Mary Anne Weaver

Copyright (c) 1999 by Delphi Technologies


For a number of years now, researcher and former NASA consultant Richard C. Hoagland of the Enterprise Mission, has observed what he has identified as a "pattern" in star positions at the time of NASA launches and/or landings. He published his discovery a few years ago as an article entitled "Kennedy's Grand NASA Plan", and has since written many more articles and performed extensive research into this topic. Later on, Mike Bara, an Aerospace design engineer with many years experience in his field, joined the research effort into this stellar alignment phenomenon and has assisted Richard Hoagland in investigating its attributes and the frequency of its occurrence.

Both Richard Hoagland and Michael Bara note that specific stars are found at particular elevations above the horizon, namely 19.5 and 33.0 degrees but also directly on the horizon and meridian, at the precise moment of a NASA launch or landing. Since 1996, Michael Bara and Richard Hoagland have done considerable research into the "star alignment" theory and posted a large amount of data. Also since that time and based on copious research into this topic, they have proposed a number of theories to account for this phenomenon.

I was intrigued by the data posted on the Enterprise Mission website and decided to investigate for myself. If there was a solid, numerical basis for the assertions made by the Enterprise Mission, it should show up in the numbers -- or not. I decided that a thorough numeric investigation could end the debate over whether such a pattern exists or not.

I have always been interested in space exploration, and have followed NASA's activities with great interest as a child and later as an adult. This interest, and my technical abilities, lead me to pursue a career in electromagnetics research and development. In 1985, I graduated from the University of Washington in Seattle, with a Bachelor's Degree in Electrical Engineering. Prior to graduation, I participated in a competitive and prestigious research and development program sponsored by GTE Labs, in which I was 1 in 50 selected nationwide. After graduation, I accepted a research position in the Antenna division at Boeing Aerospace Company in Seattle. At Boeing, I was responsible for: 3-D computer modelling, computational analysis, data analysis of radar patterns, and developing equations and analytic methods to solve for optimal antenna parameters. At Boeing, I gained experience in using rigorous experimentation and data-gathering methods to determine how accurate our "models" of the situation were, based on the radar patterns we were getting. Thus, I learned how to develop and write equations to model real life situations and how to test them; I also learned how to analyze data and real results, and make determinations based upon those analyses. These are the abilities I bring to bear on this analysis. In addition to my duties at Boeing, I've performed independent probability studies, such as a project to determine the probability of the shape and size of an object based on its radar pattern.

Since Boeing, I have worked as a computer professional, and most recently as a software design lead for a data management company. I have continued to use my mathematic and analytic skills in the computer industry, and still write programs to do computational analysis and data analysis. In order to design software of this kind, I utilize my ability with mathematics and analysis on a daily basis.

My career took a different turn in my adult years, so I never did work for NASA or help advance the space program. But, my interest in it never waned. Thus, when I learned of Richard Hoagland's research, I was intrigued and wanted to know the truth for myself. Was there really any correlation between NASA events, such as launches, and star positions? If so, what does it mean?

I don't propose to answer the "what does it mean" question in this article. I felt it important to determine first, if there a solid scientific basis for Richard Hoagland's hypothesis that these alignments form a pattern. If so, this solid foundation will provide the basis for asking and finding the answers to more questions, such as "what does it mean?"

Because of my interest in the space program, and because NASA launches and events lend themselves more easily to statistical analysis (since there are a set number of them), I decided I would limit myself to analyzing NASA events (launches, and mission activities). My task, then, was to determine numerically whether or not these NASA alignments occur more often than random chance; and if so, how much more. If there were quite a few more alignments than random chance would allow, then this could be an indication that a pattern was being followed, if all the right factors were present.

To do this, I first had to develop a correct model of the odds. For those readers who wish to view the scientific method and proofs in detail, read my technical paper. Otherwise, this summary will mention in less detail what I did and what I found.

In order to correctly model the odds, I had to come up with a simplified and restricted version of the Hoagland "star alignment model." This was for the sake of consistency, and meant to be a preliminary investigation that could catch at least some of these repetitive 19.5 and 33.0 degree star alignments.

A Brief Review of the Hoagland "Star Alignment Model"

The Hoagland "star alignment model" angle selections are based on tetrahedral geometry; specifically, a tetrahedron inscribed inside of a sphere. The circumscribed tetrahedron is descriptive of a kind of "hyperdimensional physics," which is simply a physics that takes higher (thus unseen!) spatial dimensions into account. I will not get into detail about the theory here, but rather summarize it briefly so that the reader will understand the basis of the related Hoagland/Bara star alignment work.

Hoagland's original "hyperdimensional physics" theory states that "rotation," such as that of a planet on its own axis, creates higher-dimensional dynamic forces inside a planet, which ultimately conform to a specific 3-D geometry; as a result, phenomena appear on the planets' surfaces in accordance with the geometric contact points of that geometry -- two interlaced 3-D tetrahedra inscribed inside a sphere. The lowest order touch points of these circumscribed tetrahedra (beside the poles of rotation) are at approximately 19.5 degrees, North and South latitude. Refer to Figure 1, below.

Figure 1: Two tetrahedra inscribed inside a sphere. Copyright (c) 1998 by the Enterprise Mission, used with permission.

The actual "touch points" of these tetrahedra are at 19.47 degrees N or S latitude on any particular planetary body, rounded to "19.5 degrees." This is the source of the number 19.5. Next, when one takes the Sine of the tetrahedral angle, the following number results:

Sine (19.4712) = 0.33333...

