Sonic Inspection of Spectra for Uncovering Hidden and Historic Quasars

声波检测光谱以发现隐藏的和历史的类星体

• 批准号: 2889070
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889070
• 关键词: Sonic; Inspection; Spectra; Uncovering; Hidden

Current and future spectroscopic surveys, including DESI, WEAVE, 4MOST, MOONS and PFS, will produce millions of galaxy spectra over the coming decade. Additionally, an influx of large datacubes, produced by Integral Field Units (IFUs), will each contain thousands of individual spectra. Traditional methods of visual inspection are becoming increasingly unfeasible for these large data volumes. Whilst Machine Learning (ML) tools are developed, manual data inspection is still required for tasks including, assessment of data quality, verification of fitting results, and producing the training datasets needed for ML approaches. Automated methods alone also limit discovery. They are optimised to identify known features of interest, and there is the risk of misclassification of rare or previously unknown objects. Audio inspection of spectroscopic data is an alternative method to visual inspection, with the potential to be more comfortable and efficient. For example, it may be possible to peripherally monitor sonified data, enabling other tasks to be performed simultaneously, increasing efficiency. This project will develop approaches for sonification (representing data with sound) of galaxy spectra. The initial focus will be searching for red quasars (which masquerade as galaxies) through the inspection of 1D spectra, and identifying and characterising Extended Emission Line Regions (EELRs) in spectral datacubes. The latter could help to identify 'historic' quasars which are no longer active and therefore no longer visible. The methods developed could be applied and adapted for a broad range of scientific cases extracted from the forthcoming spectroscopic surveys.

目前和未来的光谱调查，包括DESI，WEAVE，4MOST，MOONS和PFS，将在未来十年产生数百万个星系光谱。此外，由积分场单元（IFU）产生的大量数据管将包含数千个单独的光谱。传统的目视检查方法对于这些大数据量变得越来越不可行。虽然机器学习（ML）工具已经开发出来，但仍然需要进行手动数据检查，包括评估数据质量、验证拟合结果以及生成ML方法所需的训练数据集。自动化方法本身也限制了发现。它们经过优化以识别感兴趣的已知特征，并且存在对罕见或以前未知的物体进行错误分类的风险。光谱数据的音频检查是视觉检查的替代方法，具有更舒适和高效的潜力。例如，可以在外围监视声化数据，从而使其他任务能够同时执行，从而提高效率。该项目将开发星系光谱的声化（用声音表示数据）方法。最初的重点将是通过检查1D光谱来寻找红色类星体（伪装成星系），并在光谱数据管中识别和表征扩展发射线区域（EELR）。后者可以帮助识别“历史性”类星体，这些类星体不再活跃，因此不再可见。所开发的方法可适用于从即将进行的光谱调查中提取的广泛的科学案例。