The very recent discoveries of monocyclic and bicyclic aromatic molecules in rotational emission suggest that small aromatic molecules may actively form in prestellar dark clouds (McGuire et al. 2018, 2021). Although the high densities ( ∼ 10⁴ cm⁻³) allow molecules in dark clouds to collide and stick to grains and the low temperature ( ≤ 10 K) further precludes thermal evaporation (Bergen 2003), benzonitrile, a monocyclic aromatic molecule, is still observed toward multiple prestellar environments (Burkhardt et al. 2021). In this talk, we will present our efforts to numerically model these simple aromatic molecules with the rate-equation-based kinetic code, Nautilus, in dark cloud environments. To produce a more comprehensive chemical network for aromatics, we systematically added new species that may serve as precursors to the aromatic species and pathways leading to the formation of the most fundamental aromatic species, benzene (C₆H₆), under low temperature and high density conditions. In particular, we examined possible formation pathways, including the ion-molecule reaction of ethynyl radical (CCH) and 1,3-butadiene (CH₂CHCHCH₂), the neutral-neutral reaction of acetylene (HCCH) and vinyl acetylene (CH₂CHC₂H), and the dissociative recombination reaction of benzenium ions (C₆H₇⁺). Although the current model under-predicts the observed abundance of both monocyclic and bicyclic aromatic molecule, the updates we have made to the reaction network of related species have shown substantial progress in bringing the models into alignment with observations. Our results suggest that viable reactions forming and releasing small aromatic molecules on grains may be necessary to bridge the gap between models and observations in prestellar dark clouds.

Recent detections of aromatic species in dark molecular clouds suggest formation pathways may be efficient at very low temperatures and pressures, yet current astrochemical models are unable to account for their derived abundances, which can often deviate from model predictions by several orders of magnitude. The propargyl radical, a highly abundant species in the dark molecular cloud TMC-1, is an important aromatic precursor in combustion flames and possibly interstellar environments. In this talk, we present the results of astrochemical modeling of TMC-1 using the NAUTILUS code and an updated chemical network, with a focus on the propargyl radical and chemically related species. Predicted abundances for the connected C₄H₃N isomers within an order of magnitude of observed values corroborate the high efficiency of CN addition to closed-shell hydrocarbons under dark molecular cloud conditions. In addition to other resonance-stabilized radicals, the propargyl radical remains an important potential precursor to aromatic formation.

Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. However, in interstellar sources further along the path of star-formation, such as those where a protostar itself has been formed, the chemistry is known to be drastically different from that of largely quiescent dark clouds. In this talk, I will describe the ability of various machine learning models to fit the column densities of the molecules detected in source B of the Class 0 protostellar binary IRAS 16293-2422. By including a simple encoding of isotopic composition in the molecular feature vectors, I also examine for the first time how well these models can replicate the isotopic ratios. Finally, these trained models provide a list of predicted high-abundance molecules that may be excellent targets for laboratory spectroscopy and subsequent radioastronomical detection in IRAS 16293-2422 B.

Almost 300 molecules have been detected in the interstellar medium, with an exponential explosion in recent years thanks to rapid innovation in technology and technique. However, detections alone only offer tantalizing hints to future chemistry; we generally have been unable to make reliable predictions of the chemical species that will be subsequently detected. Prior work in the McGuire group was able to use Machine Learning approaches to accurately reproduce the inventories of 87 known species in TMC-1. The model went on to make over 1500 predictions of potential targets. Not only is this more than can efficiently be pursued, but these predictions provided only column densities and did not account for other key factors of detectability such as how many spectral lines fall within the range of our astronomical observations and the intensities of those lines. My work takes the results of this machine learning model, and others like it, and further narrows the list of candidates for detection by applying detectability metrics. Through automation, this workflow leads to extended usability of ML inventory predictions.

Lille Spectroscopic Database (https://lsd.univ-lille.fr)We thank the Mésocentre de Calcul Scientifique Intensif de l'Université de Lille for hosting the database.osts the predicted rotational spectra studied by our group in Lille during last 15 years. We will present recent data updates to the database as well as to the application programming interface (API) that allows the integration of the database into third party software. The question of degeneracy factors in case of molecules exhibiting large amplitude motions will be also discussed. We thank the Mésocentre de Calcul Scientifique Intensif de l'Université de Lille for hosting the database.h

Splatalogue is an online database for astronomical spectroscopy created and maintained by the National Radio Astronomy Observatory (NRAO) that contains over 11 million spectral lines of over 1300 different species; these lines are from over 15 different linelists. The Splatalogue user interface (splatalogue.online) was built on an old interface and while it has worked very well for the community over the course of the past 15 years, it does lack basic functionality that will allow for more scientific analysis done with the data contained in the database. This project involved working to complete four goals to facilitate both future development of this website and the work of astronomers who use it; the first, second, and fourth goals were fully completed within the time of this Research Experience for Undergraduates (REU) project at the NRAO in the summer of 2022. The third was partially completed in that the switch was made from PHP to Python as the base of the web server technology, but the feature integration was not fully completed.

