arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2602.23767astro-ph.GA
收藏

利用MaNGA数据通过本地星系属性预测解析尘埃消光

Predicting Resolved Dust Attenuation from Local Galaxy Properties Using MaNGA

Macquarie University(麦考瑞大学);University of Groningen(格罗宁根大学);University of California, Santa Cruz(加州大学圣克鲁兹分校)

Anilkumar Mailvaganam, Tayyaba Zafar, Pablo Corcho-Caballero, Tamal Mukherjee, Jahang Prathap, Kyle B. Westfall, Kevin Bundy

AI总结:

本文提出一个经验模型,利用MaNGA数据预测恒星形成星系的解析尘埃消光,通过局部恒星形成率面密度作为预测因子,实现高精度的尘埃校正。

摘要:

准确的空间解析尘埃校正对于解释恒星形成星系的结构和演化至关重要。我们提出一个经验模型,利用Apache Point Observatory Mapping Nearby Galaxies (MaNGA)调查的积分场光谱数据,预测恒星形成星系的解析尘埃消光(A_V)。使用5155个星系样本(7.20 < M* < 11.14 和 0.0002 < z < 0.1444),我们从巴尔末消光率中推导出超过1,898,954个恒星形成spaxels的A_V地图。使用局部恒星形成率面密度(Σ_SFR)作为预测因子,模型达到R²=0.69和RMSE=0.22 mag,残差近似高斯分布且中心接近零。模型在kpc尺度上预测A_V的因子约为1.3。我们还证明该关系可以迭代应用以从未校正的值中恢复尘埃校正的Σ_SFR,第四次迭代时残差偏置最小(-0.01 mag)且RMSE低(0.42 mag)。该模型在多样化的形态和取向中准确再现A_V地图,包括边缘视星系。它还恢复了观测到的径向A_V分布,捕捉其对恒星质量及相对恒星形成活动的依赖性,更高质量和更强烈恒星形成星系显示出更陡的梯度。

英文摘要:

Accurate spatially resolved dust corrections are critical for interpreting the structure and evolution of star-forming galaxies (SFGs). We present an empirical model for predicting spatially resolved dust attenuation ($A_V$) in SFGs using integral field spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Using a sample of 5,155 galaxies over $7.20<M_\ast<11.14$ and $0.0002 < z < 0.1444$, we derive $A_V$ maps from the Balmer decrement across more than 1,898,954 star-forming spaxels. Using local star formation rate surface density ($Σ_{\text{SFR}}$) as a predictor, the model achieves $R^2 = 0.69$ and RMSE $=0.22$ mag, with residuals that are approximately Gaussian and centred near zero. It predicts $A_V$ within a factor of $\sim$1.3 on kpc scales. We also demonstrate that the relation can be applied iteratively to recover dust-corrected $Σ_{\mathrm{SFR}}$ from uncorrected values, converging by the fourth iteration with minimal residual bias ($-0.01$ mag) and low RMSE ($0.42$ mag). The model accurately reproduces $A_V$ maps across diverse morphologies and orientations, including edge-on systems. It also recovers the observed radial $A_V$ profiles, capturing their dependence on stellar mass and relative star formation activity, with more massive and more strongly star-forming galaxies showing steeper gradients.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