adsabs/AstroCLIMB huggingface dataset:
Paper: Radiation Hydrodynamics of Self-gravitating Protoplanetary Disks. I. Direct Formation of Gas Giants via Disk Fragmentation
Authors: Yang Ni, 阳 倪, Hongping Deng, 洪 平 邓, Xue-Ning Bai, 雪 宁 白
Figure caption: The histograms of the initial fragment mass mfrag in Jupiter mass (top) and the analytically estimated fragment mass Manalytical (bottom). From left to right, the first five columns are the distribution of the initial fragment mass in m15k1, m17k1, m20k1, m17k5, and m20k5. Labels over the first row indicate the simulations in the five columns. The histogram in the last column stacks the initial fragment mass from all simulations. The solid line in the last column is a lognormal fitting of the distribution with ﹩\sigma [{\mathrm{log}}({m}_{{\rm{frag}}}/{M}_{{\rm{analytical}}})]\sim 0.3﹩.
Paper: Radiation Hydrodynamics of Self-gravitating Protoplanetary Disks. I. Direct Formation of Gas Giants via Disk Fragmentation
Authors: Yang Ni, 阳 倪, Hongping Deng, 洪 平 邓, Xue-Ning Bai, 雪 宁 白
Figure caption: Time-averaged radial profiles of Σ, T, τmid, βcool, and Q in all the simulations. Profiles of the three nonfragmented disks, i.e., m13k1, m13k5, and m15k5, are time-averaged during the last 1 kyr of the simulation (4–5 kyr) to capture the GI-saturated quasi-steady state. Profiles of the fragmented disks, i.e., m15k1, m17k1, m17k5, m20k1, and m20k5, are time-averaged during the last 0.1 kyr before the first clumps in these simulations emerge to capture the instantaneous disk properties generating the fragmentation. Black circles indicate locations of the first clumps in the fragmented disks.
Paper: Radiation Hydrodynamics of Self-gravitating Protoplanetary Disks. I. Direct Formation of Gas Giants via Disk Fragmentation
Authors: Yang Ni, 阳 倪, Hongping Deng, 洪 平 邓, Xue-Ning Bai, 雪 宁 白
Figure caption: The gas surface density maps at the end of the simulations as labeled in the upper left corner. The box size is 225 au × 225 au, and a scale bar of 100 au is included.
Paper: Radiation Hydrodynamics of Self-gravitating Protoplanetary Disks. I. Direct Formation of Gas Giants via Disk Fragmentation
Authors: Yang Ni, 阳 倪, Hongping Deng, 洪 平 邓, Xue-Ning Bai, 雪 宁 白
Figure caption: Time-averaged Fourier amplitudes of the gas density in the three nonfragmented disks during 4–5 kyr. Labels at the upper left corner of each panel indicate the names of these simulation runs. We cover the radius range 35 au < R < 100 au to exclude the effect of disk outer boundaries. Ticks of m = 1, 2, 3, 4, 5, 6 are additionally denoted to show the exact values of the dominant m modes.
Paper: Radiation Hydrodynamics of Self-gravitating Protoplanetary Disks. I. Direct Formation of Gas Giants via Disk Fragmentation
Authors: Yang Ni, 阳 倪, Hongping Deng, 洪 平 邓, Xue-Ning Bai, 雪 宁 白
Figure caption: Time-averaged Reynolds stress profiles (top left), gravitational stress profiles (top right), viscous α profiles (bottom left), and α–﹩{\beta }_{\mathrm{cool}}^{\mbox{--}1}﹩ relation (bottom right) in the three nonfragmented disks during 4–5 kyr.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Vertical distance above the 40% critical isochrone (solid orange curve in the left panel of Figure 4) versus GBP − GRP. Color-mapped points are stars with a vbroad measurement. Star points are δ Scuti stars, and circular points and gray pluses are nonpulsators. Stars with red edges are δ Scuti stars that suffer from blending (described in Section 2.4.3). This scatter plot shows that the δ Scuti stars fall farther from the isochrone and rotate more rapidly than the nonpulsators on the lower branch. This is most apparent at GBP − GRP ≲ 0.25. To show this more explicitly, we found the mean vbroad of stars on the upper branch, designated as the region between the two gray dotted lines, where 〈vbroad〉 ≈ 184 km s−1. This is about twice the rotation seen on the lower branch, where 〈vbroad〉 ≈ 95 km s−1. Roughly two-thirds of the δ Scuti stars in NGC 3532 fall on the upper branch.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Histograms, with numbers on the left axis, showing the population distribution of δ Scuti stars (dashed red) and non δ Scuti stars (black) as a function of GBP − GRP. The pulsator fraction in each bin is overplotted as open circles connected by the dotted blue line, with values on the right axis. The pulsator occurrence is shown as the blue dashed line and points points. The pulsator fraction is heavily dependent on color, and peaks at 60% ± 11%. The pulsator occurrence peaks at 67% ± 5%. Across the center of the instability strip, (GBP − GRP = [0.15, 0.4]) the pulsator fraction and occurrence is 50% ± 5% and 59% ± 5%, respectively.