adsabs/AstroCLIMB huggingface dataset:
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: 2D histogram comparing the ground truth and the HDD output electric current density. Columns from left to right show the x-, y-, z-components, and absolute current magnitude, while rows from top to bottom represent results for the quiet Sun, plage region, and sunspot, with Pearson correlation coefficients specified for each case. Red- and blue-dashed lines denote the identity and the best-fit linear relation, respectively.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: 2D histogram of predicted versus true heights, ZHDD and ZTrue, for all points across all sampled optical depths in each case. Panels (a)–(c) show results for quiet Sun, plage region, and sunspot, respectively. Red- and blue-dashed lines denote the identity and best linear fit (using ∣Bt∣ as the weighting factor), and R marks the Pearson correlation coefficients for each case.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: 2D histogram of the residual magnetic flux across cell surface versus the average magnetic field strength in the corresponding cell. (a) and (b) represent the distributions for plage from HDD’s solution and MURaM simulation, respectively.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 10, but for the Lorentz force distributions.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 5, but for the quiet Sun dataset shown in Figure 4(d).
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 5 but for the sunspot dataset shown in Figure 4(f).
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 7 but for the quiet Sun results.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 11, but for the Lorentz force comparison.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: The same as Figure 19 but showing the Lorentz force.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: The same as Figure 18 but showing the Lorentz force.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 10 but for the quiet Sun results.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 10 but for the sunspot results.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 6 but for the results from the noisy, rebinned dataset.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Columns from left to right show the predicted azimuth angle on ﹩{\mathrm{log}}_{10}\tau =-1﹩ surfaces ((a)–(f)), geometric height on ﹩{\mathrm{log}}_{10}\tau =-1﹩ surfaces ((g)–(l)), and angle distribution along vertical cuts ((m)–(s)), respectively. Rows from top to bottom display the ground truth and predictions for the quiet Sun (QS), plage region (PR), and sunspot (SS), respectively. Black-dashed lines enclose the regions used for model training, while the white-dashed line marks the location of the vertical slice.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 7 but for the sunspot results.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: Same as Figure 9, but for the quiet Sun and sunspot. Panels (a) and (b) show the results for the quiet Sun, while panels (c) and (d) correspond to the sunspot.
Paper: Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). II. A Physics-informed Machine Learning Method for 3D Solar Photosphere Reconstruction
Authors: Kai E. Yang, 凯 杨, Xudong Sun, 旭东 孙, Lucas A. Tarr, Jiayi Liu, 嘉奕 刘, Peter Sadowski, S. Curt Dodds, Matthias Rempel, Sarah A. Jaeggli, Thomas A. Schad, Ian Cunnyngham, Yannik Glaser, Linnea Wolniewicz
Figure caption: The Same as Figure 8 but for the results from the noisy, rebinned dataset.
Paper: The Enhancement of Ion Heating in Kinetic, Antiparallel Reconnection in the Presence of a Flow Shear
Authors: Colby C. Haggerty, Derek Sikorski, Michael A. Shay, Tai D. Phan, Paul A. Cassak, Giulia Murtas, Prayash S. Pyakurel
Figure caption: 2D snapshots from different simulations: the out of plane magnetic field Bz (top row), the ion bulk flow in the outflow/x-direction ux (middle row), and the xx-element of the ion temperature tensor Tixx (bottom row). From left to right, the simulations were initialized with sear flows of 0, (2/5), and (4/5) VA. This overview plot shows the key effects of increasing flow shear on reconnection, including the tilting of the current sheet, the reduction of the outflow velocity, and the significant enhancement in ion exhaust heating.
Paper: The Enhancement of Ion Heating in Kinetic, Antiparallel Reconnection in the Presence of a Flow Shear
Authors: Colby C. Haggerty, Derek Sikorski, Michael A. Shay, Tai D. Phan, Paul A. Cassak, Giulia Murtas, Prayash S. Pyakurel
Figure caption: Diagram of how the exhaust temperature is determined from the inflowing ion populations. The blue distribution function represents ions with a flow shear opposite that of the outflow velocity, while the red distribution function shows ions with a flow shear in the same direction; however, both populations are parallel to the local magnetic field, as shown in the corresponding distribution functions on the left. The distributions are boosted into the outflow frame, as shown on the right side of the diagram. The composite, counterstreaming distribution is shown in the upper box, with the shaded red region showing ions that never reach the exhaust because their parallel speed is greater than the outflow speed. The two distribution functions are taken to isotropize and produce a Maxwell–Boltzmann distribution at rest in this frame with the same kinetic energy as the combined inflowing distribution functions, as shown in the purple box in the bottom right.
