Day 1-2 (07/11-12)
- Day 1: Reviewed papers Chris sent, met with Chris and Eske on Zoom to discuss expectations, project outline on wiki page
- Day 2: Set up FASRC account. Followed the "User Quick Start Guide" on FASRC page
- set up 2FA, FASRC VPN, terminal access, and watched a few data transfer videos
- Used rsync to load Eske's target directory /n/holystore01/LABS/stubbs_lab/Lab/Auxtel_data/spectrum_data onto computer
- Went into lab and met Eske, Ali, and Mark in person. Very cool people.
- Opened Jupyter notebook; looked up astropy documentation to open fits files
- Successfully opened fits file to view contents inside, retrieved and displayed table data, displayed file header information, and plotted data
Day 3 (07/13)
- In Jupyter notebook, filtered all the fits files in '/spectrum_data' to print out star name and date/time of observation
- Stored names of files in record type that are all associated with one star; looks like there are 4 observed stars in the dataset Eske gave me.
Day 4 (07/14)
- Found data files missing the table of equivalent widths that Eske gave me
spec_data_2022062800336.fits HD205905 2022-06-29
- Plotted H2O and O2 equivalent widths against air masses for each star on each night (four stars on four nights based on data Eske gave me)
- Noticed that some files do not have O2 data (may have variants like O2(Z) or O2(B), though)
First Impressions of Data:
- The equivalent widths of H20 seem to have much more variability than those of O2
- May be some outliers in the data (particularly for the first two plots with the negative equivalent widths, may need to check on those data points
Findings
Links to Notebook:
Github repo of project code: https://github.com/ariscjj/stubbs
In notebook: https://github.com/ariscjj/stubbs/blob/master/Extract_H20.ipynb (added Chris as a collaborator on the github)
07/19/2022
- Met with Chris over zoom, got a few tasks to do:
Plot H- alpha, H-beta- should have no dependence on airmass; for quality control
Separate O2 lines (B and Z) lines
perform five sigma clipping (probably in sci py or astropy to clean data); data trimming
five sigma clipping: remove outliers outside of five STDs of data
If in future there are uncertainties in equivalent widths to be reported, compare reported with experimental STDs
Add error bars to plots, error bars represent underlying Gaussian distribution
create linear fit through data
a * (airmass) + b * \sqrt{airmass} + c
create a polynomial fit
Create list of questions for data reduction
Where are the uncertainties in equivalent widths?
Why are there different O2 lines for some plots and not others?
Keep track of stars and airmass span