Wolfram Computation Meets Knowledge

Wolfram Summer School


Jalil Villalobos Alva

Science and Technology

Class of 2019


Jalil Villalobos graduated with a degree in engineering physics from the Universidad Iberoamericana in Mexico City. His research background is comprised of quantum physics, bionformatics, proteomics and protein design. His academic interests cover the topics of quantum technology, bioinformatics, machine learning, stochastic processes and space engineering. During his idle hours he likes to play soccer, swim and listen to music.

Computational Essay: Protein Data Analysis

Project: Astro Location


In this project, we determine the coordinates of a configuration of stars, that is, its right ascension and declination, through images of stars using the Wolfram Language. The use of the Wolfram Language was implemented for an image pattern recognition to mark the potential places that correspond specifically with the referenced contents. A triangular mesh for the subject image and the reference image are created, and they are compared to localize the specific pattern. Overall, the project consists of receiving an image of a configuration of stars and returns the coordinates where that set of points is most probably located.

Summary of Results

The program generates an image where the possible location of the given set of stars stands out. With this information, we obtain an approximation of probable zones and extract the name of the star and its respective coordinates.

Future Work

As future work, it is possible to add reference stars, bright guide stars and stars. Consider factors such as the brightness of the stars. The algorithm works, but for specific cases. In this study it was assumed that the stars have the same brightness. If we want to extend the use of the program by adding the concept that some stars are brighter than others, the algorithm fails to find specific patterns since it is not capable of distinguishing the difference, whether one point is brighter than another point. Especially when the neighboring stars are very close, the selection of the configuration close to another neighborhood that seems to be similar may be incorrect. As an alternative to solving the problem, it is possible to create an artificial neural network in which the training parameters would be images that would be associated with the coordinates of where the images were taken. Finally, the network would return as a result the most probable location for that configuration of stars.