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Hi Will,<br>
<br>
If you're using a recent version of the code (revision 7156 from
Dec. 15th or later) then you do not need to add anything and you
will automatically get the KalmanSIMD fitter. If you're using a
64bit OS, then you just need something after revision 6834 from Oct.
25th. If you're using an earlier revision than that then you should
really be up considering an upgrade! :)<br>
<br>
To know exactly which factories are being used, you can add the
janadot plugin when you run the program via the -PPLUGINS=janadot
command line argument. You'll need to have the graphviz package
installed which includes the "dot" program. But this will give you a
nice picture with the complete dependency chart.<br>
<br>
Regards,<br>
-David<br>
<br>
On 1/20/11 5:19 PM, Will Levine wrote:
<blockquote
cite="mid:AANLkTinEax1qPW9ro77Yc0biR5in18E2cU5TASYEyu4F@mail.gmail.com"
type="cite">
<pre wrap="">Huh, I just figured out that I'm supposed to use
DTrackFitter=KalmanSIMD and not DTrackFitter=Kalman. That seems to fix
the problem of the absurdly large error matrices.
Will
On Thu, Jan 20, 2011 at 1:52 PM, Will Levine <a class="moz-txt-link-rfc2396E" href="mailto:wilevine@andrew.cmu.edu"><wilevine@andrew.cmu.edu></a> wrote:
</pre>
<blockquote type="cite">
<pre wrap="">Hi,
I've been looking into kinematic fitting and just thought I should share a
couple issues I've been running into, in case anyone has any ideas or
thoughts.
Using the error matrices from the Kalman fitter, if I run a kinematic fit,
and cut out events with confidence level<.01, it seems to do its basic job
of cutting out most "bad" events and keeping some good events.
However, there are a few problems, which lead to too few events passing the
cut.
Pulls indicate that the error matrices are somewhat off, in particular, that
the errors are too small. Simply scaling the error matrix (as given in
px,py,pz) does not solve the problem, so it will be necessary to look at the
errors of the tracking variables.
A few of the error matrices are totally bogus. i.e.
7x7 matrix is as follows
| 0 | 1 | 2 | 3 | 4 |
------------------------------------------------------------
0 | 9.425e+141 -4.773e+142 2.402e+142 -6.569e+140 1.786e+143
1 |-4.773e+142 2.379e+143 -1.197e+143 3.273e+141 -9.047e+143
2 | 2.402e+142 -1.197e+143 6.031e+142 -1.649e+141 4.555e+143
3 |-6.569e+140 3.273e+141 -1.649e+141 4.507e+139 -1.245e+142
4 | 1.786e+143 -9.047e+143 4.555e+143 -1.245e+142 3.473e+144
5 | -8.99e+143 4.554e+144 -2.293e+144 6.266e+142 -1.748e+145
6 |-4.053e+141 2.053e+142 -1.033e+142 2.826e+140 -7.805e+142
I'll look into whether these tracks are identifiable or bogus in any other
way.
If any one has any insight into these issues, I'd be happy to hear it.
Will
</pre>
</blockquote>
<pre wrap="">
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</blockquote>
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