Precision tests with a new class of dedicated ether-drift experiments
M. Consoli, E. Costanzo
Eur. Phys. Journ. Chttp://arxiv.org/abs/0804.0979

Abstract
In principle, by accepting the idea of a non-zero vacuum energy, the
physical vacuum of present particle physics might represent a
preferred reference frame. By treating this quantum vacuum as a
relativistic medium, the non-zero energy-momentum flow expected in a
moving frame should effectively behave as a small thermal gradient and
could, in principle, induce a measurable anisotropy of the speed of
light in a loosely bound system as a gas. We explore the
phenomenological implications of this scenario by considering a new
class of dedicated ether-drift experiments where arbitrary gaseous
media fill the resonating optical cavities. Our predictions cover most
experimental set up and should motivate precise experimental tests of
these fundamental issues.

On Mon, 12 May 2008 08:45:09 -0700 (PDT), Dono <sa... at (no spam) comcast.net
wrote:

On May 8, 8:58 am, Surfer <n... at (no spam) spam.net> wrote:

That was back in 2003. In 2008 it was discovered that 3-space speeds
in the range 420..450 km/s, perfectly resolved hitherto unresolved
spacecraft earth flyby anomalies
http://www.scieng.flinders.edu.au/cpes/people/cahill_r/Cahill_flyby.pdf

snip

Peter, check out the relativistic Doppler formula and its experimental
verifications (many)
Contrast with Cahill's formula pulled out of his butt.

I have been doing that.

From:http://en.wikipedia.org/wiki/Relativistic_Doppler_effect

For an observer moving TOWARD the source

where
fs = source frequency
fo = observed frequency
V = relative speed

the above page would give the relativistic Doppler formula as:

fo = fs Sqrt[(1+V/c)/(1-V/c)]

When a radar signal is reflected from an incoming spacecraft, the
above formula is applied twice.

Let

f = frequency of transmitted radar signal
fi = frequency of reflected radar signal received back at ground
station
V = speed of spacecraft relative to ground station

Then

fi = f (1+V/c)/(1-V/c)
= f (c+V)/(c-V) (2)

That is identical to equation (2) in Cahills paper.
[So here Cahill is using the relativistic Doppler formula as used by
space agencies]

However, he notes:

"The use of (2) instead of (1) is the origin of the
putative anomalies."

So what is (1)?

As before let:
f = frequency of transmitted radar signal
fi = frequency of reflected radar signal received back at ground
station
V = speed of spacecraft relative to ground station

Also let:

v = velocity of 3-space in ground frame
theta_i be an angle between v and direction of space craft velocity

vi = component of v in same direction as space craft velocity
= v cos(theta_i)

Then (see paper for derivation)

fi = f (c+vi)/(c+vi-V) * (c-vi+V)/(c-vi) (1)

Since (1) and (2) are different they will cause different velocities
to be calculated for a given Doppler shift.

This difference is small, however in the case of an inertial flyby,
the incoming speed Vi of a spacecraft is predicted to be EXACTLY the
same as the outgoing speed Vo, so if Vi as measured by doppler shift
is not exactly the same as the Vo as measured by doppler shift, we
have an anomaly.

Such are being repeatedly observed for spacecraft earth flybys, when
velocities are calculated using equation (2).

So if equation (1) was used instead of (2) what would the difference
be?

In the paper it is the value Delta_V_infinity, given by equation (7)
(see the paper for derivation)

Let
v = speed of 3-space in ground frame
V_infinity = asymptotic speed of spacecraft
theta_i = angle between incoming velocity and 3-space velocity
theta_f = angle between outgoing velocity and 3-space velocity

Then (7) is

Delta_V_infinity
= (v^2/c^2) ((cos(theta_f))^2 - (cos(theta_i))^2) V_infinity

It turns out that values of v in the range 420..450 km/s give values
for Delta_V_infinity that are equal to the observed anomalies.

In other words, Cahills formula (1) gives more accurate results in
such cases than (2).

