centrality determination for √s nn = 200gev d+au collisions at rhic
DESCRIPTION
Centrality Determination for √s NN = 200GeV d+Au Collisions at RHIC. Richard S Hollis University of Illinois at Chicago. Collaboration. Birger Back, Mark Baker, Maarten Ballintijn, Donald Barton, Russell Betts, Abigail Bickley , - PowerPoint PPT PresentationTRANSCRIPT
CentralityDetermination for √sNN = 200GeV
d+Au Collisions at RHIC
Richard S Hollis
University of Illinois at Chicago
Collaboration
Birger Back, Mark Baker, Maarten Ballintijn, Donald Barton, Russell Betts, Abigail Bickley,
Richard Bindel, Wit Busza (Spokesperson), Alan Carroll, Zhengwei Chai, Patrick Decowski,
Edmundo Garcia, Tomasz Gburek, Nigel George, Kristjan Gulbrandsen, Stephen Gushue,
Clive Halliwell, Joshua Hamblen, Adam Harrington, Conor Henderson, David Hofman, Richard
Hollis,
Roman Hołyński, Burt Holzman, Aneta Iordanova, Erik Johnson, Jay Kane, Nazim Khan, Piotr
Kulinich, Chia Ming Kuo, Willis Lin, Steven Manly, Alice Mignerey, Gerrit van Nieuwenhuizen,
Rachid Nouicer, Andrzej Olszewski, Robert Pak, Inkyu Park, Heinz Pernegger, Corey Reed,
Michael Ricci,
Christof Roland, Gunther Roland, Joe Sagerer, Iouri Sedykh, Wojtek Skulski, Chadd Smith,
Peter Steinberg, George Stephans, Andrei Sukhanov, Marguerite Belt Tonjes, Adam Trzupek,
Carla Vale, Siarhei Vaurynovich, Robin Verdier, Gábor Veres, Edward Wenger, Frank Wolfs,
Barbara Wosiek, Krzysztof Woźniak, Alan Wuosmaa, Bolek Wysłouch, Jinlong Zhang
ARGONNE NATIONAL LABORATORY BROOKHAVEN NATIONAL LABORATORYINSTITUTE OF NUCLEAR PHYSICS, KRAKOW MASSACHUSETTS INSTITUTE OF TECHNOLOGYNATIONAL CENTRAL UNIVERSITY, TAIWAN UNIVERSITY OF ILLINOIS AT CHICAGO
UNIVERSITY OF MARYLAND UNIVERSITY OF ROCHESTER
Detector
Analysis Apparatus:
4 Multiplicity Array
Central Octagon Barrel
6 Rings at higher (Pseudo) Rapidity
Trigger Apparatus:Paddles → One Hit on
Each Array is the Minimum-Bias Trigger
d
Au
Centrality: What is it?
These Geometrical
Quantities are not
Directly Measurable
b
Ncoll
Npart
Npart
How can we ‘measure’ these variables in d+Au collisions?
• Choose a pseudorapidity region– here it’s |η|<3.0
• Slice the MC Multiplicity into desired percentile cross-section bins
• Map this to Npart
• Determine the mean and width of the resulting Npart distributions
• Finally, slice DATA Multiplicity into same cross-section bins
– Associate Npart from same MC cross-section bins
• More Npart details tomorrow
– See Aneta Iordanova’s Talk “Npart Determination and Systematic Error”
d + Au at √s = 200GeV
Npart
Npart
30
η Coverage
Octagon
Rings Rings
Primary Trigger(Scintillator) Paddles
ηSchematic Plot
not to scale
The aim of the talk is to demonstrate the
reconstruction of the Min-Bias Multiplicity Distribution
5.4-5.4 -3.2 3.2
Which Region of η is best?Five distinct methods
• Should not matter when reconstructing a Min-Bias result– If calculated efficiencies
are correct!
• Unique PHOBOS coverage– Many regions to pick from
• All used a basic algorithm– Sum of all merged hits– Cut noise and background
ETot
EOct
ERing
EAuHem
EdHem
Schematic Distributions
Which Region of η is best?Why do we need so many?
