# 3.4. List of All Questions¶

Todo

1. For each file name below explain why the name would or would not match the pattern twi-.*bias.*

• Twi-bias.fit

• twi-diffuser-flat-001bias.fit

• twi-diffuser-flat-001bias_flt.fit

• twi-diffuser-flat-001bIas.fit

2. Look at the list of the files in the folder for_gain. For each of the items below come up with a pattern that matches the right files. Check your patterns by starting to open an image sequence in AstroImageJ, selecting the folder for_gain, entering the pattern in the right place, and verifying that the correct number of files matches.

1. All bias images that have “twi” in the name (should be 11 files)

2. The images in the R band (R is in the file name) that also have “flood” in the name and are number 001 through 003 (should be 3 files).

3. The images in the I band with “flood” in the name (10 items)

4. The images in the I band with “flood” in the name whose number is odd (001, 003, etc) (should be 5 images)

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/aij_sequences.rst, line 55.)

Todo

1. Write down the names of the images you chose.

2. Select a region and write down its width, height, and x and y position.

3. Find the average pixel value in that region in each image you have open. Write them down, clearly indiciating which measure goes with which image.

4. Calculate the difference between the two bias images.

5. Calculate the difference between the two flat images.

6. Measure the standard deviation in the region you chose in each of the two difference images. Write the result down, clearly indicating which measurement goes with which difference image.

7. Calculate the gain for the CCD using the formula in Howell’s book.

8. Calculate the read noise for the CCD using the formula in Howell’s book.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/gain_calc_detail.rst, line 24.)

Todo

1. Imagine subtracting an image from itself. What should the result be? Use AstroImageJ to subtract an image from itself. Does the result match your expectaion?

2. Imagine dividing an image by itself. What should the result be? Use AstroImageJ to divide an image by itself. Does the result match your expectation.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/imagej_arithmetic.rst, line 13.)

Todo

1. Where is the origin used for the x and y position? In other words, where in the image is the point (0,0)?

2. What is the position of the center of the image?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/imagej_measurements.rst, line 18.)

Todo

1. Measure the mean pixel value and the standard deviation of the pixel value for one of the images you have open.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/imagej_measurements.rst, line 37.)

Todo

1. Make a histogram for a single bias image. Note the width of the bias histogram, which is twice the standard deviation, and the mean.

2. Combine four of the bias images. Do this by:

• clicking on sequence window so that it is on top

• clickking on the AstroImageJ toolbar to make sure the correct menus are displayed

• selecting Z-Project from the Stacks menu in the Image menu (i.e. Image -> Stacks -> Z-Project) and...

• ...choosing appropriate values for the starting and ending image in the sequence, and...

• ...choose average as the method of combining.

• Be sure to write down the name of the combined image.

3. Make a histogram of the combination of four bias images. Compare the mean and standard deviation of the distribution for the combined images with the same numbers for the single image.

4. Repeat for 8 bias images; note the mean and standard deviation.

5. Repeat again for all 16 bias images; note the mean and standard deviation.

6. Describe in words:

1. How the histogram changes as you increase the number of images in the stack.

2. How the mean changes as you increase the number of images.

3. How the standard deviation changes as you increase the number of images.

7. For this type of error it can be shown that the amount of noise (i.e. the standard deviation) should be inversing proportional to the square root of the number of images. Does this seem to be true, at least roughly, for the histograms you just compared?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/stats_1.rst, line 18.)

Todo

1. Combine four images using median to Z-project the images, and make a histogram of the combined image.

2. Combine 16 images using median to Z-project the images, and make a histogram of the combing image.

3. Does the pattern you observed for the change in noise as you increase the number of images still hold for median combining?

4. Compare the mean and standard deviation of these histograms with the corresponding mean and standard deviation from the images combined using the average. Which results in less noise, combing by average or combining by median?

5. Why might you median combine images even though there is slightly less noise if you combine images by averaging?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/01_image_statistics/stats_1.rst, line 44.)

Todo

1. What is different about the first image in the sequence? Look near pixel coordinates (1480, 820)

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/02_master_frames/cosmic_ray.rst, line 12.)

Todo

1. Can you see the defect in the image made by averaging the stack? Why?

2. Can you see the defect in the image made by median combining the stack? Why?

3. In which stacked image is the defect more visible, the average or the median?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/02_master_frames/cosmic_ray.rst, line 26.)

