adsabs/AstroCLIMB huggingface dataset:
Paper: Bridging Unstratified and Stratified Simulations of the Streaming Instability for τs = 0.1 Grains
Authors: Jeonghoon Lim, 정훈 임, Stanley A. Baronett, Jacob B. Simon, Chao-Chin Yang, 朝欽 楊, Debanjan Sengupta, Orkan M. Umurhan, Wladimir Lyra
Figure caption: Similar to Figure 4 but for the dispersions (Equations (8), (9), and (14)). Note that unlike Figure 4, we show the dispersion profile within ±ΠH, and velocities are in units of cs. Near the midplane, the dispersions measured in the unstratified and stratified cases are nearly identical.
Paper: Bridging Unstratified and Stratified Simulations of the Streaming Instability for τs = 0.1 Grains
Authors: Jeonghoon Lim, 정훈 임, Stanley A. Baronett, Jacob B. Simon, Chao-Chin Yang, 朝欽 楊, Debanjan Sengupta, Orkan M. Umurhan, Wladimir Lyra
Figure caption: Similar to Figure 4 but showing velocities in the COM frame for the gas and dust. The top two panels display the radial velocities of gas and dust, respectively; the bottom two panels show the azimuthal velocities. Horizontal dotted lines mark the corresponding values from B24. Note that ux, uy, vx, and vy are density-weighted. At the midplane, the azimuthal velocities differ by ∼2.5% of Πcs and about 1σ temporal variability, whereas the radial velocities agree much more closely, showing that stratified and unstratified simulations remain consistent in the COM frame.
Paper: Bridging Unstratified and Stratified Simulations of the Streaming Instability for τs = 0.1 Grains
Authors: Jeonghoon Lim, 정훈 임, Stanley A. Baronett, Jacob B. Simon, Chao-Chin Yang, 朝欽 楊, Debanjan Sengupta, Orkan M. Umurhan, Wladimir Lyra
Figure caption: Radial (top) and vertical (bottom) diffusion coefficients as a function of Π for simulations with τs = 0.1 and Z/Π = 0.24. In each panel, black and light blue markers represent the diffusion coefficients from this work and B24, respectively. Solid lines indicate the best-fitting power-law models, defined by Equation (15) (see figure title), with the best-fitting parameters shown in the legends. Despite minor differences in the data points and fitting lines, the unstratified and stratified models yield similar diffusion coefficients and exhibit consistent trends with Π.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Redshift vs. T90 for Sa and Sw GRBs. The solid circles represent the Sa sample, and the hollow circles represent the Sw sample. The top panel shows the normalized cumulative distribution function (CDF) of their redshifts, with the thick line representing Sa and the thin line representing Sw. The right panel illustrates the distribution of their durations, where the filled histogram represents Sa and the grid histogram represents Sw.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Comparison of the Amati relation for Sa and Sw GRBs. The open circles are GRBs without SN association, i.e., the Sw sample. The corresponding data are taken from Tables 2, 3, and Y. Li et al. (2016). The filled circles are GRBs with SN association, i.e., the Sa sample. The data are listed in Table 1. The solid and dashed lines represent the original Amati relation obtained by L. Amati et al. (2008; with a PL index of m = 0.57 ± 0.01) and the 2σ uncertainty.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Redshift z, duration T90, peak energy Ep, and isotropic energy Eiso plotted versus the corresponding bolometric peak luminosity ﹩{L}_{{\rm{p}},{\rm{SN}}}﹩ of the accompanying SN for the SN-GRBs (i.e., the Sa sample). The bolometric peak luminosity of SNe is taken from S. Klose et al. (2019) and Y. Aimuratov et al. (2023a). The dashed line represents the average value of SN peak luminosity, i.e., ﹩{L}_{{\rm{p}},{\rm{SN}},{\rm{avg}}}=9.42\times 1{0}^{42}{\rm{erg}}\,{{\rm{s}}}^{-1}﹩.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Normalized CDF of the projected offset for Sa, Sw, and SLSNe samples. The thickest line represents the GRB sample without an SN association, i.e., Sw. The medium-thick line represents the sample with SN association, i.e., Sa. The thinnest line represents SLSNe (B. Hsu et al. 2024). The dashed line represents SGRBs (N. Gaspari et al. 2025).
