The goal of my research project is to develop a software package that will help simulate the data that will be produced by the NEID spectrometer. The NEID is a NASA-funded project that is being built, in part, at the University of Pennsylvania. This spectrometer will be used to detect planets orbiting stars other than our sun. The software that I am developing will simulate the data that is to be produced by the spectrometer. This will enable the team to comprehend the subtle, but important, sources of measurement noise related to the digital detector inside the instrument and how to correct for them.
The part of the research project that I have completed over the summer is a simulation of what a spectrum from a far-away light source goes through before we see it through our telescopes here on Earth. To this end, I utilised various statistical methods and coding techniques in generating a code packet that demonstrates the distortion on a spectrum.
To begin with, my research advisor, Dr. Cullen Blake provided me with a model of the sun’s spectrum as a fits file, a format commonly used by astronomers to store large amounts of data in a compact file. I then started writing out and testing various functions meant to distort the data as would happen in reality. These consisted of shifting the spectrum, modelling the spectrum’s absorption by Earth’s atmosphere, convolving the spectrum and adding noise to the spectrum. All of this finally led to a Monte Carlo simulation that takes blocks of the original spectrum and distorts it with the aforementioned techniques. The code then correlates the distorted spectrum with the original spectrum to see how badly it would be affected in a real world situation. The next part of the project, which I am eagerly looking forward to, will consist of working backwards from this distorted spectrum and trying to find ways to restore it to where it should have been originally.
During this project, I have gained incredible insight into the work that astronomers and astrophysicists do. As such, I have been able to understand, in great depth, statistical and computational techniques used in data science. Most of all, using computation programs such as Python and Mathematica has taught me a lot about working on, simplifying and representing large amounts of data.
Overall, this summer has been incredibly rewarding as I had the opportunity to work with people at the forefront of astronomy and astrophysics. The project that I am working on excites me greatly and everything that I have learnt will undoubtedly be invaluable to me as I pursue other research projects and forge a career in astrophysics. I would really like to thank my advisor, CURF and the University for giving me the opportunity to further my passion for astrophysics.
The goal of my research project is to develop a software package that will help simulate the data that will be produced by the NEID spectrometer. The NEID is a NASA-funded project that is being built, in part, at the University of Pennsylvania. This spectrometer will be used to detect planets orbiting stars other than our sun. The software that I am developing will simulate the data that is to be produced by the spectrometer. This will enable the team to comprehend the subtle, but important, sources of measurement noise related to the digital detector inside the instrument and how to correct for them.
The part of the research project that I have completed over the summer is a simulation of what a spectrum from a far-away light source goes through before we see it through our telescopes here on Earth. To this end, I utilised various statistical methods and coding techniques in generating a code packet that demonstrates the distortion on a spectrum.
To begin with, my research advisor, Dr. Cullen Blake provided me with a model of the sun’s spectrum as a fits file, a format commonly used by astronomers to store large amounts of data in a compact file. I then started writing out and testing various functions meant to distort the data as would happen in reality. These consisted of shifting the spectrum, modelling the spectrum’s absorption by Earth’s atmosphere, convolving the spectrum and adding noise to the spectrum. All of this finally led to a Monte Carlo simulation that takes blocks of the original spectrum and distorts it with the aforementioned techniques. The code then correlates the distorted spectrum with the original spectrum to see how badly it would be affected in a real world situation. The next part of the project, which I am eagerly looking forward to, will consist of working backwards from this distorted spectrum and trying to find ways to restore it to where it should have been originally.
During this project, I have gained incredible insight into the work that astronomers and astrophysicists do. As such, I have been able to understand, in great depth, statistical and computational techniques used in data science. Most of all, using computation programs such as Python and Mathematica has taught me a lot about working on, simplifying and representing large amounts of data.
Overall, this summer has been incredibly rewarding as I had the opportunity to work with people at the forefront of astronomy and astrophysics. The project that I am working on excites me greatly and everything that I have learnt will undoubtedly be invaluable to me as I pursue other research projects and forge a career in astrophysics. I would really like to thank my advisor, CURF and the University for giving me the opportunity to further my passion for astrophysics.