adsabs/AstroCLIMB huggingface dataset:
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Up-to-date (2023 August 29) CRAFTS sky coverage in equatorial coordinates. Search areas in CRAFTS and FASHI regions are highlighted by orange and green labels, respectively. Blue regions refer to CRAFTS sky that has not been searched. Zenith angles of 40° (maximum zenith angle for FAST) and 26﹩\mathop{.}\limits^{\unicode{x000b0}}﹩4 (zenith angle within which FAST has full gain) are shown as black circles and red circles, respectively.
Paper: The FAST H I 21 cm Absorption Blind Survey. II. Statistical Exploration for Associated and Intervening Systems
Authors: Wenkai Hu, Yougang Wang, Yichao Li, Wenxiu Yang, Yidong Xu, Fengquan Wu, Ue-Li Pen, Jie Wang, Yingjie Jing, Chen Xu, Qingze Chen, Zheng Zheng, Di Li, Ming Zhu, Xin Zhang, Xuelei Chen
Figure caption: Left panel: the same as Figure 3, but for NVSS J134035+444817. Middle-left panel: radio map from VLASS centered at NVSS J134035+444817. Middle-right panel: W2 band infrared map of WISEA J134035.20+444817.3 (WISE counterpart to NVSS J134035+44481). from WISE. Right panel: SDSS optical map of the optical counterpart of WISEA J134035.20+444817.3.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Weight assigned to each algorithm by SuperLearner for zt = 3.5 with the raw data set without the M-estimator (top left), the raw data set with the M-estimator (top right), MICE-imputed data set (bottom left), and SMOTE-balanced data set (bottom right). The red vertical line shows the weight cutoff line of 0.05 for algorithm selection. Algorithms above this threshold are selected as the best algorithms.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: For zt = 2.0. Top row: raw without M-estimator. Second row: raw with M-estimator. Third row: MICE imputed. Bottom row: SMOTE balanced. First column: ROC–AUC curve with best algorithms chosen by SuperLearner with weights >0.05 in each data set. Top right: ROC–AUC curve without “caret.rpart.” Second right: ROC–AUC curve without “caret.rpart.” Third right: ROC–AUC curve without “kernelKnn” and “xgboost.” Bottom right: ROC–AUC curve without “kernelKnn,” “ksvm,” “svm,” and “qda.”
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: For zt = 2.5. Top row: raw without M-estimator. Second row: raw with M-estimator. Third row: MICE imputed. Bottom row: SMOTE balanced. First column: ROC–AUC curve with best algorithms chosen by SuperLearner with weights >0.05 in each data set. Top right: ROC–AUC curve without “caret.rpart.” Second right: ROC–AUC curve without “svm” and “xgboost.” Third right: ROC–AUC curve without “kernelKnn” and “xgboost.” Bottom right: ROC–AUC curve without “svm,” “qda,” “xgboost,” and “earth.”
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: For zt = 3.5. Top row: raw without M-estimator. Second row: raw with M-estimator. Third row: MICE imputed. Bottom row: SMOTE balanced. First column: ROC–AUC curve with best algorithms chosen by SuperLearner with weights >0.05 in each data set. Top right: ROC–AUC curve without “ksvm.” Second right: ROC–AUC curve without “earth.” Third right: ROC–AUC curve without “svm.” Bottom right: ROC-AUC curve without “qda” and “xgboost.”
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: For zt = 3.0. Top row: raw without M-estimator. Second row: raw with M-estimator. Third row: MICE imputed. Bottom row: SMOTE balanced. First column: ROC–AUC curve with best algorithms chosen by SuperLearner with weights >0.05 in each data set. Top right: ROC–AUC curve without “ksvm” and “svm.” Second right: ROC–AUC curve without “caret.rpart.” Third right: ROC–AUC curve without “ksvm.” Bottom right: ROC–AUC curve without “ksvm” and “svm.”
