The discussion focuses on calculating the signal-to-noise ratio for 1D DEEP2 DEIMOS FITS files using Python 2.7. Participants suggest calculating the RMS (Root Mean Square) value as a method to quantify noise. Additionally, they emphasize the need for a general method to exclude both noisy and erratic data, which includes identifying instrumentation effects and developing corrections based on a theoretical understanding of these effects.
PREREQUISITESAstronomers, data scientists, and researchers working with astronomical data who need to analyze and correct noisy datasets from DEEP2 DEIMOS observations.
Calculate the RMS value?Curtnos said:I'm currently working with the 1d DEEP2 DEIMOS fits files (see http://deep.ps.uci.edu/deep3/specprimer.html) and am trying to define some exclusion criteria in Python 2.7 for the data based on the noise. What's the best way to quantify the noise in order to do this?
Thank you!
I should have added, a lot of the data is showing very odd and unpredictable behaviour. I'm unsure how to edit the original question, however I was hoping to find a more general method to both discount noisy data and bad data which fluctuates unpredictably. Some have sudden, repeated drops to zero flux, some have fluxes which appear symmetric about the flux = 0 line, some look like step functions, etc.berkeman said:Calculate the RMS value?