NOTE:
The relativistic Doppler formula incorporates the effects of time
dilation in the source. This seems to be confirmed by relativistic
Doppler experiments.

However, this means that double application of the formula to doppler
radar measurement of spacecraft speed incorporates:

- time dilation of the transmitter relative to the spacecraft
- time dilation of the spacecraft relative to the receiver

But if the receiver and transmitter are in the SAME frame, there
should be NO relative time dilation between them.

Hence this circular inclusion of time dilation effects, where none are
required, would seem to be an error.

Is there an implication here that the relativistic Doppler formula
should not be used for Doppler radar?

-- Surfer

On Mon, 12 May 2008 08:45:09 -0700 (PDT), Dono <sa... at (no spam) comcast.net
wrote:

On May 8, 8:58 am, Surfer <n... at (no spam) spam.net> wrote:

That was back in 2003. In 2008 it was discovered that 3-space speeds
in the range 420..450 km/s, perfectly resolved hitherto unresolved
spacecraft earth flyby anomalies
http://www.scieng.flinders.edu.au/cpes/people/cahill_r/Cahill_flyby.pdf

snip

Peter, check out the relativistic Doppler formula and its experimental
verifications (many)
Contrast with Cahill's formula pulled out of his butt.

I have been doing that.

From:http://en.wikipedia.org/wiki/Relativistic_Doppler_effect

For an observer moving TOWARD the source

where
fs = source frequency
fo = observed frequency
V = relative speed

the above page would give the relativistic Doppler formula as:

fo = fs Sqrt[(1+V/c)/(1-V/c)]

When a radar signal is reflected from an incoming spacecraft, the
above formula is applied twice.

Let

f = frequency of transmitted radar signal
fi = frequency of reflected radar signal received back at ground
station
V = speed of spacecraft relative to ground station

Then

fi = f (1+V/c)/(1-V/c)
= f (c+V)/(c-V) (2)

That is identical to equation (2) in Cahills paper.
[So here Cahill is using the relativistic Doppler formula as used by
space agencies

On May 8, 8:58 am, Surfer <n... at (no spam) spam.net> wrote:

That was back in 2003. In 2008 it was discovered that 3-space speeds
in the range 420..450 km/s, perfectly resolved hitherto unresolved
spacecraft earth flyby anomalies
http://www.scieng.flinders.edu.au/cpes/people/cahill_r/Cahill_flyby.pdf

snip

Peter, check out the relativistic Doppler formula and its experimental
verifications (many)
Contrast with Cahill's formula pulled out of his butt.

I have been doing that.

On Wed, 21 May 2008 14:40:17 -0500, Tom Roberts

Nonsense. You have DELUDED yourself into thinking this. There is no
"extra premise" required to compute the errorbar associated with
averaging of data,

I disagree.

Suppose the tax department wants to know my average monthly income for
the year and asks me to tabulate amounts rounded to the nearest
dollar.

What is the error bar associated with the average?

The worst cases would be if all values had been rounded up, or if all
values had been rounded down.

Therefore the maximum possible error would be +/- 12 * 50c = +/-
$6.00.

Now suppose the actual amounts were:

100
1000
100
1000
100
1000
100
1000
100
1000
100
1000

Average = 550.

What would the errorbar be using your method? I am sure it would be
wildly inflated.

On May 22, 1:40 am, Surfer <n... at (no spam) spam.net> wrote:
On Wed, 21 May 2008 21:49:34 -0700 (PDT), Jerry





Cephalobus_alie... at (no spam) comcast.net> wrote:
On May 21, 6:18 pm, Surfer <n... at (no spam) spam.net> wrote:
On Wed, 21 May 2008 14:40:17 -0500, Tom Roberts

Nonsense. You have DELUDED yourself into thinking this. There is no
"extra premise" required to compute the errorbar associated with
averaging of data,

I disagree.

Suppose the tax department wants to know my average monthly income for
the year and asks me to tabulate amounts rounded to the nearest
dollar.

