92 research outputs found
Determining the evolutionary history of galaxies by astrocladistics : some results on close galaxies
Astrocladistics, a methodology borrowed from biology, is an objective way of
understanding galaxy diversity through evolutionary relationships. It is based
on the evolution of all the available parameters describing galaxies and thus
integrates the complexity of these objects. Through the formalization of the
concepts around galaxy formation and evolution, and the identification of the
processes of diversification (build up, secular evolution, interaction,
merging/accretion, sweeping/ejection), galaxy diversity can be expected to
organize itself in a hierarchy. About 500 galaxies described by about 40
observables have now been analysed and several robust trees found. For
instance, we show that the Dwarf Galaxies of the Local Group all derive from a
common ancestral kind of objects. We identify three evolutionary groups, each
one having its own characteristics and own evolution. The Virgo galaxies
present a relatively regular diversification, with rather few violent events
such as major mergers. Diversification in another sample made of gas-poor
galaxies in different environments appears to be slightly more complicated with
several diverging evolutionary groups. Work on a large sample of galaxies at
non-zero redshifts is in progress and is pioneering a brand new approach to
exploit data from the big extragalactic surveys.Comment: To be published online at http://www.sf2a.asso.fr
Astrocladistics: Multivariate Evolutionary Analysis in Astrophysics
The Hubble tuning fork diagram, based on morphology and established in the
1930s, has always been the preferred scheme for classification of galaxies.
However, the current large amount of data up to higher and higher redshifts
asks for more sophisticated statistical approaches like multivariate analyses.
Clustering analyses are still very confidential, and do not take into account
the unavoidable characteristics in our Universe: evolution. Assuming branching
evolution of galaxies as a 'transmission with modification', we have shown that
the concepts and tools of phylogenetic systematics (cladistics) can be
heuristically transposed to the case of galaxies. This approach that we call
"astrocladistics", has now successfully been applied on several samples of
galaxies and globular clusters. Maximum parsimony and distance-based approaches
are the most popular methods to produce phylogenetic trees and, like most other
studies, we had to discretize our variables. However, since astrophysical data
are intrinsically continuous, we are contributing to the growing need for
applying phylogenetic methods to continuous characters.Comment: Invited talk at the session: Astrostatistics (Statistical analysis of
data related to Astronomy and Astrophysics
Clustering with phylogenetic tools in astrophysics
Phylogenetic approaches are finding more and more applications outside the
field of biology. Astrophysics is no exception since an overwhelming amount of
multivariate data has appeared in the last twenty years or so. In particular,
the diversification of galaxies throughout the evolution of the Universe quite
naturally invokes phylogenetic approaches. We have demonstrated that Maximum
Parsimony brings useful astrophysical results, and we now proceed toward the
analyses of large datasets for galaxies. In this talk I present how we solve
the major difficulties for this goal: the choice of the parameters, their
discretization, and the analysis of a high number of objects with an
unsupervised NP-hard classification technique like cladistics. 1. Introduction
How do the galaxy form, and when? How did the galaxy evolve and transform
themselves to create the diversity we observe? What are the progenitors to
present-day galaxies? To answer these big questions, observations throughout
the Universe and the physical modelisation are obvious tools. But between
these, there is a key process, without which it would be impossible to extract
some digestible information from the complexity of these systems. This is
classification. One century ago, galaxies were discovered by Hubble. From
images obtained in the visible range of wavelengths, he synthetised his
observations through the usual process: classification. With only one parameter
(the shape) that is qualitative and determined with the eye, he found four
categories: ellipticals, spirals, barred spirals and irregulars. This is the
famous Hubble classification. He later hypothetized relationships between these
classes, building the Hubble Tuning Fork. The Hubble classification has been
refined, notably by de Vaucouleurs, and is still used as the only global
classification of galaxies. Even though the physical relationships proposed by
Hubble are not retained any more, the Hubble Tuning Fork is nearly always used
to represent the classification of the galaxy diversity under its new name the
Hubble sequence (e.g. Delgado-Serrano, 2012). Its success is impressive and can
be understood by its simplicity, even its beauty, and by the many correlations
found between the morphology of galaxies and their other properties. And one
must admit that there is no alternative up to now, even though both the Hubble
classification and diagram have been recognised to be unsatisfactory. Among the
most obvious flaws of this classification, one must mention its monovariate,
qualitative, subjective and old-fashioned nature, as well as the difficulty to
characterise the morphology of distant galaxies. The first two most significant
multivariate studies were by Watanabe et al. (1985) and Whitmore (1984). Since
the year 2005, the number of studies attempting to go beyond the Hubble
classification has increased largely. Why, despite of this, the Hubble
classification and its sequence are still alive and no alternative have yet
emerged (Sandage, 2005)? My feeling is that the results of the multivariate
analyses are not easily integrated into a one-century old practice of modeling
the observations. In addition, extragalactic objects like galaxies, stellar
clusters or stars do evolve. Astronomy now provides data on very distant
objects, raising the question of the relationships between those and our
present day nearby galaxies. Clearly, this is a phylogenetic problem.