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Distribution of host metallicity (﹩\mathrm{log}({Z}/{{Z}}_{\odot })﹩) for the Sa and Sw GRBs. The hollow histogram and the solid-filled histogram represent Sa and Sw samples, respectively. The dotted–dashed line and the dashed line represent the normalized cumulative distribution curve of Sa and Sw GRBs, respectively.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Distribution of SFR of host galaxy for the Sa and Sw GRBs. The hollow histogram and the solid, filled histogram represent Sa and Sw samples, respectively. The dotted–dashed line and the dashed line represent the normalized cumulative distribution curve of Sa and Sw, respectively.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Event rates of GRBs derived from the Sa and Sw samples. The scattered points represent the observational data of SFR (X. F. Dong et al. 2022). Note that the GRB event rates were scaled by an arbitrary factor.
Paper: Simulation of Solar Surface Flux Transport Constrained by Magnetic Power Spectra. I. Flux Transport Parameter
Authors: Yukun Luo, Jie Jiang, Ruihui Wang
Figure caption: Variation of the consistency percentage f with dynamo effectivity range λR.
Paper: A Statistical Study of the Gamma-Ray Burst and Supernova Association
Authors: Xiao-Fei Dong, Yong-Feng Huang, Zhi-Bin Zhang, Jin-Jun Geng, Chen Deng, Ze-Cheng Zou, Chen-Ran Hu, Orkash Amat
Figure caption: Event rates derived from the Sa, SNIcBL, and SNSL samples. The SLSN data are taken from S. Gomez et al. (2024). The Ic-BL SNe data are presented in Table 5. The scattered points represent the observational data of SFR (X. F. Dong et al. 2022). The y-axis is SFR. Note that the event rates derived for GRBs and SNe are scaled by an arbitrary factor.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The resolved mass–metallicity relation (rMZR) in IllustrisTNG. The rMZR in TNG at z = 0 with spaxel resolution of 1 kpc (shifted by −0.2 dex). The solid line shows the median value of ﹩12\,+\,{\rm{log}}({\rm{O}}/{\rm{H}})﹩, and the shaded region indicates the 16th–84th percentile scatter of the background distribution of spaxels. For comparison, the dashed line is the analogous result from the EAGLE simulation (J. W. Trayford & J. Schaye 2019; which itself is shifted by −0.6 dex from the true metallicity values of EAGLE). The dotted line is from the MaNGA survey (J. K. Barrera-Ballesteros et al. 2016), and the dashed–dotted line is from the CALIFA survey (S. F. Sánchez et al. 2013). We caution against too strong a comparison against observational results, however; see Section 3.2 for more details.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The rSFMS for galaxies binned by total mass. The rSFMS for galaxies within different mass bins as indicated by the legend. The gray solid line plots the median rSFMS relation, and the black dashed line indicates the 1σ scatter. The grayscale background shows the distribution of spaxels, the same as Figure 2.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The gas mass main sequence in IllustrisTNG. The fgas–Σ⋆ relation in TNG at z = 0 with 1 kpc spaxel resolution. The distribution in the background is the full population of spaxels from TNG, and the solid black line is the median of this distribution. The dashed colored lines represent the median fgas–Σ⋆ relation in different stellar mass bins.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: Resolved Schmidt–Kennicutt (SK) relationship in TNG. The SK relation for spaxels in TNG at z = 0 with 1 kpc spaxel resolution. The colored lines represent the median SK relation in different mass bins, while the black line represents the median relation including all spaxels. The background histogram is the full population of spaxels.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The rMZR for galaxies binned by total mass. The rMZR is split into different global stellar mass bins (colored lines). The black solid line represents the median rMZR, and the gray shaded region is the 1σ scatter (same as Figure 5). The background histogram is the full population of spaxels.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The gas mass main sequence in a resolved closed box model. Evolution of simulated closed boxes on the MZR plane. The grayscale background displays our IllustrisTNG spaxels. The dashed lines track the evolution of individual closed-boxed spaxels. The colored solid lines show our closed box predictions at various times, as labeled.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The radial dependence of the rSFMS. Same as Figure 4, but now binned by total stellar mass of the host galaxy (top left is 109.0 < M⋆/M⊙ < 109.5, top right is 109.5 < M⋆/M⊙ < 1010.0, bottom left is 1010.0 < M⋆/M⊙ < 1010.5, and bottom right is 1010.5 < M⊙ < 1011.0). Additionally, each panel is color-coded by the average galactocentric radius of the spaxels.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: The resolved fundamental metallicity relation (rFMR) in IllustrisTNG. Left: the rMZR for different ΣSFR bins. The 2D histogram in the background is the full distribution of metallicities from TNG (same as Figure 5), while the gray lines are the median (solid) as well as 16th and 84th percentiles (dashed) of the distribution.
