Profile Fitting with Genetic Algorithms

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23 Οκτ 2013 (πριν από 4 χρόνια και 7 μήνες)

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Profile Fitting with Genetic

Barry Fitzgerald


Genetic Algorithms

an explanation

Applications to Astrophysics

P cygni profiles

Results of fitting


In the early 1960’s computer scientists
tried to find new ways of tackling
optimization problems.

Many looked to nature for ways of
solving problems

Obvious example was Darwin’s
“survival of the fittest”

Genetic Algorithms


Create a “population” of possible solutions.

Allow the solutions breed (mix) and mutate.

Apply a selection criteria which gives better
solutions greater chance of surviving.

Repeat until adequate solution is reached

GA’s a general plan



Completely general problem solving

Gives multiple solutions

Needs little information about the

GA’s Disadvantages

Hard to tailor to specific problems

Needs to maintain a population of

can be slow


run it twice and get
two different answers

Application to Astrophysics

Common problem for fitting spectra is
trying to fit theory to reality.

This can be transformed into an
optmization problem by plotting the
theoretical spectrum against the
observed one and then calculating a
numerical value of “goodness of fit”.

Least square fitting

cygni profiles

Many stars emit not just radiation but
also matter. Often in the form of stellar

A sensitive indicator of stellar winds are
P Cygni profiles.

These are resonance lines usually seen
in the UV range with a distinctive

Typical P Cygni profile

P Cygni emission
line profile of triply
ionized carbon at
1548.2 Å in the
central star of the
cat’s eye planetary
nebula, NGC 6543.


Expanding envelope
surrounding stellar surface.
Observer views system from
bottom of figure. Color ranges
from blue for largest blueshift
to white for zero Doppler shift
to red for largest redshift.
(constantly accelerating radial
wind case)


Fitting P cygni profiles

To fit these profiles a program that
reads in 16 parameters about a stellar
wind and outputs a theoretical profile
was used.

Using a GA this process was run in

a profile was read in to the
computer then the set of parameters
that best fit that profile was searched

Sample run theoretical data

Real Observed Data


Genetic algorithms provide a effective
means of fitting profiles

In theory!

However as they are a blind method
unphysical solutions are often found.

Further Work

Apply program to fit doublets

remove some amibugities.

Try to solve the program in stages first
fitting one part of spectrum then next.

Add restrictions to stop non physical