Astro 3310 Fall 2015
LAB #2
-----Please copy this document to the REPORT sub-directory from the
expanded LAB2_Data_Package_FA15.tar.gz. Then, edit it to write your
answers in all the "______" areas. When finished, create a tar.gz
archive of the REPORT directory and all of its contents, then scp
the file to datafarm.astro.cornell.edu and place it in:
/data/Courses/A3310/FA15/”your netid”/LAB2/
Remember that you will only get credit for the files that you put in
the REPORT sub-directory and copy to datafarm. Please make sure
that you keep a Matlab workbook with all of the commands you used to
answer the questions in the lab. Feel free to comment and organize
your workbook so that it will be easy for us to follow your
algorithms when we execute the doe. If you generate any functions
for the lab, ensure that they are also in the REPORT sub-directory
and properly called from the workbook file. For you convenience,
there is a template for the workbook file already in the REPORT subdirectory.
YOUR NAME: _______________________________
Your NetID: __________
Problem 1: Read Noise and Bias
1. Why do we divide by √2?
Hint: Recall that noise adds in quadrature but also is reduced by
the number of frames taken.
Answer:
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2. What is the problem of using spatial sampling to determine the
bias and read noise?
Hint: What assumptions are you making about the read noise and
bias and detector? You should notice something very clear about
the detector that is mentioned in “Preparation Readings”.
Answer:
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4. Report your values for the bias and read noise:
Answer: Bias = ______________ +/- ____________ [DN]
Read Noise = ____________ +/- ____________ [DN]
5. What are the advantages or disadvantages of temporal sampling?
Hint: Think about the answer to Question 3.
Answer:
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6. Which method, spatial or temporal sampling, is more
appropriate for a CMOS detector? Why?
Answer:
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7. Do you notice any differences about the bias and read noise in
the temporally averaged image? Is spatial sampling justified?
Answer:
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8. Save your bias image (as Bias.png) and read noise image (as
Read_Noise.png) in the REPORT subdirectory. Make sure they
have appropriate titles and colorbars. Make sure to title
them accordingly.
Answer: Insert a copy of your two Matlab figures here.
9. How should the bias and read noise vary in time? Why?
Answer:
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Problem 2: Dark Current
1. How should the noise in the dark current vary in time?
Answer:
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2. How do you think the dark current will vary as a function of
temperature?
Answer:
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3. Make a plot for a few pixels of the dark current as a function
of time. Save your figure (as Dark_vs_Time.png) to the REPORT
subdirectory. Make sure the axes are clearly/correctly labeled
and that the figure is titled and has a legend.
Answer: Insert a copy of your Matlab figure here.
4. Describe the similarities, differences, and features of the
curves you see.
Answer:
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5. How could we have reduced the error in our LSCOV fits to the
slope and offset of the dark current vs time curves?
Answer:
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6. Make an image of the slope (as Dark_Slope.png) and offset (as
Dark_Offset.png) of the dark current of the chip. Make sure
they each have an appropriate title and colorbar. Save the
images to the REPORT subdirectory.
Answer: Insert a copy of your two Matlab figures here.
7. Describe what you notice about the dark current across the
array. Are there any peculiarities, interesting features, or
questionable values?
Answer:
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8. Report the average dark current you measure. What are the
units?
Answer: Dark current = ____________ +/- ____________ [____]
9. What do you notice about the offset? Why is this?
Hint: Think about how the offset of the dark current curve
relates to the bias.
Answer:
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10.
Make a plot for a few pixels of the dark current as a
function of temperature. Save your figure (as
Dark_vs_Temp.png) to the REPORT subdirectory. Make sure the
axes are clearly/correctly labeled and that the figure is
titled and has a legend.
Answer: Insert a copy of your Matlab figure here.
11.
Describe what you notice about the dark current across
the array. Are there any peculiarities, interesting features,
or questionable values?
Answer:
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12.
[Optional] If you could not see or determine a trend from
CMOS data, load and make a plot for a few pixels of the dark
current as a function of temperature for the CCD detector.