This turns out to be the vertical "height" of the 19.5 angle within a unit sphere.

Richard Hoagland and Mike Bara utilize the "shortened" version of this, which is the angle 33. However, note how many "3's" there are in the above. Since the source of the Hoagland/Bara "angle 33" and angle "3 deg 30 min" is this "repeating 3's" in the Sine of the tetrahedral angle, that will be a key thing to look for in the data.

The source of the "horizon" and "meridian" alignment emphasis is Egyptian ritual practice; specifically, Egyptian star lore. Stars, to the ancient Egyptians and Sumerians, were quite important and their position in the skies were an integral part of temple layout and design and well as ceremonial applications (sources: Star Names: Their Lore and Meaning by Richard Hinckley Allen, Astrological Origins by Cyril Fagan, and historical texts on ancient Egypt and Sumer). The horizon and meridian had important symbolic values. Stars that rose were considered to be "born," stars at the meridian had reached their "peak," and stars that were setting were considered to be "dying," or about to go into the underworld. This is common knowledge to scholars of these ancient belief systems. The horizon and meridian are, of course, the basic dividing lines for the celestial sphere as well as for terrestrial geography.

Additionally, according to Egyptian belief and mythology, the stars were actually the "abode of the gods," and in many cases, were identified with the gods themselves. The constellation of Orion and Osiris were actually identified with one another, such that the constellation was considered to be Osiris. Also, Sirius was identified with "Isis."

The Hoagland/Bara hypothesis is, then, that these star alignments are symbolic of ancient star lore and hyperdimensional physics geometry. Why such an unlikely combination? No one knows for sure at this point, but if the reader wishes to embark on further explorations of these possible connections (that have been discovered and published by Richard Hoagland, as well as others), I direct them to read the other articles on the Enterprise Mission website.


Now that I have ascertained the source of the angles in question, I will define a restricted version of the above Hoagland/Bara model, which I will be using in my analysis. This model is not complete because I do not use all the "temple" locations that Richard Hoagland and Mike Bara have used in their work, nor do I use all the celestial objects they do. For example, the Hoagland/Bara temples which I do not use consist of the Mars temples -- the Viking I and II sites, and Cydonia -- as well as the Earth "temple" locations, Phoenix, JPL (Pasadena, California) and Houston. I do use Houston at one point when I am analyzing the Apollo mission events data, but not as a general rule in this analysis.

There are a total of ten "temple" sites. However, it was necessary to simplify this model for ease of numeric analysis, and to get a feel for the situation, so I only used four in this restricted model. Because this is a restricted model, I do not "catch" all the alignments (nor would I expect to) that Hoagland and Bara do; nevertheless, I did expect that this "limited" approach would at least determine whether or not the "Egyptologically-important" stars I chose to examine (in the constellations of Canis Major, Orion, and Leo -- Sirius, Mintaka, Alnilam, Alnitak, and Regulus) do appear more times than random chance would allow.

In order for a "ritual" star alignment to occur in my restricted version of the Hoagland/Bara model, the limited criteria listed below must be met.

Celestial Object must be at these Angles:

Zero degrees (either horizon)
19.5 degrees (above or below either horizon)
33.0 degrees (above or below either horizon)
Meridian (highest or lowest point a star can reach in the sky)
Other angles (such as 3 deg 30 min) which are symbolic of the numbers "33" or "19.5"

The Enterprise Mission lists many celestial bodies and stars that are used in its model, but for the purposes of this analysis, and to keep it simple, I restricted myself to specific celestial objects.

Stars used in this simplified model:

Sirius (brightest star in Canis Major)
Alnitak (Orion belt star)
Alnilam (Orion belt star)
Mintaka (Orion belt star)
Regulus (brightest star in Leo)

Locations from which stars are observed:

Giza, Egypt
Planned Apollo 11 landing site
Planned Apollo 12 landing site
Planned Apollo 13/14 landing site

As previously noted, the above locations represent only four (not all ten) of the Hoagland/Bara "temple" sites, from which these alignments are observed.

Below (Figures 2 - 5) are some examples of what these configurations look like in the sky over the Earth or Moon. All of these pictures were obtained from the program RedShift, and represent the sky at the particular "temple" of interest, at the date and time I specify.

Figure 2: The above picture depicts the sky over Giza, Egypt, during the launch of Pioneer 5. Of course, this is an example of the Orion constellation, with the Orion belt star at 33 degrees altitude.


Figure 3: The above depicts the sky over the planned Apollo 12 landing site on the Moon, at the time of the launch of Apollo 15. This is an example of the constellation Canis Major, and the star Sirius, which here appears 33 degrees below the lunar horizon.


Figure 4: The sky above the Apollo 11 planned landing site on the Moon, at the time of the launch of the Mercury program's Atlas 7. An example of 19.5 degrees above the horizon, this time with the belt star Mintaka.


Figure 5: The sky above the Apollo 12 planned landing site on the Moon, once again at the time of the launch of the Mercury program's Atlas 7. This is a meridian alignment, which means that (in this case) the star Sirius has reached the highest point in the sky that it can reach. The "meridian" line is the line drawn from the Zenith through North and South. Notice that it intersects the star Sirius, indicating that Sirius has risen "to the meridian."