Paper: Star Formation Rates, Metallicities, and Stellar Masses on Kiloparsec Scales in TNG50
Authors: Jia Qi, Alex M. Garcia, Davis Robinson, Paul Torrey, Jorge Moreno, Kara N. Green, Aaron S. Evans, Z. S. Hemler, Lars Hernquist, Sara L. Ellison
Figure caption: Radial dependence of the SK relation. The SK (same as Figure 8) colored-coded by the average galactocentric distance of spaxels.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: The light curve of EP240617a. Panel (A) shows the background-subtracted light curves for WXT and GBM, with the red solid line indicating the Bayesian block result. The green-shaded region indicates the T90 interval of the GBM data. Panel (B) shows the GBM light curve without background subtraction. The blue curve represents the orbit-averaged background, while the orange line indicates the background-derived count rate corresponding to SNR = 2. The black arrow indicates a time around 80 s, at which a corresponding increase is also observed in the WXT data.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: Comparison of EP240617a with historical XRRs and XRFs. Panels (A) and (B) show the distributions of fluence ratios and Ep, respectively, for Fermi-GBM detected GRB samples. The red dashed vertical lines mark the subclass boundaries of GRBs and are labeled accordingly. The blue vertical lines and shadow area indicate the position of EP240617a within each distribution. Panel (C) shows the CPL parameter space, with photon index and Ep on the x- and y-axes, respectively. The color scale represents the fluence ratio S(25–50 keV)/S(50–100 keV). Solid and dashed lines indicate subclassification boundaries based on fluence ratio and Ep. EP240617a is marked by a blue star with 1σ error bars.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: The Ep,z–Eγ,iso diagram. The light blue, light red, and purple points represent the data of Type I and Type II GRBs and soft gamma-ray repeater giant flares (SGR GFs), respectively, with known distances (B. Zhang 2006; B. Zhang et al. 2009; P. Y. Minaev & A. S. Pozanenko 2020a, 2020b). The corresponding dashed lines indicate the 2σcor confidence regions of the correlations. The black pentagrams represent the calculated results for EP240617a at different redshifts, while the black curve indicates the results for redshifts varying from 0.01 to 5.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: GBM visibility map at the time of EP240617a. In the upper panel, the blue area indicates the region obscured by the Earth, while the red pentagram marks the source position. In the lower panel, the red area represents the SAA region, and the satellite icon traces the orbit 20 minutes before and after the event.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: The posterior distributions from fitting the CPL model using response matrices generated from historical pointing files and real-time pointing files, corresponding to the left and right panels, respectively. The red line represents the median value, while the green dashed lines indicate the 1σ confidence interval.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: Light curve of EP240617a generated from nb and b1 detector TTE data. The blue shaded area indicates the interval used for background fitting, the blue and orange solid lines are the fitted background and the count rate corresponding to SNR = 3, respectively, and the red solid line is the trigger time of EP240617a.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: Distribution of angular deviations obtained by simulating the use of historical poshist files.
