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The Analysis of Astrophysical ‘Weeds’ Using 3-D Submillimeter Spectroscopy SARAH M. FORTMAN, JAMES P. MCMILLAN, CHRISTOPHER F. NEESE, and FRANK C. DE LUCIA.

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Presentation on theme: "The Analysis of Astrophysical ‘Weeds’ Using 3-D Submillimeter Spectroscopy SARAH M. FORTMAN, JAMES P. MCMILLAN, CHRISTOPHER F. NEESE, and FRANK C. DE LUCIA."— Presentation transcript:

1 The Analysis of Astrophysical ‘Weeds’ Using 3-D Submillimeter Spectroscopy SARAH M. FORTMAN, JAMES P. MCMILLAN, CHRISTOPHER F. NEESE, and FRANK C. DE LUCIA The 67 th International Symposium on Molecular Spectroscopy June 21, 2012 The Ohio State University

2 Motivations Primary: Understand the complete contribution of each ‘Weed’ to the Astrophysical data Bonus: Obtaining Dipole Moments and Lower State Energies which may aide in QM assignments Methodology: A Temperature Dependent Approach to Spectroscopy ALMA Science Verification Data

3 Acquiring the Intensity Calibrated Complete Experimental Spectrum (CES) 10 Heaters Sample Transmitter THz Source Detector 6 meters Peak rate:~1.5 K / Spectral Average 166 Spectral Averages Steps: Butterfly Valve

4 Catalog Line Approach Peak Integrated Absorption Coefficient : Doppler width:

5 Catalog Line Approach Input Output Best fit over 10’s -> 100’s of lines Best fit over 100’s -> 1000’s of temperatures Measured

6 Catalog Line Approach Input Output Finally Make a Prediction: Measured Prediction Measured

7 Point by Point Approach

8 Making A Prediction 2.)Choose your temp and plot! Good Agreement of the data (green line) and prediction (black line) The process agrees with the catalog lines (red dash) and picks up more Key Notes:

9 Comparisons with the Astrophysical Data Strategy: Identify the line shape for the molecule Convolve the line shape with the CES Scale/Fit the prediction to the astrophysical data MeCN EtCN VCN “An Analysis of a Preliminary ALMA Orion KL Spectrum via the use of Complete Experimental Spectra from the Laboratory” Fortman S. M., McMillan J.P., Neese C.F., Randall S., Remijan A.J., Wilson T.L., De Lucia F.C. (Submitted to J. Mol. Spec.)

10 Convolution Predicted Spectrum Width: ~1’s MHz Line Shape Width : ~10’s MHz The purpose of the convolution is to predict the SHAPE and RELATIVE intensities of spectral features. Convolution Definition: The relative width of the CES lets it act much like a delta function Assume Local Thermodynamic Equilibrium (LTE) 190K

11 Scaling for Intensity Recall: Perform best fit of : Astrophysical Data = scaling factor* Predicted Convolved Data This region is dominated by EtCN Others clearly by MeCN, VCN Band Studied: 213739.8 – 246654.6 MHz Confirm Local Thermodynamic Equilibrium (LTE) 190K

12 Conclusion


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