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configuration, and ± refers to the boost projection. This procedure was repeated for each value of the external field. We have performed multiple differing procedures to analyze the data, of which we detail only one method thoroughly in the text. Consistent results were obtained from the other procedures. 

To enforce invariance under parity transformations, for which E → −E , we took the geometric mean of correlators calculated at n and − n on each configuration. 5 Specifically from the set of g$_{±}$ ( x$_{4}$, n )$_{i}$, we form 

for n ≥ 0. This reduces the nine field values to five, corresponding to the integers n = 0 , . . . , 4. This ensemble of correlation functions was then used to generate 200 bootstrap ensembles. For the ensemble averaged correlation functions, we use the same notation but without a configuration label, namely g $_{±}$( x$_{4}$, n ). Fits to the bootstrapped ensemble are performed as described below. 

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Fits to correlation functions in vanishing electric field are often guided by effective mass plots. Ordinarily one looks for a plateau in the effective mass, M$_{eff}$ ( t ), to ascertain when the excited state contributions have dropped out of the correlator. For the boost projected correlation functions, we define two different effective masses 

<img src='content_image/404513.jpg'>

The situation is quite simple for the case of vanishing electric field; thus we handle this case first. 

When the external field vanishes, the two effective masses are identical, M + $_{eff}$( t ) = M − $_{eff}$( t ) ≡ M$_{eff}$ ( t ), and the standard analysis applies. In Fig. 1, we show the effective mass plot for the nucleon in zero electric field. Statistical noise dominates the correlator beyond the window of time depicted. As we are limited in statistics, we perform a two-state fit to extract the mass of the ground state. The fit function, G ( t, n = 0), has the form 

<img src='content_image/404516.jpg'>

