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Building and Testing the Spectrograph for the New Robotic Telescope

构建和测试新型机器人望远镜的光谱仪

基本信息

批准号：
2903361
负责人：
金额：
--
依托单位：
Liverpool John Moores 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-2903361
关键词：
Building Testing Spectrograph New Robotic

项目摘要

There are two min aims in this project, to develop a novel spectrograph optimised for the most efficient classification of transient astronomical phenomena and to use the newly developed spectrograph to carry out a programme of astronomical observations that prove the efficiency of the design. This spectrograph has a brief design concept that needs to be finalised then verified with analytical tools such as Zemax Optic Studio. A bench prototype will be assembled in-house and tested with a beam simulator to confirm the optical performance of the instrument matches the design. Once the performance has been assessed and approved the prototype will be mounted on the LT to be evaluated. Despite design differences in the two telescopes, the LT provides a good substitute to the NRT for testing purposes. On the LT the prototype will observe a test set of data taken of standard calibration sources and transient targets of opportunity. Once testing is complete the spectrograph is complete and is ready to be mounted onto the NRT. Before the spectrograph will be used by observers a data reduction pipeline will be developed in python which will calibrate the data from the instrument and examine calibration parameters for systematic and random errors affecting data quality. The final step would be a comparison to asses the performance of NR-SPRAT compared to LT-SPRAT.

该项目有两个主要目标：开发一种新型光谱仪，用于最有效地分类瞬态天文现象，并使用新开发的光谱仪进行天文观测计划，以证明设计的效率。这台光谱仪有一个简短的设计概念，需要最终确定，然后用Zemax Optic Studio等分析工具进行验证。将在内部组装台架原型，并使用光束模拟器进行测试，以确认仪器的光学性能与设计相匹配。一旦性能得到评估和批准，原型将安装在LT上进行评估。尽管两台望远镜在设计上存在差异，但LT为NRT提供了一个很好的测试替代品。在LT上，原型将观察一组从标准校准源和瞬态目标获取的测试数据。测试完成后，光谱仪完成并准备安装到NRT上。在观测者使用光谱仪之前，将在python中开发一个数据简化管道，该管道将校准仪器的数据，并检查影响数据质量的系统和随机误差的校准参数。最后一步将是比较，以评估NR-SPRAT与LT-SPRAT相比的性能。