• Auto-Correlations!– Could this introduce a Centrality
Bias?
• Method (here)– Cut on Npart directly (Black)
• Form <dN/dη>• Calculate the <Npart>
– Cut on all the other variables such that all have the same <Npart>
• Form <dN/dη>
• Each method derives a different <dN/dη> for the same <Npart>
• ERing yields the closest shape
<Npart> ≈ 3.1
<Npart> ≈ 15.5
NpartEOctETot
ERingAuHemdHem
Which Region of η is best?Let’s try one, any one
• For illustration, look at ‘EOct’– |η|<3.0 – we started here in January!
• Hijing reproduces the Data well in this region.
• Inherent Problem:– How do we make cross-
section bins if we are inefficient?
EOct
EOct
MC – HIJINGData
MC – HIJING Same cuts as data
Data
Problem: Data is not 100% Efficient
• Making a measurement from the whole range of centralities was crucial, for all our d+Au papers.
• For Au+Au data, assumed all inefficiency was in the last bin → thrown away.
• Two Options presented themselves:– PHOBOS ‘min bias’
• Divide our data into our cross-section bins– don’t know the true cross-section– how to deduce Npart?
– Hijing ‘zero-bias’• Divide this distribution into cross-section bins,
– can estimate the true cross-section,– have to carefully estimate the error associated
with this
• Au+Au (Trigger) Efficiency = 97%• d+Au (Trigger + Vertex) Efficiency = 82.5%
Data
MC
EOct
EOct
Solution: Use Hijing
• Use the Hijing Cross-section as true
• Apply a small scale factor (along x-axis) to match the distributions
• Use a Glauber calculation to estimate the uncertainty in overall efficiency
EOct
EOct
MC
Data
How do we know we are correct?
• There are some issues to consider with this technique– Is the efficiency correct?– Is Hijing Correct?– Does Hijing reproduce the shape well enough?
• Best way to address them is to try another method!– Different (shape matched) efficiencies
• Different efficiencies per centrality bin– Each of the 5 methods have different shape features
Forming the “minimum bias” result
• Form the normalized distributions in 10 equal (Hijing) cross-section bins.
• Sum these and divide by 10 (bins).
• All five methods reconstruct within a few percent of each other (in Hijing).
• Reconstruct to within 2.5% of Hijing zero bias.
• We can then hypothesize:– We have seen the intermediate
centrality distributions are very different for each centrality method
– As are the efficiencies per bin– The final (reconstructed) zero-biases
are the same– Conclusion: the method seems to work
Why do we need bins for “minimum bias” reconstruction
• If we put all the data into one bin– automatically bias
ourselves away from the low multiplicity events
– ~15% rise over zero-bias
• Folding in the efficiency helps
• Increase number of bins → get closer to ‘truth’
All in 1 BinZero Bias
η
dNch
dη
Conclusions
• We have developed a new analysis to determine bins of cross-section for d+Au collisions.
• We can analyse data even when the data falls in an inefficient bin.
• Biased Hijing reconstructs 2.5% higher than unbiased with our methodology.
• Distinct centrality methods in Hijing Reconstruct and agree to within a few percent of each other.
INAL DATA RESULT
If you want tosee it …
Aneta IordanovaSession CC
Tomorrow Morning
PRELIMINARY
INAL DATA RESULT
PRELIMINARY
INAL DATA RESULT
PRELIMINARY
INAL DATA RESULT
PRELIMINARY
INAL DATA RESULT
-4 -2 0 2 4 η
Npart
How do we ‘measure’ these variables in Au+Au collisions?
• Choose a pseudorapidity region (here it’s |η|<3.0)
• Slice the MC Multiplicity into desired percentile cross-section bins
• Map this to Npart
• Determine the mean and width of the resulting Npart distributions
• Finally, slice DATA Multiplicity into same cross-section bins
– Associate Npart from same MC cross-section bins
Au + Au at √s = 200GeV
Npart
400