M101 Data Reduction

In this project, we will take raw(ish) data from the Burrell Schmidt telescope, reduce it, and then analyse it to build a surface brightness profile and color map of the nearby spiral galaxy M101.

After the data reduction and analysis is complete, you will write an ApJ-style paper writing up not just the analysis, but giving the scientific context and interpreting your results in light of other studies of M101 and of galaxies in general. Here are the details of the writeup assignment.

Things you type in a terminal window are written in bold Courier font.
When you get to a HOLD, stop and wait for class discussion.

Step 0: Setting up
  1. Open up a terminal window (right click on desktop, choose "Open in Terminal".
  2. In that window, move to the M101 directory: cd ~/Desktop/M101
  3. Then start a jupyter notebook session: jupyter notebook
  4. Open another terminal window and move into the B2009 directory: cd ~/Desktop/M101/Bdata

Step 1: Zero/Bias subtraction and Flat Fielding
  1. Start by using ds9 to load all the raw images: ds9 pobj*.fits &
  2. HOLD
  3. Use ds9 to open an individual zero and work out the read noise (in ADU) by doing statistics in a region.
  4. Open a different zero and do the same thing, to check for consistency.
  5. Open the flat field image (SkyFlat2009B.fits) and inspect it.
  6. HOLD
  7. In your jupyter notebook browser, open the notebook ReduceImages.ipynb. Edit the directory (first line of block 3) to point to your Bdata directory, then run the notebook.
  8. Open the master zero (Zero.fits) and inspect it. Work out the read noise. Did it scale down properly?
  9. Quit ds9, then restart it and load all the reduced images: ds9 rpobj*.fits &   
  10. Do they all look good?
  11. HOLD
  12. Now edit the directory in the notebook to point to your Vdata directory, and rerun the notebook to reduce the V band data.

Step 2: Sky Subtraction and Photometric Calibration
  1. In your jupyter notebook browser, open the notebook CalibrateOne.ipynb. Again, make sure the directory (in block 2) points to your Bdata directory, and that calband is set to 'B'
  2. Run the notebook.
  3. HOLD
  4. In ds9, open crpobj0419029.fits. What is the sky level (do regions stats in a blank region)?
  5. In your jupyter notebook browser, open the notebook CalibrateImages.ipynb. Make sure the directory (in block 3) points to your Bdata directory, and that calband is set to 'B'. Then run it.
  6. Then edit the directory to point to the Vdata directory, change calband to be 'V', and re-run the notebook.

Step 3: Image Registration, Photometric Scaling, and Combining
  1. In your jupyter notebook browser, open CombineImages.ipynb.
  2. Set the directory (in block 4) to point at your Bdata directory and run.
  3. HOLD
  4. Set the directory to point at your Vdata directory and re-run.

Step 4: Examine the B image stack
  1. Quit ds9, then restart it to open the stacked image: ds9 c*.fits stack_med.fits &
  2. Set ds9 to show only one frame at a time: Frame --> Single Frame
  3. Lock all the frames to the same ra,dec coordinate system (Frame --> Lock --> Frame --> WCS)
  4. Set the intensity scale to show a range of light levels (Scale --> Scale Parameters, and then set it to scale -5 to 3000)
  5. Set the intensity scaling to be logarithmic (Scale --> Scale Parameters --> Scale, and set it to log)
  6. Lock the scalebar (Frame --> Lock --> Scale)
  7. Lock the colorbar (Frame --> Lock --> Colorbar)
  8. Play around with the image, zoom in, zoom out, and keep toggling back and forth between the two images using the tab key.
  9. Notice differences (artifacts gone, noise levels down, etc)

Step 5: Move your final stacks to the main M101 directory and compare

  1. mv ~/Desktop/M101/Bdata/stack_med.fits ~/Desktop/M101/M101_B.fits
  2. mv ~/Desktop/M101/Vdata/stack_med.fits ~/Desktop/M101/M101_V.fits
  3. cd ~/Desktop/M101/
  4. ds9 M101*.fits &