What is the error bar associated with the average?

The worst cases would be if all values had been rounded up, or if all
values had been rounded down.

Therefore the maximum possible error would be +/- 12 * 50c = +/-
$6.00.

Now suppose the actual amounts were:

100
1000
100
1000
100
1000
100
1000
100
1000
100
1000

Average = 550.

What would the errorbar be using your method? I am sure it would be
wildly inflated.

You are being horribly stupid.

One of the fundamental problems in statistics is how to estimate
the "true" value of a computed quantity (such as a mean) given
imperfect measurements and/or incomplete samplings of a
population, and to quantify the reliability of your estimates.

In your above example, you have presented a COMPLETE sampling of
the ENTIRE population of your paychecks for the year, the amounts
being rounded to a KNOWN PRECISION. This is not at all the same
situation as occurs in real measurement problems.

1) Suppose the IRS department demands an audit, and wants to see
your actual paystubs. Being very sloppy and haphazard in your
recordkeeping, you've managed to scrounge up only three paystubs,
for February, June and November. The average of the amounts
showing on these three paystubs is (1000 + 1000 + 100)/3 = $700,
so the IRS concludes that your yearly income must have been in
the vicinity of $8400.

How reliable is this estimate of your yearly income, based on
three random samples?

2) The IRS has Supoenaed the bank's security camera videotapes
showing the bulges in your back pocket after you cash each
monthly paycheck. Video measurements show the following bulges:

February    2.06 cm     $1000.00
June        2.43 cm     $1000.00
November    0.92 cm      $100.00

From the above measurements, the agent calculates a linear
regression line

y = 641.71555465x - 457.2270355

The rest of the year, your pocket bulges are measured to be
the following, along with the IRS estimate of your income for
the month based on the computed regression line.

January     1.24 cm      $338.50
March       1.02 cm      $197.32
April       2.67 cm     $1256.15
May         0.96 cm      $158.82
July        0.81 cm       $62.56
August      2.38 cm     $1070.06
September   1.00 cm      $184.49
October     4.33 cm     $2321.40
December    3.11 cm     $1538.51
                    -------------
Total all months:       $9227.81

3) The two IRS measurements both agree that you have understated
your yearly income. You claimed a yearly income of $6600, while
the paystub sample shows a yearly income of $8400, and precision
measurements of your pocket bulges show a yearly income of over
$9200. As a result, you are convicted of income tax fraud and
are sent to Ossining Prison, better known as the Sing Sing
Correctional Facility, for twenty years.

4) Was your conviction justified? Why or why not?

Why would the IRS estimates be the only ones possible?

If it came to court, I would expect a statistician hired by my legal
defence team to prove that the IRS estimates were entirely
unreasonable.

She would of course base her analysis on different premises to those
used by the IRS.

An experienced statistician would testify that the data available
to the IRS do not justify their conclusions.

I would hope so.

An experience statistician, Tom Roberts, has testified that
Miller's data do not support his conclusions,

Tom Robert's analysis shows that Miller's data is not consistent with

and that Cahill's
later work based in part on Miller's data is total fantasy.

Robert's analysis fails to show this because Cahill's theory predicts

On Fri, 23 May 2008 04:16:38 -0700 (PDT), Jerry

Cephalobus_alie... at (no spam) comcast.net> wrote:
On May 23, 6:04 am, Jerry <Cephalobus_alie... at (no spam) comcast.net> wrote:

This is the essence of your argument, as I understand it:

1) Tom Roberts analyzes the data in terms of a model in which the
measurement readings are the sum of a presumptive periodic signal
plus random measurement error plus systematic drift. You agree
that the data do not allow extraction of any sort of periodic
signal from the measurement readings.

2) You, on the contrary, assert that the data should be analyzed
in terms of a model in which the measurement readings are the
sum of a presumptive periodic signal plus random gravitational
wave signals plus systematic drift. Under these circumstances,
you assert that the data DO allow extraction of a periodic signal
from the measurement readings.