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Left: period–luminosity (P–L) relation for δ Scuti stars in NGC 3532. Colors show the SNR of the dominant mode. The fundamental mode P–L relation and uncertainty from N. Barac et al. (2022) are shown in red. Points with red edges are blended δ Scuti stars (see Section 2.4.3). Right: same as the left panel but using the N. Barac et al. (2022) P–L relation to search for modes that could be the fundamental mode. Colors here denote the ratio SNR of the chosen mode to the SNR of the dominant mode.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption:  ﹩{\nu }_{{\rm{\max }}}﹩–Teff relation for δ Scuti stars in NGC 3532 (color-mapped points) and Cep-Her Complex (gray points; S. J. Murphy et al. 2024). Colors show the vertical distance from the isochrone fit. Stars with red edges are blended δ Scuti stars (see Section 2.4.3). Two main branches can be seen in the ﹩{\nu }_{{\rm{\max }}}﹩–Teff relation in NGC 3532, and we plot two quadratic functions for each branch to help guide the eye.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Maximum pulsator occurrence versus age for the Cep-Her Complex (S. J. Murphy et al. 2024), NGC 2516 (G. Li et al. 2024), the Pleiades (T. R. Bedding et al. 2023), NGC 3532 (this work), and field stars (S. J. Murphy et al. 2019; A. K. Read et al. 2024; P. Mani et al. 2025). Cep-Her, NGC 2516, and the Pleiades show pulsator fraction measurements, but the occurrence corrections are likely small.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Recovery rates, shown as the contour plot, as a function of maximum amplitude and apparent Gaia G magnitude. Nondetections are plotted as the gray circles, and δ Scuti stars are plotted as the cyan stars.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Maximum amplitude versus Gaia GBP − GRP for nondetections and δ Scuti stars in NGC 3532 as the black points and red stars, respectively. For comparison, δ Scuti stars in the Pleiades and the Cep-Her Complex are overplotted as the blue triangles and gray pluses, respectively.
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Distributions of maximum amplitudes for NGC 3532 (black) and the Kepler Field (red; S. J. Murphy et al. 2019). The dotted lines show the median amplitude for each distribution, approximately 650 and 1100 ppm, respectively. The Kepler amplitudes have been passband corrected, using an amplitude ratio of 0.8 (M. N. Lund 2019).
Paper: Discovery of 79 δ Scuti Stars in NGC 3532 Suggests a Decrease of Pulsator Occurrence with Age
Authors: Ian Berry, Daniel Huber, Yaguang Li, Daniel Hey, Timothy R. Bedding, Simon J. Murphy
Figure caption: Pulsator occurrence (contours) as a function of pulsator fraction and median amplitude for the sample of stars in NGC 3532. Contours of constant pulsator occurrence are shown as the white dashed curves (black dashed for 100%). The cyan star shows the maximum pulsator fraction and median amplitude for NGC 3532.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: Mass accretion rate onto the protostar, ﹩{\dot{M}}_{{\rm{ps}}}﹩, for models B2H (red), B2 (black), and B2L (blue). The left panel shows ﹩{\dot{M}}_{{\rm{ps}}}﹩ plotted against protostellar mass Mps, and the right panel is against time since protostar formation. Thin lines indicate the mass accretion rate onto the protostars at each time. Thick lines are moving averages along the horizontal axis. The white-edged circles mark the epochs at which the envelope mass Menv has decreased to 50% of the initial cloud mass M2BE,0 for each model, corresponding to protostellar masses of approximately 0.32 M⊙ (B2H), 0.12 M⊙ (B2), and 0.11 M⊙ (B2L). The white-edged squares mark the epochs when Menv has decreased to 35% of M2BE,0, corresponding to protostellar masses of approximately 0.85 M⊙ (B2H), 0.24 M⊙ (B2), and 0.16 M⊙ (B2L). The subsequent evolution beyond these epochs is shown with lighter colors.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: Ratio of mass outflow rate to inflow rate, ﹩{\dot{M}}_{{\rm{out}}}/{\dot{M}}_{{\rm{in}}}﹩, plotted against the protostellar mass Mps. Solid and dotted lines represent box side lengths of L = 6000 au and L = 750 au. Red, black, and blue lines correspond to models B2H, B2, and B2L.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: Time evolution of the angular momentum flux ratios at two different envelope scales: large (L = 6000 au; right) and small (L = 750 au; left). The solid and dotted lines represent the ratios of magnetic braking and outflow angular momentum fluxes to the inflow angular momentum flux (FJ,out/FJ,in and FJ,mb/FJ,in). Red, black, and blue lines correspond to models B2H, B2, and B2L.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: Mass accretion rates onto the protostar plotted against protostellar mass for models B2H (red), B2 (black), and B2L (blue). The shaded regions indicate the range of Bondi accretion rates estimated for each model using the gas density at r = R1BE (upper bound) and r = R2BE (lower bound).
