Newsgroups: alt.ufo.reports,alt.paranet.ufo
From: kymhorsell@gmail.com
Subject: how many flying saucers are there?

[uploaded 45 times; last 19/10/2024]

EXECUTIVE SUMMARY:
- A perhaps chance -ve corr between N Hem and S Hem UFO sightings allows
  us to "guesstimate" a rough upper bound on the number of objects that
  may be responsible for UFO sightings.
- UFO sightings density wrt US military bases allows us to categorize
  types of UFO.

The coverage of the S Hem in some of the big ufo sighting datasets is
a bit spotty. Looking at the NUFORC data upto mid 2020 it seems only
about 1/10th of sightings reported to AUS- and NZ-based organisations
seem to be making it into N Hem databases.

While sightings in at least AUS appear to be very much on the decline
(and there seem to be interesting reasons for this that will be
covered in a later post) the data I managed to scrape, beg, borrow or
the other thing suggest an even more interesting possibility.  That
the number of "objects" related to UFO sightings may be finite and
relatively small.

This finding comes from the observation that N Hem and S Hem sightings
are negatively correlated. I.e. the data shows when there are more
sightings in the N Hem there are fewer in the S Hem, and vice versa.

Here is the basic data for sightings in the S Hem from the New
Zealand, NSW, Qld, Vic and WA ufo orgs:

Year.mm	#sightings
1978.79	1	1988.04	2	1993.04	2	1993.62	2	1992.12	1	
1992.88	1	1997.71	5	1997.96	1	1997.54	1	1997.46	3	
1998.04	6	1998.12	4	1998.29	12	1998.38	26	1998.46	10	
1998.54	18	1998.62	40	1997.62	1	1997.79	1	1998.71	18	
1998.79	26	2000.62	16	2000.71	10	1999.79	6	1998.88	24	
1998.96	22	1999.04	24	1999.12	20	1999.21	18	1999.29	48	
1999.38	30	1999.46	37	1999.54	25	1999.62	20	1999.71	12	
1999.88	7	1999.96	26	2000.04	23	2000.12	15	2000.21	22	
2000.29	12	2000.38	23	2000.46	9	2000.54	14	2000.79	4	
2001.04	10	2001.12	18	2001.21	18	2001.29	24	2001.38	10	
2001.46	14	2001.54	18	2000.88	10	2000.96	14	2002.46	22	
2002.62	12	2002.71	12	2002.79	14	2002.88	2	2003.04	4	
2003.12	4	2003.21	6	2003.29	12	2003.38	12	2003.46	14	
2002.54	28	2001.62	10	2001.71	12	2001.79	14	2001.96	4	
2002.04	2	2002.12	12	2002.21	10	2002.29	14	2002.38	12	
2004.21	12	2003.62	22	2003.71	4	2003.79	6	2003.88	2	
2003.96	6	2004.04	10	2004.12	18	2003.54	14	2005.21	10	
2004.29	12	2004.38	12	2004.46	8	2004.54	6	2004.62	20	
2004.71	12	2004.79	4	2004.88	14	2004.96	18	2005.04	2	
2005.12	2	2005.46	10	2005.54	4	2005.62	16	2005.71	6	
2005.79	12	2005.88	2	2005.96	20	2006.04	4	2006.12	12	
2006.29	16	2006.46	8	2006.54	14	2006.62	8	2006.71	16	
2006.79	10	2006.88	10	2006.96	10	2007.04	10	2007.12	8	
2006.21	8	2007.21	14	2006.38	38	2007.29	16	2007.46	2	
2007.54	2	2007.62	14	2007.71	14	2007.79	8	2007.88	24	
2007.38	14	2009.79	4	2008.88	10	2007.96	4	2008.04	18	
2008.12	12	2008.21	10	2008.29	8	2008.38	14	2008.46	12	
2008.54	12	2008.62	6	2008.71	12	2008.79	8	2008.96	16	
2009.04	8	2009.21	24	2009.29	16	2009.38	6	2009.46	4	
2009.54	6	2009.62	22	2009.71	8	2009.96	6	2010.04	8	
2010.12	2	2010.21	6	2010.29	14	2009.88	12	2010.62	2	
2010.71	6	2010.88	2	2011.12	2	2011.21	2	2012.21	2	
2011.29	8	2011.38	4	2011.46	2	2011.79	2	2012.12	2	
2012.29	16	2012.38	8	2012.46	8	2012.54	4	2012.62	8	
2015.62	4	2015.54	2	2015.79	2	2016.38	4	2016.54	2	
2016.62	2	2016.79	4	2016.88	4	2017.04	4	2017.12	4	
2017.21	18	2017.38	20	2017.46	6	2017.62	10	2017.79	4	
2016.96	6	2016.12	2	2016.29	2	2018.04	2	2018.29	2	

And when you compare the NHem (e.g. from NUFORC) and SHem data you get
the regression model:

Durbin-Watson d = 1.388633
d < dL (1.683684):  Positive auto-corr at 5%
Auto corr detected: estimated rho = 0.312167
y = -0.00623419*x + 10.0599
beta in -0.00623419 +- 0.00440448  90% CI
alpha in 10.0599 +- 1.43569 
P(beta<0.000000) = 0.989607
r2 = 0.0549856
calculated Spearman corr = -0.286898
Critical Spearman = 0.306000 2-sided at 5%

The "critical number" is the \beta. It says for each 100 sightings in
the N Hem there are about 0.62 (+-.44) *fewer* sightings in the S Hem
in the month-by-month numbers.

