adsabs/AstroCLIMB huggingface dataset:
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: Result of the SALT3-NIR simulation. Difference between the simulated and trained M 0 (top) and M 1 (bottom) model components, integrated over the BYJH bandpasses, and divided by the M 0 component to show fractional differences. Each curve is the average of 10 training sets simulated using SALT3-NIR as the source SED. The B band is shown as a target model fidelity for cosmology.
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: Result of the SALT2-Extended simulation. Difference between the simulated and trained M 0 (top) and M 1 (bottom) model components, integrated over the BYJH bandpasses, and divided by the M 0 component to show fractional differences. Each curve is the average of 10 training sets simulated using SALT2-Extended as the source SED. The B band is shown as a target model fidelity for cosmology.
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: Hubble residuals (with respect to ΛCDM) when fitting only rest-frame YJ filters with SALT3-NIR (red diamonds) and SNooPy (green squares), and optical filters with SALT3 from K21 (blue circles). Here the stretch parameter is not allowed to float for SALT3-NIR and SNooPy.
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: Example of observed (black) and simulated (red) NIR spectra for SIRAH SN 2020aczg. The lower spectra are from the WFC3/IR grism, while the upper spectra are from Keck NIRES. The gaps in the NIRES spectra correspond to atmospheric absorption windows. Every spectrum has a random offset applied to visually distinguish them.
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: Hubble residuals (with respect to ΛCDM) when fitting optical+NIR filters with SALT3-NIR (red diamonds), and optical filters with SALT3 from K21 (blue circles). Here all model parameters are allowed to float.
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: Distributions of (a) redshift, (b) SALT2-m  B , and (c) maximum S/N for training sample data (filled circles), SNANA simulation with SALT3-NIR model (red histogram), and simulation with SALT2-Extended model (blue histogram).
Paper: SALT3-NIR: Taking the Open-source Type Ia Supernova Model to Longer Wavelengths for Next-generation Cosmological Measurements
Authors: J. D. R. Pierel, D. O. Jones, W. D. Kenworthy, M. Dai, R. Kessler, C. Ashall, A. Do, E. R. Peterson, B. J. Shappee, M. R. Siebert, T. Barna, T. G. Brink, J. Burke, A. Calamida, Y. Camacho-Neves, T. de Jaeger, A. V. Filippenko, R. J. Foley, L. Galbany, O. D. Fox, S. Gomez, D. Hiramatsu, R. Hounsell, D. A. Howell, S. W. Jha, L. A. Kwok, I. Pérez-Fournon, F. Poidevin, A. Rest, D. Rubin, D. M. Scolnic, R. Shirley, L. G. Strolger, S. Tinyanont, Q. Wang
Figure caption: The SALT3-NIR model flux integrated over optical+NIR bandpasses, as a function of phase and the light-curve stretch parameter, x 1.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Left panel: central temperature vs. central density. Right panel: maximum temperatures occurring at the base of the convective envelope (T BCE), on the thermally pulsing AGB. Variation in elemental abundance is heavily dependent on uncertainties in opacity, mixing, and atmospheric boundary conditions, which we have not focused on replicating identically. Here, we show that our central conditions are almost identical, and so any potential variance in surface abundances is due to these mixing uncertainties, rather than differences in the conditions for nuclear burning. We note that the extreme variations in luminosity (appearing as sharp vertical lines) observed in the Monash model during the AGB are numerical artifacts; these are also present in Figure 2. They are the result of convergence issues, driven by rapid, high-amplitude variations of the luminosity. Such artifacts are typical of most calculations in this regime and are often removed by hand. It is important to note, however, that MESA does not show these variations at the same resolution. In short, this is because MESA was designed with convergence as the primary goal, but this success carries the consequence of much longer run times.