Astrocladistics 1 aims at exploring the use of phylogenetic tools in
astrophysics (Fraix-Burnet et al., 2006a,b). We have proved that Maximum
Parsimony (or cladistics) can be applied in astrophysics and provides a new
exploration tool of the data (Fraix-Burnet et al., 2009, 2012, Cardone \&
Fraix-Burnet, 2013). As far as the classification of galaxies is concerned, a
larger number of objects must now be analysed. In this paper, IComment: Proceedings of the 60th World Statistics Congress of the
International Statistical Institute, ISI2015, Jul 2015, Rio de Janeiro,
Brazi
Phylogenetic Applications of the Minimum Contradiction Approach on Continuous Characters
We describe the conditions under which a set of continuous variables or
characters can be described as an X-tree or a split network. A distance matrix
corresponds exactly to a split network or a valued X-tree if, after ordering of
the taxa, the variables values can be embedded into a function with at most a
local maxima and a local minima, and crossing any horizontal line at most
twice. In real applications, the order of the taxa best satisfying the above
conditions can be obtained using the Minimum Contradiction method. This
approach is applied to 2 sets of continuous characters. The first set
corresponds to craniofacial landmarks in Hominids. The contradiction matrix is
used to identify possible tree structures and some alternatives when they
exist. We explain how to discover the main structuring characters in a tree.
The second set consists of a sample of 100 galaxies. In that second example one
shows how to discretize the continuous variables describing physical properties
of the galaxies without disrupting the underlying tree structure.Comment: To appear in Evolutionary Bioinformatic
Multivariate Evolutionary Analyses in Astrophysics
The large amount of data on galaxies, up to higher and higher redshifts, asks for sophisticated statistical approaches to build adequate classifications. Multivariate cluster analyses, that compare objects for their global similarities, are still confidential in astrophysics, probably because their results are somewhat difficult to interpret. We believe that the missing key is the unavoidable characteristics in our Universe: evolution. Our approach, known as Astrocladistics, is based on the evolutionary nature of both galaxies and their properties. It gathers objects according to their "histories" and establishes an evolutionary scenario among groups of objects. In this presentation, I show two recent results on globular clusters and earlytype galaxies to illustrate how the evolutionary concepts of Astrocladistics can also be useful for multivariate analyses such as K-means Cluster Analysis
Galaxies and Cladistics
The Hubble tuning fork diagram, based on morphology and established in the
1930s, has always been the preferred scheme for classification of galaxies.
However, the current large amount of multiwavelength data, most often spectra,
for objects up to very high distances, asks for more sophisticated statistical
approaches. Interpreting formation and evolution of galaxies as a ?transmission
with modification' process, we have shown that the concepts and tools of
phylogenetic systematics can be heuristically transposed to the case of
galaxies. This approach, which we call ?astrocladistics', has successfully been
applied on several samples. Many difficulties still remain, some of them being
specific to the nature of both galaxies and their diversification processes,
some others being classical in cladistics, like the pertinence of the
descriptors in conveying any useful evolutionary information.Comment: Talk given at the "12th Evolutionary Biology Meeting" held in
Marseille, France, Sept. 24-26, 200
Concepts of Classification and Taxonomy. Phylogenetic Classification
Phylogenetic approaches to classification have been heavily developed in
biology by bioinformaticians. But these techniques have applications in other
fields, in particular in linguistics. Their main characteristics is to search
for relationships between the objects or species in study, instead of grouping
them by similarity. They are thus rather well suited for any kind of
evolutionary objects. For nearly fifteen years, astrocladistics has explored
the use of Maximum Parsimony (or cladistics) for astronomical objects like
galaxies or globular clusters. In this lesson we will learn how it works. 1 Why
phylogenetic tools in astrophysics? 1.1 History of classification The need for
classifying living organisms is very ancient, and the first classification
system can be dated back to the Greeks. The goal was very practical since it
was intended to distinguish between eatable and toxic aliments, or kind and
dangerous animals. Simple resemblance was used and has been used for centuries.
Basically, until the XVIIIth century, every naturalist chose his own criterion
to build a classification. At the end, hundreds of classifications were
available, most often incompatible to each other. The criteria for this
traditional way of classifying is the subjective appearance of the living
organisms. During the XVIIIth a revolution occurred. Scientists like Adanson
and Linn{\'e} devised new ways of classifying the objects and naming the
classes. Adanson realised that all the observable traits should be used, giving
birth to the mutivariate clustering and classification activity (Adanson,
1763). Linn{\'e} based his binomial nomenclature on neutral names unrelated
whatsoever to any property of the classes. We can realise the success of these
two ideas more than two centuries and a half later
Hints for families of GRBs improving the Hubble diagram
As soon as their extragalactic origins were established, the hope to make
Gamma - Ray Bursts (GRBs) standardizeable candles to probe the very high - z
universe has opened the search for scaling relations between redshift
independent observable quantities and distance dependent ones. Although some
remarkable success has been achieved, the empirical correlations thus found are
still affected by a significant intrinsic scatter which downgrades the
precision in the inferred GRBs Hubble diagram. We investigate here whether this
scatter may come from fitting together objects belonging to intrinsically
different classes. To this end, we rely on a cladistics analysis to partition
GRBs in homogenous families according to their rest frame properties. Although
the poor statistics prevent us from drawing a definitive answer, we find that
both the intrinsic scatter and the coefficients of the \,-\,
and \,-\, correlations significantly change depending on which
subsample is fitted. It turns out that the fit to the full sample leads to a
scaling relation which approximately follows the diagonal of the region
delimited by the fits to each homogenous class. We therefore argue that a
preliminary identification of the class a GRB belongs to is necessary in order
to select the right scaling relation to be used in order to not bias the
distance determination and hence the Hubble diagram.Comment: 10 pages, 6 figures, 4 tables, accepted for publication on MNRA
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
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