adsabs/AstroCLIMB huggingface dataset:
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: Performance of the calibrated CNN+LR ensemble on historical (top) and HMI (bottom) data, evaluated on test A over the full time span (November–December 1975–2022). Left: reliability diagram for the ensemble members and median. The dashed horizontal line indicates the climatological flare rate, the dashed diagonal line indicates no skill, and the solid line indicates perfect reliability. The histogram shows the distribution of output probabilities for the ensemble median. Middle: ROC curve. Left: precision-recall curve for the calibrated ensemble median, with no skill indicated by a dashed line.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: Performance of the calibrated CNN+LR ensemble on historical (top) and HMI (bottom) data, evaluated on test A over the HMI era (November–December 2010–2022). Left: reliability diagram for the ensemble members and median. The dashed horizontal line indicates the climatological flare rate, the dashed diagonal line indicates no skill, and the solid line indicates perfect reliability. The histogram shows the distribution of output probabilities for the ensemble median. Middle: ROC curve. Left: precision-recall curve for the calibrated ensemble median, with no skill indicated by a dashed line.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: Performance of the calibrated CNN+LR ensemble on test B (November–December 1975–2022) as a function of the number of years of training data. Left: metrics to be maximized (BSS, Gini, and maximum output probability). Right: metrics to be minimized (MSE, ECE, and MCE). Note that the ensemble median indicates the performance of the ensemble, not the median performance of the individual models.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: Performance of the calibrated CNN+LR ensemble on historical (top) and HMI (bottom) data, evaluated on test B (2016–2017). Left: reliability diagram for the ensemble members and median. The dashed horizontal line indicates the climatological flare rate, the dashed diagonal line indicates no skill, and the solid line indicates perfect reliability. The histogram shows the distribution of output probabilities for the ensemble median. Middle: ROC curve. Left: precision-recall curve for the calibrated ensemble median, with no skill indicated by a dashed line.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: Reliability diagram, ROC curve, and precision-recall curve for the uncalibrated (top) and calibrated (bottom) CNN+LR ensemble using all input features and evaluated on the holdout set. In the reliability diagram on the right, the dashed horizontal line indicates the climatological flare rate, while the dashed diagonal line indicates no skill, and the solid line indicates perfect reliability. In the ROC and precision-recall curves, the dashed lines indicate no skill.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: The 2D histograms of flare intensity vs. total unsigned flux on flaring samples and 1D distributions of total unsigned flux for flaring and nonflaring samples over the period of overlap between MWO, SPMG, and MDI (1996–1999). Left: before smoothing and an MWO calibration factor of 2. Right: after smoothing/calibration.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: The 2D histograms of flare intensity vs. total unsigned flux on flaring samples and 1D distributions of total unsigned flux for flaring and nonflaring samples over the data set.
Paper: Probabilistic Solar Flare Forecasting Using Historical Magnetogram Data
Authors: Kiera van der Sande, Andrés Muñoz-Jaramillo, Subhamoy Chatterjee
Figure caption: Preprocessing steps applied to sample magnetograms to synthesize instruments into ML-ready data. Image sizes are given in the top left corner.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: The dimensionless sensitivity curves ﹩\sqrt{{S}_{n}\times f}﹩ and the characteristic signals ﹩{h}_{c}=f\times | \tilde{h}(f)| ﹩ as a function of frequency f. The solid lines correspond to the Phenom-NSBH model and the dotted–dashed lines to the corresponding BBH binary modeled signal IMRPhenomD for a system at z = 0.02 (d  L  ≈ 100 Mpc). As the mass ratio of the system increases, the mergers change from total disruption of the NS, with the amplitude of the waveform being exponentially suppressed at high frequencies, to nondisruptive NS for which the waveform is comparable to the BBH waveform. The shaded blue and green lines correspond to cutoff frequencies f cut(Q = 2) = 1535 Hz and f cut(Q = 4) = 2000 Hz.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: The posterior probability density and the 90% credible regions of Λ parameter as measured for Q = 2 (disruptive), Q = 4 (mildly disruptive), and Q = 6 (nondisruptive) systems located at z = 0.07 (top) and z = 0.2 (bottom). The orange, pink, and green shaded regions correspond to the choice of χ BH = 0, χ BH = 0.5, and χ BH = −0.5, respectively. The black vertical lines correspond to the injected value of Λ = 490 and the gray-shaded regions show the implied prior on the Λ parameter.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: The combined mass–radius constraints found by LIGO/Virgo and NICER observations, together with examples of possible viable EoS relations (pink curves) based on Raaijmakers et al. (2021a). The black line indicates the EoS with the highest posterior support (the blue-shaded region indicates the posterior distribution). The green regions on the right plot indicate the multi-messenger constraints found from the NS event GW170817.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: The fractional statistical error of z (red) and H 0 (blue) as a function of d  L  for a subset of runs with Q = 2. The injected parameters are the same as those of Section 2.3. The gray-shaded region shows the threshold of SN = 30.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: (Single runs) The posterior probability density and the 68% credible intervals of {d  L , θ ij, Λ, z, H 0} as measured for the Q = 2 (purple) and Q = 6 (green) systems located at z = 0.07 with θ ij = 30. The plot shows the results of direct sampling.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: (Single runs) The posterior probability density and the 68% credible intervals of {d  L , θ ij, Λ, z, H 0} as measured for the Q = 2 (purple) and Q = 6 (green) systems located at z = 0.2 and θ ij = 30. The plot shows the results of direct sampling.
