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Hi all,<br>
<br>
An old idea - why we cannot use beam background from the data, using
the random trigger events?<br>
<br>
Stepan<br>
<br>
<div class="moz-cite-prefix">On 3/6/19 3:27 PM, Solt, Matthew Reagan
wrote:<br>
</div>
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<p style="margin-top:0;margin-bottom:0">Hi Takashi,</p>
<p style="margin-top:0;margin-bottom:0"><br>
</p>
<p style="margin-top:0;margin-bottom:0">Thanks for thinking
about this. You are correct in saying that a x10 sample of
tritrig-wab-beam is computationally difficult (if not
impossible using our resources). However, I think training on
a x10 sample of tritrig is sufficient. The goal of these ML
studies is to distinguish between multiple scattered tracks
that produce a downstream vertex (from a prompt trident) and a
true displaced vertex. So on that principle alone, I think a
x10 sample of tritrig should be enough for training.</p>
<p style="margin-top:0;margin-bottom:0"><br>
</p>
<p style="margin-top:0;margin-bottom:0">But of course we have to
take wabs and beam backgrounds into account somehow. In
principle, I will find a way to get rid of the tracks that
pick up the wrong hit due to a beam background (a more
sophisticated isolation cut), and make it such that wabs are
not such a big deal for the vertexing. I will of course need
to justify that these will not be backgrounds in the vertexing
analysis (with the ML method). One way to do this is to test
on the full 100% tritrig-wab-beam sample. I think this should
be enough to justify just training on a very large sample of
pure tridents, but someone may come with a counter argument. </p>
<p style="margin-top:0;margin-bottom:0"><br>
</p>
<p style="margin-top:0;margin-bottom:0">More ideas are welcome.
Thanks.</p>
<p style="margin-top:0;margin-bottom:0"><br>
</p>
<p style="margin-top:0;margin-bottom:0">Matt Solt<br>
</p>
</div>
<hr style="display:inline-block;width:98%" tabindex="-1">
<div id="divRplyFwdMsg" dir="ltr"><font style="font-size:11pt"
face="Calibri, sans-serif" color="#000000"><b>From:</b>
Hps-analysis <a class="moz-txt-link-rfc2396E" href="mailto:hps-analysis-bounces@jlab.org"><hps-analysis-bounces@jlab.org></a> on behalf
of Maruyama, Takashi <a class="moz-txt-link-rfc2396E" href="mailto:tvm@slac.stanford.edu"><tvm@slac.stanford.edu></a><br>
<b>Sent:</b> Wednesday, March 6, 2019 11:54:47 AM<br>
<b>To:</b> <a class="moz-txt-link-abbreviated" href="mailto:hps-analysis@jlab.org">hps-analysis@jlab.org</a><br>
<b>Subject:</b> Re: [Hps-analysis] HPS Analysis meeting March
5 @ 9am/noon PST/EST</font>
<div> </div>
</div>
<div class="BodyFragment"><font size="2"><span
style="font-size:11pt;">
<div class="PlainText">After hearing Matt S. talk on Machine
Learning, I realized there is a big problem in MC
production. To train Machine Learning, you need a huge
statistics of MC sample, especially if you want to train
in each mass bin. Furthermore, the MC sample should have
beam-background overlaid; it should be tritrig-wab-beam
not tritrig-without-wab-beam. A high statistics 1.05 GeV
tritrig-wab-beam sample with roughly equivalent to 2015
data statistics was generated last year. It took about 3
weeks to just generate wab-beam background and another
week to generate tritrig-wab-beam recon files. It
required 50 TB to store wab-beam.SLIC files. Since there
were no 50 TB space, earlier wab-beam files were deleted
as the tritrig-wab-beam recon files were completed. Since
2016 run is higher energy and 4 times higher current, it
will take more CPU time and need more disk space. If we
clean-up disk space, MC production with data equivalent
statistics could be doable, but significantly higher
statistics (10x data) is difficult.<br>
<br>
Takashi <br>
<br>
-----Original Message-----<br>
From: Hps-analysis [<a
href="mailto:hps-analysis-bounces@jlab.org"
moz-do-not-send="true">mailto:hps-analysis-bounces@jlab.org</a>]
On Behalf Of Graham, Mathew Thomas<br>
Sent: Tuesday, March 05, 2019 5:55 AM<br>
To: <a class="moz-txt-link-abbreviated" href="mailto:hps-analysis@jlab.org">hps-analysis@jlab.org</a><br>
Subject: [Hps-analysis] HPS Analysis meeting March 5 @
9am/noon PST/EST<br>
<br>
<br>
Hi All, <br>
<br>
Meeting today, here’re the details: <br>
<br>
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* Bluejeans <br>
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* Phone: 8882402560 722990411 ## *4<br>
* Details: <a
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<br>
* Meeting Rooms <br>
<br>
* JLAB: F228<br>
* SLAC: Ballam<br>
<br>
<br>
Agenda<br>
<br>
<br>
* Machine Learning in Vertexing Analysis – MattS<br>
* Relative SVT-ECal alignment in 2016 Data – Norman<br>
* Bugfixes in beamspot-constrained vertexing – MattG<br>
<br>
<br>
<br>
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