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Here, we define the evolutionary points at which we compare the models numerically. In the upper left panel, we show where points A through F fall in the context of the global evolutionary sequence. Physically, the points are defined as follows: A denotes the termination of core He-burning; B is measured at the luminosity maximum of the first thermal pulse; C, D, and E are taken at the local luminosity minimum, luminosity maximum, and median of the interpulse quiescent region for the fourth thermal pulse; and F is taken at the local luminosity minimum of the final thermal pulse, regardless of pulse number (i.e., if the MESA model experiences 26 pulses but the Monash model experiences 28, F is measured at pulse 26 for MESA and pulse 28 for Monash). The top right panel zooms in to show the core He-burning phase, where A is defined. The bottom two panels show where C, D, E, and F fall during the thermally pulsing phase.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Left panel: central temperature vs. central density. Right panel: maximum temperatures occurring at the base of the convective envelope (T BCE), on the thermally pulsing AGB. Variance in elemental abundance is heavily dependent on uncertainties in opacity, mixing, and atmospheric boundary conditions, which we have not focused on replicating identically. Here, we show that our central conditions are almost identical, and so any potential variance in surface abundances is due to these mixing uncertainties, rather than differences in the conditions for nuclear burning.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Comparison of the density profiles between the 2 M ⊙, Z = 0.03 models. The panels on the left show density as a function of radius for given points in evolution: A (top), B (center), and C (bottom). The panels on the right show a closer version of the same plots, identifying the small deviations in density in the outermost region of the envelope.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: A comparison of the surface properties of the two 7 M ⊙, Z = 0.03 models. We show luminosity (top panel), effective temperature (center panel), and radius (bottom panel) as functions of time on the thermally pulsing AGB. We note that both evolution runs were halted at the 12th thermal pulse, which is sufficient to demonstrate our point without excess computing time.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Comparison of the density profiles between the 7M ⊙, Z = 0.03 models. The panels on the left show density as a function of radius for given points in evolution: A (top), B (center), and C (bottom). The panels on the right show a closer version of the same plots, identifying the small deviations in density in the outermost region of the envelope.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Same as for Figure 6, but for points D, E, and F.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Same as for Figure A4, but for points D, E, and F.
Paper: Bridging the Gap between Intermediate and Massive Stars. I. Validation of MESA against the State-of-the-Art Monash Stellar Evolution Program for a 2M ⊙ AGB Star
Authors: Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas
Figure caption: Stellar tracks of the two 7 M ⊙ models.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption: Left panel: ﹩\bar{\chi }(L)﹩: ﹩\bar{\chi }﹩ as a function of the PTA pulsar distance L (Equation (75)) in the near-field regime for a source at a distance of r = 8 kpc (red solid line) and r = 50 kpc (green solid line), respectively. Right panel: ﹩\bar{\chi }(r)﹩: ﹩\bar{\chi }﹩ as a function of the GW source distance r in the near-field regimes for a PTA obtained by averaging results from 50,000 different parameters sets, with pulsars randomly distributed on sky at a distance of L = 2 kpc (blue solid line) and L = 10 kpc (cyan solid line), respectively. The black dashed horizontal line shows ﹩\bar{\chi }﹩ obtained by adopting the far-field approximation, i.e., ﹩{\bar{\chi }}_{{\rm{f}}}=0.365﹩. The ratio ﹩{\bar{\chi }}_{{\rm{n}}}/{\bar{\chi }}_{{\rm{f}}}﹩ as a function of L is indicated by the right vertical axis, where averaged ﹩{\bar{\chi }}_{{\rm{f}}}\simeq 0.365﹩. The top and bottom horizontal axes indicate source distance r and pulsar distance L, respectively. Different realizations for the sky locations of the PTA pulsars may result in different ﹩{\bar{\chi }}_{\ {\rm{n}}}﹩, especially when L is comparable to r. To illustrate this, the uncertainties due to different realizations for some cases with fixed r but different L and with fixed L but different r are shown in both the left and right panels (solid circles with error bars). The solid circles and its associated error bars represent the median and the 16%–84% range of ﹩{\bar{\chi }}_{{\rm{n}}}﹩ obtained from 50 realizations of the PTA pulsar sky locations (see Section 5.2.2).
