ORIGIN：在MUSE数据管中暗发射线星系的盲探测
ORIGIN: Blind detection of faint emission line galaxies in MUSE datacubes
摘要：
MUSE积分场摄谱仪的主要科学案例之一是在高红移下探测莱曼α发射体。正在进行的和计划进行的深场观测将允许对这些源进行一次大规模取样。一个有效的工具来执行盲检测微弱的发射器在MUSE数据管是这样的努力的先决条件。存在几种线检测算法，但它们在最深MUSE曝光期间的性能很难量化，特别是关于它们的实际错误检测率或纯度。{The该工作的目的是设计和验证一种算法，该算法有效地检测微弱的空间-光谱发射特征，同时允许在数据立方体上的稳定的错误检测率，并同时提供纯度的自动和可靠的估计。在提供地面真实数据的模拟数据立方体上的实验结果表明，该方法在纯度和完整性方面达到了目的。当应用于哈勃超深场的30小时曝光MUSE数据立方体时，该算法可以确认检测到133个中间红移星系和248个莱曼阿尔法发射体，其中包括86个没有HST对应物的源。该算法在检测能力和可靠性方面实现了其目标。因此，它被实现为Python包，其代码和文档可以在GitHub和readthedocs上获得。
英文摘要：
One of the major science cases of the MUSE integral field spectrograph is the detection of Lyman-alpha emitters at high redshifts. The on-going and planned deep fields observations will allow for one large sample of these sources. An efficient tool to perform blind detection of faint emitters in MUSE datacubes is a prerequisite of such an endeavor. Several line detection algorithms exist but their performance during the deepest MUSE exposures is hard to quantify, in particular with respect to their actual false detection rate, or purity. {The aim of this work is to design and validate} an algorithm that efficiently detects faint spatial-spectral emission signatures, while allowing for a stable false detection rate over the data cube and providing in the same time an automated and reliable estimation of the purity. Results on simulated data cubes providing ground truth show that the method reaches its aims in terms of purity and completeness. When applied to the deep 30-hour exposure MUSE datacube in the Hubble Ultra Deep Field, the algorithms allows for the confirmed detection of 133 intermediate redshifts galaxies and 248 Lyman Alpha Emitters, including 86 sources with no HST counterpart. The algorithm fulfills its aims in terms of detection power and reliability. It is consequently implemented as a Python package whose code and documentation are available on GitHub and readthedocs.
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。
