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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