Though the Enterprise Mission website also lists the Sun, Comet Encke, and Mars as celestial objects used in the pattern, they were excluded from this analysis to keep the model simple.

Next, I developed a mathematical model of the odds based on how long a star stays at the elevations in question; in this case, the horizon, 19.5, 33.0, and the meridian. In the above case, the probability is expressed as the time that all stars of interest to the Hoagland/Bara ritual star alignment model stay at the altitudes of interest, at each location that Hoagland and Bara use.

NASA Programs to be Analyzed

I also limited myself to Apollo and Apollo preparation missions such as Pioneer, Ranger, Surveyor, Lunar Orbiter, Mercury, Gemini, and so on. I did this because of a time limit on how long I could spend on this project, and because all these launches have a "theme" . . . in this instance, getting ready for sending men to the Moon. Because they all had the same theme, I could therefore place them all in the same statistical grouping.

I obtained launch times (as well as landing dates and times) for Apollo, from the book "To a Rocky Moon" (Don E. Wilhelm, University of Arizona Press, Tucson, 1993). For all other spacecraft launch times, I consulted the National Space Science Data Center (NSSDC). Star positions for all the dates and times analyzed in this paper were obtained using the commercially available astronomy software program " RedShift." RedShift uses NASA measurements of star and planetary positions, in order to very accurately calculate celestial coordinates as seen from specified locations on Earth for specific dates and times, as well as for locations on other celestial bodies, such as the surface of the Moon.

I strongly encourage anyone who has an interest in this to research it themselves, as there is much more work to be done. I present the tip of the iceberg here, because I only had so much time to devote to this project; still, what I found is quite amazing.


In order to explain how I would detect a "pattern," I will proceed to a simpler example -- a coin toss.

Suppose I enter into a coin toss game with a friend, and I want to figure out the odds of winning. If the coin is perfectly balanced, then the odds should be 1 to 1 for tossing "heads" -- in other words, I have equal chances of tossing "heads" or "tails" on any given toss.

Next, suppose I want to compute the probability of tossing "heads". Probability is given by:

     Probability = Outcomes Favorable/Total Outcomes

In a coin toss, there are only two outcomes -- Heads or Tails -- and I'm just interested in computing the probability that the coin I toss will come up "heads". Then,

     Probability        = Outcomes Favorable/Total Outcomes


     Probability(Heads) = 1/2 = 0.500.

But how can I be sure the model is right? That's straightforward, and in order to do that, I employ the Law of Large Numbers.

The Law of Large Numbers

It is a statistical fact that, the more observations I make of an event, the closer I can come to correctly defining the probability of that event's occurrence. Thus, the Law of Large Numbers states that I should draw closer and closer to the actual probability of an event with each observation I make.

So, to determine whether the formula for predicting odds is correct, I toss the coin as many times as I can in order to find the true probability. In this case, I did 100 sample "throws" with a coin-toss simulation program, and plotted the results. Refer to Fig. 6, below: Note how the graph closes in on the value 0.500, the actual probability that I computed using the "Outcomes Favorable/Total Outcomes" equation.

Fig. 6: Perfectly Weighted Coin
Number of Heads Tossed plotted against Total Tosses

To further illustrate the importance of the Law of Large Numbers, and how this Law works, refer to Fig. 7, below. It is the same as Fig. 6, except that I highlighted toss # 13.

Fig. 7: Highlight of Toss 13. Note its probability falls at 0.63 on the graph.

If I were to stop tossing coins at toss # 13, and I didn't know what the correct odds were supposed to be for a coin toss, I would incorrectly deduce that the probability of tossing heads is 0.63, or about 1.6 to 1, "for" tossing heads. Obviously, this isn't right! So, when calculating probabilities, it is important to 1) have the correct model for the odds, and 2) take enough samples to be able to make a correct estimate of the situation. Notice that, by the time I reach 80 or more samples, the probability has very closely approached what it should be -- 0.5, or 1 to 1 odds.


How can patterns or "non-random" occurrences then be separated out from a set of data? The first thing to do is determine, as I did for the "coin" above, what the "random" situation looks like. Then I can test the new situation to see if it fits the "random model."

Suppose I enter a coin-tossing game with a dishonest person who has "weighted" the coin, causing it to come up "heads" 2 times out of 3, giving 2 to 1 odds "for" tossing heads instead of 1 to 1. The odds are expressed as 2 to 1 because odds are expressed as the "number of successes to the number of failures". Probability is expressed differently; i.e. the ratio of the "number of successes"/"total events".

In order to test my theory, I have to take a few samples and see for myself, what the odds turn out to be. I already know what the true random situation is supposed to look like. In order to really test this coin and to be sure it's weighted, I should take a large number of samples. Recall how, in the "balanced coin" example, if I had only progressed to "toss 13" I would have made an incorrect assumption about the probability. I still could have estimated the trend by taking the "average" of the points, but it's far more accurate to use a large number of sample "tosses" in order to truly establish that the coin is weighted.

Again, using a coin-toss simulation program, I weighted the "coin" as described above and plotted the results in Fig. 8.

Fig. 8: Plot of Weighted Coin, weighted to produce 2 to 1 occurrence of heads

Notice how the graph closes in on the correct value for this "weighted" coin, which is 2/3 or 0.67. (Recall: Heads occur two times out of three in this example.) Note also that the data points all cluster in on a line, and that the line does not waver very far from the 0.67 value. It does not drop down to the 0.500 value, for example, but instead holds its position at 0.67. This will be important to remember later.