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: The top panel shows the differential histogram of log10Ta, with the red line corresponding to 3.85 (2 hr) and 4.94 (1 day). The bottom panel shows the plot of the luminosities in optical with the current nominal limiting flux of the Prime Focus Spectrograph (PFS).
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Top-left panel: the distribution of ﹩\mathrm{log}({\rm{NH}})﹩ vs. z. Top-right panel: ﹩\mathrm{log}({\rm{NH}})-1.39* \mathrm{log}(z+1)﹩ vs. z. Middle-left panel: ﹩\mathrm{log}({T}_{90})﹩ vs. z. Middle-right panel: ﹩\mathrm{log}({T}_{90}^{* })﹩ vs. z. Bottom-left panel: the distribution of ﹩\mathrm{log}({\rm{PeakFlux}})﹩ vs. z. Bottom-right panel: ﹩\mathrm{log}({\rm{Luminosity}})﹩ vs. z for the GRBs in our sample.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Left panel: shows the web-app interface when the user chooses to apply both the M-estimator and MICE-imputation before running SuperLearner. Right panel: depicts the web-app interface where the user chooses to apply the M-estimator, MICE-imputation, and SMOTE balancing, followed by SuperLearner. The user can select the desired value zt within the web app before running SuperLearner.
Paper: GRB Redshift Classifier to Follow up High-redshift GRBs Using Supervised Machine Learning
Authors: Maria Giovanna Dainotti, Shubham Bhardwaj, Christopher Cook, Joshua Ange, Nishan Lamichhane, Malgorzata Bogdan, Monnie McGee, Pavel Nadolsky, Milind Sarkar, Agnieszka Pollo, Shigehiro Nagataki
Figure caption: Confusion matrix in relation to our data where the class 0 refers to low-z and 1 to high-z GRBs.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: (Left) Difference between zspec and zphot,RF as a function of σDT. For clarity, we display only 5% of the data points. The blue line indicates the mean distribution, and the orange and green areas correspond to 1σspz and 3σspz distributions. The vertical dashed lines indicate the σDT = 0.3. (Middle) The distributions of σspz (open blue circle), the uncertainties of zphot,SED (open orange triangles), and the uncertainties of zspec (open green rectangular boxes) across zspec bins. The error bars indicate 1σ scatter. (Right) Normalized differences between spectroscopic and photometric redshifts using σspz. The dashed curve indicates the Gaussian curve fitted to the normalized differences.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: (Top) The redshift difference as a function of rHSC in three zspec bins. Open blue circles, orange squares, and green triangles with error bars indicate the median redshift difference across rmag with 0.0 < zspec < 0.4, 0.4 < zspec < 0.8, and 0.8 < zspec < 2.0 with their 1σ distribution. (Bottom) The redshift difference as a function of zspec in three rmag bins. Open blue circles, orange, squares, and green triangles indicate the median redshift difference across zspec with 15 < rHSC < 21, 21 < rHSC < 23, and 23 < rHSC < 25 with their 1σ distribution.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: Comparison between zphot,RF and zphot,SED for two groups with σDT < 0.3 (filled orange triangles) and with σDT > 0.3 (filled gray circles). For clarity, we display only 5% of the data. Open red circles mark the median zphot,RF with σDT < 0.3 at each zphot,SED bin. In the lower panel, blue dashed lines indicate the maximum and minimum offset between zphot,RF and zphot,SED at each zphot,SED introduced because of the maximum spectroscopic redshift (zspec < 2) of our training sample.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: (Top) Distribution of the magnitude difference between HSC z and Maidanak I normalized by their uncertainties. The blue histogram shows the distribution of 30,406 objects with Maidanak I. The black dashed curve indicates the Gaussian best fit for the distribution. The orange histogram displays the distribution of 5σ outliers. The inset image shows the same distributions but on a linear scale for the number of objects. (Bottom) Same as the top panel, but for HSC i and MegaCam i.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: The accuracy metrics of the photometric redshift estimation for five different input data sets that handle the problematic HSC photometry differently. The top, middle, and below panels show the medians of σz/(1+z), η, and 〈Δz/(1 + z)〉 of each set with their 1σ  scatter, respectively. The dotted line indicates the accuracy metrics from the SED fit of S. C. C. Ho et al. (2021).
