Chris Beaumont's IDL Library

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result = upperlim(signal, noise, n, mu [, confidence=confidence] [, plot=plot])

This function computes an upper limit for the strength of a signal embedded in a sequence of noisy measurements.

Signal is assumed to be the sum of n independent measurements from a process given by y = mu + source_flux + eps, where eps is gaussian noise with mean zero and variance sigma. The procedure employs a Bayesian approach to find an upper limit for source_flux * n. A prior enforces that source_flux >= 0. The program returns the value f_crit = source_flux_crit * n such that the posterior probability that f_true < f_crit is equal to confidence

TODO: Add in Poisson (and other) noise models

Return value

An upper limit for any INTEGRATED source flux (that is, summed up over all measurements) embedded in the signal.


signal in required

The SUM of n independent measurements.

noise in required

The per-measurement noise standard deviation. Currently, the noise is assumed to be normally distributed.

n in required

The number of measurements that went into the sum

mu in required

The per-measurement background level.


confidence in optional

The posterior probability that the true flux is less than the reported upper limit. The default is .998650 (3-sigma, one tailed).

plot in optional

If set, plot the posterior distribution and upper limit.

Author information


Sep 2009: Written by Chris Beaumont Sep 14 2009: Added input parameter checking. cnb.

File attributes

Modifcation date: Mon Mar 22 16:17:13 2010
Lines: 89