Prof. Patrick Irwin

Abstract:

An analysis of currently available ammonia (NH3) visible-to-near-infrared gas absorption data was recently undertaken by Irwin et al. (2018) to help interpret Very Large Telescope (VLT) MUSE observations of Jupiter from 0.48–0.93 µm, made in support of the NASA/Juno mission. Since this analysis a newly revised set of ammonia line data, covering the previously poorly constrained range 0.5–0.833 µm, has been released by the ExoMol project, “C2018” (Coles et al., 2018), which demonstrates significant advantages over previously available data sets, and provides for the first time complete line data for the previously poorly constrained 5520- and 6475-Å bands of NH3. In this paper we compare spectra calculated using the ExoMol–C2018 data set (Coles et al., 2018) with spectra calculated from previous sources to demonstrate its advantages. We conclude that at the present time the ExoMol–C2018 dataset provides the most reliable ammonia absorption source for analysing low- to medium-resolution spectra of Jupiter in the visible/near-IR spectral range, but note that the data are less able to model high-resolution spectra owing to small, but significant inaccuracies in the line wavenumber estimates. This work is of significance not only for solar system planetary physics, but for future proposed observations of Jupiter-like planets orbiting other stars, such as with NASA’s planned Wide-Field Infrared Survey Telescope (WFIRST).

Abstract:

We present a new method, based on a joint application of a principal component analysis (PCA) and Gaussian mixture models (GMM), to automatically find similar groups of spectra in a collection. We applied the method (condensed in the public code chopper.py ) to archival Jupiter spectral data in the 2–5 µm range collected by NASA Juno/JIRAM in its first perijove passage (August 2016) and to mosaics of the great red spot (GRS) acquired by JWST/NIRSpec (July 2022). Using JIRAM data analyzed in previous work, we show that using a PCA+GMM clustering can increase the efficiency of the retrieval stage without any loss of accuracy in terms of the retrieved parameters. We show that a PCA+GMM approach is able to automatically identify spectra of known regions of interest (e.g., belts, zones, GRS) belonging to different clusters. The application of the method to the NIRSpec data leads to detection of substructures inside the GRS, which appears to be composed of an outer halo characterized by low reflectivity and an inner brighter main oval. By applying these techniques to JIRAM data, we were able to identify the same substructure. We remark that these new structures have not been seen before at visible wavelengths. In both cases, the spectra belonging to the inner oval have solar and thermal signals comparable to those belonging to the halo, but they present broadened 2.73 µm solar-reflected peaks. Performing forward simulations with the NEMESIS radiative transfer suite, we propose that the broadening may be caused by differences in the vertical extension of the main cloud layer. This finding is consistent with recent 3D fluid dynamics simulations.

Abstract:

Jupiter, the largest planet in our solar system, is a vital reference point for understanding gaseous exoplanets and their atmospheres. While we know its upper tropospheric chemical composition well, the nature and structure of its clouds remain puzzling. We, therefore, rely on theoretical models and remote sensing data to address this.While traditional equilibrium chemistry condensation models (ECCM) are sensitive to input parameters, advanced models [1] offer more realistic cloud property predictions. Remote sensing data can help determine cloud properties and test theoretical predictions thanks to the application of multiple scattering atmospheric retrieval. Still, the process is highly degenerate and, therefore, computationally demanding. The predicted tropospheric layers are upper ammonia ice (∼0.7 bar) and ammonium hydrosulfide (∼2 bar) clouds [2], but their spectral detection has been limited to small, dynamically active regions (

Abstract:

Titan is renowned for its complex atmosphere, where ongoing photochemistry leads to a rich mixture of organic molecules. Beginning with the splitting of methane by sunlight and other energetic particles, multi-carbon molecules are built up by successive addition of CxHy radicals and ions to one another. This process leads to the formation of ever-larger  molecules and eventually particulates, that sediment out on the surface. Our experimental knowledge of the molecular inventory comes from two techniques: direct sampling mass spectrometry, and remote sensing.  While the former has shown the presence of species at a very wide range of masses from 1-100+ Da, their structure and even stoichiometry is poorly known. In this respect, remote sensing spectroscopy is more robust, providing definitive detections of individual molecular types via unique patterns of IR and sub-millimeter energy transitions, however for a more limited range of species. Currently, 25 species have been definitively identified by remote sensing, ranging in size from H2 to benzene (C6H6). These include 12 hydrocarbons, with the rest a mixture of diatomics, nitriles and small oxygen compounds (H2O, CO, CO2). With direct sampling currently impossible before the Dragonfly mission returns a spacecraft to Titan in 2034, astronomers have been pushing forward with chemical identifications using a range of ground and space-based observatories. We report here on recent attempts to identify new C3 and C4 hydrocarbons in Titan’s atmosphere using the high-resolution (R~100000) TEXES spectrometer at the Infrared Telescope Facility (IRTF) – see examples in Fig. 1. Associated laboratory spectroscopy work is ongoing at the Jet Propulsion Laboratory (JPL) using a Bruker FTS spectrometer to identify the positions and intensities of the strongest gas bands, to assist with targeting the telescope searches, and interpretation of the data.  Identifications of new, heavy molecular species are urgently needed to constrain photochemical and dynamical models, and make advances in our understanding of the workings of Titan’s atmosphere, and its potential for astrobiology. Such work is also important for planning data collection and analysis from the upcoming NASA Dragonfly mission, where a sensitive mass spectrometer will assess the composition of surface materials and their relation to the atmospheric constituents, as well as Titan atmospheric data from other telescopes such as ALMA and JWST.Figure 1: Examples of currently undetected molecules in Titan's atmosphere: isomers of C4H8 and C4H10. We report on ongoing searches for these species with IRTF/TEXES.

Abstract:

Over four decades have elapsed since the last in situ spectrophotometric observations of the Venusian atmosphere, specifically from the Venera 11 (1978) and Venera 13 and 14 (1982) missions. These missions recorded spectral data during their descent from approximately 62 km to the surface. Unfortunately, the original data were lost; however, a portion has been reconstructed by digitising the graphical outputs that were generated during the initial data processing phase of each of the three missions [1]. This reconstructed data is crucial as it remains the sole set of in situ spectrophotometric observations of Venus’s atmosphere and is likely to be so for the foreseeable future.While re-analysing the reconstructed Venera datasets, we identified several artefacts, errors and sources of noise, necessitating the implementation of some corrections and validation checks to isolate the most unaffected part of the reconstructed data. Then, using NEMESIS, a radiative transfer and retrieval tool [2], we conducted a series of retrievals to simultaneously fit the downward-going spectra at all altitudes. During this process, several parameters were retrieved. The first set of retrievals focused on the structure of the main cloud deck (MCD), which includes the cloud base altitude and abundance profiles of all four cloud modes. Previous corrections that were used to account for the effect of the unknown UV absorber did not result in good fits with the spectra shortward of 0.6 µm. Hence, we derived a new correction by retrieving the imaginary refractive index spectra of the Mode 1 particles.In the next phase, the MCD retrievals were used to update the model atmospheres for each of the missions. Then, the H2O volume mixing ratio profiles were retrieved and compared with the previous retrievals using the same data by [1] along with other remote sensing observations. The final retrieval phase concentrated on characterising particulate matter in the deep atmosphere. In [3], we outlined a methodology for retrieving a near-surface particulate layer using the reconstructed Venera 13 dataset. In this new work, we apply this methodology to encompass the Venera 11 and 14 datasets and compare the retrievals from the three datasets.This research thus provides a comprehensive overview of three distinct retrievals: 1) main cloud deck, 2) H2O, and 3) near-surface particulates using the reconstructed spectrophotometric data of Venera 11, 13, and 14.References: [1] Ignatiev, N. I., Moroz, V. I., Moshkin, B. E., Ekonomov, A. P., Gnedykh, V. I., Grigor’ev, A. V., and Khatuntsev, I. V. Cosmic Research 35(1), 1–14 (1997).[2] Irwin, P. G., Teanby, N. A., de Kok, R., Fletcher, L. N., Howett, C. J., Tsang, C. C., Wilson, C. F., Calcutt, S. B., Nixon, C. A., and Parrish, P. D. Journal of Quantitative Spectroscopy and Radiative Transfer 109(6), 1136–1150 (2008).[3] Kulkarni, S. V., Irwin, P. G. J., Wilson, C. F., & Ignatiev, N. I. Journal of Geophysical Research: Planets, 130, e2024JE008728, (2025).