Ive tried posting this to the original thread but it doesnt seem
to post so hopefully this gets through ...
I would like to take the opportunity to show how a recent
published paper has confirmed earlier predictions of mine,
made here on sci astro and astro-research newsgroups and
at my website since 2001 at www.gammarayburst.com .
Theoretical predictions based on my theory predict that
grb lightcurves have numerous rebrightenings and that this
must be visible in the observed data. I then explained
at numerous times and at my website how these flucuations
could be seen in the observed availble grb data.
The new paper (some quotes from author K Stanek later here
in this post) also confirm how wrong my critics were
to suggest that my predictions of these rebrightenings
were unverifiable and unscientific. Staneks paper also
shows how wrong they were to suggest that my use of
incorporating the fluctuations in observed lightcurves
In fact Stanek shows that my method, outlined by me in
quotes going back here to 2001 is the only correct one.
Stanek has has not only confirmed my predictions, but
has supplied firm evidence that supports my theoretical
model of a variable c in a non big bang universe.
Indeed his paper also admits that current beamed
theory cannot explain these observations. Please note
since 2000 and before I have not only been able to
explain all observations made till then but alsoI
predicted into the future till now, including those
now "discoverd for the first time " in Staneks
Without realizing it Stanek has just supplied the
first verifiable evidence that the speed of light is
not constant for the observor.
(Stanek et al; astro-ph/0602495 2006)
Note from my quotes that I have stated that rapid
variations in the optical lightcurve should be
seen if the time scale of the sampling were short
enough. One of my theoretical criticisms of current
procedures and beamed theory was that the power law
fitting of optical data employed by the astrophysics
community was in fact a misguided procedure
that disguises the true variability of magnitude within
the optical lightcurves by incorrectly smoothing out
the observed variability in the the optical lightcurve,
particularly in early time lightcurves where
flucuationsare more easily seen due to the relative
brightness of grb afterglow.
I have supplied illustrated graph samples at my website
www.gammarayburst.com since 2000 of how if one takes into
account the observed variability in the lighthcurve data one
can extrapolate numerous, rapid and pronounced variabilty
in brightness of the grb afterglow as it decays.
As you can see, my critics not only said that my theory was
wrong and proof of this was that their evidence (power law
chi squared smoothed decays ) showed that there were no
fluctuations as predicted by their beamed theory.
They alsotried to suggest that my attempts to argue
that one shouldnt apply power law smoothing was unscientific.
As you can see if you look at Staneks quotes, in fact now
the only acceptable way to analyse grb optical decay is by
using the very methods I proposed back in 2001 and not
the misguided method of power laws as championed by my narrow
minded sceptics since 2001.
Here are samples of my quotes
below. In addition my quotes below implicitly
predict that X ray lightcurves will also exhibit dramatic
rebrightenings and at later times than expected under
beamed theory allows. This prediction is also
rubbished as impossible and unverifiable by one critic
below. Yet as we know now from several recent Swift
bursts (outlined in Staneks paper) that this is not
only possible but most likely the norm
My first quotes below are from my 1st post from the
thread `Swift grb satelitte` started on nov16 2004
the url is below, unwrapped ...
Sean Nov 16 2004...
" the shorter the time frame of the exposure of the CCD
the more detail will emerge..... more `peaks` will emerge
in shorter ccd exposure times for SWIFT. This will give
the appearance of more numerous rapid rebrightenings
than current *theory allows*.."
"...SWIFT will also see these rebrightenings always
occuring at later times in longer wavelengths. Ie/ a
rebrightening observed in UV will appear to peak
slightly later in optical.
If SWIFT observes a burst with enough detail in its
Gamma X UV OT filter bands it should be possible to
chart features that first occur in gamma then appearing
seconds later in X..."
".. This will be a
progression directly proportional to wavelength so that
if it takes 10 seconds for the `spike` to move from
1nm to 10nm then it will take 100 seconds to move
from 10nm to 100nm... "
And here are some quotes from Stanek which show that new
analyses of data can only confirm my predictions made
here in sci.astro 2001 and 2004. Please note that the
opinions of my critics are now confirmed, as I always
suspected,.. as being dogmatic , incorrect and unscientific.
Stanek et al; astro-ph/0602495 2006....