-----------------------------------------------------------------

In terms of the target statistics (the phase and amplitude of a
presumptive periodic signal buried in the measurement readings),
your distinction between random measurement error versus random
gravitational wave signals is a meaningless one. You have no
means of modeling the random gravitational wave signals, so there
is no way of "subtracting out" their contribution to the data.
Whether random measurement error or random signal, they
contribute to the error bars OF THE TARGET STATISTICS.

Your problem is that you are confusing your target statistics.
If your target statistics are, say, the amplitude and frequency
distribution of the presumed gravitational wave fluctuations,
then these statistics may be determined with high precision.

But these are not your target statistics. What is signal in one
context is noise in another context.

Let me illustrate:

Suppose the target statistic is "the mean weight of men attending
the upcoming commencement ceremonies at my school." I gather a
group of ten men and find their weights vary from 131 lbs to 245
lbs with a mean of 162 lbs. My weights are accurate to within
+/- 0.5 lbs, so these are genuine fluctuations in weight that I
measure. Nevertheless, in terms of the target statistic, these
fluctuations are noise and contribute to the error bars of the
target statistic.

snipped my goof

Sorry, I hit the "send" button too soon. Normally I double check
what I write before sending it.

The mean weight of the SAMPLE of men may indeed be reported as
162 lbs with a precision limited only by the accuracy of the
scales. But the mean weight of the SAMPLE of men is used as an
estimator of the mean weight of the POPULATION. So let me rewrite
my last paragraph:

Your fallacy, Surfer, is assuming that since I am measuring
genuine fluctuations in weight rather than experiencing random
measurement error, these fluctuations in weight do not contribute
to the error bars of the target statistic. In other words, you
believe that the mean weight of the target population of men as
estimated by my sample should be reported as 162 +/- 0.5 lbs.

My position is that:

1) Tom Roberts' analysis is invalid

2) If the velocity vector is varying due to wave effects, then it
cannot be effectively sampled by a single rotation of the
interferometer, because the vector will change during the course of
the rotation.

3) Therefore data collected during a single rotation, should not be
regarded as a complete measurement.

4) If data collected during a single rotation is not a complete
measurement, then it is meaningless to apply error analysis to such
data.

5) However, if a run of twenty rotations is performed, then the
average values obtained at each marker MIGHT be representive of the
average component of velocity in the plane of the interferomenter
during the time it took to perform the rotations.

There is no way to prove whether that would be the case. The only way
to find out is to try the procedure and see if sensible results are
obtained.

7) Miller performed runs at different sideral times so that the plane
of the interferometer would sample different components of the
velocity vector as the earth turned on its axis. However, if the
velocity vector is varying due to wave effects, then it cannot be
effectively sampled by a single rotation of the earth, because again
the vector will change during the course of the rotation.

8) Therefore velocity vector data obtained during a single rotation of
the earth, should again not be regarded as a complete measurement.

9) If velocity vector data collected during a single rotation off the
earth is not a complete measurement, then it would again seem
meaningless to apply error analysis to such data also.

10) So I come to the conclusion that error analysis should only be
applied to FINAL values for the 3-space velocity vector. I believe
this was the approach of Allais.

Surfer wrote:
My position is that:
1) Tom Roberts' analysis is invalid

That's an ABSURD "position", as it implies you don't believe in
elementary mathematics.

No. The reason I regard your analysis as invalid is because it is

[I am only discussing the error analysis of Miller's data.]


2) If the velocity vector is varying due to wave effects, then it
cannot be effectively sampled by a single rotation of the
interferometer, because the vector will change during the course of
the rotation.

Then Miller's entire approach is invalidated. So you cannot use his result.

I think it is more correct to say that the original theory justifying

3) Therefore data collected during a single rotation, should not be
regarded as a complete measurement.

This is in conflict with your (2) -- if the velocity vector is varying
significantly within the 20 seconds of a turn, then the entire
measurement approach is invalidated, it's not merely an "incomplete
measurement".

