  We present a new deep learning method, dubbed FibrilNet, for tracing
chromospheric fibrils in Halpha images of solar observations. Our method
consists of a data pre-processing component that prepares training data from a
threshold-based tool, a deep learning model implemented as a Bayesian
convolutional neural network for probabilistic image segmentation with
uncertainty quantification to predict fibrils, and a post-processing component
containing a fibril-fitting algorithm to determine fibril orientations. The
FibrilNet tool is applied to high-resolution Halpha images from an active
region (AR 12665) collected by the 1.6 m Goode Solar Telescope (GST) equipped
with high-order adaptive optics at the Big Bear Solar Observatory (BBSO). We
quantitatively assess the FibrilNet tool, comparing its image segmentation
algorithm and fibril-fitting algorithm with those employed by the
threshold-based tool. Our experimental results and major findings are
summarized as follows. First, the image segmentation results (i.e., detected
fibrils) of the two tools are quite similar, demonstrating the good learning
capability of FibrilNet. Second, FibrilNet finds more accurate and smoother
fibril orientation angles than the threshold-based tool. Third, FibrilNet is
faster than the threshold-based tool and the uncertainty maps produced by
FibrilNet not only provide a quantitative way to measure the confidence on each
detected fibril, but also help identify fibril structures that are not detected
by the threshold-based tool but are inferred through machine learning. Finally,
we apply FibrilNet to full-disk Halpha images from other solar observatories
and additional high-resolution Halpha images collected by BBSO/GST,
demonstrating the tool's usability in diverse datasets.

Solar and Stellar Astrophysics;Machine Learning