Todo

1. If you haven’t yet, normalize the sequence of flat images you have open. Is the average pixel value in the image roughly the same in each image after normalization? Why isn’t the average pixel value the same as the median pixel value you set in the normalization dialogue box?

2. Suppose one of the original images had a median pixel value of 23,200 and that you want to have want the median after normalization to be 2. Describe in detail what you need to do to the image to normalize (detail in the sense of the math operations you would need to carry out if you were to normalize by hand, not details of how to do it in AstroImageJ) and explain why your answer is correct.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/02_master_frames/cosmic_ray.rst, line 52.)

Todo

1. Make a master bias frame using all of the bias frames in the folder and save the result as a FITS image. Make a note of:

• Which files went into the master.

• How you combined the files.

• How many files went into the master.

• What you called the master bias frame.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/02_master_frames/masters.rst, line 15.)

Todo

1. Make a master dark. Use all of the files in the float folder that start with twi and have D15 in the name. Combine using a median, and save the result as a FITS image. Make a note of:

• Which files went into the master.

• How you combined the files.

• What the exposure time was for these darks.

• What you called the master dark frame.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/02_master_frames/masters.rst, line 43.)

Todo

1. Double-check the exposure time of the flat you opened. Does it match the exposure time of the master dark you created?

2. Use the image calculator to subtract your master dark from the flat frame that you opened.

3. In principle, the pixel values are now all due to light that fell on the CCD. Pick a pixel, and convert the value from ADU to photons using the gain you calculated earlier. How many photons was it?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/02_master_frames/reduce_a_flat.rst, line 8.)

Todo

1. Open up SIMBAD and look up the information on your target. Make note of any other common names for your target, its position (in J2000 coordinates), and its magnitude (NOTE: You may want to save a bookmark to your object’s SIMBAD page).

2. Write down a list of the kind of observations you will need to make for your project (NOT INCLUDING the biases, darks, and flats you would have to shoot for any telescopic observations). Include the number of images and which filters you will need to use. You should also note if the images need to be in very dark skies or if you can shoot in brighter (possibly moonlit) skies. WARNING: One part of planning we are not addressing yet is estimating how long you need to make your exposures. This is critical because it affects how much you can actually shoot each night. We will be addressing this next week.

3. Edit the my_objects_list.txt text file to include your target(s) as the first object(s) on the list. You can also delete any lines for objects you do not intend to observe if you wish.

4. Open up jSkyCalc and load the objects list. Set the date in jSkyCalc to the appropriate date. When is sunset and sunrise for the night you are checking? You may need to know this to plan for twilight flats.

5. When does twilight end (evening) and begin (morning)? You may need these times to plan for twilight flats and to know when science observing can occur.

6. Set the time in jSkyCalc to twilight for that evening. What is the Hour Angle of your target? What does this mean?

7. What is the airmass profile for your target? What time of night will your target be high enough for it to be a good time to observe your object, if any? Clarify how you reached this decision (e.g. - What did you look for to make this decision?)

8. Is the moon going to be a problem for you on March 1-3?

9. Check the “seasonal observability” and confirm when during the spring semester would be a good time of year to observe the target. Make note of those times as well as how you reached the decision (e.g. - Did you consider just airmass or did you also need to consider the phase of the moon?)

10. Lastly, to help with you identifying the target at the telescope, put together a Finder Chart with a 20 arcminute wide field of view with a limit of about 17th magnitude. Use one of the following services:

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/03_planning_observations/checking.rst, line 8.)

Todo

1. What project do you think you would like to attempt?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/03_planning_observations/choosing.rst, line 67.)

Todo

1. Suppose your dark frames were exposed for 30 seconds but your science images were 90 second exposures. Explain how you would need to adjust the dark current images to use them for removing dark current from your science image.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/04_reduction_by_hand/reduce_science_overview.rst, line 44.)

Todo

1. If for some reason you wanted to could you still, even in case 2, do the data reduction the way it is in case 1? Explain.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/04_reduction_by_hand/reduce_science_overview.rst, line 59.)

Todo

1. Examine the FITS headers of the images. Which of the two data reduction cases in the previous discussion applies here? Explain.

2. Reduce the science image.

1. First, subtract of the bias/dark current or the dark frame (depending on which case applies here). Identify one or more features of the image that visibly changed when you did this step of the calibration.

2. Second, use the flat field to correct the illumination. Describe what changes, if any you see in the image. Hint: As with the previous step, you will need to use the image calculator.