What can I deduce from this? First of all, that the coin is weighted, and that it's not behaving like a perfectly balanced coin would. It is very unlikely, given the behavior of this graph, that the coin would not be weighted.

For a more detailed discussion on aspects of this, such as variability of random data and so on, consult my technical paper. Basically, without getting into this in great depth, I can say that there are two factors that tell me this is probably not random: high odds against this much deviation, and the fact that the curve does not show a tendency toward convergence on the expected random value.


Before I began analyzing NASA launch data, I had to make sure that my equations for predicting the number of these "ritual" star alignments that I should get by random chance, were correct. It's not enough to develop equations I think are accurate, i.e. that will predict the number of occurrences of 19.5 and 33.0 degree alignments. I also need to verify the "theory" by taking 100 random sample measurements. This will tell me, in effect, if I'm modelling "real life" accurately.

It's analogous to tossing a coin 100 times, in order to be certain that the probability of heads really is 1/2 or 0.5. Recall from the previous discussion, then, that the first thing I need to do is use the Law of Large Numbers to find out what the "random" situation looks like. (For details on the actual equations used to model the odds, please refer to my technical paper.)

Part of my calculation process for determining the probable occurrence of an alignment was setting "error margins" around the angles. For example, suppose I am a hypothetical conspirator who wishes to perform NASA events at the same time as specific stars are at alignment positions. Because of real world considerations, such as crew safety, device limitations, physical obstacles or unexpected delays, I can't always hit an alignment dead on. Therefore, there will be a margin of error around each angle. In order to find this hypothetical "margin" of error, I decided to analyze the data for each angle, to see what the values near 0, 19.5, 33, and the meridian would "center" around. I did not really expect to see any kind of "centering" tendencies if the data was truly random, anyway; if the data is random, then data could center around 32.7 just as easily as 33.0, for example -- and in that case, the arbitrary margins of +-0.25 degrees or +-0.5 degrees would be suitable to analyze the data. However, if someone was trying to hit the angles "dead on," the data would tend to "cluster" around 19.5, 33.0, and the horizon and meridian. Therefore, if I did see "centering," I could proceed from there on the supposition that someone was "trying" to hit these angles dead on, perhaps accepting certain limits or criteria for each angle. For a detailed explanation of this process I went through to select these "error margins," as well as a complete listing of the error margins, see my technical paper.

Why select error margins based on real data, instead of arbitrary fixed ones like +-0.5 and +-0.25?

Because the probability of an alignment event occurring within an actual observed error margin (not a "made up" fixed one) is expressed as

                                     Time for star to traverse error margin
P(alignment within error margin) = ------------------------------------------
                                          Total rotation time

See the picture of a circle (Figure 9, below). Note how I've highlighted a small portion of that circle. That "portion" is the amount of error that I will accept around an angle; in the example below, 33 degrees.

Figure 9: Circle of 360 degrees, showing error margin around 33 degrees. Error margin is replicated as many times as it will fit into the circle of 360.

Now, if I replicate that error margin, note that it fits into the circle so many times. Recall the basic equation for probability, which is

     Probability = Outcomes Favorable/Total Outcomes

In this case, "Outcomes Favorable" is that small highlighted piece of the circle; that's the error margin I'm accepting around 33 degrees. "Total Outcomes" is given by the total number of times that the error margin "fits" into a circle of 360. This describes the true probability of this event.

Of course, the above circle diagram represents the situation where I'm only looking at one occurrence of 33 degrees. In my mathematical model (see technical paper ) I also account for 33 degrees above or below the east or west horizons, which means that 33 occurs a total of four times. All this is already accounted for in my calculations; that's one reason why my equations work, and work well, in approximating the real situation. Figure 10 below illustrates this concept of "33 degrees" occurring 4 times at any given location.

Figure 10: Number of occurrences of 33 degrees, at a given observation site.

The first thing I found was that the launch data did precisely center around the Hoagland/Bara angles; i.e. 19.5, 33.0, the horizon and the meridian. This was a clear indication to me that it was entirely possible that someone had been trying to "hit" these angles dead on. I used my analysis and observations of this "centering" phenomenon to determine the appropriate error margins (see the technical paper for a listing of these error margins).

Once I had determined these "real life" error windows, in order to better approximate the real situation, I calculated from the equations I developed for star motion on the Earth and Moon, what the Probability of an alignment at Giza, Egypt or the planned Apollo 11, 12 or 13/14 lunar landing sites would be. I obtained Probability = 0.32 (or 32 hits out of 100).

Then, I took 100 sample measurements of star positions, the 100 randomly chosen dates and times spanning the years 1958 to 1978.

Just as my equations predicted, I did indeed get 32 hits out of my 100 measurements. So, now I know what the random situation looks like, and that my equations are modelling the real situation quite well. I plotted my results from my random data on the graph below (Figure 11). Notice that the data converges on the expected probability value of 0.32.

Figure 11: Graph of Random Data for the years 1958 through 1978

This convergence on the expected random value is of course due to the Law of Large Numbers, which states that if I take enough samples of an event over time, I'll figure out how often the event occurs and be able to predict it accurately. This is what science itself is based on, at least with respect to testing theories with experiments. The greater the number of experiments performed that have a specific outcome, the more likely it is that the scientist is correct in his theory.

