diff --git a/calculations/ACCC.jl b/calculations/ACCC.jl
index 2952da9..4f506bf 100644
--- a/calculations/ACCC.jl
+++ b/calculations/ACCC.jl
@@ -20,7 +20,7 @@ c0 = find_zero(quick_extrapolate, 0.85)
 # Calculation of training and extrapolating E
 training_c = range(1.2, 0.9, 9) # original: range(1.35, 0.9, 5)
 training_E = [quick_pole_E(V_system(c)) for c in training_c]
-training_k = new_sqrt.(2μ .* training_E)
+training_k = alt_sqrt.(2μ .* training_E)
 
 extrapolating_c = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
 exact_E = [quick_pole_E(V_system(c)) for c in extrapolating_c]
@@ -28,7 +28,7 @@ exact_E = [quick_pole_E(V_system(c)) for c in extrapolating_c]
 order::Int = ceil((length(training_c) - 1) / 2) # order of the Pade approximant
 
 # Solve coefficients as a linear system
-M_left_element(c, i) = complex(c - c0)^(i/2)
+M_left_element(c, i) = alt_sqrt(c - c0)^i
 M_left = M_left_element.(training_c, (0:order)')
 M_right = -training_k .* M_left[:, 2:end] # remove the first column
 M = hcat(M_left, M_right) # M = [M_left | M_right]
@@ -37,14 +37,11 @@ a = sol[1:order+1]
 b = [1; sol[order+2:end]]
 
 # Pade approximant
-polynomial(a, c) = sum(i -> a[i+1] * complex(c - c0)^(i/2), 0:order)
+polynomial(a, c) = sum(i -> a[i+1] * alt_sqrt(c - c0)^i, 0:order)
 pade_approx(c) = polynomial(a, c) / polynomial(b, c)
 
 # Extrapolate
 extrapolated_k = pade_approx.([training_c; extrapolating_c])
-if real.(extrapolated_k[end]) < 0 # flip if following anti-resonance
-    extrapolated_k = -conj.(extrapolated_k)
-end
 extrapolated_E = (extrapolated_k .^ 2) / (2μ)
 
 # Plotting
diff --git a/common.jl b/common.jl
index c5e537d..97d82c3 100644
--- a/common.jl
+++ b/common.jl
@@ -2,8 +2,8 @@ using LinearAlgebra, DelimitedFiles, SparseArrays
 
 @enum coordinate_system jacobi src
 
-"Square root function with the branch cut along the postive real axis"
-new_sqrt(x::Number)::ComplexF64 = im * sqrt(complex(-x))
+"Square root function with the branch cut along the postive imaginary axis"
+alt_sqrt(x::Number)::ComplexF64 = sqrt(im * x) / sqrt(im)
 
 "Sum over array while minimizing catastrophic cancellation as much as possible"
 function better_sum(arr::Array{T}) where T<:Real