[Hps-analysis] HPS Analysis meeting March 5 @ 9am/noon PST/EST

Wed Mar 6 17:32:06 EST 2019

We should. It isn't completely straightforward, but we should.

-----Original Message-----
From: Hps-analysis [mailto:hps-analysis-bounces at jlab.org] On Behalf Of Stepan Stepanyan
Sent: Wednesday, March 06, 2019 1:25 PM
To: hps-analysis at jlab.org
Subject: Re: [Hps-analysis] HPS Analysis meeting March 5 @ 9am/noon PST/EST

Hi all,

An old idea - why we cannot use beam background from the data, using the random trigger events?

Stepan

On 3/6/19 3:27 PM, Solt, Matthew Reagan wrote:

	Hi Takashi,

	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.

	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. 

	More ideas are welcome. Thanks.

	Matt Solt

________________________________

	From: Hps-analysis <hps-analysis-bounces at jlab.org> <mailto:hps-analysis-bounces at jlab.org>  on behalf of Maruyama, Takashi <tvm at slac.stanford.edu> <mailto:tvm at slac.stanford.edu> 
	Sent: Wednesday, March 6, 2019 11:54:47 AM
	To: hps-analysis at jlab.org
	Subject: Re: [Hps-analysis] HPS Analysis meeting March 5 @ 9am/noon PST/EST 

	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.

	Takashi     

	-----Original Message-----
	From: Hps-analysis [mailto:hps-analysis-bounces at jlab.org] On Behalf Of Graham, Mathew Thomas
	Sent: Tuesday, March 05, 2019 5:55 AM
	To: hps-analysis at jlab.org
	Subject: [Hps-analysis] HPS Analysis meeting March 5 @ 9am/noon PST/EST

	Hi All,  

	Meeting today, here're the details: 

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	*       Meeting Rooms 

	        *       JLAB: F228
	        *       SLAC: Ballam

	Agenda

	*       Machine Learning in Vertexing Analysis - MattS
	*       Relative SVT-ECal alignment in 2016 Data - Norman
	*       Bugfixes in beamspot-constrained vertexing - MattG

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