A probability of 0.32 translates into roughly 2 to 1 odds against (odds = 1/probability - 1, to 1). This does not seem like much. But, in statistics, the important thing is -- does something beat the odds all the time?

To get an idea what this means in a "real life" situation, imagine that you really did play an unfair coin toss game with someone, where "heads" came up 2 times more often (and you bet on "tails"!). In such a case, you would be within your rights as a player in the coin-toss game, to cry "foul!" and demand that a balanced coin be used. This is because you'd know it was more likely that the coin was unbalanced, than it would that it just "coincidentally" came up heads that much of the time! A few times -- yes. A hundred times -- no.

In the previously mentioned case, where the coin comes up heads 2 times out of 3 in a set of 100 tosses, recall that the odds against that (for a perfectly balanced coin) would be 2,182 to 1. And, probably long before 100 tosses, you'd be suspecting that this coing toss game was rigged.


Choosing to analyze the Pioneer, Mercury, Ranger, Surveyor, Gemini, Lunar Orbiter and Apollo programs, resulted in a sample set of 82 NASA launches. Figure 12 (below) is a graph, plotting the NASA launch alignment hits, of which (in this set) there were 44, against the predicted random value of 0.32 (or 32 hits/100).

Figure 12: Graph of Actual NASA Launch Data

The odds against this much deviation from the random value are 40,192 to 1. That is approximately 20 times higher than the odds against the "rigged" coin toss!

So, this is very significant. And, while 82 launches does not in itself indicate that all of NASA follows this 19.5 and 33.0 pattern, it is enough to indicate to me that the pre-Apollo and Apollo mission launches did.

How can I tell that these results follow the pattern outlined by Richard Hoagland and Mike Bara? Because of the numbers and analysis method I selected. My analysis approach only emphasizes the frequency of star positions at specific locations. This serves as a 'filter,' because of the fact that NASA launches supposedly are tied to the position of planets, weather, and lighting conditions (the position of the Sun), NOT 'star positions.' The method of analysis that I chose emphasizes star positions and nothing else; not weather, lighting, planets, or other factors. Unless one accepts the validity of Astrology, where the positions of stars do have an effect on weather conditions or so on, there is no reason to tie star positions in with launch conditions.

Patterns within Patterns

See Figure 13, below. On the left side of the figure, there are randomly arranged rectangles with mission names in them. The rectangles are oriented in different "random" directions, visually representing the concept of randomly organized launch times. Next, in the center of the Figure 13, the rectangles all assume an ordered format, which is non-random by its very nature because it is organized in a pattern. This "pattern" of rectangles represents the pattern found in the 82 launches, because in the case of the 82 launches, the missions were seen as being represented only by their launch times. When this was done, a pattern emerged in the 82 launches; therefore, the missions can be seen as being ordered in this fashion. Finally, at the far right, I decide to "magnify" one of these small "mission" rectangles in order to scrutinize it further. When I do, I find that specific events in the mission are organized in the same pattern. Thus, a "pattern" has been found within a larger pattern.

Figure 13: Illustration of the "pattern with a pattern" concept.

What if there were such a "smaller" pattern within the larger? What would this mean for the odds?

I know that the larger pattern must occur first, so that must be expressed in a probability equation, P(large pattern). Then, the probability of the "smaller" pattern that existing inside the Apollo program must be written out as P(small pattern). To find out the probability of both happening at once, I multiply them together:
P(large+small) = P(large) x P(small)

In order to test the Hoagland/Bara theory at yet another level of detail, I decided to do just that -- to see if the pattern carried itself on down through other layers of detail, into actual mission activities themselves. I analyzed the Apollo missions specifically. I was interested in finding trends, or non-random tendencies in the data within the context of the "star ritual" pattern itself. I looked for these things because non-random tendencies in the star alignment data would be extra supporting evidence of planned (with respect to star alignments) versus random launch times. Or, if I found random data, this would become apparent very quickly.

The type of events which I looked up star alignments for were activities such as docking, course corrections, landings, splashdowns, etc. Because of time constraints, I chose to focus only on the successful lunar landing missions; i.e. Apollo 11, 12, 14, 15, 16 and 17. In just these six Apollo missions, there were a total of 112 mission activities (including launches). Grouping these six missions together was appropriate, as they are all of the same "type" -- i.e., each one was a successful manned lunar landing mission, and therefore could be grouped in the same statistical category for analysis.

To do this portion of the analysis, I consulted the NSSDC again and obtained mission summaries. From these, I extracted the times of docking, engine firing, landings, etc., and looked up the corresponding star alignments that occurred at those times. If no pattern was being followed, the data should converge on a random value, just as in the "coin toss" example. Also, I felt if a pattern was followed in this data as well, it would be just as interesting, in that it indicates that the times I retrieved for mission activities from the NSSDC were selected based on a pattern!

I ask the reader to imagine the improbability of timing many maneuvers and mission activities to correspond with star alignments! If something was found, I thought it would be very unlikely.

In order to fully analyze the Apollo mission events, I decided to add "Houston" to the list of star observation sites. It is considered a major "temple" in the Hoagland/Bara model, because it was the literal "Center" of NASA's whole manned space effort, including the Apollo Program: for instance, within seconds of liftoff of every manned mission from Cape Canaveral, Houston Mission Control took over total command of all subsequent aspects of these flights. Not by accident did the phrase 'Houston' become immortalized in the language of every space enthusiast, because for over forty years Houston was also carefully designed to be the sole radio communications link between all NASA crews in space, and all the rest of us on Earth. If any location could be considered a "temple" for a NASA ritual, it would undoubtedly be "Houston."

