SBIR Phase I: Efficient Multi-Spectral Holographic Filters

这项小型企业创新研究(SBIR)第一阶段项目建议开发天文全息多光谱滤光片的制造技术，通过抑制大气OH自由基发出的窄线来提高近红外波长地面观测的信噪比(SNR)。许多天文学家已经认识到，如果能够抑制OH本底，就可以获得很大的信噪比增益。信噪比与望远镜的直径成正比。因此，通过在凯克10米望远镜上添加所提出的滤光片，潜在的信噪比将提高三倍，相当于将其直径增加到30米，这估计需要花费5亿美元。相比之下，建议的高效多光谱滤光器的成本要低三个数量级。该项目应该会对遥感和生命科学中需要精细多光谱信息以准确识别物质的应用产生直接影响。在遥感中，任意窄带滤光片的设计和制造是全球测量地球和其他行星的大气气体以及为国土安全部遥感有毒气体的有力工具。全息滤光片的光谱响应可以被定制，以精确匹配给定气体的高灵敏度的吸收光谱。由于同时检测到多个吸收峰或发射峰，与传统方法相比，检测灵敏度将大大提高，所需数据量将减少几个数量级，这使得它在遥感应用中具有很大的吸引力。

This Small Business Innovation Research (SBIR) Phase I Project proposes to develop the fabrication technology for an astronomical holographic multi-spectral filter that increases the signal to noise ratio (SNR) of ground-based observations at near-infrared wavelengths by suppressing the narrow lines emitted from atmospheric OH radicals. Many astronomers have recognized that large gains in SNR can be obtained if the OH background could be suppressed. SNR is proportional to the diameter of the telescope. The potential threefold gain in SNR achieved by adding the proposed filter to the Keck 10 meter telescope would thus be equivalent to increasing its diameter to 30 meters, which is estimated to cost five hundred million dollars. In contrast, the cost of the proposed efficient multi-spectral filters is three orders of magnitude less.This project should have a direct impact on applications requiring fine multi-spectral information for accurate substance identification in remote sensing and life sciences. In remote sensing, the design and fabrication of arbitrary narrow multi-band filter profile are powerful tools for global measurements of atmospheric gases of Earth and other planets as well as remote sensing of toxic gases for Homeland Security. The spectral response of the holographic filter can be tailored to match precisely the absorption spectrum of given gases with high sensitivity. With multiple absorption or emission peaks detected simultaneously, the detection sensitivity will be increased greatly compared with traditional methods, and the required data volumes will decrease by several orders of magnitude, which makes it very attractive for remote sensing applications.