Paper: Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers
Authors: Banafsheh Shiralilou, Geert Raaiijmakers, Bastien Duboeuf, Samaya Nissanke, Francois Foucart, Tanja Hinderer, Andrew R. Williamson
Figure caption: The single and stacked H 0 PDFs for simulated NSBH mergers in the ET+CE detector era with N = 10, 50, and 100 simulated events. The vertical line corresponds to the true value of H 0 = 67.4  km s−1 Mpc−1. The estimated precision on H 0 are ≈13%, 6.6%, and 4% for the N = 10, 50, and 100 cases, respectively.
Paper: A Surprising Periodicity Detected during a Super-outburst of V844 Herculis by TESS
Authors: Anousha Greiveldinger, Peter Garnavich, Colin Littlefield, Mark R. Kennedy, Jules P. Halpern, John R. Thorstensen, Paula Szkody, Arto Oksanen, Rebecca S. Boyle
Figure caption: The MDM light curves of V844 Her from the four individual nights. The cadence of the time series is 23 s. The system is seen to vary in quiescence with an amplitude of ±0.2 mag.(The data used to create this figure are available.)
Paper: Flares from Merged Magnetars: Their Prospects as a New Population of Gamma-Ray Counterparts of Binary Neutron Star Mergers
Authors: Shu-Xu Yi, Zhen Zhang, Xilu Wang
Figure caption: The flare rate as function of fluence limit is plotted in Figure 2. Note that the occurrence rate is calculated under a strong assumption that the entire magnetic field energy in the magnetosphere is released as flares. The blue band denotes the possible range of burst rates that are associated with GW observation in LVK-O4, and the dashed dark lines indicate the range of burst rates regardless of GW counterparts. The upper and lower limits of the range correspond to the 86% quantiles (1σ) in a Monte Carlo simulation.
Paper: Flares from Merged Magnetars: Their Prospects as a New Population of Gamma-Ray Counterparts of Binary Neutron Star Mergers
Authors: Shu-Xu Yi, Zhen Zhang, Xilu Wang
Figure caption: Illustrations of two scenarios in which the fast spinning-down millisecond magnetar is more probable to have gamma-ray flares. (a) Crust crack scenarios: Crustal defective events occurs more frequently in the stage when the magnetar fast spins down, since the centrifugal force which is counteracting the NS’ self-gravity is rapidly decreasing. (b) Magnetosphere instability scenarios: Instabilities will be triggered frequently in the stage when the magnetar fast spins down, the boundary of the magnetar’s magnetosphere is rapidly expanding.
Paper: Flares from Merged Magnetars: Their Prospects as a New Population of Gamma-Ray Counterparts of Binary Neutron Star Mergers
Authors: Shu-Xu Yi, Zhen Zhang, Xilu Wang
Figure caption: Plot of the flare spectra flux f  E /f 0 vs. energy E before (emitted, black) and after absorption by a 0.01 M ⊙ BNS merger ejecta with 0.3c expansion velocity and a robust main r-process components, at five different times after BNS collision: 0.2 day (red), 0.3 day (orange), 0.6 day (green), 1 day (cyan), 2 days (blue).
