Using machine learning and neural networks to efficiently search for pulsars in processed radio telescope data.
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Updated
Jan 30, 2021 - Python
Using machine learning and neural networks to efficiently search for pulsars in processed radio telescope data.
This is an experimental python script predicts the Time of Arrival (TOA) and gate boundaries for a pulsar for gated imaging applications. Currently, the code is tailored for GMRT observations
Now populate and survey RRATs along with pulsars!
Supplementary material for the paper "The UTMOST pulsar timing programme I: Overview and first results".
HarvardX DataScience Professional Certificate - Final Capstone IDV Project - Predicting Pulsars using machine learning algorithms. In this project we determine which machine learning algorithm has the highest prediction accuracy in predicting Pulsars.
Machine Learning projects for Data-Driven Astronomy
Mean of a Stack of FIT file to detect pulsar in space
This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.
Python implementation of pulse profile scattering deconvolution
Pipeline scripts for processing pulsar and FRB dynamic spectra using modules defined in psrdynspec
Python scripts to perform Pulsar Data analysis.
Pipeline with scripts for running pulsar search software (PRESTO and custom Python routines) on HPC nodes.
Supplementary material for the paper "Spectral properties of 441 radio pulsars".
Machine Learning Tutorial for radio astronomers
Study of different machine learning algorithms for the detection of Pulsars. Final project for the Bachelor's degree subject Machine Learning.
Python 3 tools for processing dynamic spectra of radio transients
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