Deriving Surface Temperature from Spectroscopic Analysis of Stellar Flux Sahil Hegde COSMOS 2015 Cluster 4.

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Presentation transcript:

Deriving Surface Temperature from Spectroscopic Analysis of Stellar Flux Sahil Hegde COSMOS 2015 Cluster 4

Spectroscopy the production and investigation of emitted light waves Merriam-Webster

Stellar Spectra Emitted wavelengths of light from star

Applications and Uses Surface Temperature Wien’s Displacement Law Chemical composition Absorption line analysis Spectral Classification Temperature and absorption lines

Wilhelm Wien German quantum physicist Experimentation with blackbody radiation Nobel Prize in Physics

Wien’s Displacement Law

Wien’s Constant Peak Wavelength Temperature

Data Reduction/Analysis vs.

Planck’s Law

Temperature Speed of Light Planck’s Constant Wavelength Boltzmann Constant

Results (I) Star 2Arcturus K Giant 4000± 1000 K Star 6Vega A Star 8500± 1000 K

Results (II) Ionized Calcium H and K Sodium

Importance/Analysis (I) Find surface temperature of star Measure energy output of star Lifespan of Star

Importance/Analysis (II) Take spectrum of flux vs. wavelength Calibrate and plot spectrum Analyze absorption and emission lines Stellar structure

Sources of Error The detector is more sensitive in the center of the spectrum, so in neither the shorter and longer wavelength ranges. Because of this, the data sets could have dropped off and there may have been no data near the edges of the spectra. This would have left us with some incomplete data to match and calibrate with our spectra. Furthermore, the error could have also been caused by the fact that we had to match sample spectra to our uncalibrated curve by eyeballing the shapes. This could have been a source of human error. This similar error could have come with the blackbody curve matching by eye as well.

Images

Images (II)

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