diff --git a/Utils.jl b/Utils.jl
index 4f31ade..9654456 100644
--- a/Utils.jl
+++ b/Utils.jl
@@ -125,7 +125,7 @@ function lowpass_filter(i; σ=1)
 end
 
 function residuals(I_data, I_model, err)
-  return (I_data .- I_model ./ err) .^ 2
+  return (I_data .- I_model ./ err)
 end
 
 function chi2(I_data, I_model, err)
@@ -199,7 +199,7 @@ function add_log_barriers(f::Function, constraints::Vector{Tuple{T,T}}; padding_
     if !isempty(ub_violations)
       err_msg = ""
       for i in ub_violations
-        err_msg *= " · x[$i] = $(x[i]) > $(constraints[i][2]), width = $(barrier_widths[i])"
+        err_msg *= "\n · x[$i] = $(x[i]) > $(constraints[i][2]), width = $(barrier_widths[i])"
       end
       @error err_msg
       throw("upper bound violation for indice(s) $(ub_violations)")
@@ -226,4 +226,11 @@ function print_check_bounds(param_names, param_values, lb, ub)
   end
 end
 
+function box_projector(lower::Vector{T}, upper::Vector{T}) where {T<:Real}
+  function clamp(x::Vector{Float64})
+    return max.(min.(x, upper), lower)
+  end
+  return clamp
+end
+
 end # module Utils
diff --git a/test.jl b/test.jl
index 69310bd..da28c66 100644
--- a/test.jl
+++ b/test.jl
@@ -59,6 +59,8 @@ w_all = Utils.compute_logscale_weights(q_all)
 
 intensity_reduced, P_reduced, lb_reduced, ub_reduced = Utils.reduce_to_free_parameters(meta, PLUV.intensity, params_init, lower_bounds, upper_bounds, q)
 
+projector = Utils.box_projector(lb_reduced, ub_reduced)
+
 simple_bounds = collect(zip(lb_reduced, ub_reduced))
 
 simple_init = 0.5 * (lb_reduced .+ ub_reduced)
@@ -74,6 +76,8 @@ scaling_factors = fill(0.0, length(simple_init))
 # scaling_factors[6] = 1e1
 #scaling_factors[7] = 1e3
 
+# scaling_factors[18] = 1e4
+
 bounds = MH.boxconstraints(lb=lb_reduced, ub=ub_reduced)
 
 function obj_χ2(P)
@@ -107,11 +111,13 @@ I_mean_5k, _ = PLUV.intensity(mean_5k_full, q_all)
 
 # Gauss-Newton
 if true
-  initial_guess = Utils.reduce_to_free_parameters(meta, collect(Float64, values(params_init)))
-  initial_guess = simple_init
-  # initial_guess = best_5k_params
+  data_file_init = Utils.reduce_to_free_parameters(meta, collect(Float64, values(params_init)))
+  #initial_guess = simple_init
+  initial_guess = best_5k_params
 
-  _, result = GN.optimize(barriered_obj, initial_guess; show_trace=true, iscale=1, ZCP=1e-2, ZCPMIN=1e-2)
+  _, result = GN.optimize(obj_residuals, initial_guess; projector=projector, autodiff=:central)
+  # _, result = GN.optimize(barriered_obj, initial_guess; show_trace=true, iscale=2, D0=scaling_factors, ZCP=1e-2, ZCPMIN=1e-2)
+  # _, result = GN.optimize(barriered_obj, initial_guess; show_trace=true, iscale=1, ZCP=1e-2, ZCPMIN=1e-2)
   # _, result = GN.optimize(barriered_obj, initial_guess)
 
   if GN.has_converged(result)
@@ -128,6 +134,11 @@ if true
   else
     @error "Gauss-Newton did not converge"
   end
+
+  @info "Best result GN: Σr² = $(sum(x -> x^2, obj_residuals(P_best)))"
+  @info "Best result SA: Σr² = $(sum(x -> x^2, obj_residuals(best_5k_params)))"
+  @info "Result box middle: Σr² = $(sum(x -> x^2, obj_residuals(simple_init)))"
+  @info "Result data file: Σr² = $(sum(x -> x^2, obj_residuals(data_file_init)))"
 end
 
 # Metaheuristics
@@ -147,7 +158,7 @@ if true
   I_initial, _ = intensity_reduced(initial_guess)
 
   M.lines!(ax, q, I_initial, label="initial", linestyle=:dash, linewidth=2)
-  M.lines!(ax, q, I_best, label="MH best (julia)")
+  M.lines!(ax, q, I_best, label="GN best (julia)")
   M.scatter!(ax, q, I_data, label="data")
   M.lines!(ax, q, I_best_5k, label="TSA best (5k)")
   M.lines!(ax, q_all, I_mean_5k, label="TSA mean (5k)")