4) If data collected during a single rotation is not a complete
measurement, then it is meaningless to apply error analysis to such
data.

But it is NOT meaningless to apply error analysis to MILLER'S ANALYSIS
or his conclusion.

5) However, if a run of twenty rotations is performed, then the
average values obtained at each marker MIGHT be representive of the
average component of velocity in the plane of the interferomenter
during the time it took to perform the rotations.

OK. But such an average is only "representative" to the accuracy of an
error analysis performed n the data. That is, for Miller it is NOT
significant, and the AVERAGE is fully consistent with zero.

Only if you believe the above false premises.

There is no way to prove whether that would be the case. The only way
to find out is to try the procedure and see if sensible results are
obtained.

Sure there is! -- Perform the error analysis and look for statistical
significance.

I find it hard to see how a statistical error analysis of unprocessed

different from zero means your hopes and dreams are not realized by
Miller's measurements.


7) Miller performed runs at different sideral times so that the plane
of the interferometer would sample different components of the
velocity vector as the earth turned on its axis. However, if the
velocity vector is varying due to wave effects, then it cannot be
effectively sampled by a single rotation of the earth, because again
the vector will change during the course of the rotation.

See above -- an error analysis will still tell you whether or not the
result is significant.

10) So I come to the conclusion that error analysis should only be
applied to FINAL values for the 3-space velocity vector. I believe
this was the approach of Allais.

Allais screwed up but did not realize it. So did Cahill. So do you. <shrug

I find it hard to see how a statistical error analysis of unprocessed
partial values of a quantity could tell us anything useful about the
final processed value.

Suppose our equipment can only separately measure hundreds, tens and
units, so prior to processing we have the partial values 5, 9, 8 and
6, 1, 2.

The final processed values differ by only 7.1%.

In contrast, the unprocessed partial values differ by 10%, -80% and
-60% of their full range. This tells us nothing useful about the
validity of the final processed value.

Similarly, in the case of the Miller experiments, a small change in
direction of the velocity vector can produce a huge change in the
components measured by the interferometer.

So I'd say the same kind of problem applies here.

On May 24, 7:34 pm, Surfer <n... at (no spam) spam.net> wrote:

It has nothing to do with what I want.
His paper contains false premises.

You are in broken record mode, Surfer. All of your points have
either been previously answered, or are irrelevant "chaff"
arguments thrown up to distract attention from the weaknesses
of Miller's paper. So I am snipping.

[BIG SNIP]

You have NOT, however, adequately answered Tom or myself on the
following. I challenge you to come up with a valid critique of
Tom's procedure for determining errorbars.

If you cannot refute this procedure of elementary statistical
analysis, then Miller's entire thesis falls apart.

I will begin with a "Director's Cut" quotation from myself,
(replacing my blooper with my corrected remarks) followed by a
quote from Tom's post:

-----------------------------------------------------------------
I wrote:

Suppose the target statistic is "the mean weight of men attending
the upcoming commencement ceremonies at my school." I gather a
group of ten men and find their weights vary from 131 lbs to 245
lbs with a mean of 162 lbs. My weights are accurate to within
+/- 0.5 lbs, so these are genuine fluctuations in weight that I
measure. Nevertheless, in terms of the target statistic, these
fluctuations are noise and contribute to the error bars of the
target statistic.

Your fallacy, Surfer, is assuming that since I am measuring
genuine fluctuations in weight rather than experiencing random
measurement error, these fluctuations in weight do not contribute
to the error bars of the target statistic. In other words, you
believe that the mean weight of the target population of men as
estimated by my sample should be reported as 162 +/- 0.5 lbs.

-----------------------------------------------------------------
Tom replied:

Imagine that one selected a different group of ten men. The
average for this second group is almost surely not 162 lbs.
Consider a third, fourth, fifth,... group of ten men, and plot
the distriution of the averages for the different groups. The
AVERAGES will display a variance, and that variance is related
to the variance of the weights of the individual men. THIS is
what statistics does: it tells you what the variance of the
average will be, given the variance of the individual
measurements (here mens' weights).

