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Published in Journal of Cosmology and Astroparticle Physics, 09(2024), 057, 2024
This work applies machine learning to classify cosmic topologies, focusing on small toroidal universes as a case study.
Recommended citation: Tamošiūnas, A., et al. (2024). "Cosmic topology. Part IVa. Classification of manifolds using machine learning: a case study with small toroidal universes." Journal of Cosmology and Astroparticle Physics. 09(2024), 057.
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Published in Journal of Cosmology and Astroparticle Physics, 11(2024), 020, 2024
This study investigates microwave background parity violation in cosmic topology without requiring parity-violating microphysics.
Recommended citation: Samandar, A., et al. (2024). "Cosmic topology. Part IIIa. Microwave background parity violation without parity-violating microphysics." Journal of Cosmology and Astroparticle Physics. 11(2024), 020.
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Published in Journal of Cosmology and Astroparticle Physics, 01(2025), 004, 2025
This paper sets limits on lens spaces in cosmic topology using circle search techniques with CMB data.
Recommended citation: Saha, S., et al. (2025). "Cosmic topology. Part Ic. Limits on lens spaces from circle searches." Journal of Cosmology and Astroparticle Physics. 01(2025), 004.
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Published in Journal of Cosmology and Astroparticle Physics, 2025 (08), 015, 2025
This paper analyzes eigenmodes and correlation matrices of spin-2 perturbations in orientable Euclidean manifolds within cosmic topology.
Recommended citation: Samandar, A., et al. (2025). "Cosmic topology. Part IIIb. Eigenmodes and correlation matrices of spin-2 perturbations in orientable Euclidean manifolds." Journal of Cosmology and Astroparticle Physics. 2025 (08), 015.
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Published in Under review at Journal of Cosmology and Astroparticle Physics, 2025
This paper explores eigenmodes and correlation matrices to assess the detectability of non-orientable Euclidean manifolds in cosmic topology.
Recommended citation: Samandar, A., et al. (2025). "Cosmic topology. Part IIb. Eigenmodes, correlation matrices, and detectability of non-orientable Euclidean manifolds." Under review at Journal of Cosmology and Astroparticle Physics.
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Published in Under review at ICLR 2026, 2025
This paper introduces a novel Bayesian framework for evaluating LLMs, improving robustness and reducing computational costs with fewer trials.
Recommended citation: Hariri, M., Samandar, A. (2025). "Don’t Pass@k: A Bayesian Framework for Large Language Model Evaluation." Under review at ICLR 2026.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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