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# Sphinx build info version 1 # Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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You can adapt this file completely to your liking, but it should at least You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive. contain the root `toctree` directive.
Welcome to Beamforming Cookbook Welcome to LOFAR Beamforming Cookbook
=============================== =====================================
.. only:: html
Beamforming means "pointing" an antenna array and shaping the beam by giving each antenna the right delay, then adding the signals. Beam-formers in place at the LOFAR stations and the correlator apply phase offsets to the signal to "point" beam(s) in particular directions and track known sources. The beam-formed data from LOFAR are used for applications requiring high time and/or frequency resolution, such as pulsars, dynamic spectra of solar and planetary emission, and scintillation. This cookbook provides a description of the beam-forming capabilities of LOFAR including the format of recorded data and the reading of these data using Python. A "dynamic spectrum toolkit" developed for reduction and visualisation of these data is also demonstrated.
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<p>Beamforming means “pointing” an antenna array and shaping the beam by giving each antenna the right delay, then adding the signals. Beam-formers in place at the LOFAR stations and the correlator apply phase offsets to the signal to “point” beam(s) in particular directions and track known sources. The beam-formed data from LOFAR are used for applications requiring high time and/or frequency resolution, such as pulsars, dynamic spectra of solar and planetary emission, and scintillation. This cookbook provides a description of the beam-forming capabilities of LOFAR including the format of recorded data and the reading of these data using Python. A “dynamic spectrum toolkit” developed for reduction and visualisation of these data is also demonstrated.</p>
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<p class="caption"><span class="caption-text">Contents:</span></p> <p class="caption"><span class="caption-text">Contents:</span></p>
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