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1.
2.
Decaying fermionic dark matter search with CALET
Saptashwa Bhattacharyya, 2017, original scientific article

Found in: osebi
Keywords: cosmic rays detectors, dark matter detectors, dark matter simulations
Published: 06.01.2021; Views: 1008; Downloads: 0
.pdf Fulltext (4,84 MB)

3.
An interpretation of the cosmic ray e + + e − spectrum from 10 GeV to 3 TeV measured by CALET on the ISS
Saptashwa Bhattacharyya, 2019, original scientific article

Found in: osebi
Keywords: CALET, cosmic rays, dark matter
Published: 06.01.2021; Views: 991; Downloads: 0
.pdf Fulltext (2,76 MB)

4.
Searching for cosmic-ray signals from decay of fermionic dark matter with CALET
Saptashwa Bhattacharyya, Holger Motz, Shoji Torii, Yoichi Asaoka, 2017, published scientific conference contribution

Found in: osebi
Keywords: dark matter, cosmic-rays, CALET
Published: 08.02.2021; Views: 960; Downloads: 0
.pdf Fulltext (2,36 MB)

5.
Self consistent simulation of dark matter and background
Saptashwa Bhattacharyya, Holger Motz, Shoji Torii, Yoichi Asaoka, Yuko Okada, 2015, published scientific conference contribution

Found in: osebi
Keywords: dark matter, GALPROP, cosmic-rays
Published: 04.02.2021; Views: 931; Downloads: 0
.pdf Fulltext (527,13 KB)

6.
Localisation and classification of gamma ray sources using neural networks
Guõlaugur Jóhannesson, Roberto Ruiz de Austri, Gabrijela Zaharijas, Sascha Caron, Boris Panes, Saptashwa Bhattacharyya, Chris van den Oetelaar, 2021, published scientific conference contribution

Abstract: With limited statistics and spatial resolution of current detectors, accurately localising and separating gamma-ray point sources from the dominating interstellar emission in the GeV energy range is challenging. Motivated by the challenges of the traditional methods used for the gamma-ray source detection, here we demonstrate the application of deep learning based algorithms to automatically detect and classify point sources, which can be applied directly to the binned Fermi-LAT data and potentially be generalised to other wavelengths. For the point source detection task, we use popular deep neural network structure U-NET, together with image segmentation, for precise localisation of sources, various clustering algorithms were tested on the segmented images. The training samples are based on the source properties of AGNs and PSRs from the latest Fermi-LAT source catalog, in addition to the background interstellar emission. Finally, we have created a more complex but robust training data generation exploiting full detector potential, increasing spatial resolution at the highest energies.
Found in: osebi
Keywords: gamma-rays, deep learning, computer vision
Published: 01.10.2021; Views: 540; Downloads: 16
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