leave alone spending time on their computers spamming people for

your questions contain some myths that need to be addressed before they can be answered.

1) the speed of light is constant (the same) for all colors, the frequency and wavelength change but not the speed.

2) there is no physical parameter called “speed of sight”. It is just the speed of light + the processing of the light by the eye and nervous system. Not a meaningful parameter to consider.

3) Temperature does not have an affect on the speed of light. So there is a high degree of confidence that distances to stars are not exaggerated by this.

4) Mars is red because the material it is composed of. Therefore it would look the same from Earth and Mars.

Hope that helps.

The authors delve into things, and cite Sean’s paper.

But supernovae by themselves are not extremely statistically significant indicators of the existence of dark energy; I don’t know how many sigma, but it’s just a few. But that’s because you can come close to fitting them if you assume the universe is nearly empty of matter and highly spatially curved. And we know those things just aren’t true: we’ve measured the matter density from dynamics, and the CMB tells us that there is not appreciable spatial curvature. In a flat universe, the supernovae require dark energy at more than ten sigma.

In another issue, if theory predicts that scatter in the GRB magnitude estimates will increase with increasing redshift, it will require an enormous amount of high red shift GRB data to reach the very low probability values that the physics community appears to demand for accepting the evidence for a significant difference between observation and CC predictions. Will such additional high red shift GRB data become available in the foreseeable future? Do you believe that the substantial background gamma count really represents presently unresolved GRBs, implying that there is an enormous number of high red shift GRBs just waiting for detection and measurement with improved techniques and continued observation? Is, in fact, the backgound gamma count higher than that predicted for black body radiation derived from the various big bang models and cosmological background radiation observations?

Questions, questions!

It was phrased earlier as information “skewed” but held in context of that implcation, might we have rasied the bar?

You had to look at dark energy in context, a little differently, as it pervades the universe in that expansitory progress?

I don’t know what the GRB Hubble Diagram has to say about photon-axion oscillations.

The effects of gravitational lensing are to magnify and demagnify the GRB, making them appear brighter or dimmer than would be deduced from the GRB luminosity indicators. This will cause some ‘random’ noise in the vertical direction of the Hubble Diagram. This noise gets larger as we go to higher redshift because the line of sight will pass near more-and-more galaxies. The same problem exists for supernovae, but for GRBs the effects will be larger due to the higher redshifts. Premadi & Martel (astro-ph/0503446) show that the effects don’t rise linearly; with the 1-sigma scatter (in the distance modulus) in the Hubble Diagram at z=1 is ~0.10 mag and at z=5 is ~0.34 mag. This is generally smaller than my typical error, so the effect won’t dominate. Lensing conserves flux, and with the *average* magnification of unity there should simplistically be no effect on the observed shape of the Hubble Diagram. But with the distribution being slightly skewed, any offsets will depend on the numbers of GRBs (of which I have 52 [9 in the z>3 'bin']) as described by Holz & Linder (2005, ApJ, 631, 678). The real story is more complex as it depends on whether more GRBs just outside the limiting distance are magnified into the sample than the GRBs just inside the limiting distance that are demagnified out of the sample. The limits for inclusion in the sample of GBRs with redshifts are fuzzily known, so this calculation is not easy. Holz tells me that he would expect the effects to be small (perhaps 0.05 mag in the distance modulus at z~6) when comparing high and low redshifts; and such effects are not significant given my larger error bars. A real calculation is needed to be sure of all this.

Steve asked about whether I used ordinary least squares techniques for fitting the five calibration relations. As he points out, the error bars vary substantially from burst-to-burst, so some account must be made of this. I do so by minimizing the chi-square of the fit, as this has the natural variance in the denominator. Another point is that the observed scatter about the best fit calibration relations is substantially larger than expected from the measurement errors on the indicators alone. This implies that there is some additional scatter, caused for example by gravitational lensing. This is no surprise, as for example we don’t know what the best way to define the ‘variability’. I model this by adding in quadrature a constant plus the Premadi & Martel lensing scatter to the scatter from the measurement errors. I vary the constant until the best fit reduced chi-square is unity. Another point is that I do the fit in log-log space. This is because (a) the expected relations are power laws from theory, (b) the error sources are multiplicative, and (c ) the observed error distributions appear Gaussian in log-space. From the best fit calibration curves, I get values of Log(L) for each measured indicator for each burst. Each will have an associated error bar, generally dominated by the systematic errors. Then I combine the various Log(L) values for each burst in a weighted average, to get a combined Log(L) which will then yeild a distance modulus.

The GRB diagram presented above presents a “best fit” curve for magnitude vs. redshift. As both Dan and others attending the original meeting have already commented, the variability of the magnitude data is quite high, especially the next to largest redshift datum at z~5.xx.

I wonder if Brad fitted his data using linear ordinary least squares, linearizing the model in the variables (using log transformations, for example), or used a form of non-linear regression. Looking at the “details” of “calibrating the luminosity relations” provided in the link, I’d guess he used a linear fitting technique linearized by transformation in the redshift variable.

If so, I then wonder if Brad took advantage of the variability of the magnitude data in fitting his model. A well-studied fitting technique called variance weighted least squares is designed to use variance in the variable on the left-hand side of the model to inversely weight the importance of each data point in the fit according to its error of measurement, data points with high error contributing less to the fit and data points with low error contributing more.

Given the nature of the GRB data distribution, the sparse and highly variable data at high red shift will unduly influence the entire fit of a linear ordinary least squares regression. Those high-z points will have what statisticians call “high leverage” in the model. Weighted least squares fitting would address some of that problem, could definitely change the difference between the “cosmological constant” curve and the observed curve, and could also alter the calculated probability that the two curves differ by chance alone if only ordinary least squares fitting was used for the presented fits.

It would also appear that the fitted log(L) – log(V) relationship shown in “calibrating the luminosity relations” violates the assumption of homogeneity of error variance that ordinary least squares fitting requires, so perhaps I’m wrong in assuming that this was the fitting technique used for the magnitude – z relationship.

Though I’m writing from ignorance of details that might have already been presented and of standard practices in handling such data in physics, I’d be interested in hearing comments or corrections from Dan, Sean, and the rest of the list.

I’m recovering from CSL-1 infor..ma..tion…….:)

Ya Paul I’m having hard time accepting how this information can be read, considering the lensing that goes on. If from the distance information is to travel, how do we know that the fastest route is not being considered, while the influences along the way can hold this information?

Would this not Probably be one of these stupid statements that we make sometimes?:)Galaxie formations as part of the larger expansion process create curvature parameter readings askew?

I dunno…..

So thus, the increasing expansion, must be calibrated in some way to the appearence/increase of excess GRB’s?

The Luminosity Function must itself, be tuned into the expansion rate, and therefore the light emmanating from a part of the Universe that is expanding at a greater rate, would have its light “stretched” locally, at the farthest location from the Milky Way, and could give the impression that its luminosity is greater than it actually is?

Good to hear from you!

Thanks for the reply. I’ll go hunt down Don’s comment.

On your question about w’ being the parameter for varying DE, it’s the standard thing that people do. But that does not mean it’s the only thing nor the right thing. In fact, once you parameterize this way, (say by taylor expanding it), you are secretly restricting the possible class of w(z) in the total parameter space. For example, if you parameterize it using w = w_0 + w’z, fit your observations to it, and you find w’ has to be very small, then if you conclude that rapidly evolving w(z) is ruled out, you are making a mistake. This is because you have ruled out rapidly evolving w(z) by the choice of models.

It’s legit, but one has to becareful about what it means.