Lentes para ver cómo se distribuye la materia en el universoReconstruyendo las órbitas galácticas con Inteligencia ArtificialAnálisis exhaustivo de eyecciones de plasma solar

Lenses to see how is matter distributed in the universe

An international research team led by Elizabeth Gonzalez of the Institute of Theoretical and Experimental Astronomy (IATE) analysed how matter is distributed in galaxy clusters, using an effect called 'gravitational lensing'.

Reconstructing orbits of galaxies with Artificial Intelligence

A research team from the Institute of Theoretical and Experimental Astronomy (IATE), with the collaboration of researchers from La Plata and Chile, developed a machine learning method that allows the reconstruction of the galaxy orbits from their observed positions.

Comprehensive analysis of solar plasma ejections

Researchers from the Institute of Theoretical and Experimental Astronomy (IATE), together with an international team, published an article in which, based on observations, the deviations of solar material ejections are studied.

Codes

ROGER: Reconstructing Orbits of Galaxies in ExtremeRegions using machine learning techniques

Galaxies in the Universe show a wide variety of properties as a result of the action of both, internal and environmental processes. Clusters of galaxies constitute the most extreme environments in the Universe for galaxy evolution. Galaxies in clusters exhibit different properties compared to galaxies that reside in the field, or in less massive systems.

On the other hand, machine learning techniques (machine learning) represent a new way of analyzing big data-sets in an agnostic and homogeneous way. Taking into account the amount of data generated by current and future surveys and simulations, the data-driven techniques will become a fundamental tool for their analysis.

Here we present ROGER (Reconstructing Orbits of Galaxies in Extreme Regions), a machine learning technique that relates the two-dimensional PPSD position of galaxies to their 3D orbits. The code retrieves the probability for each galaxy to belong to each class using only its position on the projected phase-spacei.e, distance to the cluster center (normalized to R200) and relative LOS velocity to the cluster center (normalized to the velocity dispersion).

This code was trained and calibrated using a synthetic catalog of clusters and galaxies constructed using the semi-analytic model of galaxy formation and evolution SAG on the Multidark MDPL2 cosmological simulation.

The code is completely free and public. Can be used as an R package (for more details see https://github.com/Martindelosrios/ROGERor through its online version at https://mdelosrios.shinyapps.io/roger_shiny/.

This project was developed by Martín de los Rios, Julián Martínez, Valeria Coenda, Hernán Muriel, Andrés Ruiz, Cristian Vega and Sofia Cora and was published in the international journal  MNRAS.

MeSsI (Merging Systems Identification)

de los rios+16 2016MNRAS.458..226D 

Merging galaxy systems provides observational evidence of the existence of dark matter and constraints on its properties. Therefore, statistical uniform samples of merging systems would be a powerful tool for several studies. In this work, we present MeSsI (Merging Systems Identification algorithm) a new machine learning method for merging systems identification. 

We use as a starting point a mock catalog of galaxy systems, identified using traditional FoF algorithms, which experienced a major merger as indicated by its merger tree. This code is completely free and public and can be downloaded and used as an R package (https://github.com/Martindelosrios/MeSsI).

This project was developed by Martín de los Rios, Mariano Domínguez, Dante Paz and Manuel Merchán.

Fargo 3D

fargohomeA versatile multifluid HD/MHD code that runs on clusters of CPUs or GPUs, with special emphasis on protoplanetary disks. FARGO3D is the successor of the FARGO code, that you can still find in the legacy part of this site. FARGO3D. The main features of FARGO3D are: FARGO3D son:

Cartesian, cylindrical or spherical geometry. As in FARGO, a simple Runge-Kutta N-body solver may be used to describe the orbital evolution of embedded point-like objects. Multifluid capability. 1, 2 or 3 dimensional calculations. Orbital advection (aka FARGO) for HD and MHD calculations. No need to know CUDA: you can develop new functions in C and have them translated to CUDA automatically to run on GPUs. FARGO3D was written by Pablo Benítez Llambay (main developper) and by Frédéric Masset. The multifluid version was developed by Pablo Benítez Llambay and Leonardo Krapp.

 

 

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