3. Summarize, in your own words, the steps needed to reduce a science image.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/04_reduction_by_hand/reduction_by_hand.rst, line 6.)

Todo

1. As a refresher, write down the three basic types of calibration you do to images.

2. Open one of the science images you have been asked to reduce.

3. Based on the exposure time and filter of the image, what calibration files will you need?

4. Are there additional calibration images you need to calibrate the calibration images in the previous question? If yes, what are they?

5. Are there calibration images you need that do not depend on exposure time or filter? What are they?

You should end this section of questions with a clear list of the calibration images you will need (e.g. flats in the Z filter, darks with an exposure time of 120 hours).

Check your list with any one of the instructors before you move on.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/automated_reduction.rst, line 53.)

Todo

1. How many bias frames should be included by the this automated tool? Note: there is no fancy way to figure this out. Look at the file names.

2. Does the master bias image that you produced look like a master bias should? By this point you should have some rough idea of what a master bias looks like and you should have notes on what the typical pixel value is in a bias frame. Check both of those things.

3. Compare your master bias image to the appropriate Reference master images to make sure they are really the same. Do that by loading both your master bias and ours and using AstroImageJ to take the difference or the ratio (what would you expect for either?).

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/master_bias.rst, line 25.)

Todo

1. Review the list of calibration files you made in Get organized before you get going. How many master dark images will you need to make?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/master_dark.rst, line 4.)

Todo

1. How many dark frames should be included by the this automated tool for each of the master darks you need to create? Note: there is no fancy way to figure this out. Look at the file names.

2. Does the master dark image(s) that you produced look like a master dark should? By this point you should have some rough idea of what a master dark looks like and you should have notes on what the typical pixel value is in a dark frame. Check both of those things.

3. Compare your master dark image(s) to the appropriate Reference master images to make sure they are really the same. Do that by loading both your master dark(s) and ours and using AstroImageJ to take the difference or the ratio (what would you expect for either?).

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/master_dark.rst, line 32.)

Todo

1. Review the list of calibration files you made in Get organized before you get going. How many master flat images will you need to make? What filter(s)?

2. What calibration frames will you need to make your master flat?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/master_flat.rst, line 6.)

Todo

1. How many flat frames should be included by the this automated tool for each of the master flats you need to create? Note: there is no fancy way to figure this out. Look at the file names.

2. Does the master flat image(s) that you produced look like a master flat should? By this point you should have some rough idea of what a master flat looks like and you should have notes on what the typical pixel value is in a flat frame. Check both of those things.

3. Compare your master flat image(s) to the appropriate Reference master images to make sure they are really the same. Do that by loading both your master flat(s) and ours and using AstroImageJ to take the difference.
• NOTE: If there turn out to be tiny differences, they may appear to be more significant than they are when displayed, since AstroImageJ scales the image to the range of values in the image. A better test might be taking the RATIO of your flat versus the reference flat image if your flat and our reference flat are just offset from each other by a tiny but constant fraction.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/master_flat.rst, line 31.)

Todo

1. How many science frames should be included by the this automated tool? Note: there is no fancy way to figure this out. Look at the file names.

2. Does the calibrated science image look sensible? By now you should be able to recognize if there are obvious defects (e.g. dust donuts)

3. Compare your science image(s) to the appropriate Reference master images to make sure they are really the same.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/05_automated_reduction/reduce.rst, line 30.)

Todo

1. If you choose $$C=0$$, what is the instrumental magnitude for the star you measured?

2. Look at the table of magnitudes handed out (also available here). Does your instrumental magnitude match the V magnitude of the star you chose? Should it?

3. What would you have to choose C to be for your magnitude to match the magnitude in the table?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/06_photometry_intro/instrumental_mags_and_the_beauty_of_photometry.rst, line 24.)

Todo

1. Talk to someone who chose a different star than you did and calculate the difference in instrumental magnitude between your two stars.

2. Compare your difference in instrumental magnitude to the difference in the reported magnitudes of the two stars in the table.

3. Try looking up your two stars in simbad and write down their V magnitudes according to simbad.

• do a coordinate search

• one format for entering coordinates is like: 2.703738h+42.77795d

• RA is first, in decimal hours, followed by declination in decimal degrees

4. Does the magnitude difference as calculated from simbad match what you get from the table we provided? Does it match your image?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/06_photometry_intro/instrumental_mags_and_the_beauty_of_photometry.rst, line 48.)

