Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728658
Title: Techniques for temporal contrast enhancement and phase characterisation of ultra-short laser pulses
Author: Sharba, Ahmed Baqer Ridha
ISNI:       0000 0004 6495 0320
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
This thesis describes methods for temporal contrast enhancement and phase characterisation of ultra-short laser pulses using nonlinear optical processes. Ultra-short laser pulses generated using the chirped-pulse amplification technique usually have unwanted emission that precedes and follows the main peak. The preceding part can cause significant changes in the conditions and the results of experiments implemented with these pulses. The first part of this thesis is devoted to the description and implementation of two techniques for enhancing the temporal contrast of ultrashort laser pulses. The first technique employs a second harmonic generation process linked to a low-gain optical parametric amplification stage. The second temporal contrast technique is one that uses cross-polarised wave generation. The second part of the thesis describes methods for phase characterisation of ultrashort laser pulses, based on the dispersion scan technique. In this method, the pulse is chirped with a set of chirp values and used to pump a nonlinear process. The phase of the pulse is extracted by linking the output of the process with simulated spectra via a phase function guessed by an iterative algorithm. This part will firstly describe and characterise a dispersion scan technique that uses second harmonic generation. Secondly, a new variant of the technique that employs self-phase modulations is introduced and characterised. The performance of both methods is optimised using a new method for phase representation, based on summing a set of Gaussian functions.
Supervisor: Sarri, Gianluca Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.728658  DOI: Not available
Share: