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.
Senior Thesis on Applying Neural Networks in Pulsar Identification
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
Detecting Radio Signals with Spectral Structure
A logistic regression model to identify possible pulsar candidates using PyTorch
Set of tools for pulsar studies,including flux energy analysis
Supplementary material for the paper "The UTMOST pulsar timing programme I: Overview and first results".
NenuPlot is a PSRFITS merging (in time and frequency) and cleaning pipeline
Toolkit for working with astronomical radio transients in the SIGPROC Filterbank format in Julia
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 based Radio Frequency Interference Mitigation Routines
Identifying Pulsar Signals from RFI noise (HTRU2 dataset)
Pipeline scripts for processing pulsar and FRB dynamic spectra using modules defined in psrdynspec
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