Drifting Features: Detection and evaluation in the context of automatic
RRLs identification in VVV
- URL: http://arxiv.org/abs/2105.01714v2
- Date: Thu, 6 May 2021 00:52:13 GMT
- Title: Drifting Features: Detection and evaluation in the context of automatic
RRLs identification in VVV
- Authors: J. B. Cabral, M. Lares, S. Gurovich, D. Minniti, P. M. Granitto
- Abstract summary: We introduce and discuss the notion of Drifting Features, related with small changes in the properties as measured in the data features.
We show that this method can efficiently identify a reduced set of features that contains useful information about the tile of origin of the sources.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As most of the modern astronomical sky surveys produce data faster than
humans can analyze it, Machine Learning (ML) has become a central tool in
Astronomy. Modern ML methods can be characterized as highly resistant to some
experimental errors. However, small changes on the data over long distances or
long periods of time, which cannot be easily detected by statistical methods,
can be harmful to these methods. We develop a new strategy to cope with this
problem, also using ML methods in an innovative way, to identify these
potentially harmful features. We introduce and discuss the notion of Drifting
Features, related with small changes in the properties as measured in the data
features. We use the identification of RRLs in VVV based on an earlier work and
introduce a method for detecting Drifting Features. Our method forces a
classifier to learn the tile of origin of diverse sources (mostly stellar
'point sources'), and select the features more relevant to the task of finding
candidates to Drifting Features. We show that this method can efficiently
identify a reduced set of features that contains useful information about the
tile of origin of the sources. For our particular example of detecting RRLs in
VVV, we find that Drifting Features are mostly related to color indices. On the
other hand, we show that, even if we have a clear set of Drifting Features in
our problem, they are mostly insensitive to the identification of RRLs.
Drifting Features can be efficiently identified using ML methods. However, in
our example, removing Drifting Features does not improve the identification of
RRLs.
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