This document discusses an approach for optimizing follow-up observations of astronomical transients using Bayesian decision theory. The approach uses transient lightcurve models, telescope noise models, and prior information to calculate the information content of potential future observations. Observations are prioritized based on their expected information content to efficiently classify transients. The presenter outlines the necessary components for a software system to implement this approach, including lightcurve generation, data fitting, calculating confusion matrices, and an observation scheduler. Future work involves integrating these components and testing the system in realistic simulations.

1. Tunable algorithms for
transient follow-up
Tim Staley
TKP Meeting, Manchester, Sept 2014
WWW: 4pisky.org , timstaley.co.uk

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Aim of this talk
A basic, intuitive understanding of
information content
and how this can be used to
optimize / automate decision
making, a.k.a.
Bayesian decision theory

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Outline
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A blueprint for automated
follow-up

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Outline
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Intrinsic lightcurves

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Noisy samples
Sampling with noise

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FoM
Information content
Evaluating each epoch

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Confusion matrices

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Confusion matrices

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Instrinsic lightcurves - ensemble

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Outline
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Required knowledge /
user-inputs
Transient rate priors.

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Required knowledge /
user-inputs
Transient rate priors.
Transient lightcurve ensemble models.

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Required knowledge /
user-inputs
Transient rate priors.
Transient lightcurve ensemble models.
Telescope / noise models.

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Required knowledge /
user-inputs
Transient rate priors.
Transient lightcurve ensemble models.
Telescope / noise models.
Follow-up prioritization weightings.

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Required software
components
Efficient lightcurve generation library.

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Required software
components
Efficient lightcurve generation library.
MCMC data fitting models and
routines.

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Required software
components
Efficient lightcurve generation library.
MCMC data fitting models and
routines.
Statistical routines for calculating
confusion matrices.

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Required software
components
Efficient lightcurve generation library.
MCMC data fitting models and
routines.
Statistical routines for calculating
confusion matrices.
Observation schedule optimization
engine.

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Required components

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Outline
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What’s next?
Finish bolting components together.
Run simulations, test in more realistic
scenarios.
Interfacing with optimizer / scheduler.

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Longer term
Variational Bayes?
Gaussian processes?

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Summary
Information content is just a penalty
function for scoring predicted
observations.
Using it to decide when to observe is
applied Bayesian decision theory.
But doing this for real requires a
number of non-trivial software
components.
Nearly ready for testing!