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: tSFEB.E. values for models B2L, B2, and B2H in this study and their corresponding nonmagnetized, nonrotating counterparts from S. Nozaki & M. N. Machida (2023). Bars with diagonal hatching indicate tSFEB.E. values at the epoch when Menv/M2BE,0 = 0.50, while solid bars correspond to the epoch when Menv/M2BE,0 = 0.35. Bars in blue show tSFEB.E. values calculated using Msys = Mps + Mdisk, and those in orange show tSFEB.E. values using Msys = Mps.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: The outflow momentum Pout is plotted against Mps. Models B2H, B2, and B2L are shown in red, black, and blue lines.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: Mass fractions of the protostar (Mps; red), disk (Mdisk; yellow), outflow (Mout; blue), ejected gas (Meject; gray), and envelope (Menv; green) relative to the initial cloud mass (M2BE,0) are plotted against protostellar mass Mps. The results are plotted up to the point where Mmps = 0.89, 0.52, and 0.27 M⊙ for models B2H, B2, and B2L, corresponding to the stage each simulation has reached. For all models, the evolution after the envelope mass Menv decreases to 35% of the initial cloud mass M2BE,0 is shown with lighter colors.
Paper: How the External Medium Outside Prestellar Cores Affects Protostellar Growth: Variations in Accretion Rate and Evolution of Disks and Outflows
Authors: Shingo Nozaki, Masahiro N. Machida
Figure caption: The disk-to-protostar mass ratio Mdisk/Mps is plotted against Menv/M2BE, 0. Models B2H, B2, and B2L are shown in red, black, and blue lines.
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: Summary of model performance. The top row displays the 2D histogram in a polar coordinate, for the azimuth angle differences between the HDD prediction and the ground truth ΔΘ = Θpred − Θtrue (as position angle), and the ground-truth transverse magnetic field strength ∣Bt∣ (as radius). From left to right, the plots show the results for quiet Sun, plage region, and sunspot, respectively. The subsequent rows show the azimuth angle recovery rate as a function of transverse field strength for the three MHD cases, for both HDD (blue) and ME0 (orange) results. The red-dashed–dotted line represents the pixel counts within the horizontal bins corresponding to the values of ∣Bt∣. The black-solid, -dashed, -dashed–dotted, and -dotted lines indicate the 100%, 90%, 80%, and 50% correct recovery rate, respectively. The gray areas indicate the range where the transverse magnetic field strength is below the estimated sensitivity of DKIST.
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: Panel (a) shows the flowchart for training the model. The inputs are the 3D magnetic vector fields with azimuthal ambiguity, and an initial guess of the geometric heights associated with each optical-depth layer. The UNet3DB network predicts 3D vector magnetic field, and the UNet3DZ network provides the geometric heights. They are combined to compute a custom loss function with physics law encoded (Equation (17)). Panel (b) shows the postprocessing step that assembles the outputs from the neural networks (prediction) and the original input magnetic fields into the final output. Panel (c) shows an example of the 3D data, split with overlaps between nearby subfields. These individual subfields are used as the input for training. Panel (d) presents the UNet3D structure, reproduced from Figure 2 in Ö. Çiçek et al. (2016).
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: Disambiguation results for the plage simulation on ﹩{\mathrm{log}}_{10}\tau =-1﹩, the same region as Figure 4(e). Columns, from left to right, show disambiguated Bx, By, and azimuth angle Θ. Rows, from top to bottom, present the ground truth, HDD results, and ME0 results, respectively. HDD’s Bx and By in panels (d) and (e) are the direct outputs of the neural network, whereas Θ represents the final result after postprocessing. Selected regions with “checkerboard” pattern are highlighted with black and white boxes in panels (d) and (i).