Paper: RapidGBM: An Efficient Tool for Fermi-GBM Visibility Checking and Data Analysis with a Case Study of EP240617a
Authors: Yun Wang, Jia Ren, Lu-Yao Jiang, Hao Zhou, Yi-Han Iris Yin, Yi-Fang Liang, Zhi-Ping Jin, Yi-Zhong Fan, Da-Ming Wei, Wei Chen, Hui Sun, Jing-Wei Hu, Dong-Yue Li, Jun Yang, Wen-Da Zhang, Yuan Liu, Wei-Min Yuan, Xue-Feng Wu
Figure caption: The spectral analysis of EP240617a for intervals a and b. The top panel shows the observed count spectra with best-fit models, with shaded regions indicating the 90% confidence intervals. The middle panel presents the corresponding νFν spectra with the same confidence regions, and the bottom panel displays the data-to-model ratios, with interval a in light blue and interval b in light orange.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Fourier transform of models with a single torus. The black curve represents the full-time signal, while the colored dashed lines correspond to five evenly divided time segments of the signal. The name of each model is indicated in the top-right corner of each subplot. Amplitudes have been scaled by a factor of 1/20 for clarity. Each simulation has a Nyquist frequency of 10−2 (except “Contra” models) in geometrized units, with M = 1.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Light curves for different model groups: Single models, Corot models, and Contra models. Each subplot shows the time evolution of the flux for several simulations, where different colors and line styles correspond to different models as labeled. The black, green, blue, red, and purple curves represent variations among models within each group. Time is shown in units of M, and flux is shown in arbitrary units (Jy).
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Time-averaged intensity profiles along the equatorial plane, computed for five equal time segments of the simulation. Each curve represents the mean intensity extracted from the central horizontal slice of IPOLE images during a different time interval. From top to bottom: Schw, Single, Corot, and Contra models.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Same figure as Figure 6 for Contra models.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Same figure as Figure 6 for Corot models.
Paper: Oscillatory Behaviors in General Relativistic Magnetohydrodynamical Simulations of Interacting Toroids
Authors: Dilshodbek Bardiev, Jan Schee, Zdeněk Stuchlík
Figure caption: Lorentzian fits to the Fourier spectra of the signals from four simulation models: Contra, ContraM, Corot, and CorotM. The top panel compares the fitted spectral profiles for Contra and ContraM during Part 2 of the evolution. The lower two panels compare Corot and CorotM during Part 3 and Part 4, respectively.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: In the top row, all functionalized cyclopentadienes: (a) fulvene (c-C5H3CH2), (b) fulvenallene (c-C5H4CCH2), (c) cyclopentadienone (c-C5H4O), and (d) fulvenone (c-C5H4CCO). In the bottom, their cyclopropene analogs: (e) triafulvene (c-C3H2CH2), (f) vinylidenecyclopropane (c-C3H2CCH2), (g) cyclopropenone (c-C3H2O), and (h) triafulvenone (C3H2CO). Three- and five-membered rings represent many of the cyclic species already detected in TMC–1. Of these, (b) and (g) have been detected, and both display aromaticity or resonance stability. (d) shows aromaticity while (h) is antiaromatic. Details in text.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: Top 20 most detectable machine learning–predicted targets by metric 1 (minimum threshold metric). The molecules in the blue box differ from the molecules recommended by the second metric (see Figure 7). In the structure figures, red indicates an oxygen atom, blue a nitrogen, yellow a sulfur, peach indicates silicon, while gray is carbon and white is hydrogen. The molecules are not sorted by visibility.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: Top 20 most detectable machine learning–predicted targets by metric 2 (line stacking). All species are the same as in Figure 6, with the exception of the five in the pink box. The molecules in the pink box differ from those in metric 1. The molecules are not sorted by visibility; a rank-ordered list is presented for these (and many additional) candidates in Appendix B.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: An unusual recommendation from the list of recommended molecules, ONNC2H6. This molecule consists of an oxygen and two nitrogens bonded one after the other. This configuration of molecules has thus far been undetected and otherwise seems unlikely to form in the gas phase in TMC–1.
Paper: Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Authors: Hannah Toru Shay, Gabi Wenzel, Ci Xue, Brett A. McGuire
Figure caption: A histogram is plotted using all the data points and their calculated percent detectability. The top indicates the histogram for the data as analyzed through the first metric and the bottom through the stacked metric.