Methods for a blind analysis of isobar data collected by the STAR collaboration
In 2018, the STAR collaboration collected data from 44 96 Ru + 44 96 Ru and 40 96 Zr + 40 96 Zr at s NN = 200 GeV to search for the presence of the chiral magnetic effect in collisions of nuclei. The isobar collision species alternated frequently between 44 96 Ru + 44 96 Ru and 40 96 Zr + 40 96 Zr ....
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Veröffentlicht in: | Nuclear science and techniques 2021-05, Vol.32 (5), p.43-50, Article 48 |
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Sprache: | eng |
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Zusammenfassung: | In 2018, the STAR collaboration collected data from
44
96
Ru
+
44
96
Ru
and
40
96
Zr
+
40
96
Zr
at
s
NN
=
200
GeV to search for the presence of the chiral magnetic effect in collisions of nuclei. The isobar collision species alternated frequently between
44
96
Ru
+
44
96
Ru
and
40
96
Zr
+
40
96
Zr
. In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data, STAR developed a three-step blind analysis procedure. Analysts are initially provided a “reference sample” of data, comprised of a mix of events from the two species, the order of which respects time-dependent changes in run conditions. After tuning analysis codes and performing time-dependent quality assurance on the reference sample, analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual
≈
30
-min data-taking runs. For this sample, species-specific information is disguised, but individual output files contain data from a single isobar species. Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage. Following these modifications, the “frozen” code is passed over the fully un-blind data, completing the blind analysis. As a check of the feasibility of the blind analysis procedure, analysts completed a “mock data challenge,” analyzing data from
Au
+
Au
collisions at
s
NN
=
27
GeV, collected in 2018. The
Au
+
Au
data were prepared in the same manner intended for the isobar blind data. The details of the blind analysis procedure and results from the mock data challenge are presented. |
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ISSN: | 1001-8042 2210-3147 |
DOI: | 10.1007/s41365-021-00878-y |