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: Panel (a) illustrates the mesh cell structure and the convention for vertex indexing. Panel (b) shows the top surfaces of a mesh cell at the center, and its projection to three planes perpendicular to ﹩\widehat{{\boldsymbol{x}}}﹩, ﹩\widehat{{\boldsymbol{y}}}﹩, and ﹩\widehat{{\boldsymbol{z}}}﹩, 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: Results for geometric heights for the plage regions. Panels (a) and (b) present the true and predicted height values for the ﹩{\mathrm{log}}_{10}\tau =-1﹩ layer. Panels (c)–(f) show vertical cuts across the domain, along the dashed lines in (a). The colors represent the Bz distribution and span the optical depths from ﹩{\mathrm{log}}_{10}\tau =0﹩ to −3.1. The five dashed black curves indicate ﹩{\mathrm{log}}_{10}\tau ﹩ values of −0.5, −1, −1.5, −2.0, and −2.5 from bottom to top, respectively. Panels (c) and (e) display ground truth at vertical cuts X = 5.6 Mm and Y = 12.9 Mm, respectively, while (d) and (f) show the corresponding HDD 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: MURaM simulations used for this study. Panels (a)–(c) present the vertical components of the magnetic field, Bz, on the ﹩{\mathrm{log}}_{10}\tau =-1﹩ layer, for the quiet Sun, the plage region, and the sunspot runs. Panels (d)–(f) show the subregion tested in Section 3.
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: An illustration of our task and the mesh grid in our computational domain. Here, x and y are image-plane axes, and z-axis is along the line of sight. The curved mesh presents the depression along the LOS for the optical-depth layers. The example given is NOAA AR 11158 as observed on 2011 February 15 in the continuum by SDO/HMI.
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 11 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: The Same as Figure 13 but for the results from the noisy rebinned dataset.
Paper: On the Origin of ∼100 TeV Neutrinos from the Seyfert Galaxy NGC 7469
Authors: Qi-Rui Yang, Xiao-Bin Chen, Ruo-Yu Liu, Xiang-Yu Wang, Martin Lemoine
Figure caption: Multimessenger emissions from NGC 7469 in the turbulence acceleration scenario. The upper panel illustrates case 1 corresponding to a high neutrino flux of 10−12 erg cm−2 s−1, while the lower panel shows case 2 corresponding to a lower neutrino flux of 10−13 erg cm−2 s−1. For both cases, the purple shaded region indicates the neutrino flux reported by IceCube (R. Abbasi et al. 2025). The blue arrows indicate the upper limits for GeV gamma-ray emission imposed by Fermi-LAT. The black solid curve represents the neutrino spectrum, whereas the blue curves show the cascade gamma-ray emission. We normalize the proton spectrum by Equation (8). The parameters are adopted as βp ≃ 0.1 (σ ≃ 1) with the Alfvén speed of vA ≃ c and ﹩{E}_{p,{\rm{\max }}}\simeq 2\,{\rm{PeV}}﹩.
Paper: On the Origin of ∼100 TeV Neutrinos from the Seyfert Galaxy NGC 7469
Authors: Qi-Rui Yang, Xiao-Bin Chen, Ruo-Yu Liu, Xiang-Yu Wang, Martin Lemoine
Figure caption: Timescales of proton acceleration, escape, and cooling in the corona of NGC 7469 in the turbulence scenario. The upper panel and lower panel represent case 1 and case 2 in the turbulence scenario, respectively. The timescale of turbulence acceleration is shown by the black dashed line, and the thin solid colored line refers to the energy-loss timescale. The shared parameters for the two cases are Rc ≈ 10Rg, βp ≃  0.1, and ℓc = Rg. In case 1, np ≃  1010 cm−3, and the magnetic field is B = 1.1 × 104 G; in case 2, np  ≃  109 cm−3, and the magnetic field is B = 3.4 × 103 G.
Paper: On the Origin of ∼100 TeV Neutrinos from the Seyfert Galaxy NGC 7469
Authors: Qi-Rui Yang, Xiao-Bin Chen, Ruo-Yu Liu, Xiang-Yu Wang, Martin Lemoine
Figure caption: Same as Figure 2 but for the reconnection scenario. The upper panel illustrates case 1 with a high neutrino flux of 10−12 erg cm−2 s−1, corresponding to ηp ≃ 0.2, while the lower panel shows case 2 with a low neutrino flux of 10−13 erg cm−2 s−1, corresponding to ηp ≃ 0.02. The parameter values used are σ = 10, np ≃ 3 × 108 cm−3, and ﹩{E}_{p,{\rm{\max }}}\simeq 60\,{\rm{PeV}}﹩.
Paper: On the Origin of ∼100 TeV Neutrinos from the Seyfert Galaxy NGC 7469
Authors: Qi-Rui Yang, Xiao-Bin Chen, Ruo-Yu Liu, Xiang-Yu Wang, Martin Lemoine
Figure caption: Same as Figure 1 but for the reconnection scenario. The parameter values used are Rc ≈ 10Rg, σ = 10, B = 7.5 × 103 G, and np = 3 × 108 cm−3.