[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124185-en":3,"doc-seo-124185-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124185,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","BEYOND THE MACHINE - AUTOMATING SPECTRA GENERATION AND ANALYSIS FROM MACHINE LEARNING RESULTS","Interstellar chemistry detections provide enticing clues, yet they rarely enable reliable forecasting of which molecular species will be observed next. Prior machine-learning efforts reproduced inventories for known targets and generated thousands of candidate predictions, but those outputs lacked detectability considerations such as the number of spectral lines within observational windows and their expected line intensities. This work uses detectability metrics to refine ML candidates and automates spectra generation and analysis, improving downstream usability of ML-based inventory predictions.","BEYOND THE MACHINE: AUTOMATING SPECTRA GENERATION AND ANALYSIS FROM MACHINE LEARNING RESULTS  \nHANNAH TORU SHAY, GABI WENZEL, CI XUE, BRETT A. McGUIRE, Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.  \nAlmost 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 ef􀀂ciently 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 work􀀃ow leads to extended usability of ML inventory predictions.","cbCaitHTOb7498u1","https://ap.wps.com/l/cbCaitHTOb7498u1","pdf",13436,1,"English","en",105,"# Background and challenge\n## Limits of detections for predictive chemistry\n# Prior machine-learning inventory work\n## Reproducing known species and generating candidates\n# Detectability-aware candidate selection\n## Using detectability metrics for spectral coverage and intensity\n# Automation and impact\n## Extending usability of ML inventory predictions","[{\"question\":\"Why are interstellar molecule detections insufficient for reliable future predictions?\",\"answer\":\"Detections mainly offer hints, while reliable prediction of the next detectable chemical species remains challenging.\"},{\"question\":\"What did earlier machine-learning work accomplish in the McGuire group?\",\"answer\":\"It accurately reproduced inventories of 87 known species in TMC-1 and produced over 1500 candidate targets for potential detection.\"},{\"question\":\"How does this work improve candidate selection from machine-learning results?\",\"answer\":\"It applies detectability metrics to narrow candidates, incorporating factors like spectral-line coverage within observational ranges and the expected intensities of those lines.\"}]","BEYOND THE MACHINE - 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