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Understanding the escape of ionizing (Lyman continuum) photons from galaxies is vital for determining how galaxies contributed to reionization in the early universe. While directly detecting the Lyman continuum from high-redshift galaxies is impossible due to the intergalactic medium, low-redshift galaxies in principle offer this possibility but require observations from space. The first local galaxy for which Lyman continuum escape was found is Haro 11, a luminous blue compact galaxy at z = 0.02, where observations with the FUSE satellite revealed an escape fraction of 3.3%. However, the FUSE aperture covers the entire galaxy, and it is not clear from where the Lyman continuum is leaking out. Here we utilize Hubble Space Telescope/Cosmic Origins Spectrograph spectroscopy in the wavelength range 1100–1700 Å of the three knots (A, B, and C) of Haro 11 to study the presence of Lyα emission and the properties of intervening gas. We find that all knots have bright Lyα emission. UV absorption lines, originating in the neutral interstellar medium, as well as lines probing the ionized medium, are seen extending to blueshifted velocities of 500 km s−1 in all three knots, demonstrating the presence of an outflowing multiphase medium. We find that knots A and B have large covering fractions of neutral gas, making LyC escape along these sightlines improbable, while knot C has a much lower covering fraction (≲50%). Knot C also has the the highest Lyα escape fraction, and we conclude that it is the most likely source of the escaping Lyman continuum detected in Haro 11. [ABSTRACT FROM AUTHOR]

We present a machine learning (ML) pipeline to identify star clusters in the multicolor images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic Ultraviolet Survey). S tarc N et (STAR Cluster classification NETwork) is a multiscale convolutional neural network (CNN) that achieves an accuracy of 68.6% (four classes)/86.0% (two classes: cluster/noncluster) for star cluster classification in the images of the LEGUS galaxies, nearly matching human expert performance. We test the performance of S tarc N et by applying a pre-trained CNN model to galaxies not included in the training set, finding accuracies similar to the reference one. We test the effect of S tarc N et predictions on the inferred cluster properties by comparing multicolor luminosity functions and mass–age plots from catalogs produced by S tarc N et and by human labeling; distributions in luminosity, color, and physical characteristics of star clusters are similar for the human and ML classified samples. There are two advantages to the ML approach: (1) reproducibility of the classifications: the ML algorithm's biases are fixed and can be measured for subsequent analysis; and (2) speed of classification: the algorithm requires minutes for tasks that humans require weeks to months to perform. By achieving comparable accuracy to human classifiers, S tarc N et will enable extending classifications to a larger number of candidate samples than currently available, thus increasing significantly the statistics for cluster studies. [ABSTRACT FROM AUTHOR]