MARVEL: Measured active rotational-vibrational energy levels. II. Algorithmic improvements

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

When determining energy levels from several, in cases many, measured and assigned high-resolution molecular spectra according to the Ritz principle, it is advantageous to investigate the spectra via the concept of spectroscopic networks (SNs). Experimental SNs are finite, weighted, undirected, multiedge, rooted graphs, whereby the vertices are the energy levels, the edges are the transitions, and the weights are provided by transition intensities. A considerable practical problem arises from the fact that SNs can be very large for isotopologues of molecules widely studied; for example, the experimental dataset for the H 2 16O molecule contains some 160,000 measured transitions and 20,000 energy levels. In order to treat such large SNs and extract the maximum amount of information from them, sophisticated algorithms are needed when inverting the transition data. To achieve numerical effectiveness, we found the following efficient algorithms applicable to very large SNs: reading the input data employs hash codes, building the components of the SN utilizes a recursive depth-first search algorithm, solving the linear least-squares problem is via the conjugate gradient method, and determination of the uncertainties of the energy levels takes advantage of the robust reweighting algorithm.

Original languageEnglish
Pages (from-to)929-935
Number of pages7
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Volume113
Issue number11
DOIs
Publication statusPublished - Jul 2012

Fingerprint

Electron energy levels
energy levels
Electron transitions
Molecules
Conjugate gradient method
molecular spectra
conjugate gradient method
molecules
apexes
high resolution

Keywords

  • MARVEL
  • Spectroscopic network
  • Water molecule

ASJC Scopus subject areas

  • Spectroscopy
  • Atomic and Molecular Physics, and Optics
  • Radiation

Cite this

@article{74d8aa6e76d04c25a0ca6b19a61e8400,
title = "MARVEL: Measured active rotational-vibrational energy levels. II. Algorithmic improvements",
abstract = "When determining energy levels from several, in cases many, measured and assigned high-resolution molecular spectra according to the Ritz principle, it is advantageous to investigate the spectra via the concept of spectroscopic networks (SNs). Experimental SNs are finite, weighted, undirected, multiedge, rooted graphs, whereby the vertices are the energy levels, the edges are the transitions, and the weights are provided by transition intensities. A considerable practical problem arises from the fact that SNs can be very large for isotopologues of molecules widely studied; for example, the experimental dataset for the H 2 16O molecule contains some 160,000 measured transitions and 20,000 energy levels. In order to treat such large SNs and extract the maximum amount of information from them, sophisticated algorithms are needed when inverting the transition data. To achieve numerical effectiveness, we found the following efficient algorithms applicable to very large SNs: reading the input data employs hash codes, building the components of the SN utilizes a recursive depth-first search algorithm, solving the linear least-squares problem is via the conjugate gradient method, and determination of the uncertainties of the energy levels takes advantage of the robust reweighting algorithm.",
keywords = "MARVEL, Spectroscopic network, Water molecule",
author = "T. Furtenbacher and A. Cs{\'a}sz{\'a}r",
year = "2012",
month = "7",
doi = "10.1016/j.jqsrt.2012.01.005",
language = "English",
volume = "113",
pages = "929--935",
journal = "Journal of Quantitative Spectroscopy and Radiative Transfer",
issn = "0022-4073",
publisher = "Elsevier Limited",
number = "11",

}

TY - JOUR

T1 - MARVEL

T2 - Measured active rotational-vibrational energy levels. II. Algorithmic improvements

AU - Furtenbacher, T.

AU - Császár, A.

PY - 2012/7

Y1 - 2012/7

N2 - When determining energy levels from several, in cases many, measured and assigned high-resolution molecular spectra according to the Ritz principle, it is advantageous to investigate the spectra via the concept of spectroscopic networks (SNs). Experimental SNs are finite, weighted, undirected, multiedge, rooted graphs, whereby the vertices are the energy levels, the edges are the transitions, and the weights are provided by transition intensities. A considerable practical problem arises from the fact that SNs can be very large for isotopologues of molecules widely studied; for example, the experimental dataset for the H 2 16O molecule contains some 160,000 measured transitions and 20,000 energy levels. In order to treat such large SNs and extract the maximum amount of information from them, sophisticated algorithms are needed when inverting the transition data. To achieve numerical effectiveness, we found the following efficient algorithms applicable to very large SNs: reading the input data employs hash codes, building the components of the SN utilizes a recursive depth-first search algorithm, solving the linear least-squares problem is via the conjugate gradient method, and determination of the uncertainties of the energy levels takes advantage of the robust reweighting algorithm.

AB - When determining energy levels from several, in cases many, measured and assigned high-resolution molecular spectra according to the Ritz principle, it is advantageous to investigate the spectra via the concept of spectroscopic networks (SNs). Experimental SNs are finite, weighted, undirected, multiedge, rooted graphs, whereby the vertices are the energy levels, the edges are the transitions, and the weights are provided by transition intensities. A considerable practical problem arises from the fact that SNs can be very large for isotopologues of molecules widely studied; for example, the experimental dataset for the H 2 16O molecule contains some 160,000 measured transitions and 20,000 energy levels. In order to treat such large SNs and extract the maximum amount of information from them, sophisticated algorithms are needed when inverting the transition data. To achieve numerical effectiveness, we found the following efficient algorithms applicable to very large SNs: reading the input data employs hash codes, building the components of the SN utilizes a recursive depth-first search algorithm, solving the linear least-squares problem is via the conjugate gradient method, and determination of the uncertainties of the energy levels takes advantage of the robust reweighting algorithm.

KW - MARVEL

KW - Spectroscopic network

KW - Water molecule

UR - http://www.scopus.com/inward/record.url?scp=84860917341&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860917341&partnerID=8YFLogxK

U2 - 10.1016/j.jqsrt.2012.01.005

DO - 10.1016/j.jqsrt.2012.01.005

M3 - Article

AN - SCOPUS:84860917341

VL - 113

SP - 929

EP - 935

JO - Journal of Quantitative Spectroscopy and Radiative Transfer

JF - Journal of Quantitative Spectroscopy and Radiative Transfer

SN - 0022-4073

IS - 11

ER -