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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>
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<hr style="display:inline-block;width:98%" tabindex="-1">
<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Hps-analysis <hps-analysis-bounces@jlab.org> on behalf of Maruyama, Takashi <tvm@slac.stanford.edu><br>
<b>Sent:</b> Wednesday, March 6, 2019 11:54:47 AM<br>
<b>To:</b> hps-analysis@jlab.org<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">mailto:hps-analysis-bounces@jlab.org</a>] On Behalf Of Graham, Mathew Thomas<br>
Sent: Tuesday, March 05, 2019 5:55 AM<br>
To: hps-analysis@jlab.org<br>
Subject: [Hps-analysis] HPS Analysis meeting March 5 @ 9am/noon PST/EST<br>
<br>
<br>
Hi All, <br>
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Meeting today, here’re the details: <br>
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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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