分类： 天文学 >> 天文学 提交时间： 2023-02-19
Hao He
Connor Bottrell
Christine Wilson
Jorge Moreno
Blakesley Burkhart
Christopher C. Hayward
Lars Hernquist
Angela Twum
摘要： We employ the Feedback In Realistic Environments (FIRE-2) physics model to study how the properties of giant molecular clouds (GMCs) evolve during galaxy mergers. We conduct a pixel-by-pixel analysis of molecular gas properties in both the simulated control galaxies and galaxy major mergers. The simulated GMC-pixels in the control galaxies follow a similar trend in a diagram of velocity dispersion ($\sigma_v$) versus gas surface density ($\Sigma_{\mathrm{mol}}$) to the one observed in local spiral galaxies in the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey. For GMC-pixels in simulated mergers, we see a significant increase of factor of 5 - 10 in both $\Sigma_{\mathrm{mol}}$ and $\sigma_v$, which puts these pixels above the trend of PHANGS galaxies in the $\sigma_v$ vs $\Sigma_{\mathrm{mol}}$ diagram. This deviation may indicate that GMCs in the simulated mergers are much less gravitationally bound compared with simulated control galaxies with virial parameter ($\alpha_{\mathrm{vir}}$) reaching 10 - 100. Furthermore, we find that the increase in $\alpha_{\mathrm{vir}}$ happens at the same time as the increase in global star formation rate (SFR), which suggests stellar feedback is responsible for dispersing the gas. We also find that the gas depletion time is significantly lower for high $\alpha_{\mathrm{vir}}$ GMCs during a starburst event. This is in contrast to the simple physical picture that low $\alpha_{\mathrm{vir}}$ GMCs are easier to collapse and form stars on shorter depletion times. This might suggest that some other physical mechanisms besides self-gravity are helping the GMCs in starbursting mergers collapse and form stars.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We use machine learning techniques to classify galaxy merger stages, which can unveil physical processes that drive the star formation and active galactic nucleus (AGN) activities during galaxy interaction. The sample contains 4,690 galaxies from the integral field spectroscopy survey SDSS-IV MaNGA, and can be separated to 1,060 merging galaxies and 3630 non-merging or unclassified galaxies. For the merger sample, there are 468, 125, 293, and 174 galaxies in (1) incoming pair phase, (2) first pericentric passage phase, (3) aproaching or just passing the apocenter, and (4) final coalescence phase or post-mergers. With the information of projected separation, line-of-sight velocity difference, SDSS gri images, and MaNGA Ha velocity map, we are able to classify the mergers and their stages with good precision, which is the most important score to identify interacting galaxies. For the 2-phase classification (binary; non-merger and merger), the performance can be high (precision>0.90) with LGBMClassifier. We find that sample size can be increased by rotation, so the 5-phase classification (non-merger, 1, 2, 3, and 4 merger stages) can be also good (precision>0.85). The most important features come from SDSS gri images. The contribution from MaNGA Ha velocity map, projected separation, and line-of-sight velocity difference can further improve the performance by 0-20%. In other words, the image and the velocity information are sufficient to capture important features of galaxy interactions, and our results can apply to the entire MaNGA data as well as future all-sky surveys.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： The conditions under which galactic nuclear regions become active are largely
unknown, although it has been hypothesized that secular processes related to
galaxy morphology could play a significant role. We investigate this question
using optical i-band images of 3096 SDSS quasars and galaxies at 0.3
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