Design and Characterisation of Calibration Systems for Small-Sized Telescope Cameras in the Cherenkov Telescope Array.

The Cherenkov Telescope Array Observatory (CTAO) represents the next generation in very-high-energy gamma-ray astronomy, promising an order-of-magnitude improvement in sensitivity over existing instruments. With a diverse array of telescopes, including the Small-Sized Telescopes (SSTs) optimized for the highest energies, CTAO will open new windows on the gamma-ray sky in the 20 GeV–300 TeV regime and beyond. A critical component enabling this scientific performance is the stability and reliability of the telescope cameras, which rely on fast photo sensors. For example - Silicon Photomultiplier (SiPM) in SST Camera. Since SiPM characteristics can drift due to temperature variations, aging, or operational conditions, a robust calibration system is essential to ensure data quality and long-term stability. This thesis presents the design, testing, and integration of the calibration system developed for the SST camera (SSTCAM). Beginning with a review of calibration strategies employed in current-generation gamma ray telescopes, the work identifies the performance requirements imposed by CTAO and adapts them for the SSTCAM. The development process is described across successive design iterations, including hardware prototypes and firmware implementations. Extensive performance studies of the calibration device are reported, covering dynamic range, operating condition dependencies, and beam profile characterization. Results demonstrate compliance with CTAO specifications while also revealing areas for improvement. Building on these findings, the thesis explores the transition from a single-channel device to a multi-channel calibration unit capable of supporting diverse activities such as flat-fielding, pixel linearity monitoring, and single photo-electron calibration. The final design integrates lessons learned from earlier versions and outlines a pathway toward full incorporation within the SSTCAM. In parallel, simulation studies are conducted to assess the feasibility of applying the calibration procedures in practice, offering guidance for future system integration. By providing a dedicated, flexible, and high-performance calibration solution, this work contributes a key technological element to the success of the CTAO’s SST program

TransCom model simulations of methane: Comparison of vertical profiles with aircraft measurements

To assess horizontal and vertical transports of methane (CH4) concentrations at different heights within the troposphere, we analyzed simulations by 12 chemistry transport models (CTMs) that participated in the TransCom-CH4 intercomparison experiment. Model results are compared with aircraft measurements at 13 sites in Amazon/Brazil, Mongolia, Pacific Ocean, Siberia/Russia, and United States during the period of 2001-2007. The simulations generally show good agreement with observations for seasonal cycles and vertical gradients. The correlation coefficients of the daily averaged model and observed CH4 time series for the analyzed years are generally larger than 0.5, and the observed seasonal cycle amplitudes are simulated well at most sites, considering the between-model variances. However, larger deviations show up below 2 km for the model-observation differences in vertical profiles at some locations, e.g., at Santarem, Brazil, and in the upper troposphere, e.g., at Surgut, Russia. Vertical gradients and concentrations are underestimated at Southern Great Planes, United States, and Santarem and overestimated at Surgut. Systematic overestimation and underestimation of vertical gradients are mainly attributed to inaccurate emission and only partly to the transport uncertainties. However, large differences in model simulations are found over the regions/seasons of strong convection, which is poorly represented in the models. Overall, the zonal and latitudinal variations in CH4 are controlled by surface emissions below 2.5 kmand transport patterns in the middle and upper troposphere. We show that the models with larger vertical gradients, coupled with slower horizontal transport, exhibit greater CH4 interhemispheric gradients in the lower troposphere. These findings have significant implications for the future development of more accurate CTMs with the possibility of reducing biases in estimated surface fluxes by inverse modelling

Advanced Analysis of Night Sky Background Light for SSTCAM

Night Sky Background (NSB) is a complex phenomenon, consisting of all light detected by Imaging Atmospheric Cherenkov Telescopes (IACTs) not attributable to Cherenkov light emission. Understanding the effect of NSB on cameras for the next-generation Cherenkov Telescope Array (CTA) is important, as it affects the systematic errors on observations, the energy threshold, the thermal control of the cameras and the ability of the telescopes to operate under partial moonlight conditions. This capacity to observe under partial moonlight conditions is crucial for the CTA transient science programme, as it substantially increases the potential observing time. Using tools initially developed for H.E.S.S. (in combination withthe prototype CTA analysis package ctapipe) we will present predictions for the NSB present in images taken by the CTA Small Sized Telescope Camera (SSTCAM), showing that SSTCAM will likely be able to meet the associated CTA requirements. Additionally, we calculate the potential observing time gain by operating under high NSB conditions

Advanced Analysis of Night Sky Background Light for SSTCAM

Night Sky Background (NSB) is a complex phenomenon, consisting of all light detected byImaging Atmospheric Cherenkov Telescopes (IACTs) not attributable to Cherenkov light emission.Understanding the effect of NSB on cameras for the next-generation Cherenkov Telescope Array(CTA) is important, as it affects the systematic errors on observations, the energy threshold, thethermal control of the cameras and the ability of the telescopes to operate under partial moonlightconditions. This capacity to observe under partial moonlight conditions is crucial for the CTAtransient science programme, as it substantially increases the potential observing time. Usingtools initially developed for H.E.S.S. (in combination with the prototype CTA analysis packagectapipe) we will present predictions for the NSB present in images taken by the CTA Small SizedTelescope Camera (SSTCAM), showing that SSTCAM will likely be able to meet the associatedCTA requirements. Additionally, we calculate the potential observing time gain by operatingunder high NSB conditions

Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS

Imaging Atmospheric Cherenkov Telescope arrays allow us to probe the gamma-ray sky from tens of GeV up to hundreds of TeV. They operate by stereoscopically imaging the Cherenkov light generated when an astrophysical gamma-ray interacts with Earth's atmosphere. In order to reject charged cosmic ray events, and to reconstruct the direction and energy of the incident gamma-ray, machine learning methods are used in combination with parametric descriptions of the detected images. One potential means of improving performance for the next-generation Cherenkov Telescope Array (CTA) is to apply new deep learning methods in place of these parametric techniques. In this thesis, we explore the complexity of deploying deep learning methods, first considering the application of high precision timing data, and then testing such methods' performance on real observations from the current generation VERITAS array. Finally, we explore improvements to the modelling of Night Sky Background observed by Cherenkov instruments, that can be used to both inform the design of the Small Sized Telescope Camera (SSTCAM) for CTA, as well as potentially augment simulated data for deep-learning-based event classification

Advanced analysis methods for Imaging Atmospheric Cherenkov Telescope data with SSTCAM and VERITAS

Imaging Atmospheric Cherenkov Telescope arrays allow us to probe the gamma-ray sky from tens of GeV up to hundreds of TeV. They operate by stereoscopically imaging the Cherenkov light generated when an astrophysical gamma-ray interacts with Earth's atmosphere. In order to reject charged cosmic ray events, and to reconstruct the direction and energy of the incident gamma-ray, machine learning methods are used in combination with parametric descriptions of the detected images. One potential means of improving performance for the next-generation Cherenkov Telescope Array (CTA) is to apply new deep learning methods in place of these parametric techniques. In this thesis, we explore the complexity of deploying deep learning methods, first considering the application of high precision timing data, and then testing such methods' performance on real observations from the current generation VERITAS array. Finally, we explore improvements to the modelling of Night Sky Background observed by Cherenkov instruments, that can be used to both inform the design of the Small Sized Telescope Camera (SSTCAM) for CTA, as well as potentially augment simulated data for deep-learning-based event classification