Save your figure (as Dark_vs_Temp_CCD.png) to the REPORT
subdirectory. Make sure the axes are clearly/correctly labeled
and that the figure is titled and has a legend.
Answer: Insert a copy of your Matlab figure here.
13.
[Optional] Describe what you notice about the dark
current across the array. Are there any peculiarities,
interesting features, or questionable values?
Answer:
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Problem 3: Gain
1. What would the gain be for a detector with a full well of
42840 electrons, read out with a 15 bit ADC?
Answer: Gain = ____________ [e-/DN]
2. Why does not subtracting the bias lead to an underestimation
in the gain? You can use words or an equation.
Hint: How does the bias change as a function of time or number of
counts being read by the CMOS?
Answer:
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3. Write and equation for our three sources of noise so far,
assuming we subtract the bias (i.e. read noise, photon noise,
and dark noise). Is our signal variance curve still linear?
Answer:
Equation:
Explanation:
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4. Now that we finally have some flux on our detector – do you
notice any immediate peculiarities in the detector? What might
these be?
Hint: What might some environmental factors be that could affect
the images?
Answer:
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5. Is your choice of uniform region for determining the gain
justified? Why or why not?
Answer:
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6. What assumption must you make about the 100x100 pixel region
we have provided to determine the gain?
Answer:
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7. Make a plot of your signal variance curve using the spatial
method. Save your figure (as Spatial_Gain.png) to the REPORT
subdirectory. Make sure the axes are clearly/correctly labeled
and that the figure is titled.
Answer: Insert a copy of your Matlab figure here.
8. Report your gain from your spatially determined signalvariance plot. Is this gain justified for the whole chip? Why
or why not?
Answer: Gain = ____________ +/- ____________ [e-/DN]
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9. What assumption are we making about our 100x100 pixel region
when we normalize the frames?
Hint: Can we take data instantly?
Answer:
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10.
Make an image of the gain (as Temporal_Gain.png) and
error (as Temporal_Gain_Error.png) for the region provided.
Make sure they each have an appropriate title and colorbars.
Save the images to the REPORT subdirectory.
Answer: Insert a copy of your two Matlab figures here.
11.
Is your error, on average, greater or less using this new
method? What do you think now of using a single gain for the
entire chip?
Answer:
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12.
Make new read noise and dark current maps that are in the
correct units of [e-] and [e-/s]. Make these maps
incorporating your two different gain calculations. That is
create a base map using the single gain calculated in Method 1
and update the region where we know the gain specifically for
each pixel. Save your updated maps as Read_Noise_Updated.png
Dark_Current_Updated.png. Make sure they each have an
appropriate title and colorbar. Save the images to the REPORT
subdirectory.
Answer: Insert a copy of your two Matlab figures here.
Problem 4: Responsivity
1. Using dimensional analysis, confirm the units of the blackbody
function are correct.
Answer:
2. Take a moment to understand the camera equation.
units of 𝐹, 𝐹̅ , & 𝐹𝑜 ?
What are the
Hint: Consider the other terms in the camera equation. Remember
we are ultimately looking for a unitless number for QE.
Answer:
3. What is the main difference between the provided spectral
curve and the blackbody curve you programmed?
Hint: Make sure your units between the spectral curve and the
blackbody curve are the same. You should still notice a
significant difference between the two curves.
Answer:
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4. Make a plot of the spectral curve, the original blackbody,
modified blackbody, and peak fitting parabola. Scale them as
needed to fit reasonably. Save your plot as
Source_Fitting.png. Make sure it has appropriate axes labels,
a clear legend, and title. Save the images to the REPORT
subdirectory.
Answer: Insert a copy of your Matlab figure here.
5. Make a map of the QE efficiency determined from your linear
regression fits. Make sure the map has an appropriate title
and colorbar. Save the map as QE.png to the REPORT
subdirectory.
Answer: Insert a copy of your Matlab figure here.
6. Is your QE reasonable? How large is the error in QE
(determined from the LSCOV fit). What do you think is our
largest source of error?
Hint: Think of the assumptions that went into the camera equation
and the modifications that needed to be made in order to
determine the QE.
Answer:
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