Optimal extinction measurements – I. Single-object extinction inference. (arXiv:1905.00669v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Lombardi_M/0/1/0/all/0/1">Marco Lombardi</a>
In this paper we present XNICER, an optimized multi-band extinction technique
based on the extreme deconvolution of the intrinsic colors of objects observed
through a molecular cloud. XNICER follows a rigorous statistical approach and
provides the full Bayesian inference of the extinction for each observed
object. Photometric errors in both the training control field and in the
science field are properly taken into account. XNICER improves over the known
extinction methods and is computationally fast enough to be used on large
datasets of objects. Our tests and simulations show that this method is able to
reduce the noise associated with extinction measurements by a factor 2 with
respect to the previous NICER algorithm, and it has no evident bias even at
high extinctions.
In this paper we present XNICER, an optimized multi-band extinction technique
based on the extreme deconvolution of the intrinsic colors of objects observed
through a molecular cloud. XNICER follows a rigorous statistical approach and
provides the full Bayesian inference of the extinction for each observed
object. Photometric errors in both the training control field and in the
science field are properly taken into account. XNICER improves over the known
extinction methods and is computationally fast enough to be used on large
datasets of objects. Our tests and simulations show that this method is able to
reduce the noise associated with extinction measurements by a factor 2 with
respect to the previous NICER algorithm, and it has no evident bias even at
high extinctions.
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