When I did the above, the probability went up to 0.48, because I added another location where an alignment could happen. That means that, at any given time, it's more likely for me to observe an alignment. This makes sense, because the more places I choose to observe stars from, the more likely I am to see a star at 19.5 or 33.0 degrees (or on the horizon or meridian).

Much to my surprise, not only did the Apollo "mission activities" data not converge on a random value, it spectacularly conformed to the Hoagland/Bara star ritual pattern yet again! The graph below (Figure 14) illustrates this, and shows that, for 112 mission activities (including launches), the curve does not converge on a random value, but stays above it, as in the other case.

Figure 14: Graph of actual star alignments taking place during day-to-day Apollo Mission events

Graph of Alignments taking place during Apollo mission activities

In this situation, I had P(align) = 0.48, and 76 hits out of 112. This came to 100,010 to 1 odds against chance. That's how unlikely it is just for the Apollo mission activities to conform this closely to the Hoagland/Bara star ritual theory, by mere random chance! This clearly indicates that Apollo mission activities cannot be accurately modelled using random chance, because it is far too improbable to do so!

Since Houston is a "temple" in the Hoagland/Bara model, that's why this data confirms that Apollo mission activities do follow the "ritual" pattern. However, if I were to leave Houston out of it, the odds against would still be very high -- as will be seen.

The Importance of Consistency

Recall the section above, when I was speaking of "patterns within patterns." In this case, I can't multiply this Apollo mission data pattern (the "small" pattern in my previous example) times the probability of the 82 launch pattern (the "large" pattern) yet, because I used Houston in addition to Giza and the three planned Apollo landing sites in the above example. Using Houston is a different different analysis approach than I performed earlier on the 82 launches, where I used only the Apollo 11, 12, and 13/14 landing sites on the Moon, and Giza, Egypt, though it still falls within the Hoagland/Bara star alignment model.

In statistics, comparing two sets of data with two different sets of locations and/or paramenters, would be like comparing apples and oranges. Though a pattern might be found in an arrangement of both, only apples or only oranges should be considered -- not both!

The solution is, then, to examine both sets of data in the same manner. Therefore, next I will examine both sets of data using only the locations of: Giza, and the planned Apollo 11, 12 and 13/14 lunar landing sites, as I did for the 82 launches. I also use the same error tolerances for both sets of data. In this case, I use a tolerance set that gives me a Probability of alignment = 0.39 for both. (It is not 0.48 anymore, because of removing Houston from the Apollo mission data as a stellar alignment observer location. As cited above, I did this so that the Apollo mission events will be analyzed using the same locations as the ones used for the launch data.) Since Houston doesn't figure directly in any launches but those of the Apollo Program, removing it "levels the playing field" for comparison with those missions NOT controlled by Houston.

Now I will be looking at exactly the same pattern, defined in exactly the same way, for both the "large" pattern of 82 launches, and the "small" pattern I found in the Apollo lunar landing missions. Now, multiplying them together is meaningful, because the patterns will match, or not (and the numbers will indicate this one way or the other).

In the case of the Apollo mission activities data, without Houston the odds come out to be 5,152 to 1 against chance. Next, for the 82 launches, the odds are 1,489 to 1 against chance.

Note how, in both sets of data, the odds are very high "against" random chance; in the thousands, rather like the "coin toss" example shown earlier. This is important because it says that both sets of data are equally non-random within this set of tolerances; therefore, both definitely follow the same non-random tendencies.

Recall, P(large+small) = P(large)*P(small), and that Probability = 1/(odds+1). Therefore, multiplying the above results yields a likelihood for the Apollo missions conforming to the same pattern as the 82 launches, as being 7.68 million to one!

So what does this mean? It means that it is extremely unlikely that the Apollo mission activities would conform to the same pattern that the 82 launches for the "Apollo preparation" missions (in this case, the Hoagland/Bara ritual star pattern). It's one thing for the 82 launches to follow the Hoagland/Bara star ritual pattern. But, for Apollo mission activities to follow that same pattern, one that belongs to a much larger grouping (82 launches spanning decades), indicates a high degree of non-random, and organized behavior in the data. Therefore, it also indicates a high degree of planning. Consistency, by its very nature, is not random. "Random" means that the data is haphazard and follows no patterns or trends, such as was the case for the sample "coin toss". In the case of missions, sometimes the activities are performed mere minutes apart. In order for something to stay consistent, it must be ordered. If it is ordered, it is not random. That's why I express the "odds against" as "odds against chance." That's because the data is behaving in too consistent a way, for too long a time, for it to be the result of random processes.

Once again, I draw the reader's attention to the fact that, in the case of Apollo mission events, it is even less likely that these events such as spacecraft docking and manuevering should all be tied in with the position of stars in the skies of the Moon, or the skies of Earth -- because there is no "weather" to be concerned about in space. For example, how could docking and manuevering a spacecraft in the vacuum of space, be in any way dependent upon the position of stars in the skies of a planet?

More "Non-Random" Trends

Additionally, in the set of 82 launches I noticed a Sirius, Alnitak and Mintaka preference over other stars. This means that the three stars Sirius, Alnitak and Mintaka appear more often than the other stars I analyzed (in this case, Regulus and one other Orion belt star, Alnilam). I noted this and calculated the odds for it, which come to 380 to 1 against chance for the set of 82 launches (not counting Cape Canaveral data). However, the trend does not stop there.