Paper: The Enhancement of Ion Heating in Kinetic, Antiparallel Reconnection in the Presence of a Flow Shear
Authors: Colby C. Haggerty, Derek Sikorski, Michael A. Shay, Tai D. Phan, Paul A. Cassak, Giulia Murtas, Prayash S. Pyakurel
Figure caption: Cuts along y of the ion temperature tensor rotated into the frame of the local magnetic field. The cuts are taken 15 di downstream of the x-line in each simulation after steady-state reconnection has been reached. Parallel Ti∥ (top row), perpendicular Ti⊥ (middle row), and average ion temperature (Ti∥ + 2Ti⊥)/3 (bottom row) for simulations with different measured flow shear speeds. The color corresponds to the measured upstream flow speed as indicated by the color bar. The ion temperature in the reconnection exhaust increases with the inflowing flow shear speed.
Paper: The Enhancement of Ion Heating in Kinetic, Antiparallel Reconnection in the Presence of a Flow Shear
Authors: Colby C. Haggerty, Derek Sikorski, Michael A. Shay, Tai D. Phan, Paul A. Cassak, Giulia Murtas, Prayash S. Pyakurel
Figure caption: 2D maps of the predicted ion exhaust temperature as a function of outflow speed (Vo, y-axis) and flow shear speed (VS, x-axis), for different inflowing ion temperatures, as labeled in the title of each subplot. The white dashed lines correspond to the pressure balance/outflow constraint being satisfied (i.e., Equation 2). The dashed white and color-coded lines provide predictions for the outflow as a function of flow shear speed for the different upstream ion temperatures.
Paper: The Enhancement of Ion Heating in Kinetic, Antiparallel Reconnection in the Presence of a Flow Shear
Authors: Colby C. Haggerty, Derek Sikorski, Michael A. Shay, Tai D. Phan, Paul A. Cassak, Giulia Murtas, Prayash S. Pyakurel
Figure caption: Cuts of the xx-element of the ion temperature tensor (top) and the x-element of the ion bulk flow vector (middle). The plots use the same conventions as Figure 2 and are taken at the same locations and times. The light dashed vertical lines show the locations that the averages were determined and are color coded with the left-hand side of the corresponding line. The bottom shows the average values measured within the exhaust as a function of upstream flow shear speed for the bulk outflow (green outlined squares) and the ion thermal speed based on the xx-element of the ion temperature tensor (brown outlined triangles).
Paper: Different Origins of Nucleated and Nonnucleated Dwarf Elliptical Galaxies: Identified by Deep Learning
Authors: Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli, Woong-Bae G. Zee, Jaewon Yoo, Binod Adhikari
Figure caption: The surface number density map of dEs is compared with the spatial distribution of massive galaxies and X-ray emission in the Virgo cluster core region. The surface number density map is constructed using 2D Gaussian kernel density estimation (KDE) with a kernel width of 0﹩\mathop{.}\limits^{\unicode{x000b0}}﹩175 (50 kpc). The color bar at the top represents the normalized density. Red contours indicate the surface flux density of hard X-ray emission (0.4–2.4 keV) in the Virgo cluster region, based on the ROSAT all-sky survey (H. Böhringer et al. 1994). Small gray dots denote individual dEs (from which the surface number density map is generated), while large black dots represent massive galaxies (M* > 1010 M⊙). Large blue dots with their names are the six main subgroups of galaxies.
