分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: 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 $r_{\rm AB}, z_{\rm AB} < 21.81$ mag and are lensed by galaxy clusters at $0.26 < z < 1$. This sample 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 $\sim$29 to $\sim$180. Combining these analyses, we derive demagnified stellar masses ranging from $\rm log_{10}(M_{*}/M_{\odot}) \sim 9.7 - 11.0$ and star formation rates in the youngest age bin ranging from $\rm log_{10}(SFR/(M_{\odot}\cdot yr^{-1})) \sim 0.4 - 1.6$, 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 Ly$\alpha$ emissions, offering unique opportunities to study Ly$\alpha$ emitters at high redshift in future work.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.