Accurate Carbon Dioxide Line Lists with 12 Isotopologues Enable Improved Radiative Transfer Codes
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Understanding the composition of exoplanetary atmospheres requires detailed knowledge of the molecular fingerprints of key gases, and a team led by Sergei N. Yurchenko, Marco G. Barnfield, and Charles A. Bowesman from University College London significantly advances this field with comprehensive data for carbon dioxide. They constructed extensive catalogues of spectral lines for twelve different forms of carbon dioxide, known as isotopologues, using highly accurate computational methods and incorporating existing experimental data. This achievement provides astronomers with a crucial tool for analysing light from distant worlds, enabling more precise identification of atmospheric constituents and ultimately, a better understanding of planetary formation and potential habitability. The resulting data, freely available to the scientific community, has already been implemented in multiple atmospheric modelling codes, promising to refine future exoplanetary observations.
Extensive Collaboration Defines Quantum Computing Research A large team of researchers, representing diverse expertise, collaborated to produce a comprehensive set of spectroscopic data for carbon dioxide, ensuring a rigorous and multifaceted approach to the project. The collaborative effort involved contributions from numerous institutions and countries, highlighting the global importance of accurate spectroscopic data for atmospheric studies and beyond.
Accurate Carbon Dioxide Rovibrational Line Lists Computed Scientists have pioneered a new approach to constructing highly accurate rovibrational line lists for twelve different forms of carbon dioxide, encompassing various isotopic compositions. Researchers employed the sophisticated TROVE program, utilizing precise calculations of molecular energy and behaviour based on the Ames-2 potential energy surface and the ab initio Ames-2021-40K dipole moment surface. For higher rotational energy levels, scientists formed rovibrational basis functions by combining vibrational and rigid-rotor functions, carefully considering molecular symmetry. Precise nuclear masses for each carbon and oxygen isotope were defined, and calculations extended to specific energy levels, generating transition intensities for all dipole-allowed transitions. This meticulous process resulted in highly accurate rovibrational data for all twelve carbon dioxide isotopes, facilitating precise modeling of atmospheric opacity and remote sensing applications. High-Accuracy Rovibrational Line Lists for CO2 Isotopologues Scientists have constructed extensive rovibrational line lists for twelve different forms of carbon dioxide, including ¹²C¹⁶O₂, ¹³C¹⁶O₂, ¹²C¹⁷O₂, and ¹⁷O¹³C¹⁸O. The work utilizes the TROVE program and the Ames-2 potential energy surface, combined with the ab initio Ames-2021-40K dipole moment surface to achieve high accuracy. These comprehensive datasets facilitate accurate modeling of carbon dioxide atmospheres and are crucial for applications ranging from atmospheric remote sensing to the study of exoplanetary atmospheres. The research delivers a significant advancement in spectroscopic data, providing a foundation for improved radiative transfer calculations and more precise atmospheric analyses. All data are freely available from the ExoMol database at www. exomol. com. The resulting datasets cover a wide spectroscopic range and are reliable up to 2000-3000 K, depending on the specific isotope, representing a substantial improvement over previously available data.
The team addressed known issues in earlier models, notably resolving an intensity anomaly in the 700nm region and avoiding spurious behaviour in high overtone bands. Furthermore, they pioneered the use of machine-learning techniques to assign quantum numbers for carbon dioxide isotopes, greatly expanding the coverage of assigned transitions in a consistent manner. Opacities generated from these line lists are compatible with four widely used atmospheric retrieval codes, facilitating applications in planetary and stellar atmosphere modelling. The authors acknowledge that further improvements are possible, and are currently applying machine-learning methods to refine the prediction of transition wavenumbers for minor isotopes. Partition functions and pressure-broadening coefficients for key perturbers have also been compiled, enhancing the utility of these datasets for a broad range of thermodynamic and radiative transfer applications. All data, including line lists, partition functions, broadening parameters, and opacities, are publicly available through the ExoMol database. 👉 More information 🗞 ExoMol line lists — LXIII: ExoMol line lists for 12 isotopologues of CO 🧠 ArXiv: https://arxiv.org/abs/2512.13889 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Cloud Quantum Computing Gains Forensic Tool, Pinpointing Hardware Noise and Resource Allocation December 18, 2025 Entangled Qubit States Enable Scalable Superdense Coding for N-bit Messages December 18, 2025 Timelens Enables Accurate Video Understanding by Addressing Data Quality in Temporal Grounding Benchmarks December 18, 2025