Magically, the Sirius, Alnitak and Mintaka trend shows up in the Apollo mission activities data as well. The odds came to 17 to 1 against chance, indicating that the data favors Sirius, Alnitak and Mintaka.

Why is this significant, since the odds are lower? It is significant because the trend is the same for both the daily mission activities and the 82 launches. This ties the day-to-day mission activities in with the launch pattern in yet another way! And because of this, it makes the data yet more consistent and less random.

To express this in equations, it's necessary to multiply probabilities again:

P(trends+pattern) = P(large pattern) x P(large trend) x P(small pattern)
                    x P(small trend)

Which yields odds of 53.5 billion to 1 against!

What do these high odds mean? For one thing, that every time I consider one more part of this picture, explaining the whole thing as being a result of "random chance" becomes more and more improbable. Also, because I'm dealing here with many samples of mission activities and launches, the message becomes all the more powerful. The high odds against express the improbability that these NASA launches I examined, plus all the Apollo lunar landing missions, all conform to random chance. These are odds against not just one mission, not just one alignment, but all those pre-Apollo launches and all the mission activities listed in the NSSDC summaries for the six Apollos I studied, behaving in the same way! The likelihood is billions to 1 against! Also, these numbers are not just saying how unlikely it is that one alignment could happen. They are expressing how unlikely it is that this much conformation to the Hoagland/Bara ritual pattern could happen by accident, in the case of the 82 launches and the Apollo lunar landing mission activities. Therefore, I must conclude that the star alignments for the mission activities and launches I studied do not happen by accident ... they must happen by design. To try and explain them via random processes results in odds of billions to one. I would not bet on the "random" side of these kind of odds ... and who would, if the odds were billions to one?

Why the Sirius, Alnitak and Mintaka Trend?

I can only speculate, but I will mention some possible connections here. There is a connection between Cape Canaveral and Egypt that goes beyond Cape Canaveral's translated "Spanish" name, which means "cape of reeds" (corresponding, perhaps, to the Egyptian "field of reeds" or the afterlife). When Mintaka is 33 degrees below the horizon at Cape Canaveral, Sirius is 33 degrees above the horizon in Giza, Egypt. Also, when Mintaka is within a degree of the meridian at Cape Canaveral, it is also at 19 degrees in Giza (depending on whether the Nadir or "Midheaven" meridian is utilized). When Alnitak is 33 degrees below the horizon at Giza, Sirius is at 19 deg 50 min below the horizon at the Cape. And so on.

I have not performed a study to be certain that Mintaka, Alnitak and Sirius have the most such alignments. So, I can only note that this is interesting. However, in light of the fact that Cape Canaveral has been and is the launch site of NASA space missions, and the fact that so many other things in this picture tie together, I would not be suprised if the Sirius, Alnitak and Mintaka alignment geometry between Cape Canaveral and Giza stood out over other possible configurations.

Another interesting fact is, the Arabs called the zodiac -- that band of sky through which the Sun and planets travel -- "Al Mintaka al Buruj," the "girdle of the Signs". (Source: Richard Hinckley Allen, "Star Names: Their Lore and Meaning," page 3.) "Mintaka" itself means "belt," and it is a star in Orion's belt. I do not know if this has significance, but I find it interesting that Mintaka is the one star in Orion's belt whose name actually means "belt," and that the entire zodiac was also named "Mintaka." Of course, "belt" is a descriptive term for the ecliptic, but the whole thing being named after "Mintaka," a star in Orion's belt, could convey a significance on Mintaka. It would be interesting to see if other mythological or statistical indicators support Mintaka's importance.

Can the Sirius/Alnitak/Mintaka trend be dismissed based on the recurring alignments at Cape Canaveral and Giza, Egypt? No, because the trend existed even after Cape Canaveral was excluded from the data, and the only remaining locations examined were Giza and lunar landing sites. This means that this improbable trend is either there by accident, which seems increasingly unlikely, or it is there by design. Naturally, since what I'm dealing with in this case is alignments pertaining to the space program, it would be consistent if the symbolism were to be interconnected in this fashion. What is more central to NASA than its Cape Canaveral launch facility?

Apollo 11

Apollo 11 was a very special mission, historic and high profile, because it was the very first time humans successfully walked upon the surface of the Moon and returned again to Earth. Because so much preparation went into this, and the majority of the 82 launches of which Apollo is a part were part of that preparation, I thought it wise to examine other aspects of Apollo 11 for correspondences to the star ritual theory. Apollo 11 should have been laced through and through with symbolism, it being part of this pinnacle of achievement that started with earlier NASA preparatory missions.

Below, I made up a table of correspondences between Apollo 11 and the Egyptian/Masonic ritual scenario as described by Richard Hoagland.


