721 research outputs found
The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression
Revealing hidden patterns in astronomical data is often the path to
fundamental scientific breakthroughs; meanwhile the complexity of scientific
inquiry increases as more subtle relationships are sought. Contemporary data
analysis problems often elude the capabilities of classical statistical
techniques, suggesting the use of cutting edge statistical methods. In this
light, astronomers have overlooked a whole family of statistical techniques for
exploratory data analysis and robust regression, the so-called Generalized
Linear Models (GLMs). In this paper -- the first in a series aimed at
illustrating the power of these methods in astronomical applications -- we
elucidate the potential of a particular class of GLMs for handling
binary/binomial data, the so-called logit and probit regression techniques,
from both a maximum likelihood and a Bayesian perspective. As a case in point,
we present the use of these GLMs to explore the conditions of star formation
activity and metal enrichment in primordial minihaloes from cosmological
hydro-simulations including detailed chemistry, gas physics, and stellar
feedback. We predict that for a dark mini-halo with metallicity , an increase of in the gas
molecular fraction, increases the probability of star formation occurrence by a
factor of 75%. Finally, we highlight the use of receiver operating
characteristic curves as a diagnostic for binary classifiers, and ultimately we
use these to demonstrate the competitive predictive performance of GLMs against
the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy
and Computin
Enabling Network Slicing Across a Disaggregated Optical Transport Network
We propose and implement a network virtualization architecture for open optical (partially) disaggregated networks, based on a device hypervisor and OpenConfig and OpenROADM data models, in support of 5G network slicing over interconnected NFVI-PoPs
A probabilistic approach to emission-line galaxy classification
We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional
emission-line classification schemes of galaxy ionization sources: the
Baldwin-Phillips-Terlevich (BPT) and vs. [NII]/H
(WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey
Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically
define classes of galaxies in a three-dimensional space spanned by the
[OIII]/H, [NII]/H, and EW(H), optical
parameters. The best-fit GMM based on several statistical criteria suggests a
solution around four Gaussian components (GCs), which are capable to explain up
to 97 per cent of the data variance. Using elements of information theory, we
compare each GC to their respective astronomical counterpart. GC1 and GC4 are
associated with star-forming galaxies, suggesting the need to define a new
starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN)
class and WHAN's weak AGN class. GC3 is associated with BPT's composite class
and WHAN's strong AGN class. Conversely, there is no statistical evidence --
based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our
sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The
GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN
diagrams respectively. Subtleties aside, we demonstrate the potential of our
methodology to recover/unravel different objects inside the wilderness of
astronomical datasets, without lacking the ability to convey physically
interpretable results. The probabilistic classifications from the GMM analysis
are publicly available within the COINtoolbox
(https://cointoolbox.github.io/GMM\_Catalogue/).Comment: Accepted for publication in MNRA
Experimental Evaluation of Dynamic Resource Orchestration in Multi-Layer (Packet over Flexi-Grid Optical) Networks?
This paper has been presented at : ONDM 2019 23rd Conference on Optical Network Design and ModellingIn future 5G infrastructures, network services will be de- ployed through sets of Virtual Network Functions (VNFs) leveraging the advantages of both Software Defined Networking (SDN) and Net- work Function Virtualization (NFV). A network service is composed of an ordered sequence of VNFs, i.e., VNF Forwarding Graph (VNFFG), deployed across distributed data centers (DCs). Herein, we present a Cloud/Network Orchestrator which dynamically processes and accom- modates VNFFG requests over a pool of DCs interconnected by a multi- layer (packet/flexi-grid optical) transport network infrastructure. We propose two different cloud and network resource allocation algorithms aiming at: i) minimizing the distance between the selected DCs, and ii) minimizing the load (i.e., consumed cloud resources) of the chosen DCs. Both algorithms run on a Cloud/Network Orchestrator and are ex- perimentally validated and benchmarked on the CTTC ADRENALINE testbed.This work is partially funded by the EU H2020 5G TRANSFORMER project
(761536) and the Spanish AURORAS project (RTI2018-099178
Multivariate side-band subtraction using probabilistic event weights
A common situation in experimental physics is to have a signal which can not
be separated from a non-interfering background through the use of any cut. In
this paper, we describe a procedure for determining, on an event-by-event
basis, a quality factor (-factor) that a given event originated from the
signal distribution. This procedure generalizes the "side-band" subtraction
method to higher dimensions without requiring the data to be divided into bins.
The -factors can then be used as event weights in subsequent analysis
procedures, allowing one to more directly access the true spectrum of the
signal.Comment: 17 pages, 9 figure
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
Experimental Evaluation of Orchestrating Inter-DC Quality-enabled VNFFG Services in Packet/Flexi-Grid Optical Networks
44th European Conference on Optical Communications (ECOC 2018)An implemented Cloud/Network Orchestrator to dynamically serve VNFFGs in remote DCs through a Multi-Layer Network (packet/flexi-grid optical) is evaluated. Two network information and path computation approaches are adopted by the Orchestrator being experimentally benchmarked with a number of performance metrics.This work is partially funded by the Spanish MINECO
DESTELLO project (TEC2015-69256-R) and the EU
H2020 5G TRANSFORMER project (761536)
Latency-Aware Network Service Orchestration over an SDN-Controlled Multi-Layer Transport Infrastructure
In this paper, we present latency-aware orchestration strategies that jointly consider satisfying both the allocation
of computing resources (in distributed DCs) and the bandwidth and latency networks requirements, which are
experimentally evaluated within a Multi-Layer (Packet over Optical Flexi-Grid) Transport Network and
considering different DC set-ups and capabilities.This work is partially funded by the EU H2020 5G TRANSFORMER project (761536)
Latency-aware resource orchestration in SDN-based packet over optical flexi-grid transport networks
In the upcoming 5G networks and following
the emerging Software Defined Network/Network
Function Virtualization (SDN/NFV) paradigm, demanded
services will be composed of a number of
virtual network functions that may be spread across
the whole transport infrastructure and allocated in
distributed Data Centers (DCs). These services will
impose stringent requirements such as bandwidth
and end-to-end latency that the transport network
will need to fulfill. In this paper, we present an orchestration
system devised to select and allocate virtual
resources in distributed DCs connected through a
multi-layer (Packet over flexi-grid optical) network.
Three different on-line orchestration algorithms are
conceived to accommodate the incoming requests by
satisfying computing, bandwidth and end-to-end latency
constraints, setting up multi-layer connections.
We addressed end-to-end latency requirements by
considering both network (due to propagation delay)
and processing delay components. The proposed algorithms
have been extensively evaluated and assessed
(via a number of figures of merit) through experimental
tests carried out in a Packet over Optical
Flexi-Grid Network available in the ADRENALINE
testbed with emulated DCs connected to it.This work has been partially funded by the EC H2020 5GTransformer Project (grant No. 761536)
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