[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126582-en":3,"doc-seo-126582-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126582,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A statistical and machine learning approach to the study of astrochemistry","A statistical and machine learning framework addresses how to infer key grain-surface chemistry parameters that govern astrochemical evolution. The work targets binding energies, whose values remain inconsistent across chemical networks and literature. Bayesian inference is used to estimate these parameters, but adequate constraints require sufficient data. To improve future detectability, the MOPED compression algorithm prioritizes species for observations. Finally, interpretable machine learning analyzes the nonlinear mapping between binding energies and resulting abundances of selected molecular species.","arXiv :2306 .05790v1 [ astro-ph .GA] 9 Jun 2023  \nA statistical and machine learning approach to the study of astrochemistry  \nJohannes Heyl, a‡ Serena Viti, b;a‡ and Gijs Vermariënb  \nIn order to obtain a good understanding of astrochemistry, it is crucial to better understand the key parameters that govern grain-surface chemistry. For many chemical networks, these crucial parameters are the binding energies of the species. However, there exists much disagreement regarding these values in the literature. In this work, a Bayesian inference approach is taken to estimate these values. It is found that this is difficult to do in the absence of enough data. The Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm is then used to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies. Finally, an interpretable machine learning approach is taken in order to better understand the non-linear relationship between binding energies and the final abundances of specific species of interest.  \n1 Introduction  \nGiant Molecular Clouds in our Milky Way as well as in other galaxies host gas which is almost entirely molecular, with densities above 􀀘 100 cm􀀀3 and temperature below 􀀘 100 K. These denser, cooler regions contain a significant fraction of the nonstellar baryonic matter in a galaxy and they are usually much more massive than large tenuous ones. The importance of these regions lie in the fact that they are key for our understanding of how galaxies form and evolve because this denser, cooler gas is the reservoir of matter that forms stars and planets, as well as the gas that fuels the centres of galaxies.  \nFrom an astrochemical point of view, due to their high densities and low temperatures, these regions are great laboratories to study the interactions of gas and dust, with species from the gas phase `freezing' onto the dust grains present, and forming icy mantles rich in hydrogenated as well as complex organic molecules (COMs), due to the many fast surface reactions that take place. As stars form in these clouds (or if any other energetic process takes place) then the dust temperature may reach the mantle sublimation temperature (􀀘 100K), and the molecules in the mantles are injected into the gas, where they react and form new, more complex, molecules. Associated with star formation, as well as with active galactic nucleus (AGN) activity, are highly supersonic collimated jets and molecular outflows. When the outflowing material encounters the quiescent gas of molec-  \na Department of Physics and Astronomy, University College London, Gower Street,  \nWC1E 6BT, London, UK; E-mail: [johannes.heyl.19@ucl.ac.uk](johannes.heyl.19@ucl.ac.uk)  \nb Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Nether lands  \n‡These authors contributed equally.  \nular cloud, it creates shocks, where the grain mantles are (partially) sputtered and the refractory grains are shattered. Again, here, the interaction of gas and dust varies within very short timescales and the effects of chemistry and dynamics are interlocked in a complex non-linear fashion. In summary, the gas and dust surface compositions exhibit a complicated time dependent, non-linear chemistry that strongly depends on the physical environment. There are many open questions - still - about such interaction: what is the unprocessed ice composition? What are the efficiencies of the viable surface reactions? And how do the energetics of the ISM (cosmic rays, UV radiation, shocks) influence the processed ices? In order to determine accurate estimates of the abundances of molecular species as a function of all the parameters that influence their chemistry we need to be able to answer such questions. In other words, we need to understand the chemical pathways towards each molecule and its dependencies on the density, temperature and energetics of the gas and dust before molecules can be ","cbCaifTsW2259XQt","https://ap.wps.com/l/cbCaifTsW2259XQt","pdf",1064716,2,1,14,"English","en",105,"# Introduction\n## Grain and dust chemistry in molecular clouds\n## Inverse problems in coupled chemical and radiative transfer modeling\n## Bayesian and machine learning progress and motivation","[{\"question\":\"Why are binding energies important in astrochemistry models?\",\"answer\":\"They are key parameters governing grain-surface chemistry, strongly affecting how chemical networks evolve and what molecular abundances result.\"},{\"question\":\"How does the document estimate uncertain binding energies?\",\"answer\":\"It applies Bayesian inference to infer binding energies, noting that reliable estimation is difficult when data are insufficient.\"},{\"question\":\"What is the purpose of using the MOPED compression algorithm?\",\"answer\":\"MOPED is used to determine which species should be prioritized for future detections to better constrain binding-energy values.\"}]","A statistical and machine learning approach to the study of astrochemistry | 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