Detecting Periodicity in Point Processes John Rice University of California, Berkeley

Detecting Periodicity in
Point Processes
John Rice
University of California, Berkeley
“All animals act as if they can make decisions.” (I. J. Good)
Joint work with Peter Bickel and Bas Kleijn. Thanks to Seth Digel, Patrick Nolan
and Tom Loredo
Introduction and motivation
A family of score tests
Assessing significance in a blind search
The need for empirical comparisons
Many gamma-ray sources are unidentified and may be
pulsars, but establishing that these sources are periodic is
difficult. Might only collect ~1500 photons during a 10 day
Frequency unknown
Spins down
Large search space
Celestial foreground
Computational demands for a blind search are very substantial. A
heroic search did not find any previously unknown gamma-ray
pulsars in EGRET data. (Chandler et al, 2001).
Detection problem
Unpleasant fact: There is no optimal test. A detection
algorithm optimal for one function will not be optimal for another
function. No matter how clever you are, no matter how rich the
dictionary from which you adaptively compose a detection statistic,
no matter how multilayered your hierarchical prior, your
procedure will not be globally optimal.
The pulse profile (t) is an infinite dimensional object. Any test
can achieve high asymptotic power against local alternatives for at
most a finite number of directions. In other words, associated with
any particular test is a finite dimensional collection of targets and it
is only for such targets that it is highly sensitive.
Consequence: You have to be a [closet] Bayesian and choose
directions a priori.
Lehman & Romano. Testing Statistical Hypotheses. Chapt 14
Specifying a target
Likelihood function and score test
Let the point spread function be w(z|e). The likelihood given times (t),
energies (e), and locations (z) of photons:
where wj = w(zj | ej).. A score test (Rao test) is formed by
differentiating the log likelihood with respect to  and evaluating the
derivative at  = 0:
Neglible if
period << T
Unlike a generalized ratio test, a Rao test does not require fitting
parameters under the alternative, but only under the null
Phase invariant statistic
Square and integrate out phase. Neglecting the second term:
Apart from psf-weighting was proposed by Beran as locally most
powerful invariant test in the direction ( ) at frequency f.
Truncating at n=1 gives Rayleigh test. Truncating at n=M gives
ZM2. Particular choice of coefficients gives Watson’s test (invariant
form of Cramer-von Mises).
Mardia (1972). Statistics of Directional Data
Relationship to tests based on
density estimation
In the unweighted case the test statistic can be expressed as
A continuous version of a chi-square goodness of fit test using
kernel density estimate rather than binning. But note that a kernel
for density estimation is usually taken to be sharply peaked and thus
have substantial high frequency content! Such a choice of ( ) will
not match low frequency targets.
And suppose the signal is
The n-th harmonic will only contribute to the power if n is
substantial and if n is small. That is, inclusion of harmonics is
only helpful if the signal contains substantial power in those
harmonics and if sampling is fine, n< 1 compared to the spacing
of the Fourier frequencies, 1/T; otherwise the cost in variance of
including higher harmonics may more than offset potential gains.
Viewed from this perspective, tests based on density estimation with
a small bandwidth are not attractive unless the light curve has
substantial high frequency components and the target frequency is
very close to the actual frequency.
Integration versus discretization
Rather than fine discretization of frequency, consider integrating the
test statistic over a frequency band using a symmetric probability
density g(f).
Requires a number of operations quadratic in the number of photons. However
the quadratic form can be diagonalized in an eigenfunction expansion, resulting
in a number of operations linear in the number of photons.
(In the case that g() is uniform, the eigenfunctions are the prolate spheroidal wave
functions.) Then
Power is still lost in high frequencies unless the support of g is small.
This procedure can be extended to integrate over tiles in the
plane when
Example: Vela
Assessing significance
At a single frequency, significance can be assessed easily through
simulation. In a broadband blind search this is not feasible and
furthermore one may feel nervous in using the traditional chi-square
approximations in the extreme tail (it can be shown that the limiting
null distribution of the integrated test statistic is that of a weighted
sum of chi-square random variables). We are thus investigating the
use of classical extreme value theory in conjunction with affordable
Gumbel Approximation
Tail Approximations
According to this approximation, in order for a Bonferonni
corrected p-value to be less than 0.01, a test statistic of about 11
standard deviations or more would be required.
log[- log F(t)] versus t
Need for theoretical and empirical
Since no procedure is a priori optimal, comparisons are needed.
Suppose we are considering a testing procedure such as that we have described
and two Bayesian procedures:
A Gregory-Loredo procedure based on a step function model for phased
light curve
A prior on Fourier coefficients, eg independent mean zero Gaussian
with decreasing variance
Also note that within each of these two, the particular prior is important. Even in
traditional low-dimensional models, the Bayes factor is sensitive to the prior on
model parameters, in contrast to its small effect in estimation.
Kass & Raftery (1995). JASA. p. 773-
We run the procedure discussed earlier with
We also use a Bayesian procedure:
The signal has
How do the detection procedures compare?
To compare a suite of frequentist and Bayesian procedures, we would like to like
to understand the behavior of the Bayes factors if there is no signal and if there is
a signal. (Box suggested that statistical models should be Bayesian but should be
tested using sampling theory). Theory for the Bayesian models above?
It might be possible to convert p-values to Bayes factors.
It might be possible to evaluate posterior probabilities of all the competing models
and perform composite inference (would involve massive computing).
Inference is constrained by computation.
Touchstone: blind comparisons on test signals. We understand that GLAST will
be making such comparisons.
Box (1981). In Bayesian Statistics: Valencia I
Good (1992) JASA
Problems are daunting, but with imagination,
some theory, and a lot of computing power, there
is hope for progress.
NERSC's IBM SP, Seaborg, has 6,080 CPUs .