Friday, September 19, 2014

Major new endorsement for The Plundering of NASA: an Exposé

Received this email from O. Glenn Smith (former manager of Space Shuttle systems engineering at NASA's Johnson Spaceflight Center):

"Hi Rick,

I just finished reading your great book.  Good going!  We are really swimming upstream, perhaps until the next election.  If you have not seen it, attached is one of my recent articles in Space News.


For those interested in reading the article of which Glenn spoke, here is a link:

It is most gratifying when significant figures in the space community have complimentary things to say about my work.

Following are some of the other kudos received by me:

"I hope you continue as a voice against pork at NASA"
Lori Garver, (former Deputy Administrator of NASA) commenting to the author about The Plundering of NASA: an Exposé

"The Plundering of NASA offers an insightful analysis of the agency's struggle with external forces tending to distort a clear, long-term US vision for space exploration. In crisp and concise prose, it calls into question NASA's current "flight plan" for reaching its stated destination, and makes the case for using the game-changing technologies needed for a truly sustainable and robust human exploration of space. An enlightening read for those who are not in the field and food for thought for those who are. " 
Dr, Franklin Chang-Diaz, former Space Shuttle astronaut, CEO of Ad Astra Rocket Company and inventor of the VASIMR electric rocket drive

"The author does an excellent job of exposing how a few individuals in the legislative branch of our government are impeding the progress of our space program. This is most evident with the Space Launch System, a project to develop a heavy lift launch vehicle in the same class as the Saturn V that sent astronauts to the moon. It is also true to a lesser extent with the Orion spacecraft that is designed for human missions beyond earth orbit. Both of these projects are based on flawed designs (more on this later). Worse yet, these projects (especially the Space Launch System) are consuming such a large portion of NASA's budget that other vitally important work is not getting accomplished. Of course the book's author is not the only one who has been pointing out these things, but I was glad to see it laid out in detail in book form. I hope that members of congress read this book and that it helps influence legislative policy."
Gerald Black, 40 year veteran aerospace engineer who worked on the ascent engine of the Apollo lunar lander for NASA

"The Plundering of NASA: An Exposé by R.D. Boozer is the indispensable source for a wide range of information about the SLS controversy and the machinations within NASA, the Congress, aerospace companies and two Administrations that have left us in the terrible mess we are in today. It is the most comprehensive single source for both SLS-Orion program information and context. I have written several articles about the SLS issue and found the book very valuable and interesting."
John K Strickland, Jr.
member National Space Society Board of Directors
Advocate: Space Frontier Foundation

Thursday, September 18, 2014

Photometry with AIP4WIN: A Tutorial – Part 2

By R.D. Boozer

In this second part of the tutorial, I cover the first process that I had to perform on data supplied by the University of Tasmania; that is, the creation of a scalable master dark frame.

Before the images were calibrated, an optimum bias frame, dark frame and flat-field frame needed to be generated.  AIP4WIN has a utility for performing these operations called the Calibration Setup Tool.  Under the Calibrate menu, the Setup option is picked to yield the dialogue box shown below.

Figure 1: The default appearance of the Calibration Setup Tool dialog box.

The dialogue box’s default setting is to follow the Basic calibration protocol in which the only calibration file produced is a master dark frame that is generated from multiple raw dark frames by either averaging the raw frames pixel by corresponding pixel or by taking the median of those pixels.  This method will not bring the background noise level down sufficiently to the degree needed for accurate stellar photometry.  A drop down list box can be used to invoke more adequate options as shown in the next illustration.

Figure 2: Selection of a calibration protocol.

Two other options are presented in the drop down list box.  The Standard protocol will automatically create a master dark frame from a series of raw dark frames and a master flat-field frame from a series of raw flat-field frames, but does not apply any bias frame compensation and thereby is not suitable for photometry.  The only option that is usable for photometric purposes is the Advanced protocol which allows everything.  The following figure shows the appearance of the dialogue box after the user chooses the advanced option.

Figure 3: Selecting the type of bias compensation.

There are four tabs offering different complementary functionality in the dialog box: The Bias frame tab, the Dark frame tab, the Flat-field frame tab, and the Defect frame tab.  Under the first tab, three bias related choices are offered: no bias subtraction at all, subtraction of a user defined number of ADUs from each pixel (only of use if no bias frames are available), or subtraction of either a raw or master bias frame.  Since precision photometry is to be done, the radio button for the third option marked Use Bias Frame is selected followed by clicking the Select Bias Frame(s) button.  At this point what is seen in the illustration after this paragraph shows up.

Figure 4: Selecting bias frames.

Since eleven bias frames were supplied, one master bias frame will be made from all of them.  To begin the process of making the master bias frame, the user highlights the filenames and clicks the Open button.  This action brings the user back to the Calibration Setup dialog box as it appears below.

Figure 5: Bias frames have been selected.

The reader will note that the dialogue box now indicates that 11 raw bias frames have been loaded.  At this point the user needs to decide how the raw bias frames are to be combined into a master bias frame.  If pixels are averaged with their corresponding pixels in the other frames, the readout noise typically decreases with the square root of the number of frames averaged. (Berry and Burnell 169-170)  Given this fact, averaging as many bias frames as possible is usually the best way to go.  One exceptional case is when the camera is being operated in an electrically noisy environment.  Under such circumstances median combining of the raw bias frames should be used because any large power spikes will manifest themselves in a master bias frame that was obtained from an averaging operation. (Berry and Burnell 170; AAVSO 3.2)

I did a visual check of the given raw bias frames that revealed no indication of power line spiking, so I decided that averaging would be used.  Clicking the Average Combine radio button causes ADU values of corresponding pixels in the frames to be automatically averaged.  Finally, the user clicks the Process Bias Frame(s) button to create the master bias frame and the view that follows is seen.

Figure 6: The master bias frame may now be saved.

As soon as the processing has been completed, the Save as Master Bias button becomes enabled so that the master bias frame can be saved as a single file.  After the master bias frame is saved, it can be used in the future instead of going through the bias frame combining process again.  Notice the check box marked Subtract Bias.  As long as that box is checked, AIP4WIN will automatically subtract the master bias frame from any dark frame or image when calibration occurs.  If the user decides at any point that he/she does not want automatic subtraction of the master bias frame to occur, the option may be unchecked.

Now the master dark frame is to be created.  Clicking the tab marked Dark will start this operation.

Figure 7: The default appearance of the Dark frame tab.

The creation of the master dark frame begins with clicking the Select Dark Frame(s) button. Since the selection of the raw dark frames that are to be combined is similar to the procedure followed for the earlier described selection of raw bias frames, this operation will not be pictorially illustrated.

Because a temperature-controlled camera was used, a master dark frame was created using raw frame files that were not necessarily shot near the same time as the research images, nor did they have the same integration time as the research images.  The standard technique (automatically done by the software) in this situation is to:
Get the number of ADU counts per second for each pixel in each frame by dividing the count of each pixel by the number of seconds of the frame integration.
Produce the master frame by averaging ADU counts per second per pixel (that were calculated in step 1) for every dark frame.

After those two steps have been executed by the software, the resultant master frame can then be scaled to calibrate any stellar image exposure by multiplying the dark frame pixel values by the image integration time.  (Walker 29).  With AIP4WIN, the above series of steps is chosen for automatic implementation when the user clicks the Automatic Dark Matching radio button.  The reader should not be misled by the Constant Dark Scaling radio button, because that option is only used when a user wants to use a raw dark frame as his master and manually inputs a scaling factor for it in the input box below that button.  Ignore it.

As was mentioned in Part 1 of this tutorial, raw dark frames were supplied that had 5, 6, 35 and 180 second integration times.  It should be mentioned that there seems to be some disagreement about what frames to combine.  One source I found says only raw dark frames that have a longer integration time than the stellar images should be used to make the scaling dark frame.  (Berry and Burnell 174-176)  Another source implies that all scaling dark frames should be used. (Walker 29)  Using techniques that will be described shortly, I calibrated some stellar images using each method and compared the signal-to-noise ratios of the stars in the calibrated images.  I decided to resolve the matter myself to my satisfaction by trying both methods and examining the final fully-processed images yielded from each of these methods.  Because I found that including all of the dark frames in the making of the scaling master frame yielded slightly improved SNRs (signal to noise ratios) compared to the other way, I chose that method.  The next figure shows what is seen after these files have been selected and the Automatic Dark Matching radio button was chosen.  At this point, the Process Dark Frame(s) radio button is clicked to indicate that a scalable master dark frame is desired.

