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
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.
Python 3 tools for processing dynamic spectra of radio transients
Heimdall-based single-pulse search pipeline.
Senior Thesis on Applying Neural Networks in Pulsar Identification
Match single-pulse detections to known sources, such as canonical pulsars or Rotating Radio Transients.
Database backend and survey analysis code for MeerTRAP.
Set of tools for pulsar studies,including flux energy analysis
Scattering fits of Fast Radio Burst and pulsar data.
Detecting Radio Signals with Spectral Structure
NenuPlot is a PSRFITS merging (in time and frequency) and cleaning pipeline
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