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: Cumulative fraction of catastrophic outliers and cumulative number of photometric measurements as a function of σDT. The orange line and shaded region indicate the cumulative fractions of catastrophic outliers and their 1σ scatter. The black line and shaded region indicate the cumulative numbers of photometric redshift measurements and their 1σ scatter. The vertical dashed lines indicate the boundary we determine the reliability of zphot,RF.
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: The accuracy metrics of the photometric redshift estimation depend on the input features. The top, middle, and bottom panels show the medians of σz/(1+z), η, and 〈Δz/(1 + z)〉 of each set with their 1σ scatter, respectively. The dotted lines in the top two panels indicate the accuracy metrics from the SED fit of S. C. C. Ho et al. (2021).
Paper: Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Authors: Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
Figure caption: The permutation importance for top five input features in our zphot,RF determination. The permutation importance for the other 51 input features are very small (< 0.03).
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Comparison of key physical quantities at 3002 resolution using different numerical schemes. First row (a)–(d): rising velocity (Vz/Cs). Second row (e)–(h): Alfvén speed (VA/Cs). Third row (i)–(l): horizontal magnetic field (Bx/B0). Fourth row (m)–(p): density (ρ/ρ0). All quantities are evaluated at ﹩x/{H}_{0}=\left({X}_{\,\rm{max}\,}/2\right)/{H}_{0}﹩, corresponding to the midpoint of the rising loop. Gray and black dashed lines represent theoretical predictions and coronal height base, respectively. Our results exhibit sharper transitions near the contact surface compared to K. Shibata et al. (1989a; see their Figures 7 and 12), demonstrating improved resolution of the magnetic loop structure.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Initial setup. (a) Schematic showing a magnetic flux sheet positioned between heights z0 and z1, with initial velocity perturbation (red patch) and temperature transition from chromospheric (Tch) to coronal (Tcor) values near zcor. (b) Vertical profiles showing density (solid blue) and pressure (dashed red) in logarithmic scale (left), and temperature (dashed black) and magnetic field strength (solid green) profiles (right), illustrating the stratified atmosphere and localized magnetic flux sheet.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: MHD shock analysis using IMWENO-P scheme at 24002 and t = 51τ0. (a) Potential shock locations. The background displays magnetic field lines (gray contours) and velocity vectors (gray arrows). Shocks are categorized as fast (red), slow (blue), and intermediate (green), with darker shades indicating higher Mach numbers (Mf, Ms, MA). Three color bars show the normalized Mach numbers for each shock type, where M > 1 signifies the presence of a shock. (b) Pressure work (−p ∇ · V) distribution with magnetic field lines (dark cyan contours), where red and blue regions indicate expansion (negative) and compression (positive) in logarithmic scale.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Two-dimensional visualization of magnetic field lines (black curves), velocity vectors (white arrows), and density distribution (color) at t = 51τ0 using different numerical schemes. Left column shows results at 3002 resolution; right column at 24002 resolution. While all schemes capture the essential features at both resolutions, the refined mesh reveals more detailed flow structures and sharper discontinuities.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Key physical quantities using the TENO-LAD scheme at 24002 resolution: (a) rising velocity, (b) Alfvén speed, (c) horizontal magnetic field, (d) density, and (e) plasma beta. Measurements are taken at the same evaluation location as in Figure 5, i.e., at ﹩x/{H}_{0}=\left({X}_{\,\rm{max}\,}/2\right)/{H}_{0}﹩.