Figure 8: Dark frames of varying integration times have been chosen.

Clicking the Process Dark Frame(s) button will start the process of automatically creating the scalable master dark frame via the aforementioned process.  The dialogue box will then look similar to what you see below.

Figure 9:  The newly created scalable dark frame can now be saved.

The Save Master Dark button has become enabled and should be used to save the scalable dark frame as a file for later use.  The Subtract Dark Frame check box was automatically checked to indicate that, during the current run of the AIP4WIN application, an image calibration would automatically scale the master dark frame and apply it to the image after the master bias frame has been applied.

Part 3 will cover the creation of the scalable flat field frame.


Berry, Richard and James Burnell, Handbook of Astronomical Image Processing, (2006) Willmann-Bell, Inc., Richmond, Virginia, USA

Walker, E. Norman, CCD Photometry, (2007)

Copyright 2014 R.D. Boozer

Wednesday, September 10, 2014

Photometry with AIP4WIN: A Tutorial – Part 1

By R.D. Boozer, MoA in astrophysics

The science of photometry can be used by both amateur astronomers and professionals for some very advanced scientific work.  You can detect the light changes caused by eclipsing binary stars, plot the changes in luminosity of a variable star and even detect an exoplanet orbiting another star.  This tutorial will be your step-by-step guide on how to employ the powerful Magnitude Measurement Tool that comes with the renowned astronomical imaging software known as AIP4WIN by Richard Berry and Robert Burnell.  Special thanks to Mr. Berry for giving me permission to include screen images and extensive operating details from AIP4WIN.

The proper equipment for this endeavor is as follows:  a telescope with an accurate tracking drive, a sufficiently sensitive CCD or CMOS camera, and a computer with AIP4WIN installed.  AIP4WIN comes on a DVD accompanying the book The Handbook of Astronomical Image Processing by the aforementioned Messrs. Berry and Burnell.

Link to site of The Handbook of Astronomical Imaging
The instructions in this tutorial are most applicable to CCD or CMOS imagers with precise temperature control.  While almost all astronomical imagers have a Peltier cooler built into them which significantly lowers the temperature of the imaging chip to help eliminate electronic noise, many just cool the imager a significant amount below the ambient air temperature without keeping the temperature at a steady value.  This tutorial is mainly geared towards those imagers with precise temperature control, though I will mention steps that users of imprecisely cooled cameras must do to get really good results.  It should be pointed out that the user of an imprecisely cooled camera will have to do a good bit of extra work to obtain accurate results, but it will be worth the effort.

Many thanks to Dr. Andrew Cole of the University of Tasmania for giving me permission to use astronomical images shot of star Gliese 876 at the university’s Canopus Hill observatory on July 24 of 2004.  Specifically, I am going to relate how I conducted the photometry part of my research project in 2009 to obtain my Masters’ degree.  Gliese 876 had already been determined to have several exoplanets via Doppler technique.  The task assigned to me was to see if the image data collected on the night in question showed a transit of the star by one of these exoplanets.  If the transit occurred, the star’s light would dim as it crossed in front of the star’s disk.

Such a transit was unlikely to be detected for two reasons: 1) the odds that the planet would have an orientation relative to Earth that would cause it to pass in front of the star were very low and 2) weather conditions were not optimal when the images were shot as there was some cloud haze.  However, in regard to the second concern, the particular method chosen to photometrically measure the stars brightness is capable of cancelling out much of the negative effects of variation in brightness due to the ever changing cloud cover.

AIP4WIN has a number of advanced automated features that boost the efficiency of photometry related research to a very high level by dispensing with much of the time consuming drudgery that normally is associated with this type of research.  The higher level of automation should also decrease the likelihood of human induced error during the collection and analysis of the photometric data.  One note before we continue: what traditional photographers refer to as “exposure time” (length of time that light is acquired from the object being observed) will be referred to as “integration time” as is common among astronomical professionals.

