superluminal motions & H_0 - comments from Ajk - Bartek's IAP talk
boud w astro.uni.torun.pl
Pon, 30 Sie 2004, 15:40:26 CEST
On Mon, 30 Aug 2004, Bartosz Lew wrote:
> On Fri, 27 Aug 2004, Boud Roukema wrote:
> > -> on month scale changes very rapidly - some blobs just stop for
> > some time, and then start up again.
> plus it often happens that they fade away very quickly or change shape so
> within a few months the recognition of the same blob mae sometimes
> be difficult.
OK, but these don't seem to affect the statistics as far as i understand.
It seems to me that blobs that fade are no longer detected, hence no
longer relevant; and if a blob changes shape without changing speed,
then it does increase the error bar (determining the centre is difficult),
or drop out from detection statistics.
> > My initial reaction is that in a statistical sample these should
> > average out, provided that the errors are random and symmetrically
> > distributed.
> > Alternatively: can we try to statistically correct for this?
> > Given that blobs stop and then start again, or change direction, but
> > are only *detected* when they are moving superluminally, then it would
> > seem to me that the true average speed is slower than estimated from
> > the doppler factor.
> hmm this I quite don't understand ? why do you say so, that they are only
> detected when they are superliuminal. in fact superluminality isn't a
> condition "sine qua non" for doing this thing.
It seems to me that they are more *likely* to be detected when they
are superluminal - and the doppler boosting has a big role in this.
> > Most practical cosmology is about statistical corrections - it requires
> > careful modelling and correct statistical analysis, but it can be done.
> > Is it reasonable to use the observational data which *shows* the
> > erratic behaviour of these objects to statistically model this?
> > If yes, then we would have the necessary correction factor.
> from what I've done and played with the real data, I see that the final
> value is quite sinsitive on precise measurments from the maps !! so
> true - the big sample is needed and this should work.
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