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| Cloak |
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Axion-like particle (ALP) is a dark matter candidate predicted by certain extensions of the Standard Model. They are emitted in core-collapse supernovae (CCSNe) via the Primakoff process and undergo a conversion into gamma rays in the presence of an external magnetic field. We observe this effect through an enhanced gamma-ray flux from a CCSN. The study of these spectral irregularities in gamma-ray signals allows for a search of traces of ALPs, as well as determination of constraints on photon-ALP coupling. In this project, we search for these irregularities in the extragalactic core-collapse supernovae with spectral peaks at around ~60 MeV, corresponding to the peak of ALP-induced gamma-ray bursts, thus considering the data both from the upper energy limit of the Fermi-GBM as well as lower energy limit of the LAT instrument. |
Ibrahim Safa
Leonardo Garcia
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| Cloak |
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Imaging atmospheric Cherenkov telescopes (IACTs), including the Cherenkov Telescope Array (CTA), detect images of the atmospheric showers generated by gamma rays and cosmic rays as they are absorbed by the atmosphere. Background cosmic rays greatly outnumber gamma rays, so correct classification of the detected images is critical for maximizing IACT sensitivity. I am exploring new event classification methods for CTA using convolutional neural networks, a class of deep learning algorithms specialized for image analysis. Unlike existing analysis methods, convolutional networks can extract useful features directly from images without having to define a preset parametrization. Initial studies using convolutional networks for gamma/hadron classification show promising performance. |
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Samuel Spencer
Tuesday, June 5
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