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Title:IDENTIFYING BROADBAND ROTATIONAL SPECTRA WITH NEURAL NETWORKS
Author(s):Zaleski, Daniel P.
Contributor(s):Prozument, Kirill
Subject(s):Spectroscopy as an analytical tool
Abstract:A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many speciesfootnote{ Perez et al. “Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer.” Chem. Phys. Lett., 2013, 571, 1–15.}. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routinesfootnote{Seifert et al. “AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and_x000d_ Applications to 1-Hexanal.” J. Mol. Spectrosc., 2015, 312, 13–21.}. In this approach, combinations of 3 transitions can be created to form a “triple”, which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2–12 hours depending on the spectral density. A new approach is to utilize machine learningfootnote{Bishop. “Neural networks for pattern recognition.” Oxford university press, 1995.} to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising._x000d_
Issue Date:6/21/2017
Publisher:International Symposium on Molecular Spectroscopy
Citation Info:APS
Genre:CONFERENCE PAPER/PRESENTATION
Type:Text
Language:English
URI:http://hdl.handle.net/2142/97003
DOI:10.15278/isms.2017.WE08
Date Available in IDEALS:2017-07-27


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