An Information Theory Approach on Deciding Spectroscopic Follow Ups. (arXiv:1911.02444v1 [astro-ph.IM])

An Information Theory Approach on Deciding Spectroscopic Follow Ups. (arXiv:1911.02444v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Astudillo_J/0/1/0/all/0/1">Javiera Astudillo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Protopapas_P/0/1/0/all/0/1">Pavlos Protopapas</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pichara_K/0/1/0/all/0/1">Karim Pichara</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Huijse_P/0/1/0/all/0/1">Pablo Huijse</a>

Classification and characterization of variable phenomena and transient
phenomena are critical for astrophysics and cosmology. These objects are
commonly studied using photometric time series or spectroscopic data. Given
that many ongoing and future surveys are in time-domain and given that adding
spectra provide further insights but requires more observational resources, it
would be valuable to know which objects should we prioritize to have spectrum
in addition to time series. We propose a methodology in a probabilistic setting
that determines a-priory which objects are worth taking spectrum to obtain
better insights, where we focus ‘insight’ as the type of the object
(classification). Objects for which we query its spectrum are reclassified
using their full spectrum information. We first train two classifiers, one that
uses photometric data and another that uses photometric and spectroscopic data
together. Then for each photometric object we estimate the probability of each
possible spectrum outcome. We combine these models in various probabilistic
frameworks (strategies) which are used to guide the selection of follow up
observations. The best strategy depends on the intended use, whether it is
getting more confidence or accuracy. For a given number of candidate objects
(127, equal to 5% of the dataset) for taking spectra, we improve 37% class
prediction accuracy as opposed to 20% of a non-naive (non-random) best
base-line strategy. Our approach provides a general framework for follow-up
strategies and can be extended beyond classification and to include other forms
of follow-ups beyond spectroscopy.

Classification and characterization of variable phenomena and transient
phenomena are critical for astrophysics and cosmology. These objects are
commonly studied using photometric time series or spectroscopic data. Given
that many ongoing and future surveys are in time-domain and given that adding
spectra provide further insights but requires more observational resources, it
would be valuable to know which objects should we prioritize to have spectrum
in addition to time series. We propose a methodology in a probabilistic setting
that determines a-priory which objects are worth taking spectrum to obtain
better insights, where we focus ‘insight’ as the type of the object
(classification). Objects for which we query its spectrum are reclassified
using their full spectrum information. We first train two classifiers, one that
uses photometric data and another that uses photometric and spectroscopic data
together. Then for each photometric object we estimate the probability of each
possible spectrum outcome. We combine these models in various probabilistic
frameworks (strategies) which are used to guide the selection of follow up
observations. The best strategy depends on the intended use, whether it is
getting more confidence or accuracy. For a given number of candidate objects
(127, equal to 5% of the dataset) for taking spectra, we improve 37% class
prediction accuracy as opposed to 20% of a non-naive (non-random) best
base-line strategy. Our approach provides a general framework for follow-up
strategies and can be extended beyond classification and to include other forms
of follow-ups beyond spectroscopy.

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