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Jonathan Tennyson Jonathan Tennyson Department of Physics and - - PowerPoint PPT Presentation

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Jonathan Tennyson Jonathan Tennyson Department of Physics and Astronomy, University College Department of Physics and Astronomy, University College London, WC1E 6BT, UK London, WC1E 6BT, UK E- -mail: mail: j.tennyson@ucl.ac.uk

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Jonathan Tennyson Jonathan Tennyson

Department of Physics and Astronomy, University College Department of Physics and Astronomy, University College London, WC1E 6BT, UK London, WC1E 6BT, UK E E-

  • mail:

mail: j.tennyson@ucl.ac.uk j.tennyson@ucl.ac.uk

Alexander Z. Fazliev Alexander Z. Fazliev

Institute of Atmospheric Optics SB RAS, Institute of Atmospheric Optics SB RAS, Akademichesky Akademichesky av.1, Tomsk 634055, Russia av.1, Tomsk 634055, Russia E E-

  • mail

mail: : faz@iao.ru faz@iao.ru CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Content Content

VAMDC

Motivation Virtual Atomic and Molecular Center Atomic & Molecular Databases A&M Challenges VAMDC End User Communities VAMDC project organisation

Molecular data and metadata

Problem Definition Molecular spectroscopy model Elementary solution of spectroscopic problem Primary information sources Upload of energy levels

  • Transitions. Comparison and Download

Line Profile Root mean square deviations Composite solution of spectroscopic problems

Validity

Properties for solution of spectroscopic problem Т6 Automatically computed root-mean square deviation of solutions Decomposition of composite data sources H2O

Automatic semantic processing Distributed information system on molecular spectroscopy

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Motivation Motivation

The existing problems can be divided into two categories: (1) data completeness and quality assessment and (2) data interface including problem specific tools for data mining. Today those issues are tackled by a number of data centres but they are highly focussed on specific applications and non-flexible. Thus, there is a strong need to: – Develop close links between the user communities, the data producers and data centres based on modern technology. – Establish better international coordination in order to promote atomic and molecule data compilation and database activities, avoid duplication of efforts and ensure the use of the best available data.

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Virtual Atomic and Molecular Center Virtual Atomic and Molecular Center

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Name of the coordinating person:

Professor M.L. Dubernet, LPMAA/CNRS

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FP7 – e-Infrastructure Program Project

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15 legal partners - 21 institutes or departments

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France, UK, Austria, Italia, Sweden, Germany, Serbia

  • Russian Federation (ISAN, IAO, IA RAS, Institute
  • f Technical Physics)

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Venezuela

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Duration 1 July 2009 – 31 December 2012

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Atomic & Molecular Databases Atomic & Molecular Databases

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Contain Atomic and Molecular Data

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Data from laboratory experiments & calculations

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Spectroscopic data: linelists and their characteristics

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Rate coefficients as a function of temperature

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Cross-sections as function of energy, angles, …

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Many processes: chemical reaction, collisional excitation, photo-chemical reaction

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Very Different Chemical Species from atomic (isotopes and ions) to molecules (various isotopologues) and even to surfaces or solids

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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A&M Challenges A&M Challenges

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A&M data underpins many areas of research

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Providing access to a wide range of users (astronomy, nuclear, climatology, biology) in academia and industry

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Data is complex and increasingly large

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Handling of data (often) involves use of applications

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Issues with ensuring data completeness & quality

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Coordination and standards – organising the A&M community Challenge: provide data access to all A&M data to all end user communities

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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VAMDC End User Communities VAMDC End User Communities

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Astrophysical, atmospheric, plasma, combustion media

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Simulations, Observations, Diagnostics

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Industrial Applications, e.g. lighting, etching, e.g. PLASIMO

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Teaching Outreach

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Range of Complexity of user requirements

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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VAMDC project organisation VAMDC project organisation

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Networking Activities

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Training, workshops

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Science requirements, strategy

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Interface to external groups (VO, Grid, etc)

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Service Activities

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Deployment of A+M services

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Support to the service and user communities

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Research Activities

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Standards (XML Schema, Dictionnaries, Query Language, Registry)

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Publishing and Data MiningTools

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Problem Definition Problem Definition Create a distribution information system on molecular spectroscopy oriented on semantic description of molecular spectroscopy problems’ solutions properties

  • 1. Create a distributed information system on molecular

spectroscopy containing the solutions of spectroscopic problems and the solution properties.

  • 2. Create an open computable logic theory of solution

properties

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Model of Atmospheric Spectroscopy Model of Atmospheric Spectroscopy (0 (0-

  • th approximation)

th approximation)

Isolated molecules spectral line parameters task (T2) Isolated molecule physical characteristics task (T1) Spectral line profile parameters task (T3) Spectral functions calculation task (T4) Direct Problems Inverse Problems Calculations

Measurements

Two chains of problems are selected for approximation for domain description. Task on isolated molecule energy level definition (T7) Einstein coefficients definition task (T6) Task on interacting molecule spectral line parameters definition (ET) Spectral functions measurement task (E) Task on quantum numbers assignment to spectral lines (T5)

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Model of Atmospheric Spectroscopy Model of Atmospheric Spectroscopy

