3,616 research outputs found
ASPECT: A spectra clustering tool for exploration of large spectral surveys
We present the novel, semi-automated clustering tool ASPECT for analysing
voluminous archives of spectra. The heart of the program is a neural network in
form of Kohonen's self-organizing map. The resulting map is designed as an icon
map suitable for the inspection by eye. The visual analysis is supported by the
option to blend in individual object properties such as redshift, apparent
magnitude, or signal-to-noise ratio. In addition, the package provides several
tools for the selection of special spectral types, e.g. local difference maps
which reflect the deviations of all spectra from one given input spectrum (real
or artificial). ASPECT is able to produce a two-dimensional topological map of
a huge number of spectra. The software package enables the user to browse and
navigate through a huge data pool and helps him to gain an insight into
underlying relationships between the spectra and other physical properties and
to get the big picture of the entire data set. We demonstrate the capability of
ASPECT by clustering the entire data pool of 0.6 million spectra from the Data
Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results
regarding quality and completeness we track objects from existing catalogues of
quasars and carbon stars, respectively, and connect the SDSS spectra with
morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and
Astrophysic
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach
The existence of multiple subclasses of type Ia supernovae (SNeIa) has been
the subject of great debate in the last decade. One major challenge inevitably
met when trying to infer the existence of one or more subclasses is the time
consuming, and subjective, process of subclass definition. In this work, we
show how machine learning tools facilitate identification of subtypes of SNeIa
through the establishment of a hierarchical group structure in the continuous
space of spectral diversity formed by these objects. Using Deep Learning, we
were capable of performing such identification in a 4 dimensional feature space
(+1 for time evolution), while the standard Principal Component Analysis barely
achieves similar results using 15 principal components. This is evidence that
the progenitor system and the explosion mechanism can be described by a small
number of initial physical parameters. As a proof of concept, we show that our
results are in close agreement with a previously suggested classification
scheme and that our proposed method can grasp the main spectral features behind
the definition of such subtypes. This allows the confirmation of the velocity
of lines as a first order effect in the determination of SNIa subtypes,
followed by 91bg-like events. Given the expected data deluge in the forthcoming
years, our proposed approach is essential to allow a quick and statistically
coherent identification of SNeIa subtypes (and outliers). All tools used in
this work were made publicly available in the Python package Dimensionality
Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and
can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).Comment: 16 pages, 12 figures, accepted for publication in MNRA
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