The sightings data is VERY noisy as with anything to do with UFO's --
most of them are mistakes and something else, after all. ;) So the R2
is very tiny at 0.05 or so. Nevertheless the stats tests say the
probability the \beta is -ve is around 99%. Even the Spearman test
shows the ordering of the data by SHem numbers is so similar to the
reverse of the NHem numbers it is unlikely to just be down to luck.

So, given the NHem and SHem sightings data appear to have a -ve
correlation we can then reason this might be because there are a
constant number of "objects" and we might try to estimate if that is
true what that number might be.

Knowing "how many" might give us a very rough idea of how many
"people" might be involved in creating these sightings. :)

It's estimated about 4500 people developed and built the 1600-odd
'747s, for example.

To estimate "how many" we take the basic model:

y = -0.00623419*x + 10.0599

where y is the number of sightings in the S Hem in a given month, x is
the number of sightings in the N Hem for that month, and imagine we
can PUSH all the S Hem sightings up to the N Hem by setting y to 0 and
solve for x:

x = 10.0599/0.00623419 = 1613.67.

Wow! A fairly smallish number and REMARKABLY similar to the number of
jumbo jets manufactured.

BUT.

There are some other wrinkles we had better take into account.

The N Hem sightings are mostly down to the USA; the S Hem sightings (I
have here) mostly from AUS. And the 2 countries are a bit different.
The populations, for one thing.

If we assume the "ufo density" per month and per km2 over the earth is
approx constant we might predict sighting numbers will increased for
countries with larger areas and/or larger populations.

If we take those factors into account the S Hem sightings numbers need
to be multiplied by around 13.5 to be comparable with the N Hem.

I.e. the total number of objects responsible for all the sightings
might be "only" 1614*13.5 = 21789.

This is still not too outlandish. The "lights in the sky" seem to
behave in many ways more like unmanned drones than 747's.  It's
estimated that (human ;) countries around the world operate a total
around 1.3 mn drone aircraft.

~ ~ ~

I set some "homework" last time to estimate the density of USA UFO
sightings as they relate to the location of military bases.  I posted
last time that the "total" of UFO sightings in the US seems to follow
a "square root" distribution -- density of sightings rises according
to the sqrt of the distance to nearby military bases.  The square root
relationship was a mild surprise but could be related to UFO's in bulk
trying to avoid possible contact with ground-to-air missiles that --
no coincidence -- have a travel time roughly proportional to the sqrt
of the distance to the target (roughly continuous acceleration after
firing).

I was wondering if anyone might be able to break down the same data
according to UFO "shape" as given in e.g. the NUFORC database.

Here are my results, along with some interpretations ;):

Type		a	R2
Cone		0.009	0.98167		avoiding sighting by any base
					no matter how distant
Disk		0.071	0.49194
Egg		0.219	0.44703
Flash		0.275	0.42819
Rectangle	0.408	0.19790
Oval		0.465	0.45325
Unknown		0.480	0.40102
Triangle	0.497	0.38545
Fireball	0.499	0.52735
Light		0.505	0.56578		avoiding missiles
					(mostly from air bases)
Changing	0.529	0.26881
Cigar		0.559	0.26097
Diamond		0.582	0.34801
Circle		0.608	0.48534
Cylinder	0.618	0.23534
Other		0.633	0.36786
Sphere		0.637	0.37910
Formation	0.694	0.34061
Teardrop	0.820	0.09685
Chevron		1.083	0.10235		avoiding intercept
Cross		1.601	0.07000		1/2 avoiding regular patrols

Each UFO type falls off depending how close it is to military bases
(mostly air bases) according to dist^a. It was previously found values
for a of 0, 1/2, 1 and -2 seemed to occur "often" in UFO behaviour.
And it was reasoned there that we can interpret that as mostly
defensive flying behaviour. If UFO density around a base falls of
going toward the base we might interpret the situation like this:
density varies as 1/threat (as perceived by the UFO); threat ~
1/arrival-time-of-threat; i.e. density ~ arrivaltime.  For a scrambled
fighter jet arrivaltime is distance/speed.  I.e. a == 1 (given top
fighter speeds are roughly set by technology at a constant M number).
For an A2G missile arrivaltime = sqrt(2*distance/acceleration) i.e. a
== 1/2.  And for discovery by CAP/area patrol arrivaltime ~ 1/patrol_area 
i.e. 1/distance^2 i.e. a == -2.  We can maybe spot mixed strategies by
averaging 2 or more of these common values.

The R2s above shows how close the observed sighting density is to the
relevant power curve.  The "cone" type of sighting seems to 98% change
with distance from military bases according to dist^0.009. This is
almost constant IOW "cone" sightings are not related much to presence
of military bases. Either they are "invulnerable" or are marginally
avoiding something that is "not dependent on distance" from a military
base. Maybe something that travels at constant speed and is very, very
fast. E.g. radar or simply being seen.

Despite ostensibly high speed, maneuverability and stealth
capability, most types seem to be operating on a defensive basis.
Only the "cone types" seem to behave as if military bases are not a
threat to them.

Tetrahedrons (sometimes called "pyramids") hovering over the Pentagon
may also fall into the same class.

--
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Space Force troops now called Guardians
Mystery Wire, 18 Dec 2020 06:25 PM MST 
The Trump administration celebrated the first birthday
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Vice President Mike Pence made the announcement at a celebratory event
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fire-hit wildlife
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[The One Basket Model:]
Cyber security experts worldwide scramble to defend networks after attack in US
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[Intel Offside!]
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Intel has famously had a long-running partnership with Microsoft as the 
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[AI Takes The Stick:]
Air Force Big News 
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According to an Air Force press release, during a training flight
Tue night, the Air Force allowed an AI algorithm named  "ARTUµ" to
the control sensor and navigation systems of a U-2 "Dragon Lady" spy
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[Even Sweden?]
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