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star-tpc-l - Re: [Star-tpc-l] Some issues with SL23d and SL23e productions

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  • From: "Ma, Rongrong" <marr AT bnl.gov>
  • To: "Fisyak, Yuri V" <fisyak AT bnl.gov>
  • Cc: "skradhakrishnan AT lbl.gov" <skradhakrishnan AT lbl.gov>, "Van Buren, Gene" <gene AT bnl.gov>, "geurts AT rice.edu" <geurts AT rice.edu>, Barbara Trzeciak <barbara.trzeciak AT gmail.com>, 张炜 <wzhang AT m.scnu.edu.cn>, "Mooney, Isaac" <isaac.mooney AT yale.edu>, Yi Yang <yiyang AT ncku.edu.tw>, "nihar AT rcf.rhic.bnl.gov" <nihar AT rcf.rhic.bnl.gov>, xuyue <xuyue AT impcas.ac.cn>, subhash <subhash AT rcf.rhic.bnl.gov>, "Tribedy, Prithwish" <ptribedy AT bnl.gov>, Zhenyu Chen <zhenyuchen AT sdu.edu.cn>, "Yiding-Han AT rice.edu" <Yiding-Han AT rice.edu>, Star-tpc L <Star-tpc-l AT lists.bnl.gov>
  • Subject: Re: [Star-tpc-l] Some issues with SL23d and SL23e productions
  • Date: Tue, 5 Dec 2023 17:01:41 +0000

Hello Yuri

When Wei looked at the eta dependence of the mean nsigma_e, he saw a non-flat distribution, which prompted him to shift the nsigma_e distribution by hand to make <nsigma_e> sit at 0 in all eta bins. It will make it easier to cut on nsigma_e for PID and calculate its efficiency. This is what "corrected" means, which is essentially a shift of nsigma_e distribution vs. eta. 

What puzzles us is that nsigma_e is more Gaussian in the old (SL21c) production than in the new production (SL23d). 

Sure, we can discuss this at the TPC meeting this Thursday. 

Best
Rongrong

On Dec 5, 2023, at 11:49 AM, Fisyak, Yuri V <fisyak AT bnl.gov> wrote:

  1. Non Gaussian nsigma_e distributions (https://drupal.star.bnl.gov/STAR/system/files/nsigmaeCorr.pdf):
  1. I don’t know what do you call “corrected” nsigma_e. 
  2. I know that with new dE/dx model I have momentum dependence overestimation for electrons with respect to the model up to 10%. The electron dE/dx is a special beast and it requires a special treatment. 
  3. Log(dE/dx) has much better match with Gaussian than dE/dx (which is Landau distribution with infinite dispersion) due to the finite dispersion and the Central Limit theorem, but it is not exactly Gaussian. How close it to Gaussian depends on resolution and statistics.




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