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ics based MC generators for detector optimization gration with the software development selected final state, with physics backgrounds event generato menology/Theory of amplitude parameterization nalysis to reach the physics goals. Framework exists but needs to be tware tools, integration with with the GRID data and MC access, visualization, fitting tools) Partial Wave Analysis
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Physics based MC generators for detector optimization Integration with the software development

Mar 19, 2016

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Partial Wave Analysis. Physics based MC generators for detector optimization Integration with the software development (selected final state, with physics backgrounds event generator). Phenomenology/Theory of amplitude parameterization and analysis - PowerPoint PPT Presentation
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Page 1: Physics based MC generators for detector optimization Integration with the software development

Physics based MC generators for detector optimizationIntegration with the software development (selected final state, with physics backgrounds event generator)

Phenomenology/Theory of amplitude parameterization and analysis (how to reach the physics goals. Framework exists but needs to be updated)

Software tools, integration with with the GRID (data and MC access, visualization, fitting tools)

Partial Wave Analysis

Page 2: Physics based MC generators for detector optimization Integration with the software development

Identify old (a2) and new (1) states

Resonances appear as a result of amplitude analysis and are identified as poles on the “un-physical sheet”

A Physics Goal

Use data (“physical sheet”) as input to constrain theoretical amplitudes

Data ResonancesAmplitudeanalysis

(… then need the interpretation: composite or fundamental, structure, etc)

Page 3: Physics based MC generators for detector optimization Integration with the software development
Page 4: Physics based MC generators for detector optimization Integration with the software development

Analyticity:

Methods for constructing amplitudes (amplitude analysis)

Crossing relates “unphysical regions” of a channel with a physical region of another another

Unitarity relates cuts to physical data

Other symmetries (kinematical, dynamical:chiral, U(1), …) constrain low-energy parts of amplitudes (partial wave expansion, fix subtraction constant)

Data (in principle) allows to determine full (including “unphysical” parts)Amplitudes. Bad news : need data for many (all) channels

Approximations:

Page 5: Physics based MC generators for detector optimization Integration with the software development

Example : 00 amplitude

Only f on C is needed !

To check for resonances:look for poles of f(s,t)on “unphysical s-sheet”

-t 4m2 Re s

Im s

s0 ! 1

Data

To remove the s0 ! 1region introduce subtractions(renormalized couplings)• Chiral, U(1)

For Re s > N use • Regge theory(FMSR)

• Unitarity• Crossing symmetry

N

Partial wave projection Roy eq.

Page 6: Physics based MC generators for detector optimization Integration with the software development

down-flatup-flat

two different amplitude parameterizations which do not build in crossing

in = theoretical phase shifts

=

out = adds constraints from crossing (via Roy. eq)

Lesniak et al.

Page 7: Physics based MC generators for detector optimization Integration with the software development

Extraction of amplitudes

t

(t)

f a ! M1,M2,(s,pi)Ea

(2mp Ea)(t)

sa

aM1

Mn

p1

Use Regge and low-energy phenomenology via FMSR To determine dependence on channel variables, sij

Page 8: Physics based MC generators for detector optimization Integration with the software development

(18GeV) p X p - p ’ p ~ 30 000 events

Nevents = N(s, t, M)

p p

- a2

-

t

M

s

Page 9: Physics based MC generators for detector optimization Integration with the software development

- p ! 0 n

Assume a0 and

a2 resonances

(A.Dzierba et al.) 2003

( i.e. a dynamical assumption)

Page 10: Physics based MC generators for detector optimization Integration with the software development

E852 data

Page 11: Physics based MC generators for detector optimization Integration with the software development

- p ! - pCoupled channel, N/D analysis with L< 3 - p ! ’- p

D

S P

D

P

Page 12: Physics based MC generators for detector optimization Integration with the software development

|P+|2

(P+)-(D+)

Page 13: Physics based MC generators for detector optimization Integration with the software development

Some comments on the isobar model

isobar

+(1)

-(3)

+(2)

s13>>s23 otherwise channels overlap : need dispersion relations (FMSR)

isobar model violates unitarity

K-matrix “improvements” violate analyticity

Page 14: Physics based MC generators for detector optimization Integration with the software development

Ambiguities in the 3 system

Page 15: Physics based MC generators for detector optimization Integration with the software development
Page 16: Physics based MC generators for detector optimization Integration with the software development
Page 17: Physics based MC generators for detector optimization Integration with the software development

- p ! -+- p

BNL (E852) ca 1985

CERN ca. 1970E852 2003Full sample

Software/Hardware from past century is obsolete

Page 18: Physics based MC generators for detector optimization Integration with the software development

Preliminary results from full E852 sample

a2(1320)2(1670)

Chew’s zero ?

Interference between elementary particle (2) with the unitarity cut

Page 19: Physics based MC generators for detector optimization Integration with the software development

s+-(1)s+-(2)

0

0

H000(ma2 - < M3 < ma2 + )

Standard MC O(105) bins (huge !) Need Hybrid MC !

Page 20: Physics based MC generators for detector optimization Integration with the software development

Theoretical work is needed now to develop amplitude parameterizations

Page 21: Physics based MC generators for detector optimization Integration with the software development

X

(a p ! X n) Im f( a ! a)

Semi inclusive measurement (all s)

Dispersion relations

Re f(M2X)

Exclusive (low s, partial wave expansion)

s = MX2

f(k) / k2L

k = (s,m21,m2

2)