Found 25 talks width keyword Big data

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Tuesday November 16, 2021
Dr. Andrea Negri, Dr. Carlos Allende
IAC

Abstract

We will introduce several ways in which trivially embarrassingly parallel tasks can be run in laptops and desktops. We will introduce command-line tools such as GNU parallel and Kiko. We will then focus on simple techniques for optimisation of scientific computations in python. We will cover parallel computing with multiprocessing, acceleration of functions via numba, and GPU computing with cupy. The goal is to provide an easy roadmap for python code optimisation methods that can applied on already existing code, without writing a single line of C or FORTRAN.


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Thursday November 4, 2021
Dr. Sergio Contreras
DIPC

Abstract

 

On the LCDM cosmology, dark matter collapses into virialised objects called haloes. The abundance and distribution of these haloes are a direct consequence of the cosmology of the Universe. By constraining the dark matter halo clustering, we could also constraint the cosmology from our Universe. Since dark matter haloes can not be observed, we need to use galaxies to trace them.

In this talk, I will present a new method that we develop capable of constraining cosmological information from the redshift space galaxy clustering.  We use the scaling of cosmological simulations and the SubHalo Abundance Matching extended (SHAMe) empirical model to produce realistic galaxy clustering measurements over a wide range of cosmologies. We generate more than 500,000 clustering measurements at different cosmological and SHAMe parameters to build an emulator capable of reproducing the projected correlation function, monopole and quadrupole of the galaxies. We run an MCMC using this emulator to constrain the cosmology of the TNG300 hydrodynamic simulation. We correctly predicted the cosmology of the TNG300 simulation constraining sigma8 between [0.75,0.83] and Omega matter h^2 between [0.127,0.162]. The best constraints are obtained when including scales below 2 Mpc/h and when combining all different clustering statistics. We conclude that our approach can be used to constrain cosmological and galaxy formation parameters from the galaxy clustering of galaxy surveys.

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Thursday September 16, 2021
Dr. Siddharth Mishra-Sharme
NYU

Abstract

The next decade will see a deluge of new cosmological data that will enable us to accurately map out the distribution of matter in the local Universe, image billions of stars and galaxies to unprecedented precision, and create high-resolution maps of the Milky Way. Signatures of new physics as well as astrophysical processes of interest may be hiding in these observations, offering significant discovery potential. At the same time, the complexity of astrophysical data provides significant challenges to carrying out these searches using conventional methods. I will describe how overcoming these issues will require a qualitative shift in how we approach modeling and inference in cosmology, bringing together several recent advances in machine learning and simulation-based (or likelihood-free) inference. I will ground the talk through examples of proposed analyses that use machine learning-enabled simulation-based inference with an aim to uncover the identity of dark matter, while at the same time emphasizing the generality of these techniques to a broad range of problems in astrophysics, cosmology, and beyond.

 

https://rediris.zoom.us/j/83193959785?pwd=TExXSDJ6UDg5a24yWDM1TnlOWkNTZz09

Meeting ID: 831 9395 9785
Passcode: 343950O

YouTube: https://youtu.be/1Nkzn-cGaIo


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Tuesday September 7, 2021
Dr. Jesús Vega-Ferrero
IAC

Abstract

Galaxy morphologies are one of the key diagnostics of galaxy evolutionary tracks, but visual classifications are extremely time-consuming. The sheer size of Big Data surveys, containing millions of galaxies, make this approach completely impractical. Deep Learning (DL) algorithms, where no image pre-processing is required, have already come to the rescue for image analysis of large data surveys. In this seminar, I will present the largest multi-band catalog of automated galaxy morphologies to date containing morphological classifications of ∼27 million galaxies from the Dark Energy Survey. The classification separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. These classifications have been obtained using a supervised DL algorithm. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications, but hese typically have mr < 17.7 mag. We overcome the lack ofa training sample by modeling fainter objects up to mr < 21.5 mag, i.e., by simulating what thebrighter objects with well-determined classifications would look like if they were at higher redshifts.The CNNs reach a 97% accuracy to mr < 21.5 on their training sets, suggesting that they are ableto recover features more accurately than the human eye. We obtain secure classifications for 87%and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively.

 


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Friday April 23, 2021
Dr. Diego Tuccillo
Instituto de Astrofísica de Canarias

Abstract

Pandas is an open source Python package that is widely used for data analysis. It is a powerful ally for data munging/wrangling and databases manipulation/visualisation, and a must-have tool for Data Scientists. In this seminar we will have a general overview on its functionality and we will run over some of the reasons of its large success in the Data Science community.


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Friday November 9, 2018
Prof. Michael Biehl
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Supervised learning: classification and regression
Lecture 4


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Thursday November 8, 2018
Prof. Michael Biehl
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Supervised learning: classification and regression
Lecture 3


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Thursday November 8, 2018
Prof. Marc Huertas-Company
Université Paris-Diderot - Observatoire de Paris

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Deep learning
Lecture 4


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Thursday November 8, 2018
Prof. Mario Juric
University of Washington

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Data challenges and solutions in forthcoming surveys
Lecture 4


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Thursday November 8, 2018
Prof. George Djorgovski
Caltech, Division of Physics, Mathematics and Astronomy

Abstract

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: General overview on the use of machine learning techniques in astronomy
Lecture 4


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