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- Aguilar, J., et al.
(författare)
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Decoherence in neutrino oscillation at the ESSnuSB experiment
- 2024
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Ingår i: Journal of High Energy Physics (JHEP). - : Springer Nature. - 1126-6708 .- 1029-8479. ; 2024:8
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Tidskriftsartikel (refereegranskat)abstract
- Neutrino oscillation experiments provide a unique window in exploring several new physics scenarios beyond the standard three flavour. One such scenario is quantum decoherence in neutrino oscillation which tends to destroy the interference pattern of neutrinos reaching the far detector from the source. In this work, we study the decoherence in neutrino oscillation in the context of the ESSnuSB experiment. We consider the energy-independent decoherence parameter and derive the analytical expressions for Pμe and Pμμ probabilities in vacuum. We have computed the capability of ESSnuSB to put bounds on the decoherence parameters namely, Γ21 and Γ32 and found that the constraints on Γ21 are competitive compared to the DUNE bounds and better than the most stringent LBL ones from MINOS/MINOS+. We have also investigated the impact of decoherence on the ESSnuSB measurement of the Dirac CP phase δCP and concluded that it remains robust in the presence of new physics.
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- Aguilar, J., et al.
(författare)
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Search for Leptonic CP Violation with the ESSnuSBplus Project
- 2024
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Ingår i: Letters in High Energy Physics. - : Andromeda Publishing And Academic Services LTD. - 2632-2714.
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Tidskriftsartikel (refereegranskat)abstract
- ESSνSB is a design study for a next-generation long-baseline neutrino experiment that aims at the precise measurement of the CP-violating phase, δCP, in the leptonic sector at the second oscillation maximum. The conceptual design report published from the first phase of the project showed that after 10 years of data taking, more than 70% of the possible δCP range will be covered with 5σ C.L. to reject the no-CP-violation hypothesis. The expected value of δCP precision is smaller than 8◦ for all δCP values. The next phase of the project, the ESSνSB+, aims at using the intense muon flux produced together with neutrinos to measure the neutrino-nucleus cross-section, the dominant term of the systematic uncertainty, in the energy range of 0.2–0.6 GeV, using a Low Energy neutrinos from STORed Muons (LEnuSTORM) and a Low Energy Monitored Neutrino Beam (LEMNB) facilities.
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- Aguilar, J., et al.
(författare)
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Study of nonstandard interactions mediated by a scalar field at the ESSnuSB experiment
- 2024
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Ingår i: Physical Review D. - : American Physical Society. - 2470-0010 .- 2470-0029. ; 109:11
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study scalar mediator induced nonstandard interactions (SNSIs) in the context of the ESSnuSB experiment. In particular, we study the capability of ESSnuSB to put bounds on the SNSI parameters and also study the impact of SNSIs in the measurement of the leptonic CP phase δCP. Existence of SNSIs modifies the neutrino mass matrix and this modification can be expressed in terms of three diagonal real parameters (ηee, ημμ, and ηττ) and three off-diagonal complex parameters (ηeμ, ηeτ, and ημτ). Our study shows that the upper bounds on the parameters ημμ and ηττ depend upon how Δm312 is minimized in the theory. However, this is not the case when one tries to measure the impact of SNSIs on δCP. Further, we show that the CP sensitivity of ESSnuSB can be completely lost for certain values of ηee and ημτ for which the appearance channel probability becomes independent of δCP.
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- Jasmine Pemeena Priyadarsini, M., et al.
(författare)
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Lung Diseases Detection Using Various Deep Learning Algorithms
- 2023
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Ingår i: Journal of Healthcare Engineering. - : Hindawi Publishing Corporation. - 2040-2295 .- 2040-2309. ; 2023
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Tidskriftsartikel (refereegranskat)abstract
- The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
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