..."the large number of anomalous optical afterglows
can no longer be seen
as a small wrinkle on the standard afterglow model.
In fact, unless there is sufficient data
to suggest otherwise, it would be only prudent to
assume that any given afterglow might
be anomalous. As a result, some of the often employed
procedures, such as deriving the jet
opening angle using a broken power-law fit to the
optical light curve, in many cases might
have a poor statistical significance and be simply
..."rapid variations often seen in Swift-XRT data
would also be seen in the optical light curves,
given good enough sampling. As a result, some of the
often employed procedures, such as deriving the jet
opening angle using a broken power-law fit to the
optical light curves, in many cases might have a poor
"..Given the unusual behavior observed in the optical
wavelengths for these two bursts, it
is useful to investigate their X-ray light curves as
well. Indeed, X-ray afterglows observed
by Swift-XRT have been shown to have features
(Nousek et al. 2006) not expected in the
standard afterglow models, including giant ares such as
observed in GRB 050502B (Falcone
et al. 2006). The origin of the ares is still under
investigation (e.g. Zhang et al. 2005)..."
"..the overall behavior between the two bands is similar,
but with clear short-timescale variations, as
reported before by Morris et al. (2006b). Trying to
describe these erratic events with smoothpower-law fits
is often a dubious statistical proposition..."
As you can see from Staneks paper on all 3 points my
critics are proved wrong and my predictions, made before
Swift was even online, are not just confirmed as possible
but are the norm for grb phenomena.
Finally, here are samples of what can only be confirmed
now as `unscientific` critisms made on newsgroups
from members of the self styled `scientific` community.
It makes one wonder how they got their jobs.
Craig Markwardts post of mar 28 2005 of the same
thread as my quotes...
"...Scientifically speaking, if you were claiming a
"good fit," then you would be giving proportionality
parameters, confidence limits on those parameters,
and goodness-of-fit measures (i.e. chi-square statistics).
I note that you continue to ignore such formal measures
and instead speculate wildly...."
.. and here Craig tries to push the theoretical approach
of power law smoothing as being correct over my prediction
that in fact there is significant small scale variabilty in
optical lightcurves that has been erased by `smoothing`...
(Craig Markwardt april 11 2005)"..
And the point is that
right now you are using a trial-and-error approach which:
(a) ignores many possible combinations of valid parameters
which may not be so flattering to you; (b) ignores
uncertainties on measurements; and (c) ignores possible
systematic biases. "
Craig Markwardt feb 22 2005 sci.astro..
" In fact, if you
had read some of the literature, you would have found
measured hard-to-soft gamma-ray lags to be significantly
less than 0.1 seconds. (Norris 2000). But it appears that
you have not even tried to consult the literature. When you
get your wavelength values right, you will see that you
would predict significant lags down to 0.5 keV or to
As you can see above, Craig stands by his claim that
my predictions of time lags between hard and soft high
energy observations of than 0.1 seconds are incorrect
and not possible. As seperate 060218 data shows he is
completely wrong and the data verifies my predictions
that some grbs will have significantly longer delays
between all wavelengths (including in X)than believed
possible by current beamed theory.
Below are a few more quotes from M Hardcastle, J Lazio
and C Markwardt from an even earlier thread ...
...`beamed gamma ray bursts`Sat, Oct 27 2001 9:09 am...
They try here to argue that my criticisms of using power
law smoothing is unscientific and unverifiable
Looks like they were wrong and I was right, as it turns
out, as Stanek can now show, that using power law smoothing
with all its parameters and chi squaring is at best
innapropriate. His recommendation is to incorporate
the detailed rebrightenings previously smoothed out by power
law, just as I recommended.
Please read the following quotes or go to the
full thread in sci.astro oct 2001. I`ve interspersed
quotes from my posts from 2001 followed with the relevent
responses from eminent non scientists CM MH and JL
I believe in fact that if observations in the
optical spectrum, of the afterglow were to be
made in numerous very short lets say 10 sec
exposures(is that possible )every 10 secs over
the first day or so from the gamma burst trigger
We would see in the optical spectrum a
very erratic multi peaked profile in a graph of
energy flux /time that should mimic very closely
the gamma ray energy / time profile One could
just as legitimately choose other extremes within
error margins and interpret grb 970508
optical lightcurve as having at least 6 peak
flashes between 1/2 to 1 1/2 mag. If 990123
is debateable depending on error margin
interpretation, Grb 970508 is
definitive proof that both beamed theory and the
use of power law smoothing is incorrect and
misguided. These peak flashes I refer to
can be seen at approx. 0.2, 0.3, 1.05,
1.1, 1.2, 1.5, 2, 2.8, 3.5 and 5.5 days post
trigger(Pian etal: astro-ph/9710334) all within
the accepted error margins supplied.