Todo

1. What is the FWHM of the star you took the seeing profile of?

2. What does the FWHM mean? Answer both in terms of what the acronym means and an explanation in plain english what it represents.

3. What aperture size was chosen? How large is it compared to the FWHM (e.g. 2X, or half)?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/06_photometry_intro/photometry_aij.rst, line 24.)

Todo

1. Write down, from the measurement table, for the star you chose:

• the RA and Dec (in decimal hours and decimal degrees)

• net counts (Source-Sky)

• minimum and maximum sky radius

• Sky counts/pixel

• Source Error

• Source SNR

2. What was the net number of photons received from the star in the aperture? Hint: Don’t forget about the gain.

3. Pay attention to the folks up front for a brief lecture about error and signal-to-noise ratio (SNR). Take useful notes.

4. Calculate the source error and SNR and compare to the values of each from AstroImageJ

5. What is the largest source of error?

6. Of the “noise” terms (dark, sky, read noise), which makes the largest contribution?

7. How could you reduce that source of noise?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/06_photometry_intro/photometry_aij.rst, line 52.)

Todo

1. Add three columns to your data table, one for the instrumental magnitudes of each of the three stars for which you have photometry. NOTE: AstroImageJ calls the columns that contain the net counts (i.e. $$N_\text{Source}-N_\text{Sky}$$) something like Source-Sky_T1 or Source-Sky_T2. Use a value of zero for C.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/07_photometry_automated/excel_graphs.rst, line 17.)

Todo

1. Make a graph with airmass on the horizontal axis and instrumental magnitude on the vertical axis, like BGO Fig. 7.4. Use the “target” star.

• Display the points as points not a line like the book does.

• Excel will pick stupid default values for the airmass values; be sure to at least change the minimum value to something sensible.

2. Add a trendline to each of the data series. In the options for the trend line:

• Display the fit equation

• Display $$R^2$$

3. What would the instrumental magnitude of this star be if corrected for the atmosphere? Hint: What does $$m_0$$ represent, physically (mathematically it is the y-intercept of the line you fit to the data)

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/07_photometry_automated/excel_graphs.rst, line 35.)

Todo

1. Add the instrumental magnitudes of the other two stars to your graph. The graph should have three sets of points on it, one set for each of the three stars (i.e. three data series, in Excel-speak).

2. Add trendlines to the new data series.

3. Do all three stars experience the same extinction coefficient, k? Why do you think that is?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/07_photometry_automated/excel_graphs.rst, line 59.)

Todo

1. Open the image “reference-positions.fit” to get the RA and Dec for each of the stars you look at and write them down in your notebook.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/07_photometry_automated/multi_aperture_photometry.rst, line 39.)

Todo

1. Write down the FWHM, aperture size, inner sky radius and outer sky radius you chose.

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/07_photometry_automated/set_aperture_radius.rst, line 22.)

Todo

1. For my project to succeed, what do I need to actually accurately measure from my science images?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/08_planning_reductions/data_processing_questions.rst, line 6.)

Todo

1. Calibrated Astrometry: Do I need to know where my objects actually are in the sky? Do I need to be concerned with their motion? Note: None of your projects this year involved tracking moving objects, so this is not critical. That said, it is a good idea to calibrate your astrometry so that you know where each point in your image actually is in the sky. Luckily for you, we have already calibrated all your images astrometrically using a software package called astrometry.net.

2. Calibrated Photometry: Do you need to calibrate your photometry so that it can be compared to others? That is, do you need to report calibrated magnitudes or can you stick to instrumental magnitudes? If you need calibrated magnitudes, how would you go about getting them? What standard stars will you use?

3. Atmospheric Extinction Corrections: Do you need to be concerned with determining the extinction and reddening per airmass for your target? If so, how could you determine it?

4. Galactic Extinction Corrections: Do you need to be concerned with determining the amount of Galactic extinction or reddening to your target? Why or why not?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/08_planning_reductions/data_processing_questions.rst, line 11.)

Todo

1. Do you need to stack images in order to bring out faint features in your images? If so, what images do you need to stack to produce what final images. How will you make sure the stacked images are aligned?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/08_planning_reductions/image_processing_questions.rst, line 6.)

Todo

1. For my project to succeed, what do I need to actually accurately measure from my science images?

(The original entry is located in /var/build/user_builds/image-analysis/checkouts/latest/08_planning_reductions/introduction.rst, line 25.)