For the case (like Miller's) where you have only one group of ten
men to consider, honesty precludes one from claiming the average
is 162 +- 0.5, and one must claim 162 +- sigma, where sigma is
determined from the distribution of the ten mens' weights. In the
language of statistics, the mean of those ten mens' weights is
the best unbiased predictor of the true average, and the sigma
is the best unbiased predictor of how accurately the average of
those ten weights reflects the true average. Note these are
"predictors", because one does not know the true values, one only
knows the ten values one measured.

To learn how to compute that sigma you need to STUDY. If
those ten men's weights are randomly but uniformly
distributed between 131 and 245 lbs, the sigma (errorbar
on the average) will be about 10 lbs, not 0.5 lbs.

That's PRECISELY what I did for each run of Miller's data: For
each of his eight orientations he averaged 40 data points. I
computed the variance of those eight averages from the variance
of the 40 points that went into computing each one. Those
variances (errorbars) GREATLY exceed the variation among the
eight averages, showing that the variation Miller used to make
his result is not significant. This, in turn, makes any
conclusion based on his results be insignificant: Miller
concluded the average is 11 km/s, but the errorbar on that
average is something like 100 km/s; Miller determined an average
direction, but the errorbar on that direction includes all
possible directions.

-----------------------------------------------------------------

How is Tom's procedure incorrect?

In addition to the false premises, I don't think a statistical error

On Mon, 26 May 2008 16:25:15 -0700 (PDT), Jerry
Cephalobus_alie... at (no spam) comcast.net> wrote:

The goals of this exercise were:

a) To obtain, from a limited sampling of the clinic population,
an unbiased estimate of the mean weight of the total population
of women being treated for bulemia. The sample mean is the best
unbiased predictor of the population mean.

b) To obtain, from the distribution of measured weights, an
estimate of the reliability of the sample mean as a predictor
of the population mean, expressed in terms of standard error.

In terms of the target statistics, it makes absolutely no
difference whether the scatter in measured weights is derived
from sampling effects or from measurement error. The reliability
of the sample mean as a predictor of the population mean is
affected identically.

That is not what I am interested in. What I am interested in is the
underlying reality.

If the weighing machine has an errorbar of +/-117 lbs that is a very
different situation to a weighing machine with an errorbar of +/- 0.5
lbs.

If a statistical procedure can't tell the difference, then its
hindering investigation.

Examination of Miller's data from Figure 8 of his 1933 review
on a column-by-column basis shows absolutely no evidence of "wave
effects." For example, col 1, after restoration of his adjustments
on turns 5, 9 and 18, gives the following:

10 7 1 -4 -11 -15 -7 -17 -26 -25 -34 -33 -24 -26 -23 -33 -37 -43
-45 -54

The best fit line for the above is
y = -2.7827x + 7.2684 for x = 1,2,3...20

1 2 3 4 5 6 7 8 9 10 11
4.5 1.7 -1.1 -3.9 -6.6 -9.4 -12.2 -15.0 -17.8 -20.6 -23.3
10 7 1 -4 -11 -15 -7 -17 -26 -25 -34
-----------------------------------------------------------------
-5.5 -5.3 -2.1 0.1 4.4 5.6 -5.2 2.0 8.2 4.4 10.7

12 13 14 15 16 17 18 19 20
-26.1 -28.9 -31.7 -34.5 -37.3 -40.0 -42.8 -45.6 -48.4
-33 -24 -26 -23 -33 -37 -43 -45 -54
-----------------------------------------------------------------
6.9 -4.9 -5.7 -11.5 -4.3 -3.0 0.2 -0.6 5.6

The differences between the regression line and the column data
show no discernable periodicity.

That is a subjective evaluation.

A Fourier analysis shows many frequency components.