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arXiv 2606.09413astro-ph.GA
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朝向一致的活动星系核贡献分数和宿主星系性质确定框架

Towards a consistent framework of determining active galactic nucleus contribution fraction and host galaxy properties

Kapteyn Astronomical Institute, University of Groningen(格罗宁根大学卡普坦天文研究所);SRON Netherlands Institute for Space Research(荷兰空间研究研究所);National Centre for Nuclear Research(国家核研究中心);Aix-Marseille Université, CNRS, CNES, LAM(艾克斯-马赛大学);INAF - Osservatorio Astronomico di Capodimonte(意大利国家天体物理研究所卡波迪蒙特天文台);European Space Agency/ESTEC(欧洲空间局/欧洲空间研究与技术中心)

Mi Chen, Lingyu Wang, Berta Margalef-Bentabol, Katarzyna Małek, Brivael Laloux, Antonio La Marca

AI总结:

建立统一框架,结合SED分解(CIGALE和GRAHSP)与深度学习成像分解,从多波段测光中估计AGN贡献分数和宿主星系性质,揭示SED拟合中的参数简并性,并展示加入形态信息可打破简并。

摘要:

将活动星系核(AGN)发射从宿主星系光中分解出来,对于识别AGN主导的系统以及准确推导宿主星系的物理性质至关重要。然而,由于光谱能量分布(SED)拟合中固有的参数简并性,从多波段测光估计AGN贡献仍然具有挑战性。在这项工作中,我们建立了一个统一的框架,通过结合互补的诊断方法:使用两个独立拟合代码CIGALE和GRAHSP进行SED分解,以及基于深度学习的成像分解,来估计AGN贡献分数和宿主星系性质。我们将此框架应用于COSMOS-Web场中的星系,使用从紫外到远红外的多波段测光。我们计算了JWST/NIRCam F150W滤光片中的AGN贡献分数,并将SED推导的估计值与从深度学习图像分解获得的独立AGN分数进行比较。我们的结果揭示了当前基于经验或理论AGN模板的SED拟合方法中存在的显著简并性,并表明加入独立的形态信息可以帮助打破这些简并性,提高AGN和宿主星系性质估计的可靠性。

英文摘要:

Decomposing active galactic nucleus (AGN) emission from host-galaxy light is essential for identifying AGN-dominated systems and accurately deriving host-galaxy physical properties. However, estimating AGN contributions from multi-wavelength photometry remains challenging due to inherent parameter degeneracies in spectral energy distribution (SED) fitting. In this work, we establish a unified framework for estimating AGN contribution fractions and host-galaxy properties by combining complementary diagnostics: SED decomposition with two independent fitting codes, CIGALE and GRAHSP, and deep-learning-based imaging decomposition. We apply this framework to galaxies in the COSMOS-Web field using multi-wavelength photometry from the ultraviolet to the far-infrared. We calculate the AGN contribution fraction in the JWST/NIRCam F150W filter and compare the SED-derived estimates with independent AGN fractions obtained from deep-learning image decomposition. Our results reveal significant degeneracies in current SED-fitting approaches based on empirical or theoretical AGN templates and demonstrate that incorporating independent morphological information can help break these degeneracies and improve the reliability of AGN and host-galaxy property estimates.

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。

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