(1 (1-

  • approximation)

approximation) Task on isolated molecule energy level definition (T7) Einstein coefficients definition task (T6) Task on interacting molecule spectral line parameters definition (ET) Task on quantum numbers assignment to spectral lines (T5) Isolated molecules spectral line parameters task (T2) Isolated molecule physical characteristics task (T1) Spectral line profile parameters task (T3) Spectral functions calculation task (T4)

Direct Problems Inverse Problems

Root-mean-squire deviations

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Elementary solution of spectroscopic problem Elementary solution of spectroscopic problem Elementary source characteristics Elementary source characteristics

molecule – H2O the list of physical quantities – energy levels E (cm-1), Quantum numbers (v1 v2 v3 J Ka Kc), correction for the value of energy

level dE (см-1), Number of transitions

publication - Schwenke D.W., New H2O Rovibrational Line Assignments. // Journal of Molecular Spectroscopy, 1998, v. 190, no. 2,

  • p. 397-402

data - ………………………………………………………………

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Primary information sources Primary information sources

Title and link to publication

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Primary information source Primary information source

Additional data (Metadata) formed by user

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Upload of energy levels Upload of energy levels

Choice of substance and description of data file structure

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Data file schema and file upload Data file schema and file upload

0 5 0 2 0 2 7612.724380 0.003027 1 0 5 0 2 1 1 7683.029190 0.003006 1 0 6 0 2 2 1 9271.260790 0.003156 1 2 0 1 2 1 1 10703.137068 0.004243 1 0 5 0 3 1 3 7724.699470 0.003006 1 0 6 0 3 0 3 9009.105574 0.003179 1 0 7 0 3 0 3 10224.562788 0.120009 1 0 8 0 3 0 3 11390.579637 0.009867 1

Data file structure Description of file structure

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Review of uploaded energy levels Review of uploaded energy levels

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Transitions Transitions.

Comparison and Download

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Line Profile Line Profile

Root mean square deviations

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Line Profile Line Profile

Root mean square deviations

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Composite solution of spectroscopic problems

1.HITRAN, GEISA, …. 2.Implicit solutions, unknown solutions or unpublished solutions

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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Validity

Formal constraints Data type –

quantum numbers – natural numbers, intensity, half-width, frequency, energy levels – positive real numbers, ….

Variation interval –

0 < frequency < 45000 cm-1, 10-16 cm/mol < intensity <10-30

Selection rules -

normal modes - ka+kc=J or J+1, ….. precise quantum numbers – J < 60, 0 < s < 5, ……

Publication constraints Whether a result is published or not Non-formal constraints. Experts’ opinion

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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  • J. Tennyson, P.F. Bernath, L.R. Brown, A. Campargue, M.R.

Carleer, A.G. Császár, R.R. Gamache, J.T. Hodges, A. Jenouvrier, O.V. Naumenko, O.L. Polyansky, L.S. Rothman, R.A. Toth, A.C. Vandaele, N. Zobov, L. Daumont, A.Z. Fazliev, T. Furtenbacher, I.F. Gordon, S.N. Mikhailenko, S.V. Shirin, IUPAC Critical Evaluation of the Rotational-Vibrational Spectra of Water Vapor. Part I. Energy Levels and Transition Wavenumbers for H2

17O and H2 18O

Journal of Quantitative Spectroscopy and Radiative Transfer, July 2009, V.110, no.9-10, P.573-596.

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

Non Non-

  • formal constraints

formal constraints

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Properties for solution of Properties for solution of spectroscopic problem spectroscopic problem Т Т6 6

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Automatically computed root-mean square deviation of solutions

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Decomposition of Decomposition of composite data sources H composite data sources H2

216 16O

O

L.S. Rothman, I.E. Gordon, A. Barbe, D.Chris Benner, P.F. Bernath, M. Birk, V. Boudon, L.R. Brown,

  • A. Campargue, J.-P. Champion, K. Chance, L.H. Coudert,
  • V. Dana, V.M. Devi, S. Fally, J.-M. Flaud, R.R. Gamache,
  • A. Goldman, etc,

The HITRAN 2008 molecular spectroscopic database. Journal of Quantitative Spectroscopy and Radiation Transfer, 2009, v. 110, Issue 9, p. 533-572.

  • N. Jacquinet-Husson, E. Arié, J. Ballard,
  • A. Barbe, G. Bjoraker, B. Bonnet, L. R. Brown,
  • C. Camy-Peyret, J. P. Champion, A. Chédin,
  • A. Chursin, C. Clerbaux, G. Duxbury, J. -M. Flaud,
  • N. Fourrié, A. Fayt, G. Graner, et al,

The 1997 spectroscopic GEISA databank. Journal of Quantitative Spectroscopy and Radiation Transfer, 1999, v. 62, Issue 2, p. 205-254

181 data sources (including HITRAN 2004, GEISA 1997)

~ 4000 lines WN – 0.4012 -19000 Intensity 10-20 – 10-32 ~ 25 lines WN -11000-25000 Intensity 10-25 – 10-28

183 data sources (including HITRAN 2004, HITRAN 2008)

Total number of data sources ~ 250 Residual Tdecomposition ~ 10 min

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk

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The authors acknowledge Prof. M. The authors acknowledge Prof. M.-

  • L.

L. Dubernet Dubernet ( (LPMAA/CNRS LPMAA/CNRS) for her ) for her efforts which underpin the VAMDC efforts which underpin the VAMDC project project

The End

CITES CITES – – 2009, Krasnoyarsk 2009, Krasnoyarsk