1. Security: Ensuring the site was safe from attacks so it can be consistently available as a secure resource for the astronomical community.
2. Documentation: Documenting the project code to facilitate future development of the site.
3. Modern Technology Base: Switching the base of the web server technology from PHP to Python for security and feature integration reasons.
4. User Interface (UI) Improvements: Facilitating the use of the website through user interface style improvements.

Overall, this project represents a valuable advance in astronomical software that will facilitate both future modifications to the website and astronomical research going forward.

Interstellar molecules are often highly reactive species, which are unstable under terrestrial conditions, such as radicals, ions and unsaturated carbon chains. Their detection in space is usually based on the astronomical observation of their rotational fingerprints. However, laboratory investigations have to face the issue of efficiently producing these molecules and preserving them during rotational spectroscopy measurements. A general approach for producing and investigating unstable/reactive species is presented by means of selected case-study molecules. The overall strategy starts from quantum-chemical calculations that aim at obtaining accurate predictions of the missing spectroscopic information required to guide spectral analysis and assignment. Rotational spectra of these species are then recorded by exploiting the approach mentioned above, and their subsequent analysis leads to accurate spectroscopic parameters. These are then used for setting up accurate line catalogs for astronomical searches.

Massive star-forming regions are hotbeds of interstellar chemistry where unique physical conditions allow for the formation of a wide variety of molecular species. Accurately characterizing the morphology of these molecules allows us to glean information from the energetic physical processes taking place during massive star formation as well as achieve a better understanding of how such relatively large molecules are formed in the interstellar medium. In order to more efficiently process the vast amounts of data that modern interferometers such as ALMA are producing, we have developed a least-squares fitting routine capable of fitting the combined spectra of a given list of molecules across a broadband line survey for thousands of pixels in a data cube. Results of the application of this routine to high mass star-forming regions will be presented in the form of spatial maps for the derived parameters incuding excitation temperature, kinematics, and column densities. Most previous approaches to analyzing the spatial morphology rely on spectra extracted from a handful of pixels and extrapolating across the field of view with a moment map, using the intensity of a single transition as a proxy for the abundance of a molecule. In contrast, the maps generated by our fitting routine utilize the full suite of available lines and will showcase the physical parameters of the region in an unbiased manner. Maps showcasing the physical column density ratios will be presented as well; these unique products of our approach will give insight into how the protostellar environment influences the relative abundance of interstellar molecules.

Collisional energy transfer in volatized exospheric materials dominates the uncertainty of comet models that trace comae composition back to surface composition. Methods for ab initio and semi-empirical calculation of quantum-state dependent collisional efficiencies are typically benchmarked to pressure broadening experiments when available. Last year we detailed experimental efforts to determine collisional efficiencies for selected transitions of water at temperatures demonstrative of the comet environment and well below the water condensation temperature. This year we add two HDO transitions to the data set. The method utilizes a collisional cooling cell with water and/or deuterated water injected into a bath gas at the target temperature or held at temperatures that produce relevant vapor pressures for measurement. THz radiation is passed twice through the cooled gas to record a transmission spectrum exhibiting the Lamb dip effect. The sub-Doppler feature is subject to collisional broadening at pressures commensurate with the partial pressure of water and deuterated water in the system. Data analysis involves extraction of intensity and pressure broadening information. The method, results and comparisons to calculated values will be discussed.

Methylamine (CH₃NH₂) was first detected in the interstellar medium (ISM) toward Sgr B2 almost 50 years ago by observation of its v_t = 0 rotational transitionsKaifu, et al. 1974, ApJ, 191, L135; Fourikis et al. 1974, ApJ, 191, L139 Methylamine exhibits two large amplitude motions, methyl torsion and amine wagging, that complicate the spectral analysis especially in excited vibration states. This work aims to study experimentally and theoretically the terahertz rotational spectrum of methylamine to provide a reliable basis for the ISM detection of rotational transitions in v_t = 1 state. The terahertz spectrum of methylamine was measured from 150 to 1520 GHz with the Lille fast scan spectrometer. Using a new “hybrid” Hamiltonian model, we were able to fit accurately the rotational spectrum of the v_t= 1 state of methylamine including the analysis of the nuclear quadrupole hyperfine structure. The results of this spectroscopic analysis allowed us to search for rotational transitions of methylamine in its first torsionally excited state toward the high-mass star forming region Sgr B2(N) that was the target of the imaging spectral line survey ReMoCA performed with the Atacama Large Millimeter/submillimeter Array (ALMA). We report the first interstellar detection of methylamine in its v_t = 1 state on the basis of this interferometric data set. Kaifu, et al. 1974, ApJ, 191, L135; Fourikis et al. 1974, ApJ, 191, L139.