Differences

This shows you the differences between two versions of the page.

--- archive:appds:wp3:main [05/07/2024 22:03] – removed - external edit (Unknown date) 127.0.0.1
+++ archive:appds:wp3:main [05/07/2024 22:03] (current) – ↷ Page moved from grants:archive:appds:wp3:main to archive:appds:wp3:main admin
@@ Line 1: / Line 1: @@
+====== WP3: Multi-Messenger Data Analysis ======
+For a detailed reliability test this work package aims for common data analyses using the
+complementary information of TAIGA and KASCADE, in particular to apply deep learning (machine
+learning) methods to multi-messenger analyses. Such specific analyses of the data provided by the
+new data centre will be performed to test the entire concept resulting in full reviewed journal
+publications. This will give important contributions and confidence to the project as a valuable
+scientific tool. In addition, having young scientists working directly with the provided hard- and
+software environment will be the best advertisement of the infrastructure to the community.
+The KASCADE and TAIGA experiments have already shown significant achievements in using the
+experimental data for explaining the origin of high-energy cosmic rays and gamma rays. We
+propose to perform within this project a full analysis of the data provided by KCDC (TAIGA and
+KASCADE) to test the entire data centre and to show the proof-of-principle that a real scientific
+multi-messenger analysis is possible by using data from a public data centre. Until now, no one
+performed the physics analysis with an entire data set in the wide energy range of cosmic rays.
+A scientific example could be the search for the hidden hot spots and asymmetry on the sky map
+by combination the data from both experiments. Since KASCADE and TAIGA are located in the
+same hemisphere, and observe almost the same part of the sky, the uncorrelated background
+could be rejected using multi-dimensional convolution neural networks (CNN). Simulating different
+populations and intensity of Galaxy accelerators, we obtain a large set of possible frames, which
+can be observed experimentally. The CNN trained on this set will allow filtering out background
+and classifying all possible hidden anomalies. With careful treatment of hardware response, the
+statistics of Tunka (10 years) can be add to the statistics of KASCADE, this way, we obtain a
+virtual detector with significantly improved exposure. This multivariate data analysis can restrict
+possible configurations of possible Galaxy accelerators of cosmic rays and PeVatrons, and test the
+models of the magnetic fields in the Galaxy.
+Another application could be the implementation of the system for online search of transients or
+counterparts of electromagnetic or gravitational bursts. The model and corresponding software will
+be feed with the configuration of possible transients (energy, duration, location, etc.), and will
+return the possible CNN filter for the rejection of regular background. The implementation and test
+of this model will result a standard software library, which can be applied for the future searches.
+Such analyses would derive an important combined result of various experiments such as public
+standard spectra of cosmic rays, which is demanded in the astroparticle physics community.
+Hence, the data centre is a valuable and even required part of any analysis considering data from
+many different experiments, i.e. multi-messenger data analyses. The data centre will be also very
+useful for theoreticians to interpret experimental results. A full analysis published in a reviewed
+journal will be performed entirely based on data provided by the public data centre. This will give
+important input to the consolidation of the data centre.
+Specific tasks:
+  * Defining appropriate physics questions, where the data centre is used (KIT-IKP, MSU-SINP, ISU)
+  * Cross-checks of the reliability of all the specific user functions (KIT-IKP, MSU-SINP, ISU)
+  * Performing the combined TAIGA-KASCADE data analysis (KIT-IKP, ISU)
+  * Performing the multi-messenger data analysis (ISU, MSU-SINP)