Basically, my assignment at CERN was divided into two sections:
I installed gAn (gravity analysis) in offline and used it to analyse the data from the fast annihilation cryogenic tracking (FACT) detector. A FACT detector is used to detect the annihilation of antihydrogen in the trapping production. Firstly, I need to understand the digital response of FACT by studying the time over threshold (TOT) distribution which exhibits a two-peaks distribution. I must understand the origin of those two peaks by first looking at the correlation between the time difference of one peak to the other, and the TOT of the second peak. When antihydrogen is trapped inside the detector, at certain times it will collide with the detector’s wall and annihilate to produce two pions. Each pion will pass through the scintillating fiber and it creates an event called hit. The light intensity proportional from radiation to the deposited energy makes it possible to differentiate pions produced by annihilation from other types of particles going through the detector. The light intensity produced inside a scintillating fiber that was hit by either a pion or a number of photons (gamma burst) is estimated.
One of the first tasks given by my supervisor is to develop the code for the measurement of time differences between two hits in the same fiber (ToT vesus time differences between two hits in the same fiber). This task is quite challenging, where most of the code used for the pointer and reference are in C++ which was not taught in my university. At the same time, I need to refer to the source of gAn code that has already been developed by other developers which I will then use the functions contained within to measure the time differences between two hits in the same fiber after the antihydrogen annihilation process.
From prior studies proposed by Francesco Guatieri, only three- and four-layer tracks were considered, assuming that I have a reasonable sample of all detected tracks and an efficiency value for the fiber. There are several possibilities for N-tracks passing through a certain fiber that it should be seen:
However, cases c) and d) are not detectable in the real world case. Hence, the this method produces a biased estimator, which is the inverse of efficiency. The efficiency given by a rational function with an asymptote around 0.66, however this technique will fail for fiber efficiencies below 2/3. Regardless of the presence of the studied fiber, I have chosen to observe only events in which all three fibers are present in order to overcome this.
Currently, the idea by Philip Hackstock is to reconstruct tracks and to count the hits and misses for each fiber. This way has successfully produced an unbiased estimator with a low uncertainty. Two cases 4/4 and 3/4 are possible fibers firing were considered. Generally, this approach is not limited to FACT; it can be implemented to any pixel-like tracking detector in order to measure its efficiency on the level of its smallest unit. The current method proposed by Philip Hackstock is the best method.
For my second task, I need to come up with a new method to prove the non-biased estimator for fiber efficiency based on my assumption and mathematical derivations. I need to brainstorm and discuss with my supervisor to ensure all the equations are valid and can be implement in the code of analysis. However, this task is under progress to prove it.
Almost every week, I spent my free time by travelling and visiting places of interest in Switzerland and France. Before leaving to travel for the weekend, I need to get permission from my supervisor and get some recommendations from him about the place that I should visit. We must ask permission from our supervisor because they might need help for about six hours on a shift in the control room to make online analysis during the experiment. So far, I don’t have any shifts on the weekends. Weekend is the most suitable time for traveling because I have enough time to travel within 18 hours and I can manage my time to travel any places that I want to visit. On weekdays, I spend most of my free time after work to prepare my dinner and sometimes I hang out with other summer students in Geneva.
The CSSP programme provides lectures and introductory courses about antimatter at low energy in order to ensure that they get some fundamental knowledge. This is important for me as I am working on AEgIS which requires using FACT detector to study and detect antihydrogen produced in the antiproton trap.
I am grateful that CERN is positively impacting my current studies, especially in learning how to analyse and interpret data, which gives me a better understanding of what kind of process and interactions are happening inside the detector. Through analysis, several questions will arise, which will tell us more about what’s happening inside the detector. In addition, analysis also provides information about the efficiencies of fibers inside the detector.
I must gain more knowledge and learn more on how to analyse data of particle physics using C++ and Python which are quite useful tools. Analysis and interpretation skills that I learnt from CSSP will give me a huge advantage for my future studies on the Master’s level.
Now, I have found my passion in a field that I am interested in: I would like to pursue my interest in nuclear physics for my Master’s Degree next year.
I would like to say thank you to the Science and Technology Facilities Council (STFC) for the sponsorship they provided for my two months at CSSP. The STFC was formed in 2007 and is one of Europe’s largest multidisciplinary research organisations. Their vision is 'to maximise the impact of our science and technology for the benefit of the UK and its people through their three strategic goals of world class research, innovation and skills, to generate knowledge, solutions and skilled people. The STFC is supporting the Non-Member State Summer Student Programme as part of their portfolio of activities from the UK Global Challenges Research Fund. I am using this opportunity to express my deepest gratitude and special thanks to CERN because they giving me the opportunity to be part of CERN research group which in AEgIS and provide an excellent platform for summer students to learn and develop their skills. Thank you to my supervisor, Micheal Doser and AEgIS team because they are very supportive and guide me on the track during the past two months. I am very happy and thankful to be a part of their team.
My heartfelt appreciation extends to the Academy of Science Malaysia (ASM); I feel honoured and thankful that I have been chosen to be part of CSSP. ASM is very supportive and concerned about science that handles programmes such as the CSSP to promote engagement, understanding and literacy in science, technology and innovations so that we as young scientists and future makers will not be left behind in shaping the better future for humanity.
Thank you to University of Technology Malaysia (UTM), especially to the lecturers of the Physics Department that have encouraged me to apply for the CSSP. Special thanks to my supervisor Dr Izyan Hazwani and my senior Nurdyana Ramlee who recommended this programme to me. To be able to reach this level brings be great honour.
Last but not least, I humbly extend my love and appreciation to my beloved family, and friends who have always pushed me to be better in this regards.