We report the discovery of five bright, strong gravitationally lensed galaxies at 3 < z < 4: COOL J0101+2055 (z = 3.459), COOL J0104−0757 (z = 3.480), COOL J0145+1018 (z = 3.310), COOL J0516−2208 (z = 3.549), and COOL J1356+0339 (z = 3.753). These galaxies have magnitudes of r AB, z AB < 21.81 mag and are lensed by galaxy clusters at 0.26 < z < 1. This sample nearly doubles the number of known bright lensed galaxies with extended arcs at 3 < z < 4. We characterize the lensed galaxies using ground-based grz/giy imaging and optical spectroscopy. We report model-based magnitudes and derive stellar masses, dust content, and star formation rates via stellar population synthesis modeling. Building lens models based on ground-based imaging, we estimate source magnifications ranging from ∼29 to ∼180. Combining these analyses, we derive demagnified stellar masses in the range log 10 ( M * / M ⊙ ) ∼ 9.69 − 10.75 and star formation rates in the youngest age bin in the range log 10 ( SFR / ( M ⊙ yr − 1 ) ) ∼ 0.39 − 1.46 , placing the sample galaxies on the massive end of the star-forming main sequence in this redshift interval. In addition, three of the five galaxies have strong Lyα emissions, offering unique opportunities to study Lyα emitters at high redshift in future work.
In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such as convolutional neural networks (CNN), for denoising has become a promising area of research. We investigate the feasibility of CNN-based self-supervised learning algorithms (e.g., Noise2Noise) for denoising astronomical images. We experimented with Noise2Noise on simulated noisy astronomical data. We evaluate the results based on the accuracy of recovering flux and morphology. This algorithm can well recover the flux for Poisson noise (98.13 − 0.90 + 0.77 % ) and for Gaussian noise when image data has a smooth signal profile (96.45 − 0.96 + 0.80 % ).
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