Paper: Different Origins of Nucleated and Nonnucleated Dwarf Elliptical Galaxies: Identified by Deep Learning
Authors: Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli, Woong-Bae G. Zee, Jaewon Yoo, Binod Adhikari
Figure caption: The neural network architecture of our deep learning framework for dE galaxy detection and localization. Our framework extends the R-CNN to predict both segmentation masks and class probabilities. (a) The input training RGB image is processed by a custom layer CNN to produce multiscale feature maps. (b) A region proposal network (RPN) identifies candidate regions likely to contain galaxies. (c) An example of the input inference image. (d) The probability map produced after processing through CNN and ROI alignment. (e) The potential dE candidates are identified. (f) An external attention network classifier, trained on a dataset of visually identified dE, assigns each region to a class (dE or non-dE). The final output shows “Accepted” (blue) and “Removed” (red) regions, effectively distinguishing true detections from false positives.
Paper: Different Origins of Nucleated and Nonnucleated Dwarf Elliptical Galaxies: Identified by Deep Learning
Authors: Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli, Woong-Bae G. Zee, Jaewon Yoo, Binod Adhikari
Figure caption: Top: fraction of nucleated dEs as a function of magnitude. The black line shows the nucleated fraction measured by R. Sánchez-Janssen et al. (2019) in the Virgo cluster core, while the red line represents our measurement for the full dE sample. The error bar represents the fraction of dEs that we were not able to classify into nucleated and nonnucleated. Bottom: g-band magnitude distributions of nucleated (red histogram) and nonnucleated (blue histogram) dEs, which we have calculated assuming all dEs are located at the average distance of the Virgo cluster, i.e., 16.5 Mpc.
Paper: Different Origins of Nucleated and Nonnucleated Dwarf Elliptical Galaxies: Identified by Deep Learning
Authors: Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli, Woong-Bae G. Zee, Jaewon Yoo, Binod Adhikari
Figure caption: All sky distribution of identified dEs in our sample. A subset of the full sample that has a line-of-sight radial velocity measurement is shown in black. We also highlight the six main subgroups of galaxies in the Virgo cluster area with large blue dot symbols and their names.
Paper: Different Origins of Nucleated and Nonnucleated Dwarf Elliptical Galaxies: Identified by Deep Learning
Authors: Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli, Woong-Bae G. Zee, Jaewon Yoo, Binod Adhikari
Figure caption: The g-band magnitude distributions of our dE sample (gray) and a subset having radial velocity information (black).
Paper: Earth Analogs in Reflected Light: Insights from Early Spectral Characterization in Unconstrained Orbits
Authors: Arnaud Salvador, Tyler D. Robinson
Figure caption: Model-generated (synthetic) reflected-light spectrum of our fiducial case (plain line): an Earth-like planet at quadrature (i.e., at a planetary phase angle α = 90°; see Table 1 for fiducial input values), and assuming gray cloud properties (dashed line), which notably do not capture the absorption feature of water-ice clouds near 1.6 μm. The colored areas indicate the spectral imprints of the gas species by showing the difference between the baseline spectrum (plain line) and the flux measured if they were absent from the atmosphere and replaced with Ar, the background gas. The top of the figure indicates the spectral ranges considered, along with their resolving power res = λ/δλ (Table 2): the blue (λ = [0.43–0.53] μm, res = 140) + red (λ = [0.87–1.05] μm, res = 140) bandpasses, visible (λ = [0.45–1] μm, res = 140) + near-infrared (NIR; λ = [1–1.80] μm, res = 70) bandpasses (together denoted vNIR), or red, visible, and NIR bandpasses observed individually. The error bars represent the nonrandomized, wavelength-independent noise levels corresponding to specific signal-to-noise ratios (S/Ns), set at the lower bound of each spectral coverage (see Appendix A and Figure 8): λ0 = 0.43 μm for the blue+red bandpass, λ0 = 0.45 μm for the visible and vNIR spectral ranges, λ0 = 0.87 μm for the red bandpass, and λ0 = 1.00 μm for the NIR bandpass.
Paper: Earth Analogs in Reflected Light: Insights from Early Spectral Characterization in Unconstrained Orbits
Authors: Arnaud Salvador, Tyler D. Robinson
Figure caption: Posterior distributions of gas abundances obtained from observations in different color-coded spectral coverages at signal-to-noise ratios (S/Ns) of 10 (left side of the split-violins, without contour lines), 15 (right side, with dashed contours), and 20 (right side, with solid contours). From top to bottom: (left column) molecular nitrogen, molecular oxygen, water vapor, (right column) carbon dioxide, methane, and ozone. The horizontal dashed lines spanning each violin row indicate Earth-like input values, while the dashed lines within the violins show the 25th, 50th, and 75th percentiles of the data (the first quartile, median, and third quartile, respectively). The density has been normalized across all kernel density plots so that each has the same area.