None of the initial images had any processing done to them.  Instead, they were supplied with support files that included bias frame files, dark frame files, and flat frame files.  The assumption being that whoever uses the images would calibrate them sufficiently by applying the support files to obtain final images that were of a quality high enough to perform photometric research.  Immediately following is a general discussion of the purpose of the support files.  Actual descriptions of how these frames were used and illustrated depictions of them will be included later in this tutorial within more specific discussion.

Bias frames are images with no exposure time and no actual light exposure.  Having no dark current (because of the zero exposure time) and no light induced signal, they show only the random noise produced by the CCD’s amplifier and any underlying fixed pattern variation in base pixel ADU values.  (Berry and Burnell 124)

Dark frames have an exposure time greater than zero, but just as with bias frames no light is admitted into the camera during the so called “exposure time”.   In fact, the lack of light exposure for certain types of CCD frames is a good part of the reason why the time during which the CCD is accumulating new pixel data is often not referred to as an exposure time, but instead as an integration time.  As I mentioned earlier, this tutorial will use the latter term.  Dark frames are used to remove quantum mechanical noise induced by the temperature of the CCD chip. (Berry and Burnell 124)    The best kind of dark frame is one where a bias frame has been subtracted from it on a pixel-by-pixel basis, so that amplifier noise and fixed pattern bias has been removed from it.  Specifics of how dark frames are created depend on the technique used to cool the CCD detector described as follows.

Cooling the CCD detector helps to remove some of the unwanted effects from thermal noise. Had the camera merely been imprecisely cooled by some amount below ambient air temperature, the temperature of the CCD would change from one image to the next as the ambient air temperature changed.  This temperature change would cause each image to have different amounts of thermal noise.  In such situations it is necessary to shoot a couple of dark frames before and after each main image exposure with the same integration time as the main exposure.  Those four dark frames would then be averaged to make a master dark frame that can only be used for that one particular image. (Walker 28)

However the U of T images were shot with a CCD camera held at a specific temperature for all exposures; therefore, only one set of dark frames were supplied (all of them shot within a short time period before the observation run was begun) because the constant temperature would have insured the thermal noise would not change for the entire multi-exposure observing session.

None of the dark frames had exposure times that were equal to the image exposure times.  In fact, dark frame integration times spanned a range of 5, 6, 35 and 180 seconds.  When a precisely temperature controlled camera is used, such a variety of dark frame exposures is standard procedure and the supplied dark-frames are to be used to make one scalable master dark frame that can be applied to any image regardless of integration time. (Walker 29)  A description of how and why this process works will be explained in due course.

Once a suitable bias-subtracted dark frame has been produced, it is subtracted from the image frame on a pixel-by-pixel basis to obtain a stellar image that contains very little equipment-derived noise.

Flat-field frames are true light exposures, but are not exposures of the image to be studied.  These frames are used to counteract unwanted imaging artefacts due to imperfections in the combined telescope/camera optical system.   To produce a flat-field frame, light of uniform intensity over its cross-sectional area is sent through the entire optical system.  In this scenario, the optical system must be assembled in the identical configuration under which it will be used during observation.  The flat frame will reveal such problems as vignetting in the outer frame of the image, reduction of pixel sensitivity due to shadows of dust particles on the camera’s optical window, uneven pixel sensitivity in the CCD sensor, etc. (Berry and Burnell 180-181) Before the flat frame is used, it must be calibrated with the bias and dark frames to remove noise generated by the amplifier and thermal conditions.  Forthcoming will be instructions on how to shoot a flat frame and the mathematical application of the flat frame to the subject image for removal of the effects of the aforementioned artefacts (don’t worry, the software will do the mathematics).  In fact, instructions on actual implementation of the above described steps will begin with the next part of this tutorial.


Berry, Richard and James Burnell, Handbook of Astronomical Image Processing, (2006) Willmann-Bell, Inc., Richmond, Virginia, USA

Walker, E. Norman, CCD Photometry, (2007)