Difference between revisions of "Delta mix calculation"

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<math> a^2\Delta_{\mbox{mix}}(m_v) = m_{vs}^2 - \frac{1}{2}(m_{vv}^2 + m_{ss}^2)  </math>  
 
<math> a^2\Delta_{\mbox{mix}}(m_v) = m_{vs}^2 - \frac{1}{2}(m_{vv}^2 + m_{ss}^2)  </math>  
  
where <math> m_v </math> is the valence quark mass, <math> m_{vv} </math>, <math> m_{vs} </math>, <math> m_{ss} </math> are the overlap, mix and domain wall pion mass respectively. There were 16 values of valence masses <math>m_v </math>, and one value for the sea mass, yielding 16 separate <math>m_{vv} </math> and <math> m_{vs} </math> masses and one <math>m_{ss} </math> mass. For each jackknife bin we computed ∆mix (i.e. 12 values). From this we computed cross correlations among the configurations and masses by constructing the following covariance matrix
+
where <math> m_v </math> is the valence quark mass, <math> m_{vv} </math>, <math> m_{vs} </math>, <math> m_{ss} </math> are the overlap, mix and domain wall pion mass respectively. There were 12 values of valence masses <math>m_v </math>, and one value for the sea mass, yielding 12 separate <math>m_{vv} </math> and <math> m_{vs} </math> masses and one <math>m_{ss} </math> mass. In each jackknife loop we fitted a single Cosh to extract the ground state of the system. 
 
 
 
 
<math>  C_{ij} = \frac{(n-1)}{n} \sum_{i=1}^n (\Delta_{\rm mix}(m_i)  - \bar{\Delta}_{\rm mix}(m_i)) (\Delta_{\rm mix}(m_j)  - \bar{\Delta}_{\rm mix}(m_j)) </math>
 
 
 
We minimized <math> \chi^2 </math> using a constant function to determine the value of ∆mix
 
  
 
{| class="wikitable" style="text-align: center;"
 
{| class="wikitable" style="text-align: center;"
|+ Fit details
+
|+ Fit details for extracting the pion masses.
!Here !! Fitting Range !! <math> \chi^2 </math>
+
!Action !! Fitting Range !! <math> \chi^2 </math>
 
|-
 
|-
 
| DWF || [11 - 17] || -
 
| DWF || [11 - 17] || -
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|-
 
|-
 
|}
 
|}
 +
 +
For each jackknife bin we computed ∆mix (12 values in all). From this we computed cross correlations among the configurations and masses by constructing the following covariance matrix
 +
 +
<math>  C_{ij} = \frac{(n-1)}{n} \sum_{i=1}^n (\Delta_{\rm mix}(m_i)  - \bar{\Delta}_{\rm mix}(m_i)) (\Delta_{\rm mix}(m_j)  - \bar{\Delta}_{\rm mix}(m_j)). </math>
 +
 +
The covariance matrix has an extract <math> (n-1)^2 </math> factor due to the jackknife sampling. We minimized <math> \chi^2 </math> in the usual fashion using a constant function to determine the value of ∆mix.  We find <math> \Delta_{\rm mix} = 0.000
 +
 
==Plots==
 
==Plots==
 
===Efective Mass Plots===
 
===Efective Mass Plots===

Revision as of 17:10, 25 January 2011

Ensemble Details

  • Lattice Size: 323 × 64 \( a = 0.085\rm fm\)
  • Number of Configurations: 46 each with 4 different source positions (x, y, z, t). Total of 194 configurations.
    • Position 1: (16, 16, 16, 10)
    • Position 2: (0, 0, 0, 26)
    • Position 3: (16, 16, 16, 42)
    • Position 4: (0, 0, 0, 58)
  • Masses

DWF Mass\[ma = 0.004 \] , \(m_\pi = \) 292 MeV

Overlap Masses
No. ma est \(m_\pi (\mbox{MeV})\)
1 0.00069 100
2 0.00148 140
3 0.00253 180
4 0.00349 210
5 0.0046 240
6 0.00585 270
7 0.00677 290
8 0.00765 307
9 0.00885 330
10 0.0112 364
11 0.0129 390
12 0.0152 419
13 0.018 450
14 0.024 510
15 0.03 570
16 0.036 630

Propagator Calculation Details

using rbc_conf_3264_m0.004_0.03_002190_hyp as a typical example

  • Boundary Conditions: Periodic
  • Location on Kraken: email Mike Lujan: mlujan@gwmail.gwu.edu
  • Eigensystems
    • # Hwilson: 200 available. Timing: 1.4 hours including I/O time.
    • # Overlap: 400 available. Timing: 10.4 hours including I/O time.
  • Inversions
    • CGM error: 10-8
    • Eigenvectors used: #Hwilson=85, #Overlap=150.
    • Overlap Precision: 10-9 --> Poly order 215.
    • Total # of CGM iterations: 3081 (257 average iterations per inversion).
    • Timing: 2.0 hours including I/O time.

Fitting Analysis

In analyzing \( \Delta \rm mix \) we needed to take into account cross correlations between configurations and between masses. To successfully take into account the cross correlations, we bin jackknife the configurations. Within every jackknife sample we compute \( a^2 \Delta \rm mix \) according to

\( a^2\Delta_{\mbox{mix}}(m_v) = m_{vs}^2 - \frac{1}{2}(m_{vv}^2 + m_{ss}^2) \)

where \( m_v \) is the valence quark mass, \( m_{vv} \), \( m_{vs} \), \( m_{ss} \) are the overlap, mix and domain wall pion mass respectively. There were 12 values of valence masses \(m_v \), and one value for the sea mass, yielding 12 separate \(m_{vv} \) and \( m_{vs} \) masses and one \(m_{ss} \) mass. In each jackknife loop we fitted a single Cosh to extract the ground state of the system.

Fit details for extracting the pion masses.
Action Fitting Range \( \chi^2 \)
DWF [11 - 17] -
Overlap [5, 13] -
Mix [8,15] -

For each jackknife bin we computed ∆mix (12 values in all). From this we computed cross correlations among the configurations and masses by constructing the following covariance matrix

\( C_{ij} = \frac{(n-1)}{n} \sum_{i=1}^n (\Delta_{\rm mix}(m_i) - \bar{\Delta}_{\rm mix}(m_i)) (\Delta_{\rm mix}(m_j) - \bar{\Delta}_{\rm mix}(m_j)). \)

The covariance matrix has an extract \( (n-1)^2 \) factor due to the jackknife sampling. We minimized \( \chi^2 \) in the usual fashion using a constant function to determine the value of ∆mix. We find <math> \Delta_{\rm mix} = 0.000

Plots

Efective Mass Plots

Overlap(Red). DWF(Green), Mix(Blue)

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