Paper: Earth Analogs in Reflected Light: Insights from Early Spectral Characterization in Unconstrained Orbits
Authors: Arnaud Salvador, Tyler D. Robinson
Figure caption: Posterior distributions of cloud properties obtained from observations in different color-coded spectral coverages at signal-to-noise ratios (S/N) of 10 (left side of the split-violins, without contour lines), 15 (right side, with dashed contours), and 20 (right side, with solid contours). From top to bottom: (left column) cloud-top pressure, thickness, (right column) optical depth, and cloudiness fraction. The horizontal dashed lines spanning each violin row indicate Earth-like input values, while the dashed lines within the violins show the 25th, 50th, and 75th percentiles of the data (the first quartile, median, and third quartile, respectively). The density has been normalized across all kernel density plots so that each has the same area.
Paper: Earth Analogs in Reflected Light: Insights from Early Spectral Characterization in Unconstrained Orbits
Authors: Arnaud Salvador, Tyler D. Robinson
Figure caption: Posterior distributions of surface and atmospheric condition parameters obtained from observations in different color-coded spectral coverages at signal-to-noise ratios (S/Ns) of 10 (left side of the split-violins, without contour lines), 15 (right side, with dashed contours), and 20 (right side, with solid contours). From top to bottom: surface pressure, atmospheric temperature, and surface albedo. The horizontal dashed lines spanning each violin row indicate Earth-like input values, while the dashed lines within the violins show the 25th, 50th, and 75th percentiles of the data (the first quartile, median, and third quartile, respectively). The density has been normalized across all kernel density plots so that each has the same area.
Paper: Earth Analogs in Reflected Light: Insights from Early Spectral Characterization in Unconstrained Orbits
Authors: Arnaud Salvador, Tyler D. Robinson
Figure caption: Posterior distributions of the planet radius (top) and mass (bottom) obtained from observations in different color-coded spectral coverages at signal-to-noise ratios (S/Ns) of 10 (left side of the split-violins, without contour lines), 15 (right side, with dashed contours), and 20 (right side, with solid contours). The horizontal dashed lines spanning each violin row indicate Earth-like input values, while the dashed lines within the violins show the 25th, 50th, and 75th percentiles of the data (the first quartile, median, and third quartile, respectively). The density has been normalized across all kernel density plots so that each has the same area.
Paper: Earth Analogs in Reflected Light: Insights from Early Spectral Characterization in Unconstrained Orbits
Authors: Arnaud Salvador, Tyler D. Robinson
Figure caption: Schematic of the rfast retrieval framework. Boxes denote model inputs and outputs, while circles represent components of the framework. Here, the “observed” spectrum is a synthetic observation of an Earth analog, generated using Earth-based fiducial parameters (see Table 1).
Paper: Constraining the Baryon Content of Cosmic Filaments Using Localized Fast Radio Bursts and DESI Imaging Data
Authors: Jian-Feng Mo, Weishan Zhu, Qi-Rui Yang, Yi Zheng, Long-Long Feng
Figure caption: Similar to Figure 3, the left panel presents results based on all 46 localized FRBs within the DESI sky area, using filaments identified by DisPerSE with a persistence threshold of nsig=5. In contrast, the right panel shows results for the 37 securely localized FRBs, with filaments extracted using a lower threshold of nsig=3.
Paper: Constraining the Baryon Content of Cosmic Filaments Using Localized Fast Radio Bursts and DESI Imaging Data
Authors: Jian-Feng Mo, Weishan Zhu, Qi-Rui Yang, Yi Zheng, Long-Long Feng
Figure caption: Estimated baryon mass fraction in filaments across redshift bins. Blue and orange lines show results from this work using 37 securely localized FRBs within the DESI area and DisPerSE-identified filaments (galaxy catalog from DESI imaging surveys) with persistence thresholds nsig=3 and nsig=5, respectively. The green line and red point represent estimates from simulations by W. Zhu & L.-L. Feng (2017) and N. I. Libeskind et al. (2018).