Dielectron analysis status
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Dielectron Analysis Status. WIS 21-Aug-14. Normalization. Get the normalization factors for the like sign spectra: Get in integrals of the normalized like sign spectra: Get the normalization factor for the unlike sign spectrum:. Normalization I. Normalization II. HIJING (~0-10% central).

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Dielectron Analysis Status

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Dielectron analysis status

Dielectron Analysis Status

WIS

21-Aug-14


Normalization

Normalization

  • Get the normalization factors for the like sign spectra:

  • Get in integrals of the normalized like sign spectra:

  • Get the normalization factor for the unlike sign spectrum:

Normalization I.

Normalization II.


Hijing 0 10 central

HIJING (~0-10% central)

  • Compare the combinatorial background from SAME event with the combinatorial background from MIXED event

  • How do we select the combinatorial background from SAME event:

    vert1!=vert2

    (both legs are coming from different space points)

    OR

    vert1==vert2 && vertr1==0 && vertr2==0

    (both legs are coming from exactly zero – these are pi+ and pi-)


Hijing 0 10 central1

HIJING (~0-10% central)

  • Applying the Normalization II:

    -> the ratio of the combinatorics from the SAME and the MIXED is:

    0.9898 +/- 0.0003


Hijing 0 10 central2

HIJING (~0-10% central)

  • Applying the Normalization I:

    -> the ratio of the combinatorics from the SAME and the MIXED is 1


Hijing to data

HIJING to DATA

-> Assuming the results from HIJING are correct we apply normalization I. to the data

  • Standards CA eID is applied

  • DC ghost track rejected

  • RICH ghost event rejected

  • Normalization done after CA and ghost cuts

  • HBD cuts applied later

  • In the following slides the data set is ~1B events


Cabana boy settings

Cabana Boy settings

//CabanaBoy *cb = new CabanaBoy(10,8,1,"ULMM_DataTrack");

CabanaBoy *cb = new CabanaBoy(10,8,6,"ULMM_DataTrack"); cb->setPoolType(CabanaBoy::AkibaPools);cb->setPoolDepth(100);cb->setFastMom(false);cb->setZVertexMax(20.0);//cb->setReactionPlaneSelectionType(CabanaBoy::ReactionPlaneNotUsed);

cb->setReactionPlaneSelectionType(CabanaBoy::ReactionPlaneRun7RXNEllipticSN); cb->setCentralitySelectionType(CabanaBoy::CentralityTypeRun10AuAu200);

  • Other settings

  • CA eID cuts

  • no HBD cuts applied

  • Ghost events rejected


N like b like s 0 centrality 10

Nlike/Blike , S+- (0 < centrality < 10)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


N like b like s 10 centrality 20

Nlike/Blike , S+- (10 < centrality < 20)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


N like b like s 20 centrality 40

Nlike/Blike , S+- (20 < centrality < 40)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


N like b like s 40 centrality 92

Nlike/Blike , S+- (40 < centrality < 92)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


Applying strong eid cuts

Applying strong eID cuts

  • eID cuts:

    • n0 > 3

    • sqrt(emcsdphi*emcsdphi + emcsdz*emcsdz) < 2

    • dep > -1 [#]

    • chi2/npe0 < 5

    • disp < 4

    • prob > 0.05

    • |zed| < 75

  • In the following slides the data set is ~2B events


N like b like s 0 centrality 101

Nlike/Blike , S+- (0 < centrality < 10)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


N like b like s 10 centrality 201

Nlike/Blike , S+- (10 < centrality < 20)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


N like b like s 20 centrality 401

Nlike/Blike , S+- (20 < centrality < 40)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


N like b like s 40 centrality 921

Nlike/Blike , S+- (40 < centrality < 92)

CA + ghost rej.

CA + ghost rej. + HBD (matching)

CA + ghost rej. + HBD (matching)

+ S/D rejection


Summary

Summary

  • There is some correlation in like sign?

  • It is masked by the combinatorics in the central, but becomes visible in the peripheral?

  • The like-sign ratio looks flat for the central and the normalization is reasonble?

  • Should we apply some kind of “jet-free region” normalization for the peripheral?

  • Will try to study peripheral events in HIJING


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