Paper: The RS Oph Outburst of 2021 Monitored in X-Rays with NICER
Authors: Marina Orio, Keith Gendreau, Morgan Giese, Gerardo Juan M. Luna, Jozef Magdolen, Tod E. Strohmayer, Andy E. Zhang, Diego Altamirano, Andrej Dobrotka, Teruaki Enoto, Elizabeth C. Ferrara, Richard Ignace, Sebastian Heinz, Craig Markwardt, Joy S. Nichols, Michael L. Parker, Dheeraj R. Pasham, Songpeng Pei, Pragati Pradhan, Ron Remillard, James F. Steiner, Francesco Tombesi
Figure caption: To visualize how the variability patterns changed during the NICER monitoring, the stacked bars show the number of best simulations (chosen from the best hundred out of 100,000) with each type of variability for each GTI: the number of best simulations with stable period and variable amplitude is plotted in blue, while the number of best simulations with variable period (or variable period and amplitude) is plotted in red. The upper panel represents the light curve for reference. The bottom panel shows the results for GTIs in which the signal was detected with FAP < 0.1, and the middle panel shows the result for GTI exposures with FAP < 0.001.
Paper: The RS Oph Outburst of 2021 Monitored in X-Rays with NICER
Authors: Marina Orio, Keith Gendreau, Morgan Giese, Gerardo Juan M. Luna, Jozef Magdolen, Tod E. Strohmayer, Andy E. Zhang, Diego Altamirano, Andrej Dobrotka, Teruaki Enoto, Elizabeth C. Ferrara, Richard Ignace, Sebastian Heinz, Craig Markwardt, Joy S. Nichols, Michael L. Parker, Dheeraj R. Pasham, Songpeng Pei, Pragati Pradhan, Ron Remillard, James F. Steiner, Francesco Tombesi
Figure caption: Comparison between the RS Oph spectra below 1 keV obtained on day 57 (2021 October 5) with NICER at 1:37:27 (red) and the pile-up-corrected Swift XRT spectrum of the same day during a 1 ks GTI starting at 4:37:22 (in black), extracted from an annulus around the center of the PSF with the https://www.swift.ac.uk/user_objects/ webtool.
Paper: The RS Oph Outburst of 2021 Monitored in X-Rays with NICER
Authors: Marina Orio, Keith Gendreau, Morgan Giese, Gerardo Juan M. Luna, Jozef Magdolen, Tod E. Strohmayer, Andy E. Zhang, Diego Altamirano, Andrej Dobrotka, Teruaki Enoto, Elizabeth C. Ferrara, Richard Ignace, Sebastian Heinz, Craig Markwardt, Joy S. Nichols, Michael L. Parker, Dheeraj R. Pasham, Songpeng Pei, Pragati Pradhan, Ron Remillard, James F. Steiner, Francesco Tombesi
Figure caption: On the left, the power spectrum of the 24 continuous exposures longer than 1000 s (those in Table 5), and the best fit with the model parameters in Table 6. On the right, the average rms spectrum, computed by dividing each power spectrum by the mean count rate in each GTI and fitted with a two Lorentzian model.
Paper: The RS Oph Outburst of 2021 Monitored in X-Rays with NICER
Authors: Marina Orio, Keith Gendreau, Morgan Giese, Gerardo Juan M. Luna, Jozef Magdolen, Tod E. Strohmayer, Andy E. Zhang, Diego Altamirano, Andrej Dobrotka, Teruaki Enoto, Elizabeth C. Ferrara, Richard Ignace, Sebastian Heinz, Craig Markwardt, Joy S. Nichols, Michael L. Parker, Dheeraj R. Pasham, Songpeng Pei, Pragati Pradhan, Ron Remillard, James F. Steiner, Francesco Tombesi
Figure caption: Examples of each type of variability with the best simulations (red lines) for the corresponding class of models. The top panels show stable periodicity with variable amplitude, the middle panels variable periodicity with stable amplitude, and in the bottom panels, both parameters are assumed to be variable. For each observation, the best type of simulation is the one with the label “Best.” We compare these simulations with the LS periodograms with FAP > 0.01, traced in black. The sum of residual squares was calculated over the 10 mHz shaded interval around the peak.
Paper: The RS Oph Outburst of 2021 Monitored in X-Rays with NICER
Authors: Marina Orio, Keith Gendreau, Morgan Giese, Gerardo Juan M. Luna, Jozef Magdolen, Tod E. Strohmayer, Andy E. Zhang, Diego Altamirano, Andrej Dobrotka, Teruaki Enoto, Elizabeth C. Ferrara, Richard Ignace, Sebastian Heinz, Craig Markwardt, Joy S. Nichols, Michael L. Parker, Dheeraj R. Pasham, Songpeng Pei, Pragati Pradhan, Ron Remillard, James F. Steiner, Francesco Tombesi
Figure caption: Comparison of the power spectra of the ninth GTIs (black) and the 22nd (red) of Table 5. Note that for purposes of comparison the spectrum for GTI 22 has been reduced by a factor of 2.5 to fit within the plotted range.