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 The latter could help to identify 'historic' quasars which are no longer active and therefore no longer visible.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.

当前和未来的光谱调查，包括DESI，编织，4，卫星和PFS，将在未来十年中产生数百万个星系光谱。此外，由整体场单元（IFUS）产生的大数据存储量的涌入将包含数千个单独的光谱。对于这些大数据量，传统的视觉检查方法变得越来越不可行。尽管开发了机器学习（ML）工具，但仍需要手动数据检查来进行任务，包括数据质量评估，拟合结果验证以及生成ML方法所需的培训数据集。仅自动化方法也限制了发现。它们被优化以确定感兴趣的已知特征，并且存在稀有或以前未知物体的错误分类的风险。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，有可能外围监视超声的数据，从而同时执行其他任务，从而提高效率。该项目将开发用于星系光谱的超声（表示声音数据）的方法。最初的焦点将通过检查1D光谱来搜索红色类星体（将其作为星系化妆），并在光谱数据库中识别和表征扩展发射线区域（EELRS）。后者可以帮助识别不再活跃并且因此不再可见的“历史性”类星体。可以针对从即将进行的光谱调查中提取的广泛的科学案例应用和调整所开发的方法。