diff --git a/src/spectroscopy.jl b/src/spectroscopy.jl
index 347c4f3..d6dd410 100644
--- a/src/spectroscopy.jl
+++ b/src/spectroscopy.jl
@@ -1 +1,57 @@
 # This file should contain all spectroscopy specifc stuff like correlators, fits, effective masses, ...
+function effectivemass(c)
+    N = length(c)
+    m = similar(c)
+    for i in 1:N
+        r = c[i]/c[mod1(i+1,N)]
+        m[i] = r > 0 ? abs(log(r)) : NaN
+    end
+    return m
+end
+function effectivemass_err(c,Δc;norm=1)
+    N  = length(c)
+    Δm = similar(c)
+    for i in 1:N
+        j = mod1(i+1,N)
+        Δm[i] = norm(Δc[i]/c[i],Δc[j]/c[j],p=norm)
+    end
+    return Δm
+end
+effectivemass_err_sys(c,Δc)  = effectivemass_err(c,Δc,norm=1)
+effectivemass_err_stat(c,Δc) = effectivemass_err(c,Δc,norm=2)
+"""
+    effectivemass(c)
+
+Returns the effectivemass ``m_\\text{eff}(t)`` of the data ```x``` given by ``
+m_\\text{eff}(t) = \\left| \\log \\left( \\frac{x(t)}{x(t+1)} \\right) \\right| ``. If
+```x``` is a vector of ```DataPoint```'s the statistical and systematic
+uncertainties are propagated accordingly. If ```x <: Number``` only the the mean
+value is calculated. In that case the unexported methods
+```effectivemass_err_stat(x,Δx)``` and ```effectivemass_err_sys(x,Δx)``` can be
+used for propagation of uncertainty.
+"""
+function effectivemass(c::Vector{DataPoint{T}}) where T
+    # here are some allocations that are not strictly needed
+    val = value.(c)
+    stat = staterr.(c)
+    sys = syserr.(c)
+    return DataPoint.(effectivemass(val),effectivemass_err_stat(val,stat),effectivemass_err_sys(val,sys))
+end
+function effectivemass_cosh(c)
+    T = length(c)
+    t = 1:T
+    mid  = div(T,2)+1 # 1-based indexing
+    return @. abs(acosh(c/c[mid])/(mid-t))
+end
+_acoshderiv(x) = 1/sqrt(x^2 + 1)
+function effectivemass_cosh_err(c,Δc;norm=1)
+    T = length(c)
+    mid  = div(T,2) + 1 # 1-based indexing
+    Δm = similar(c)
+    for t in 1:T
+        err1 = _acoshderiv(c[t]/c[mid])*Δc[t]/c[mid]
+        err2 = _acoshderiv(c[t]/c[mid])*c[t]*Δc[mid]/c[mid]^2
+        Δm[t] = LinearAlgebra.norm((err1,err2),norm)/abs(mid-t)
+    end
+    return Δm
+end