Sorry, I don't see any statistically significant
peaks at these positions, with the possible
exception of the one at 0.2 days. So this is not
`definitive proof' of anything. The data are
consistent with a power-law model within the errors
in the region where the authors fit one, or so it
seems to me. It's easy to check; get hold of the raw
data and some fitting software and see what the
reduced chi^2 is for the straight-line fit in this
Of course, this doesn't mean that you're wrong
that the peaks *are* there; but you have to show
that they are significant. What's the change in
reduced chi^2 when you add models of these peaks
to the overall power-law decline?
(Here I give Martin credit for at least leaving the
door open to the possibility that maybe occasionaly
there might be a lightcurve with some fluctuation.)
However if he expects me to try to show this by
then applying power law smoothing to the data then
thats counter productive as its exactly this use of
power law which I object to in the first place.
Its like trying to show that GR doesnt explain
gravitational lensing by using GR to see if it
can... And Martins next point only highlites the fact
that if I were to try to show that fluctuations do
exist I probably wouldnt find any if I did have to
do it his way with power laws..Sean)
Anyway, consider fitting a straight line to the
data points between 0.1 and 0.5 days. What is the
reduced chi^2? I think you'll find that almost any
model of these data is an acceptable fit, because the
error bars are so large. An understanding of
experimental error is absolutely key to analysing
this sort of data. I looked quickly at your plot at
your web site but it's missing the error bars, which
means it's impossible to say how real any of the
structure is that you describe.
Following on from that is it not true that
power law chooses `suitable points `in error
margin to give a straight line power law?
and J Lazio`s reply...
Not in the way you seem to be describing it. A
*fit* is an attempt to find a simple function that
describes a large number of data. One could have an
adequate fit even though the fit misses some of the
data by more than dm.
If one wants to describe the fit accurately, one
also has to quote a "goodness of fit." This is
typically done using a chi-squared statistic that
describes how good the fit is and how probable it
is that a comparable fit could be achieved from
If one were to draw a profile through the mid point
of each observation one would get a erratic multi-
peaked structure , Not a straight line power law.
And the irregular profile would be the more
`correct ` way to represent the data. Would it not?
Not really. First off, the entire purpose of
estimating error bars is to illustrate how well
measured a quantity is. We never actually know
what the "true" value of the magnitude is, but
we think it is probably "close" to m, "close"
within about dm. Second, one can always find a
function, using N parameters, to fit N data.
That's not meaningful, though. The idea behind
physics (and astronomy) is to find simple
expressions that describe experiments and
observations. If you fit all N data with an
N parameter function, then as soon as you obtain a
new datum, you need a new function. If you fit
N data with a 2 parameter function *and* it is a
good function, then you can also describe N+1
data with the same function.
(And finally Craig Markwardt replies with an
argument that we now now to be misguided at best
below about how the only way to analyse grb
optical lightcurve decays is to dogmatically
adhere to chi squaring and power law smoothing
of the data to make sure it fits a decidedly
useless beamed theory.
Below Markwardt insinuates my approach of including
the fluctuations in the observed lightcurve is dubious!
Do you still think so now Craig? )
Also, as has been pointed out, trying to infer that
something is a "peak" when the data are as noisy
and as sparse as they are, is in my view a dubious
practice. Simply "connecting the dots" will lead
to *a possible* solution, but ultimately a very
*low probability* one a priori.
A more appropriate approach would be to start with
a featureless model of the decline (say, a power law),
then add a gaussian or some other simple
parameterization of the putative peak. By computing
the F-statistic, one can then find out how significant
the additional peak is, statistically speaking,
compared to the overall decline. If it's significant
at the >95% level, then it may be worth considering
(yeah right Craig..maybe you should take a look at