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption:                          ﹩| {{\mathscr{P}}}^{+}(f)| ﹩ as a function of ϑ (Equations (14) and (B1)). Left panel: the GW source is located at r = 8 kpc (e.g., at the GC) and the pulsar distance is L = 1 kpc; a subpanel at the left-bottom corner shows the detail of the pattern, and we use several gray dashed lines to represent the 1° mesh grid. Right panel: r = 400 pc, L = 200 pc, f = 10−8 Hz, and ι E = 0. The red solid line represents the envelope curve of ﹩| {{\mathscr{P}}}_{{\rm{f}}}^{+}(f)| ﹩ obtained by adopting the far-field approximation, i.e., ﹩\rho =1+\cos \vartheta ﹩. The red dashed line represents ﹩\rho ={\rho }_{0}(1+\cos \vartheta )﹩ with ﹩{\rho }_{0}=\max (| {{\mathscr{P}}}^{+}(f)| )﹩ representing the maximum value of ﹩| {{\mathscr{P}}}^{+}(f)| ﹩ over ϑ in the range from 0 to 2π. The near-field effect is clearly shown by the difference between the red solid curve and the outer envelope of the blue curve. The rapid oscillation of ﹩| {{\mathscr{P}}}^{+}(f)| ﹩ with ϑ may suggest a high angular resolution for the detection of individual GW sources. The right panel shown here is only for illustrative purpose. See details in Section 5.1.2.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption: Top panel: FF as a function of the GW source distance (Equation (62)) obtained by adopting the far-field approximation. Bottom panel: ﹩\delta \hat{\varrho }﹩ as a function of the GW source distance (Equation (65)) obtained by adopting the far-field approximation. The MSP is assumed to be at L = 2 kpc (blue) or L = 10 kpc (green) with ﹩\vartheta =\tfrac{\pi }{2}﹩. This figure shows that near-field effect can be significant for L = 2 kpc (10 kpc) when the GW source distance r ≲ 27 Mpc (572 Mpc) or ≲91 Mpc (1.9 Gpc), with corresponding FF ≳0.944 (indicated by the red dotted–dashed line in top panel) or ≳0.995 (indicated by the red dotted line in top panel), if the S/N threshold is set to be 3 or 10. In the top panels, the black dotted–dashed lines represent FF = 1.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption:                       ﹩| {\tilde{h}}_{a,{\rm{o}}}(f)| ﹩ (Equation (78)) and ﹩| \tilde{z}(f)| ﹩ (Equation (79)) (both in units of seconds) obtained from the fast Fourier transformation of a monochromatic GW source located at r = 8 kpc with amplitude h 0 = 10−15 and frequency f = 10−8 Hz in the frequency domain “observed” by a PTA with L = 1 kpc over a time span of T = 20 yr. The ﹩| \tilde{z}(f)| ﹩ shown here is a modulated GW signal spectrum. This modulation occurs only when the pulsar term (in the term of ﹩{{ \mathcal P }}^{+}(f)﹩ in Equation (79)) is considered and the results obtained in the near-field case are different from those obtained in the case adopting the far-field approximation. See Section 6.1 for details.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption: Dependence of χ(ι E, ψ E) on ι E and ψ E for a GW source located at r = 8 kpc monitored by a PTA with MSP distances L = 2 kpc. Different colors represent different χ(ι E, ψ E) value as indicated by the right color bar.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption: The three-dimensional schematic for the configuration of a GW source and two pulsars, where two pulsars, GW sources (S), and Earth are not located in the same plane; Earth is located at the origin O of coordinate system; and pulsar 1 is located in the plane yOz. It should be noted that the positive direction of the z-axis points to the left.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption: Similar to Figure 5, except for ﹩\vartheta =\tfrac{\pi }{4}﹩ in the left panel, and ﹩\vartheta =\tfrac{3\pi }{4}﹩ in the right panel.
Paper: On Detecting Nearby Nanohertz Gravitational Wave Sources via Pulsar Timing Arrays
Authors: Xiao Guo, 潇 郭, Youjun Lu, 由俊 陆, Qingjuan Yu, 清娟 于
Figure caption: ORF ∣Γ12(f)∣ (see Section 5.2.2) dependence on f. Here we set β 12 = 1, L 1 = L 2 = 1  kpc, and ﹩{\vartheta }_{1}=\tfrac{\pi }{2}﹩, ﹩{\vartheta }_{2}=\tfrac{\pi }{4}﹩.
