The dispersion measure in broadband data from radio pulsars
- Authors: Rammala, Isabella
- Date: 2019
- Subjects: Pulsars , Radio astrophysics , Astrophsyics , Broadband communication systems
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/67857 , vital:29157
- Description: Modern day radio telescopes make use of wideband receivers to take advantage of the broadband nature of the radio pulsar emission. We ask how does the use of such broadband pulsar data affect the measured pulsar dispersion measure (DM). Previous works have shown that, although the exact pulsar radio emission processes are not well understood, observations reveal evidence of possible frequency dependence on the emission altitudes in the pulsar magnetosphere, a phenomenon known as the radius-to-frequency mapping (RFM). This frequency dependence due to RFM can be embedded in the dispersive delay of the pulse profiles, normally interpreted as an interstellar effect (DM). Thus we interpret this intrinsic effect as an additional component δDM to the interstellar DM, and investigate how it can be statistically attributed to intrinsic profile evolution, as well as profile scattering. We make use of Monte-Carlo simulations of beam models to simulate realistic pulsar beams of various geometry, from which we generate intrinsic profiles at various frequency bands. The results show that the excess DM due to intrinsic profile evolution is more pronounced at high frequencies, whereas scattering dominates the excess DM at low frequency. The implications of these results are presented with relation to broadband pulsar timing.
- Full Text:
- Date Issued: 2019
- Authors: Rammala, Isabella
- Date: 2019
- Subjects: Pulsars , Radio astrophysics , Astrophsyics , Broadband communication systems
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/67857 , vital:29157
- Description: Modern day radio telescopes make use of wideband receivers to take advantage of the broadband nature of the radio pulsar emission. We ask how does the use of such broadband pulsar data affect the measured pulsar dispersion measure (DM). Previous works have shown that, although the exact pulsar radio emission processes are not well understood, observations reveal evidence of possible frequency dependence on the emission altitudes in the pulsar magnetosphere, a phenomenon known as the radius-to-frequency mapping (RFM). This frequency dependence due to RFM can be embedded in the dispersive delay of the pulse profiles, normally interpreted as an interstellar effect (DM). Thus we interpret this intrinsic effect as an additional component δDM to the interstellar DM, and investigate how it can be statistically attributed to intrinsic profile evolution, as well as profile scattering. We make use of Monte-Carlo simulations of beam models to simulate realistic pulsar beams of various geometry, from which we generate intrinsic profiles at various frequency bands. The results show that the excess DM due to intrinsic profile evolution is more pronounced at high frequencies, whereas scattering dominates the excess DM at low frequency. The implications of these results are presented with relation to broadband pulsar timing.
- Full Text:
- Date Issued: 2019
Data compression, field of interest shaping and fast algorithms for direction-dependent deconvolution in radio interferometry
- Authors: Atemkeng, Marcellin T
- Date: 2017
- Subjects: Radio astronomy , Solar radio emission , Radio interferometers , Signal processing -- Digital techniques , Algorithms , Data compression (Computer science)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/6324 , vital:21089
- Description: In radio interferometry, observed visibilities are intrinsically sampled at some interval in time and frequency. Modern interferometers are capable of producing data at very high time and frequency resolution; practical limits on storage and computation costs require that some form of data compression be imposed. The traditional form of compression is simple averaging of the visibilities over coarser time and frequency bins. This has an undesired side effect: the resulting averaged visibilities “decorrelate”, and do so differently depending on the baseline length and averaging interval. This translates into a non-trivial signature in the image domain known as “smearing”, which manifests itself as an attenuation in amplitude towards off-centre sources. With the increasing fields of view and/or longer baselines employed in modern and future instruments, the trade-off between data rate and smearing becomes increasingly unfavourable. Averaging also results in baseline length and a position-dependent point spread function (PSF). In this work, we investigate alternative approaches to low-loss data compression. We show that averaging of the visibility data can be understood as a form of convolution by a boxcar-like window function, and that by employing alternative baseline-dependent window functions a more optimal interferometer smearing response may be induced. Specifically, we can improve amplitude response over a chosen field of interest and attenuate sources outside the field of interest. The main cost of this technique is a reduction in nominal sensitivity; we investigate the smearing vs. sensitivity trade-off and show that in certain regimes a favourable compromise can be achieved. We show the application of this technique to simulated data from the Jansky Very Large Array and the European Very Long Baseline Interferometry Network. Furthermore, we show that the position-dependent PSF shape induced by averaging can be approximated using linear algebraic properties to effectively reduce the computational complexity for evaluating the PSF at each sky position. We conclude by implementing a position-dependent PSF deconvolution in an imaging and deconvolution framework. Using the Low-Frequency Array radio interferometer, we show that deconvolution with position-dependent PSFs results in higher image fidelity compared to a simple CLEAN algorithm and its derivatives.
- Full Text:
- Date Issued: 2017
- Authors: Atemkeng, Marcellin T
- Date: 2017
- Subjects: Radio astronomy , Solar radio emission , Radio interferometers , Signal processing -- Digital techniques , Algorithms , Data compression (Computer science)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/6324 , vital:21089
- Description: In radio interferometry, observed visibilities are intrinsically sampled at some interval in time and frequency. Modern interferometers are capable of producing data at very high time and frequency resolution; practical limits on storage and computation costs require that some form of data compression be imposed. The traditional form of compression is simple averaging of the visibilities over coarser time and frequency bins. This has an undesired side effect: the resulting averaged visibilities “decorrelate”, and do so differently depending on the baseline length and averaging interval. This translates into a non-trivial signature in the image domain known as “smearing”, which manifests itself as an attenuation in amplitude towards off-centre sources. With the increasing fields of view and/or longer baselines employed in modern and future instruments, the trade-off between data rate and smearing becomes increasingly unfavourable. Averaging also results in baseline length and a position-dependent point spread function (PSF). In this work, we investigate alternative approaches to low-loss data compression. We show that averaging of the visibility data can be understood as a form of convolution by a boxcar-like window function, and that by employing alternative baseline-dependent window functions a more optimal interferometer smearing response may be induced. Specifically, we can improve amplitude response over a chosen field of interest and attenuate sources outside the field of interest. The main cost of this technique is a reduction in nominal sensitivity; we investigate the smearing vs. sensitivity trade-off and show that in certain regimes a favourable compromise can be achieved. We show the application of this technique to simulated data from the Jansky Very Large Array and the European Very Long Baseline Interferometry Network. Furthermore, we show that the position-dependent PSF shape induced by averaging can be approximated using linear algebraic properties to effectively reduce the computational complexity for evaluating the PSF at each sky position. We conclude by implementing a position-dependent PSF deconvolution in an imaging and deconvolution framework. Using the Low-Frequency Array radio interferometer, we show that deconvolution with position-dependent PSFs results in higher image fidelity compared to a simple CLEAN algorithm and its derivatives.
- Full Text:
- Date Issued: 2017
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