分类： 天文学 >> 天文学 提交时间： 2023-02-19
Zhuyun Zhuang
Nicha Leethochawalit
Evan N. Kirby
J. W. Nightingale
Charles C. Steidel
Karl Glazebrook
Tania M. Barone
Hannah Skobe
Sarah M. Sweet
Themiya Nanayakkara
Rebecca J. Allen
Keerthi Vasan G. C.
Tucker Jones
Glenn G. Kacprzak
Kim-Vy H. Tran
Colin Jacobs
摘要： Gravitational lenses can magnify distant galaxies, allowing us to discover and characterize the stellar populations of intrinsically faint, quiescent galaxies that are otherwise extremely difficult to directly observe at high redshift from ground-based telescopes. Here, we present the spectral analysis of two lensed, quiescent galaxies at $z\gtrsim 1$ discovered by the ASTRO 3D Galaxy Evolution with Lenses survey: AGEL1323 ($M_*\sim 10^{11.1}M_{\odot}$, $z=1.016$, $\mu \sim 14.6$) and AGEL0014 ($M_*\sim 10^{11.3}M_{\odot}$, $z=1.374$, $\mu \sim 4.3$). We measured the age, [Fe/H], and [Mg/Fe] of the two lensed galaxies using deep, rest-frame-optical spectra (S/N $\gtrsim$ 40\AA$^{-1}$) obtained on the Keck I telescope. The ages of AGEL1323 and AGEL0014 are $5.6^{+0.8}_{-0.8}$ Gyr and $3.1^{+0.8}_{-0.3}$ Gyr, respectively, indicating that most of the stars in the galaxies were formed less than 2 Gyr after the Big Bang. Compared to nearby quiescent galaxies of similar masses, the lensed galaxies have lower [Fe/H] and [Mg/H]. Surprisingly, the two galaxies have comparable [Mg/Fe] to similar-mass galaxies at lower redshifts, despite their old ages. Using a simple analytic chemical evolution model connecting the instantaneously recycled element Mg with the mass-loading factors of outflows averaged over the entire star formation history, we found that the lensed galaxies may have experienced enhanced outflows during their star formation compared to lower-redshift galaxies, which may explain why they quenched early.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Machine learning (ML) models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and unsupervised learning algorithms trained with the ResNetV2 neural network architecture on their ability to efficiently find strong gravitational lenses in the Deep Lens Survey (DLS). We use galaxy images from the survey, combined with simulated lensed sources, as labeled data in our training datasets. We find that models using semi-supervised learning along with data augmentations (transformations applied to an image during training, e.g., rotation) and Generative Adversarial Network (GAN) generated images yield the best performance. They offer 5--10 times better precision across all recall values compared to supervised algorithms. Applying the best performing models to the full 20 deg$^2$ DLS survey, we find 3 Grade-A lens candidates within the top 17 image predictions from the model. This increases to 9 Grade-A and 13 Grade-B candidates when $1$\% ($\sim2500$ images) of the model predictions are visually inspected. This is $\gtrsim10\times$ the sky density of lens candidates compared to current shallower wide-area surveys (such as the Dark Energy Survey), indicating a trove of lenses awaiting discovery in upcoming deeper all-sky surveys. These results suggest that pipelines tasked with finding strong lens systems can be highly efficient, minimizing human effort. We additionally report spectroscopic confirmation of the lensing nature of two Grade-A candidates identified by our model, further validating our methods.
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