Paper: Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms
Authors: Dattaraj B. Dhuri, Shamik Bhattacharjee, Shravan M. Hanasoge, Sashi Kiran Mahapatra
Figure caption: The 2003 Halloween storms. Top: NOAA AR 10486 MDI magnetograms show the AR dynamics leading to extreme flare events X17.0, X10.0, and X28.0 (outside ±45° of the central meridian, not shown) characterized by rotation of the positive polarity spot (Zhang et al. 2008; Kazachenko et al. 2010), highlighted within the red circle. Positive (white) and negative (black) polarities are saturated at 1000 G. Bottom: the CNN-estimated total unsigned flux, mean free energy density, and absolute net current helicity during these storms showing a systematic rise leading to the flares. The CNN accurately captures helicity injection due to the sunspot rotation showing a significant increase in the mean free energy density and electric current helicity. The gaps correspond to the missing observations, and 1σ error bars are shown. The plots are smoothed with a 6 hr running average.
Paper: Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms
Authors: Dattaraj B. Dhuri, Shamik Bhattacharjee, Shravan M. Hanasoge, Sashi Kiran Mahapatra
Figure caption: Schematic of ML models. Left: CNN models process LOS magnetograms as input and produce vector field features of SHARP as output. SHARP features group together in four groups based on their mutual correlations as shown. We develop four different CNN models to estimate SHARP from four different groups. All CNN models have identical architecture described in Figure 1 except the final output layer, where the number of output neurons is equal to the number of SHARP features to be estimated from the respective group. Right: baseline models using LR for estimation of two groups of SHARP features that depend on electric current and free energy, respectively, using extensive SHARP features and Schrijver’s R_value (Schrijver 2007) as an input.
Paper: Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms
Authors: Dattaraj B. Dhuri, Shamik Bhattacharjee, Shravan M. Hanasoge, Sashi Kiran Mahapatra
Figure caption: IG attribution maps. The contour plots for the IG attribution maps highlight the regions from magnetograms with the highest attributions for the CNN estimation of SHARP features—total unsigned flux, absolute net current helicity, and mean free energy density. The red/blue contours correspond to the regions with the net positive/negative attributions for the magnetograms in the bottom rows relative to the reference magnetograms in the top rows. The Δ values are the percentage change in the normalized values of the respective features between the present and the reference observations. The color bar for the magnetograms is saturated with ±500 G. The IG attribution maps are smoothed with a Gaussian filter of standard deviation 10 pixels before obtaining the contours.
Paper: Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms
Authors: Dattaraj B. Dhuri, Shamik Bhattacharjee, Shravan M. Hanasoge, Sashi Kiran Mahapatra
Figure caption: Comparison of true and CNN-estimated SHARP vector field features. Comparisons of time evolution of the CNN-estimated total unsigned flux, absolute net current helicity, and mean free energy density with true values for ARs that produce M5 or greater flares. Only AR observations within ±45° of the central meridian are considered. The left panel shows a typical result (see Figure 9 for all ARs with major flares). The right panel shows an extreme event with the largest flare observed in cycle 24. The gaps correspond to the missing observations and 1σ error bars are shown. The legend in the top left panel applies to all plots. The plots are smoothed with a 6 hr running average.
Paper: Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms
Authors: Dattaraj B. Dhuri, Shamik Bhattacharjee, Shravan M. Hanasoge, Sashi Kiran Mahapatra
Figure caption: Interpretation of the trained CNN model. Box plots showing weights in the penultimate layer of the CNN trained to reconstruct total unsigned flux, absolute net current helicity, and mean free energy density. Top five weights from the components of the penultimate layer, i.e., global average pooling, global max pooling, and fully connected layer processing AR location (see Figure 1), are shown for the 10 validation models. The red line indicates the median of the weight populations.
Paper: Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms
Authors: Dattaraj B. Dhuri, Shamik Bhattacharjee, Shravan M. Hanasoge, Sashi Kiran Mahapatra
Figure caption: Mean absolute error of the CNN-reconstructed vector field features. The CNN-estimated values of total unsigned flux, absolute current helicity, and mean free energy density for HMI and GONG are binned into 10 uniform bins as per the respective true values. Mean absolute errors as a function of the mean true values of each population bin are shown. 1σ error bars for each bin are also shown. The legend in the top left panel applies to all panels.