Machine Learning for Exoplanets
- Forestano, R. T., K. T. Matchev, K. Matcheva, E. Unlu, “Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection”, The Astrophysical Journal , v. 958, number 2, 2023.
arXiv:2308.07604 [astro-ph.EP] - Unlu, E., Forestano, R. T., K. T. Matchev, K. Matcheva, E. Unlu, “Reproducing Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with Machine Learning Surrogate Model”, accepted in Proceedings of the European Conference on Machine Learning, ECML, October, 2023.
arXiv:2310.10521 [astro-ph.EP] - Yip K., Q. Changeat, I. Waldmann, E. Unlu, R. Forestano, A. Roman, K. I. Matcheva, K. Matchev, S. Stefanov, O. Podsztavek, M. Morvan, N. Nikolaou, A. Al-Refaie, C. Jenner, C. Johnson, A. Tsiaras, B. Edwards, C. Oliveira, J. Thiyagalingam, P. Lagage, J. Cho, G. Tinetti, Lessons Learned from Ariel Data Challenge 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes, Proceedings of Machine Learning Research, volume 220, 1–17, 2023. https://proceedings.mlr.press/v220/
- K. Matchev, K. Matcheva, and A. Roman, “Transverse Vector Decomposition Method for Analytical Inversion of Exoplanet Transit Spectra ”, Astrophysical Journal 939, n 2, pp. 15, 2022.
arXiv:2203.06299 [astro-ph.EP] - K. Matchev, K. Matcheva, and A. Roman, “Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra”, Planetary Science Journal, v. 3, Issue 9, pp.12, 2022.
arXiv:2201.02696 [astro-ph.EP] - K. Matchev, K. Matcheva, and A. Roman, “Analytical Modeling of Exoplanet Transit Spectroscopy with Dimensional Analysis and Symbolic Regression”, Astrophysical Journal 930, n. 1, pp. 13, 2022.
arXiv:2112.11600 [astro-ph.EP]
Machine Learning for Physics
- Matchev, K., K. Matcheva, S. Verner, P. Ramond,”Exploring the Truth and Beauty of Theory Landscapes with Machine Learning “, submitted to Physics Letters B, January 2024.
arXiv:2401.11513 [hep-ph] - Matchev, K., K. Matcheva, S. Verner, P. Ramond,”Seeking Truth and Beauty in Flavor Physics with Machine Learning”, accepted in the workshop “AI for Scientific Discovery: From Theory to Practice”, NeurIPS conference, December 2023.
arXiv:2311.00087 [hep-ph] - Forestano, R. T., K. T. Matchev, K. Matcheva, A. Roman, E. Unlu, S. Verner, “Identifing the Group-Theoretic Structure of Machine-learned Symmetries”, Physics Letters B, v. 847, article id 138306, 2023.
arXiv:2309.07860 [hep-ph] - Forestano, R. T., K. T. Matchev, K. Matcheva, A. Roman, E. Unlu, S. Verner, “Accelerated Discovery of Machine-learned Symmetries: Deriving the Exceptional Lie Groups G2, F4, and E6”, Physics Letters B, v. 847, article id 138266, 2023.
arXiv:2307.04891 [hep-th] - Forestano, R. T., K. T. Matchev, K. Matcheva, A. Roman, E. Unlu, S. Verner, “Discovering Sparce Representations of Lie Groups with Machine Learning”, Physics Letters B, v. 844, article id 138086, 2023.
arXiv:2302.05383 [hep-ph] - Roman, A., R. T. Forestano, K. T. Matchev, K. Matcheva, E. Unlu, S. Verner, “Oracle-Preserving Latent Flows”, Symmetry, v. 15, Issue 7, p. 1352, 2023.
arXiv:2302.00806 [cs.LG] - Forestano, R. T., K. T. Matchev, K. Matcheva, A. Roman, E. Unlu, S. Verner, “Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles”, Machine Learning: Science and Technology, v. 4, Issue 2, 2023.
arXiv:2301.05638 [hep-ph - Dong, Z., K. Kong, K. T. Matchev, K. Matcheva, “Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression”, Physical Review D, v. 107, Issue 5, 2023.
arXiv:2211.08420 [hep-ph]
Quantum Machine Learning
- Unlu, E., et al., “Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics”, submitted to Axioms, Jan. 2024.
- Cara, M. C., et al., “Quantum Vision Transformers for Quark-Gluon, Classification”, submitted to Axioms, Jan. 2024.
- Forestano, R. T., et al., A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks, submitted to Axioms, Jan. 2024.
arXiv:2311.18672 [quant-ph] - Dong, C., et al., “Z2×Z2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks”, submitted to Axioms, Jan. 2024.
arXiv:2311.18744 [quant-ph]
Solar System Planets
- D. Barrow and K. Matcheva, “Modeling the effect of atmospheric gravity waves on Saturn’s ionosphere”, Icarus 224, n. 1, p. 32-42, 2013.
- K. Matcheva and D. Barrow, “Small scale variability in Saturn’s lower ionosphere”, Icarus 221, n. 2, p. 525-543, 2012.
- D. Barrow, K. Matcheva and P. Drossart, “Prospects for observing atmospheric gravity waves in Jupiter’s thermosphere using H3+ emission”, Icarus 219, n. 1, p. 77-85, 2012.
- D. Barrow and K. Matcheva, “Impact of Atmospheric Gravity Waves on the Jovian Ionosphere”, Icarus 211, n. 1, 609-622, 2011.
- J. Harrington, R. French, and K. Matcheva,” The 1998 November 14 Occultation of GSC 0622-00345 by Saturn. II. Stratospheric Thermal Profile, Power Spectrum, and Gravity Waves”, The Astrophysical Journal, v. 716, n. 1, 404-416, 2010.
- K. Matcheva, B. Conrath, P. Gierasch, F. M. Flasar, “The Cloud Structure of the Jovian Atmosphere as Seen by the Cassini/CIRS Experiment”, Icarus 179, n. 2, 432-448, 2005.
- E. Raynaud, K. Matcheva, P. Drossart, F. Roques, and B. Sicardy, A Re-analysis of the 1971 Beta Scorpii Occultation by Jupiter: Study of Temperature Fluctuations and Detection of Wave Activity, Icarus 168, n. 2, 324-335, 2004.
- E. Raynaud, P. Drossart, K. Matcheva, B. Sicardy, W. Hubbard, F. Roques, T. Widemann, G. Gladstone, J. Waite, P. Bastien, R. Doyon, and D. Nadeau, The 1999 HIP 9369 Occultation by the Northern Polar Region of Jupiter: Ingress and Egress Lightcurve Analysis, Icarus 162, n. 2, 344-361, 2003.
- K. Matcheva, D. Strobel and M. Flasar, Interaction of Gravity Waves with Ionospheric Plasma: Implications for Jupiter’s Ionosphere, Icarus 152, n. 2, 347-365, 2001.
- K. Matcheva and D. Strobel, Heating of Jupiter’s Thermosphere by Dissipation of Gravity Waves Due to Molecular Viscosity and Heat Conduction, Icarus 140, n. 2, 410-423, 1999.