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Big Data of the Huge Universe (BIDHU)When Galaxies Collide A Machine Learning Approach

Duilia F. de Mello1,2, Felipe Augusto de Castro e Silva1,3, Gabriel Alexandre Santos4,5, Pedro Xavier4,61The Catholic University of America, 2 NASA Goddard Space Flight Center, 3University of Campinas,

4Northern Virginia Community College (NOVA), 5Uniandrade PR, 6Federal Institute of Goias

We present the latest results of an ongoing research project searching for clues regarding

the evolution of galaxies. Our own galaxy, the Milky Way, is in an active process of collision

with small dwarf galaxies and will collide with the large Andromeda galaxy in approximately

4 billion years from now. In this project, we are searching for evidence that other galaxies

are going through similar processes. We are using the largest database of the universe,

known as the Sloan Digital Sky Survey, to search for colliding galaxies. First we selected

a small slice of the universe and were able to identify 100 candidates. We improved our

search code and later we were able to select 40,000 pairs of galaxies. We are now in the

process of implementing a code that is training the computer (Machine Learning) to

verify whether these pairs of galaxies are in advanced stages of collision. Here we present

the latest results on the Machine Learning Approach.

Abstract

We adopt a machine learning approach that uses a convolutional neural network todetermine, from galaxies images, whether there is or not a collision. Results were

considerable successful and some new approaches were proposed to improve the method in

future works.

Galaxies Collision through Convolutional Neural Networks

Convolutional Neural Networks are mathematical models inspired by the organization ofthe animal visual cortex that are largely used in image processing and recognition. Their

operation can be compared to the human eye; for example, if you see a dog, in a small amountof time, your eye will register the image, transform it into electric pulses, send it to some

neurons that are going to classify the data and make you understand that there is a doginfront of you. In this work, we make a learning machine see some galaxies images andclassify them as colliding or non-colliding galaxies.

To make this classification possible, we need to teach the mathematical model how tounderstand whether these galaxies are colliding or not. This is done through a process

named training, in which were presented 200 pre-classified galaxiesby eye by our team tothe machine (100 colliding galaxies and 100 non-colliding galaxies) to make it understand

which images contain colliding galaxies and which contain non-colliding galaxies.

COLLIDING and NOT

Various architectures of convolutional neural networks

were tested. In the best outcome, in a set of 80 images (40

colliding galaxies and 40 non-colliding galaxies), the

galaxies in the images classified as colliding were really in

collision, but 12 colliding galaxies were misclassified as

non-colliding, what is called underfitting.

We conclude that our trained machine was 85% of thetime correct BUT current sample is small and need to

be increased!!!

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Results

Conclusions and future work

Our method can already be used to classify real data.

Due to hardware processing narrows and the number of

available previous classified images, more complex

architectures could not be tested yet.

Many other machine learning methods can be

aggregated for more precise results.

Expand classification to 40,000 galaxies soon

We will create a GUI that will allow each user to select a

galaxy on the screen and let the device do the

classification.

Two years ago D. de Mello embarked in a BIG DATA

project that she didn't have much experience with, but

she could see the potential it had in introducing

undergrads to research.

Most of the students she talked to had heard of big data,

but when they accepted working on this project they had

no idea that their database would be the entireuniverse!

Today we are a group of 10 people spread in the US and

in Brazil working on a project we call the Big Data of the

Huge Universe (BIDHU). This is what we haveaccomplished so far:

First step: sample selection by undergraduate studens

at CUA W. Barbosa, Ana Nascimento (visiting), Rocio

Rossi : 100 colliding galaxies

Second step: expand sample with international

collaborators A. Borges, M. Goya, S. Puga: 40,000candidates

Third step: machine learning by undergraduatestudents at CUA and NOVA Felipe de Castro e Silva,

Gabriel A. Santos, Pedro Xavier

1 undergraduate thesis in computer science W.

Barbosa (UFAL)

1 honorable mention on science fair of undergrads

Ana C. Nascimento (UFRJ)

2 conference presentations Rocio Rossi (CUA) and

Ana C. Nascimento (UFRJ).

The BIDHU Project A Success Story

This work uses a machine learning approach through

Convolutional Neural Networks to classify galaxy

images as colliding and non-colliding

Convolutional Neural Networks are mathematical

models that are largely used to classify image data.

200 pre-classified images were used to train the neural

network.

Various neural network architectures were tested and

there are good outcomes for colliding images

classification (85% success), but larger sample is

required.

Future work may use other machine learning methods

to improve the results and will expand it to 40,000

galaxies.

Summary

Interested in joining the BIDHU project? Email demello@cua.edu, follow our posts on

LinkedIn and come to 206 Hannan Hall.

We have billions of galaxies to classify and many discoveries to be made!

Examples of colliding galaxies discovered by the BIDHU team