                                                   Connection to 
Item                                             "star ritual" theory
-----------------------------------           ---------------------------
Name of Lunar Module, the "Eagle"               Eagle = Phoenix = Osiris*
Date of Moon Landing, July 20                   Osiris "resurrection" day
Time for docking procedure                              33 min
Time between Moon landing and start
                        of ceremony                     33 min
Mission duration                                       195 hours
Time after ceremony starts, that Sirius
        reaches precise tetrahedral angle          14 min (14 = Osiris num.)**
                                                           14 = Number of ways
                                                                to spin 2
Apollo Program patch                               Orion constellation***
Astronaut identity                                  33rd degree Mason
The name "Apollo," meaning "Sun God"                 Horus = Sun god
Star alignments                                    (see data page for list)

*See Ben Franklin on the "Great Seal of the United States," and Egyptian literature on "Osiris"
**See Ancient Egyptian story of "Osiris," killed by his brother and torn into 14 pieces.
***See Ancient Egyptian literature on "Osiris," who "dwells in Orion"

If I am to calculate the improbability of the Apollo 11 "fitting the pattern" in the sense of star alignments, it also makes sense to calculate the improbability of it fitting all the rest of the picture, too, since it has so far demonstrated an amazing consistency throughout. However, the above items are very hard to quantify. There is one calculation that can be done, however.

A ceremony was conducted in the Apollo 11 lunar module on the day of the Moon landing -- July 20, 1969. Precisely at that time, the star of most importance to ancient Egyptians -- Sirius -- exactly reached the tetrahedral angle of 19.471 degrees. Now, without the context of a larger pattern, this could be dismissed . . . but not as easily in this case. So, what is so special about July 20?

I do not wish to reiterate here the research of Richard C. Hoagland and others into this date and its significance, so I will let the reader do this for themselves. A good place to start is with Richard Hoagland's first article on the subject of these star alignments. Suffice to say that, out of 365 days in a year, there is only one Osiris resurrection date, and this date also "happens" to be associated with the tetrahedron, the source of the numbers 19.5 and 33.0, which have demonstrably occurred in a non-random fashion throughout these missions. The odds are against there being as many tetrahedral numbers in the Apollo 11 "mission activities" table as there are, because the odds are against there being as many Apollo 11 alignments as there are. The numbers "19.5" and "33" refer to tetrahedral geometry, and Apollo 11's mission activities have more of these "tetrahedral" alignments than it should -- the odds are 153 to 1 against Apollo 11 having that many tetrahedral correlations. Since the odds support the "tetrahedral" correlation, therefore, it is consistent to look at the probability for the "tetrahedral Osiris" date being the very date on which this occurs.

Therefore, multiplying the probability of the trends within the 82 launches and the Apollo lunar landing missions, by 1/365 for the date July 20, yields the odds of all these events and patterns being unrelated to each other and to Apollo 11's ceremony on that one particular day:

Final Odds, Apollo 11: 19.5 trillion to 1 against chance.

This number expresses the improbability that the Apollo 11 mission could conform to the Hoagland/Bara theory in this way, conform to the rest of the pattern the way it does, and conform to the pattern which governs the 82 launches analyzed in the beginning of this paper.


Figure 15: The sky above Giza, Egypt, at the time of the launch of Gemini 5. This is an example of the constellation Leo, and the star of interest within it, Regulus. Note how the Sun is also within a degree of 33 degrees and is very close to Regulus. I did not include the Sun in my model, but I did observe that the Sun does appear at or near Hoagland/Bara alignment angles quite often. This is consistent with Egyptian mythology, since Osiris' son Horus was a Sun god. Also, each pharaoh was associated with Horus, which is another link of "kingship" with the "Sun" (see below).

Historically, Leo and the Sun have been considered "linked." The Sun is associated with kingliness, power, and authority in ancient texts on the subject; and Leo is also associated with kings, authority, and power; but more specifically, the star Regulus is associated with those same qualities. Leo and the Sun are also linked astrologically, where Leo is considered the zodiac sign over which the Sun presides; which of course, is just one more indication of this historical link. Because the Sun and Leo and so linked, and Regulus (in Leo) is linked with kingship, it is therefore valid to tie them together numerically. An error margin of +-1 degree gives me an "error window" of 2 degrees; therefore, the probability of the Sun being placed within the same degree as Regulus is, to a close approximation, 2/360, which of course gives odds of 179 to 1 against random chance.

This is just one of many correspondences which I have not had time to investigate thoroughly. However, there are so many of these unlikely "coincidental" tie-ins with the Hoagland/Bara star alignment theory that I think it would be worthwhile to investigate these "coincidental" occurrences in more depth.



The significance of these findings is, I have shown there to be a pattern throughout 82 launches that were part of the Apollo preparation phase (and Apollo itself). Additionally, I have shown that the Apollo missions follow this same pattern on a day-to-day, mission activities level, which is even more improbable because of its consistency with the launch data. Furthermore, it is improbable that the frequency of these stellar alignments are tied to weather or lighting conditions, because of the fact that they occur for a variety of mission events, even those that do not require specific lighting or weather conditions.

The high odds express how unlikely it is for 1) the pattern in the 82 launches to occur, 2) the Apollo lunar missions being related to this pattern, and 3) the likelihood of a mission within that Apollo lunar landing grouping to conform to yet another aspect of the Hoagland/Bara star ritual theory at the same time all the rest of this is going on.


I wish to summarize now and state this briefly. I am, as a former Boeing Engineer and computer professional, only one of many who are wondering if there is any truth to this. I started this out as an impartial investigation into the Enterprise Mission data, and found some very startling and significant results. I encourage others out there who want to know more, to do the rest of this analysis . . . to analyze other missions, other launches, and so on, in order to find out if the pattern exists in other programs . . . and what it means.


Mary Anne Weaver (left) and S. Sriram, Ph.D. (right, sitting) at GTE Labs, collaborating on a research project.

Mary Anne Weaver, email address: