PCA Tomography: how to extract information from datacubes

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies it is possible to obtain datacubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of datacube (data from single field observations, containing two spatial and one spectral dimension) that uses PCA (Principal Component Analysis) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data onto these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal and this information is fundamental for their handling and interpretation. When the datacube shows objects that present uncorrelated physical phenomena, the eigenvector's orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the LINER galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore we show that it is displaced from the centre of its stellar bulge.Comment: 13 pages, 16 figures, accepted for publication on MNRA

ClimateCompatibleGrowth/clicSAND: v1.1

<p>clickSAND v1.1</p&gt

Designing a zero-order energy transition model: How to create a new Starter Data Kit

The Paris Agreement was signed by 192 Parties, who committed to reducing emissions. Reaching such commitments by developing national decarbonisation strategies requires significant analyses and investment. Analyses for such strategies are often delayed due to a lack of accurate and up-to-date data for creating energy transition models. The Starter Data Kits address this issue by providing open-source, zero-level country datasets to accelerate the energy planning process. There is a strong demand for replicating the process of creating Starter Data Kits because they are currently only available for 69 countries in Africa, Asia, and South America. Using an African country as an example, this paper presents the methodology to create a Starter Data Kit made of tool-agnostic data repositories and OSeMOSYS-specific data files. The paper illustrates the steps involved, provides additional information for conducting similar work in Asia and South America, and highlights the limitations of the current version of the Starter Data Kits. Future development is proposed to expand the datasets, including new and more accurate data and new energy sectors. Therefore, this document provides instructions on the steps and materials required to develop a Starter Data Kit. • The methodology presented here is intended to encourage practitioners to apply it to new countries and expand the current Starter Data Kits library. • It is a novel process that creates data pipelines that feed into a single Data Collection and Manipulation Tool (DaCoMaTool). • It allows for tool-agnostic data creation in a consistent format ready for a modelling analysis using one of the available tools

CCG Starter Data Kit: Mauritania

A starter data kit for Mauritani

CCG Starter Data Kit: Papua New Guinea

A starter data kit for Papua New Guine

CCG Starter Data Kit: Uruguay

A starter data kit for Urugua

CCG Starter Data Kit: Bolivia

A starter data kit for Bolivi

CCG Starter Data Kit: Paraguay

A starter data kit for Paragua

Selected ‘Starter Kit’ energy system modelling data for South Africa (#CCG)

Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for South Africa, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020–2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work