Revisiting the identification of methane on Mars using TES data
Revisiting the identification of methane on Mars using TES data
复制标题
使用 TES 数据重新审视火星上甲烷的识别
DOI：
10.1051/0004-6361/201526235
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Blanco
中科院分区：
文献类型：
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作者：
S. Fonti;F. Mancarella;G. Liuzzi;T. Roush;M. C. Frouard;J. Murphy;A. Blanco
火星大气中甲烷的存在和变化已经由几位作者进行了研究，并引发了热烈的讨论。在这种情况下，我们解决我们以前的推断的空间和时间的CH4的变化确定从火星全球探测器热发射光谱仪测量，这是用来表明可能存在的火星甲烷循环。这一专题的重要性要求对这种可变性进行明确评估，以正确理解火星甲烷可能的产生和破坏机制。因此，重要的是要从不同的角度仔细重新审视以前的结果，以确认它们之前，他们被用于进一步的调查。我们在这里详细描述了一个新的程序，用于验证这些早期的热发射光谱仪测量和彻底分析与修订后的程序所获得的结果。尽管我们努力定义一个有效的数据分析程序，我们还没有能够确认或反驳甲烷的空间和时间变异的存在。尽管如此，我们的工作产生了新的有趣的工具，这些工具经过必要的调整，可以在处理和解释行星光谱方面提供一些帮助，并且一般来说，对于所有其他需要初步选择数据的情况，这些数据包含在非常广泛的数据集中，这些数据很难用传统技术有效处理。
The presence and variability of methane in the Martian atmosphere has been investigated by several authors and spurred a lively discussion. In this context, we address our previous inference of spatial and temporal CH4 variability identified from Mars Global Surveyor Thermal Emission Spectrometer measurements which was used to suggest the possible existence of a martian methane cycle. The importance of the topic requires a clear assessment of such variability to correctly comprehend the possible production and destruction mechanisms of Martian methane. It is therefore important to carefully revisit previous results from a different perspective to confirm them before they are used for further investigations. We here describe in detail a new procedure used to validate these earlier Thermal Emission Spectrometer measurements and thoroughly analyze the results obtained with the revised procedure. In spite of our efforts of defining an efficient data analysis procedure, we have not been able to either confirm or refute the existence of the spatial and temporal variability of methane. Nevertheless, our work has produced new interesting tools, which, with the necessary adaptation, can be of some aid in processing and interpreting planetary spectra and, in general, for all the other cases requiring a preliminary selection of data included in very extensive datasets, which are difficult to be efficiently treated with traditional techniques.