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In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data, as indicated by the high R2 score. Consequently, we propose that GANs can be employed as cost-effective models for black hole image generation and reliably augment training data sets for other parametrization algorithms.

 

Abstract Energetic particles emitted by active stars are likely to propagate in astrospheric magnetized plasma and disrupted by the prior passage of energetic coronal mass ejections (CMEs). We carried out test-particle simulations of ∼GeV protons produced at a variety of distances from the M1Ve star AU Microscopii by coronal flares or traveling shocks. Particles are propagated within a large-scale quiescent three-dimensional magnetic field and stellar wind reconstructed from measured magnetograms, and within the same stellar environment following the passage of a 10 36 erg kinetic energy CME. In both cases, magnetic fluctuations with an isotropic power spectrum are overlayed onto the large-scale stellar magnetic field and particle propagation out to the two innnermost confirmed planets is examined. In the quiescent case, the magnetic field concentrates the particles into two regions near the ecliptic plane. After the passage of the CME, the closed field lines remain inflated and the reshuffled magnetic field remains highly compressed, shrinking the scattering mean free path of the particles. In the direction of propagation of the CME lobes the subsequent energetic particle (EP) flux is suppressed. Even for a CME front propagating out of the ecliptic plane, the EP flux along the planetary orbits highly fluctuates and peaks at ∼2–3 orders of magnitude higher than the average solar value at Earth, both in the quiescent and the post-CME cases. 

Two magnetic braking models are implemented inMESAfor use in theMISTstellar model grids. Stars less than about 1.3 solar masses are observed to spin down over time through interaction with their magnetized stellar winds (i.e., magnetic braking). This is the basis for gyrochronology and is fundamental to the evolution of lower-mass stars. The detailed physics behind magnetic braking are uncertain, as are 1D stellar evolution models. Thus, we calibrate our models and compare to data from open clusters. Each braking model tested here is capable of reproducing aspects of the data, with important distinctions; neither fully accounts for the observations. The Matt et al. prescription matches the slowly rotating stars observed in open clusters but tends to overestimate the presence of rapidly rotating stars. The Garraffo et al. prescription often produces too much angular momentum loss to accurately match the observed slow sequence for lower-mass stars but reproduces the bimodal nature of slowly and rapidly rotating stars observed in open clusters fairly well. Our models additionally do not reproduce the observed solar lithium depletion, corroborating previous findings that effects other than rotation may be important. We find additional evidence that some level of mass dependency may be missing in these braking models to match the rotation periods observed in clusters older than 1 Gyr better.