candidates and the single little red dot (LRD) identified in this study (J. Scholtz et al. 2023 and P. Rinaldi et al. 2025a) are shown in green, blue, and purple, respectively.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: Top: The MEx diagram for the outflow candidates within our sample. Regions are taken from S. Juneau et al. (2014). The AGN region is shaded red while the star-forming region is shaded blue. The composite region is shaded gray. Bottom: The BPT diagram for the outflow candidates in our sample with detectable Hα. Separations are taken from G. Kauffmann et al. (2003) and L. J. Kewley et al. (2001). AGN classification is the shaded red region while the star-forming region is shaded blue. The composite AGN-SF region is left unshaded. Upper limits on line flux ratios are denoted by arrows. The only robust AGN candidate with high S/N emission lines is in blue, while the AGN and LRD identified in J. Scholtz et al. (2025) and P. Rinaldi et al. (2025a) are green and purple, respectively.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: The SFR−M* plane for the [O III] emitters considered in this work binned by redshift. Candidate outflow galaxies are shown with the same red markers as Figure 4 while the remaining [O III]-emitting sample is shown as light gray points. Blue squares and orange stars are from Xu25 and Ca24, respectively. The lower envelope of the starburst regime as empirically defined by K. I. Caputi et al. (2017, 2021) is shaded in blue. The star-forming main sequence (SFMS) from P. Popesso et al. (2023) is plotted in red, with the age parameter set as the median age of the Universe in each redshift bin. Typical errors associated with our entire sample are boxed in the leftmost panel. The known AGN have been excluded from this figure as their SFRs are likely poorly estimated.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: RGB image cutouts for a selection of outflow candidates with NIRCam coverage. Images are generated using all available photometric bands and the trilogy (D. Coe et al. 2012) package. Target galaxies are circled in red while nearby/companion objects are circled in blue. Spectroscopic redshifts are obtained from our line fitting while photometric redshifts come from the JADES public release photometric catalogs. We see a diverse range of morphologies ranging from clumpy or candidate mergers, extended or disk-like galaxies, or highly compact sources.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: Example cutouts for three of our outflow candidates. First column: cutout from the filter within which the [O III] emission line complex resides. Second column: cutout from the nearest filter to the [O III] line that maps the optical continuum without including the line itself. White contours trace the optical continuum while gray contours trace the [O III] emission. Third column: emission line map built from the subtraction of the optical continuum from the [O III] emission. The [O III] emission is blocked in blue while the surrounding continuum is blocked in red. Fourth column: the radial surface brightness profile of the continuum and emission line map. The surface brightness is normalized to the center of each respective cutout.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: An example of our outflow candidate selection process. Left: The [O III]5007 line of candidate JADES-GN+189.11339-62.22768 fitted with a single narrow Gaussian (blue). The flux excess on the blue side of the line is shaded gray. We see that the flux (top panel) and residual (lower panel) are both significantly above the associated errors (gray dashed). Right: The same emission line fit with an additional broad component (red). This second component improves the residual while remaining significantly above the associated uncertainties.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: The derived outflow velocity for our sample against host physical parameters binned by redshift. Upper: comparison with stellar mass. Middle: comparison with UV SFR. Lower: comparison with specific SFR derived from the UV SFR. Outflow candidates from Xu25 and Ca24 are shown in blue squares and orange stars, respectively. The empirical starburst separation from K. I. Caputi et al. (2017, 2021) is shaded in blue.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: The ratio of outflow velocity vs. escape velocity calculated assuming a spherical dark matter halo against stellar mass and SFR. The point of unity is plotted as a dashed line. Values above this value represent outflows that can escape the galaxy, while those below return to the interstellar medium.
Paper: High-velocity Outflows in [O III] Emitters at 2.5 < z < 9 from JWST NIRSpec Medium-resolution Spectroscopy
Authors: Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
Figure caption: The NIRSpec slit orientation plotted over one of the objects in our sample. Multiple individual features are clear across the RGB image and the F200W filter, and the NIRSpec slit overlaps all of them. Since the F200W filter covers the [O III] emission of this object, it is possible for the broadened emission line to be a result of these clumps.
Paper: The Balmer Break and Optical Continuum of Little Red Dots from Super-Eddington Accretion
Authors: Hanpu Liu, 翰溥 刘, Yan-Fei Jiang, 燕飞 姜, Eliot Quataert, Jenny E. Greene, Yilun Ma, 逸伦 马
Figure caption: Model continuum spectra in the disk scenario. Left: the MBH = 107 M⊙, λEdd = 102 case. Each curve shows the flux integrated over disk annuli from Rin to Rout = 104.3 Rg. The legend marks the inner radius, Rin, and the effective temperature at the inner radius, Teff,in, derived from Equation (3). A blackbody curve at T = 5000 K and the spectrum of an LRD, RUBIES-UDS-31747, are shown for comparison. A collection of observed LRD spectra are shown in gray in the background. Right: same as the left panel but for the MBH = 107 M⊙, λEdd = 100 case, with Rout = 103.6 Rg.