I am an active member of ATLAS and CAST Collaborations at CERN. I am interested in experimental methods and data analysis in high energy physics (particle physics). As a PhD student I work in a project which involves applying machine learning methods (particularly deep learning) to the vector boson scattering problem.
There are some fundamental issues which are unsolved in particle physics; the main ones are unifying gravity with the other fundamental forces, solution to the 'dark matter' and 'dark energy' problems and matter/antimatter asymmetry. These issues requires a new framework to incorporate new phenomena which will be probed in future collider efforts. These new physics scenarios are thought to be hinted by Vector Boson Scattering processes which are highly suppressed through interference effects. The measurement of such effects require a very good background elimination and processing of very tiny signals where powerful methods such as machine learning and novel statistical techniques come into play.
My ATLAS qualification task (in progress) is in TRT (Transition Radiation Tracker) detector. TRT is one of the inner detectors which is operated in ATLAS experiment in LHC. I am working on improving the electron identification performance of the detector using sequence based deep learning models such as Recurrent Neural Networks (RNNs) and Recursive Neural Networks (RecNN).
In my masters I worked in the CAST (CERN Axion Solar Telescope) Collaboration at CERN. I've contributed to the chameleon search effort which is lead by G. Cantatore (University of Trieste). I've contributed to the design and implementation of novel Fabry-Perrot interferometer KWISP (Kinetic Wisp Detection) which is used as a chameleon detector. Since chameleons are very weakly interacting particles, the interferometer uses a very precise mechanism of a mechanical oscillator to sense the 'radiation pressure' of the chameleon beams imparting when they interact with the matter. The chameleons are proposed to be the candidate particles for the 'dark energy problem' which is thought to be responsible for the accelerated expansion of the universe.
My research was concentrated on contributing on the design and implementation of different versions of the detector and doing the preliminary analysis of the test-data set. We have reached a conclusion that there was no significant signal compared to background and there needs to be an improvement of the detector.
I am also interested in machine learning, numerical simulation and statistical data analysis in general. As a general interest, I work on problems of statistical mechanics and probability, particularly Ising models, Markov Chain Monte Carlo Methods. I am fascinated by the power of visualisations of physical models and numerical codes in order to teach and grasp very difficult concepts.
As an undergraduate I've participated in the research group of Nihal Ercan, in Physics Department, Boğaziçi University. I've worked with Tülün Ergin (now at Tübitak Space Research Centre) on active galactic nuclei (AGN). We have conducted observations of various AGN candidate objects which we have picked from Fermi Catalog with RT150 telescope of Tübitak National Observatory. We have made both optics and gamma-ray analysis of candidate objects using time-series analysis methods. We have presented our findings in 40th COSPAR Scientific Assembly in Moscow, Russia in 2014 [Correlating between the Optical and Gamma-ray Light Curves of Fermi-LAT Unassociated Sources].
I've also contributed to a paper on supernova modelling with Tülün Ergin. It was published in Astrophysical Journal . [Ergin, Tülün & Sezer, Aytap & Saha, Lab & Majumdar, Pratik & Chatterjee, Anshu & Bayırlı, Arif & Ercan, E.Nihal. (2014). Recombining Plasma in the Gamma-ray Emitting Mixed-Morphology Supernova Remnant 3C 391. The Astrophysical Journal. 790. . 10.1088/0004-637X/790/1/65. arXiv: 1406.2179]