Re: Data reduction methodology for V-I colors

[Thomas Droege <tdroege2@earthlink.net>]

Andrew Bennett writes:

> Not for the first time, may I point out that Tom's
> repeated conclusion disagrees with my analysis as
> reported in TN94. I don't know why.
>

When I have said "no improvement" I mean no large improvement.  When one
has data with a noise floor at 0.05 for random telescope pointing and 0.005
for tracking a field, then I look for some big improvement.  I did not see
any significant. improvement for the simple cut I described.  I did see an
improvement.  But it was tiny, indicating to me that the problem was
somewhere else.  When one does n random things, then n/2 result in small
improvements.  With as much data as tass has they can even be statistically
significant.  But I am looking for a major cause.  

As Andrew says, it may be that I need to move away from Chicago.  This is
not going to happen so the tass data remains a Chainsaw.  It is useful for
some tasks but of limited use for a surgeon.  I am content to do what I can
do in Chicago.  If  I can find something to improve, I will make the
improvement if I can.  Meanwhile I will keep taking data.  

Tom Droege 


> [Original Message]
> From: Andrew Bennett <andrew.bennett@ns.sympatico.ca>
> To: <tdroege2@earthlink.net>; <tass@listserv.wwa.com>
> Date: 8/31/2004 3:20:40 PM
> Subject: Re: Data reduction methodology for V-I colors
>
> On Tue, 31 Aug 2004 12:20:51 -0500, Tom wrote:
>
> >Mike,
> >
> >Yep, this and a lot more.  I cannot identify a "bad" night.  I did a lot
of
> >work on the assumption that there was such a thing as a bad night.  For
> >example, go through all the data and look for stars with big deviations
> >from the mean.  (This should work because most stars are not variable.)
Now
> >sort all frames by fraction of stars with big deviations from the mean. 
> >Now eliminate these from the data set and look at the result. I did this
> >eliminating the noisiest 10%, 20%, 50% ...  No improvement at any cut
> >level.  Conclusion:  Eliminating frames with lots of deviant measurements
> >does not improve the quality of the data as a whole.
>
> Not for the first time, may I point out that Tom's
> repeated conclusion disagrees with my analysis as
> reported in TN94. I don't know why.
>
> I was using an ensemble calibration.
> I was "correcting" - I use the term loosely -
> for gradients within each image using linear
> (3 coefficients: zero point and two gradients)
> and, on occasion, quadratic correction (6
> coefficients, adding curvature in each coordinate
> and cross gradient.)
>
> Eliminating images with high scatter gave a
> significant improvement.
> Eliminating stars with high scatter was also
> included - later versions that I have not
> written up do not eliminate high scatter stars
> and appear none the worse ... not sure why!
>
> The improvement from eliminating high scatter
> images was nowhere near as great as I had hoped.
> The residual errors are very far from Gaussian
> and include far too many large errors. The
> detection of variable stars remains horribly
> unreliable as compared to naive calculations 
> using the observed rms errors assuming a Gaussian
> distribution.
>
> Andrew Bennett, Avondale Vineyard