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The profiles of the 3 to 12 $mu$m PAH features

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 Added by Els Peeters
 Publication date 2004
  fields Physics
and research's language is English




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We present spectra of the 3.3 $mu$m and 11.2 $mu$m PAH features of a large number of (extra-) galactic sources, obtained with ISO-SWS. Clear variations are present in the profiles of these features. The sources are classified independently based on the 3.3 and 11.2 $mu$m feature profiles and peak positions. Correlations between these classes and those based on the 6--9 $mu$m features (Peeters et al. 2002) are found. Also, these classifications depend on the type of object. The observed pronounced contrast in the spectral variations for the CH modes (3.3 and 11.2 $mu$m bands) versus the CC modes (6.2, 7.7 and 8.6 $mu$m bands) is striking : the peak wavelengths of the features attributed to CC modes vary by $sim$15--80 cm$^{-1}$, while for the CH modes the variations are $sim$4--6.5 cm$^{-1}$. We summarize existing laboratory data and theoretical calculations of PAH molecules and complexes. In contrast to the 6.2 and 7.7 $mu$m components which are attributed to PAH cations, the 3.3 $mu$m feature appears to originate in neutral and/or negatively charged PAHs. We attribute the variations in peak position and profile of these features to the composition of the PAH family. The variations in FWHM of the 3.3 $mu$m feature remains an enigma while those of the 11.2 $mu$m can be explained by anharmonicity and molecular structure. The possible origin of the observed contrast in profile variations between the CH modes and the CC modes is highlighted.



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We present here a new method to model the shape of the 3-{mu}m absorption band in the reflectance spectra of meteorites and small bodies. The band is decomposed into several OH/H2O components using Exponentially Modified Gaussian (EMG) profiles, as well as possible organic components using Gaussian profiles when present. We compare this model to polynomial and multiple Gaussian profile fits and show that the EMGs model returns the best rendering of the shape of the band, with significantly lower residuals. We also propose as an example an algorithm to estimate the error on the band parameters using a bootstrap method. We then present an application of the model to two spectral analyses of smectites subjected to different H2O vapor pressures, and present the variations of the components with decreasing humidity. This example emphasizes the ability of this model to coherently retrieve weak bands that are hidden within much stronger ones.
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