Survey design for Spectral Energy Distribution fitting: a Fisher Matrix approach

The spectral energy distribution (SED) of a galaxy contains information on the galaxy's physical properties, and multi-wavelength observations are needed in order to measure these properties via SED fitting. In planning these surveys, optimization of the resources is essential. The Fisher Matrix formalism can be used to quickly determine the best possible experimental setup to achieve the desired constraints on the SED fitting parameters. However, because it relies on the assumption of a Gaussian likelihood function, it is in general less accurate than other slower techniques that reconstruct the probability distribution function (PDF) from the direct comparison between models and data. We compare the uncertainties on SED fitting parameters predicted by the Fisher Matrix to the ones obtained using the more thorough PDF fitting techniques. We use both simulated spectra and real data, and consider a large variety of target galaxies differing in redshift, mass, age, star formation history, dust content, and wavelength coverage. We find that the uncertainties reported by the two methods agree within a factor of two in the vast majority (~ 90%) of cases. If the age determination is uncertain, the top-hat prior in age used in PDF fitting to prevent each galaxy from being older than the Universe needs to be incorporated in the Fisher Matrix, at least approximately, before the two methods can be properly compared. We conclude that the Fisher Matrix is a useful tool for astronomical survey design.Comment: Accepted by ApJ; online Fisher Matrix tool available at http://galfish.physics.rutgers.ed

A novel internet-of-things infrastructure to support self-healing distribution systems

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we present a novel distributed software infrastructure to foster new services in smart grids with particular emphasis on supporting self-healing distribution systems. This infrastructure exploits the rising Internet-of-Things paradigms to build and manage an interoperable peer-to-peer network of our prototype smart meters, also presented in this paper. The proposed three-phase smart meter, called 3-SMA, is a low cost and open-source Internet-connected device that provides features for self-configuration. In addition, it selectively run on-board-algorithms for smart grid management depending on its deployment on the distribution network. Finally, we present the experimental results of Hardware-In-the-Loop simulations we performed

A Novel Integrated Real-time Simulation Platform for Assessing Photovoltaic Penetration Impacts in Smart Grids

© 2017 The Authors. For future planning and development of smart grids, it is important to evaluate the impacts of PV distributed generation, especially in densely populated urban areas. In this paper we present an integrated platform, constituted by two main components: a PV simulator and a real-time distribution network simulator. The first simulates real-sky solar radiation of rooftops and estimates the PV energy production; the second simulates the behaviour of the network when generation and consumption are provided at the different buses. The platform is tested on a case study based on real data for a district of the city of Turin, Italy

On the Unruh effect in de Sitter space

We give an interpretation of the temperature in de Sitter universe in terms of a dynamical Unruh effect associated with the Hubble sphere. As with the quantum noise perceived by a uniformly accelerated observer in static space-times, observers endowed with a proper motion can in principle detect the effect. In particular, we study a "Kodama observer" as a two-field Unruh detector for which we show the effect is approximately thermal. We also estimate the back-reaction of the emitted radiation and find trajectories associated with the Kodama vector fields are stable.Comment: 8 pages; corrected typos; sections structure revise

Halo Clustering with Non-Local Non-Gaussianity

We show how the peak-background split can be generalized to predict the effect of non-local primordial non-Gaussianity on the clustering of halos. Our approach is applicable to arbitrary primordial bispectra. We show that the scale-dependence of halo clustering predicted in the peak-background split (PBS) agrees with that of the local-biasing model on large scales. On smaller scales, k >~ 0.01 h/Mpc, the predictions diverge, a consequence of the assumption of separation of scales in the peak-background split. Even on large scales, PBS and local biasing do not generally agree on the amplitude of the effect outside of the high-peak limit. The scale dependence of the biasing - the effect that provides strong constraints to the local-model bispectrum - is far weaker for the equilateral and self-ordering-scalar-field models of non-Gaussianity. The bias scale dependence for the orthogonal and folded models is weaker than in the local model (~ 1/k), but likely still strong enough to be constraining. We show that departures from scale-invariance of the primordial power spectrum may lead to order-unity corrections, relative to predictions made assuming scale-invariance - to the non-Gaussian bias in some of these non-local models for non-Gaussianity. An Appendix shows that a non-local model can produce the local-model bispectrum, a mathematical curiosity we uncovered in the course of this investigation.Comment: 12 pages, 4 figures; submitted to Phys. Rev. D; v2: references added; v3: some more comments on kernel-bispectrum relation in appendi

PVInGrid: A distributed infrastructure for evaluating the integration of photovoltaic systems in smart grid

© IFIP International Federation for Information Processing 2017 Published by Springer International Publishing AG 2017. All Rights Reserved. Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed infrastructure that simulates the PV production and evaluates the integration of such systems in the grid considering data provided by smart-meters. The proposed solution is able to model the behaviour of PV systems solution exploiting GIS representation of rooftops and real meteorological data. Finally, such information is used to feed a real-time distribution network simulator

Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial. While recent work has focused on general-purpose systems to learn and predict the best execution target in terms of the execution time of a snippet of code or kernel (e.g. offload OpenCL kernel on multicore CPU or GPU), in this work we focus on static compile-time features to assess if they can be successfully used to predict the minimum energy configuration on PULP, an ultra-low-power architecture featuring an on-chip cluster of RISC-V processors. Experiments show that using machine learning models on the source code to select the best energy scaling configuration automatically is viable and has the potential to be used in the context of automatic system configuration for energy minimisation

Fast fault location for fast restoration of smart electrical distribution grids

© 2016 IEEE. Distribution systems are evolving towards fault self-healing systems which can quickly identify and isolate faulted components and restore supply to the affected customers with little human intervention. A self-healing mechanism can considerably reduce the outage times and improve the continuity of supply; however, such an improvement requires a fast fault location method and also a communication and measurement infrastructure. In this paper the feasibility of fast service restoration through a fast fault location method is studied. A fast fault location method is proposed which is applicable to any distribution network with laterals, load taps and heterogeneous lines. The performance of the proposed method is evaluated by simulation tests on a real 13.8 kV, 134-node distribution system under different fault conditions. The results verify the applicability of the proposed architecture. We show that the communication delay plays a less important role in overall restoration time, and we stress the contribution of a fast fault location method in keeping the overall interruption time less than 1 minute