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 $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ 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 $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (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 $\log$ [OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), 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 ($Q$-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 $Q$-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)