Paper: A Comparative Analysis of High-resolution Shock-capturing Schemes for Two-dimensional Magnetohydrodynamic Simulation of Flux Emergence in the Solar Atmosphere
Authors: Donghui Son, Yeonwoo Jang, Tetsuya Magara
Figure caption: Time evolution of integrated pressure work (∫Ω (−p ∇ · V) dA) using different numerical schemes at 24002 resolution and t = 51τ0, where Ω denotes the computational domain, and dA = ΔxΔz is the cell area.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Comparison of true vs. predicted values of the dilution factor based on modeling 10,000 synthetic light curves. Red points denote the median value of the obtained ﹩{ \mathcal D }﹩ for a given true value, while error bars represent the 5th and 95th percentiles of this distribution. To enhance visualization, a small horizontal random offset was applied to the data points to reduce overlap.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Shifts in the obtained parameters (e, ω, i regime) relative to the dilution factor. All points represent modeling processes that included ﹩{ \mathcal D }﹩ as a free parameter. For each ﹩{ \mathcal D }﹩, we calculated the median and 16th–84th percentile range of shifts based on a sample of 10,000 synthetic light curves.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Comparison of true vs. recovered parameters after modeling about 6000 synthetic light curves using PHOEBAI. Green/red points represent results with/without the dilution factor as a free parameter. Black dashed lines show the y = x line. The upper/lower sets of plots show results for ﹩{ \mathcal D }=0.9﹩/﹩{ \mathcal D }=0.5﹩.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: Median parameter shifts as a function of the dilution fraction. Red/green lines represent cases without/with ﹩{ \mathcal D }﹩ as a free parameter in the model. For each ﹩{ \mathcal D }﹩, we calculated the median and 16th–84th percentile range of the shift based on the sample of about 6000 synthetic light curves.
Paper: The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models
Authors: Marcin Wrona, Andrej Prša
Figure caption: True vs. obtained values for the R2/R1 parameter in relation to ﹩{ \mathcal D }﹩.
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Ratio of the performance of standard library-provided functions to their NQT counterparts. Bigger is better. Left column is the portable implementation that uses frexp and ldexp and right column is the integer-aliased implementation described in the Appendix. Top row is first-order NQT and bottom row is second-order NQT. In the architecture lists, _intel implies the Intel compiler was used. On CPU systems, the gnu compiler was otherwise used and on GPU systems the relevant vendor-provided compiler (here CUDA) was used. spr stands for Sapphire Rapids. The code was built with the compilation flags inserted by the CMake build system for release mode, which is typically -O2 and -march=native. -std=c++17 is also required. For CUDA builds, we also use –expt-relaxed-constexpr -extended-lambda -Wext-lambda-captures-this. The software versions used are: gcc 12.3.0, CUDA 12.5.0, and Intel 2024.
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Linear interpolation of synthetic function (7) on a true-log grid (left), first-order NQT grid (center), and second-order NQT grid (right). The x-axis is density and the y-axis is the relative error in pressure. On average (in the L1 sense), all interpolants converge at second order, but error spikes near the control points of the NQT gridding converge more slowly. The result is that first-order NQT converges at second order in the L1 norm but slower in the Lp norm for p > 1. In second-order NQT the spikes also converge at second order, recovering second-order convergence in higher pLp norms. In all three figures, a dashed horizontal line shows the expected magnitude for second-order convergence.
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Interpolation of the tabulated equation of state for copper in the Sesame database (S. P. Lyon & J. D. Johnson 1992; J. H. Peterson et al. 2012). The left panel is the fifth-order rational function interpolant produced by the EOSPAC library (D. A. Pimentel 2021), which we treat as ground truth. The middle two panels show errors from first- and second-order NQT interpolation respectively, while the right panel shows error from true-log interpolation.
Paper: Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data
Authors: Peter C. Hammond, Jacob M. Fields, Jonah M. Miller, Brandon L. Barker
Figure caption: Integrated performance data comparing logarithmic with first- and second-order NQT interpolation in an AthenaK simulation of an isolated neutron star on various architectures. Speedups are with respect to logarithmic interpolation. Broadly NQTo2 always provides a speedup in integrated runtime, though smaller than NQTo1. The largest speedup for NQTo2 is about 30% on an RTX 3070. Performance is measured by recording the total number of zone cycles per wallsecond in each calculation.