Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories
Machine learning pipelines for astrochemical inventories have been introduced as a useful addition to the astrochemist toolbox, having first been used to model and predict column densities in the Taurus Molecular Cloud (TMC-1). Rapid changes in the field of machine learning have provided new tools i...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adc80b |
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| author | Hannah Toru Shay Haley N. Scolati Gabi Wenzel Kin Long Kelvin Lee Aravindh N. Marimuthu Brett A. McGuire |
| author_facet | Hannah Toru Shay Haley N. Scolati Gabi Wenzel Kin Long Kelvin Lee Aravindh N. Marimuthu Brett A. McGuire |
| author_sort | Hannah Toru Shay |
| collection | DOAJ |
| description | Machine learning pipelines for astrochemical inventories have been introduced as a useful addition to the astrochemist toolbox, having first been used to model and predict column densities in the Taurus Molecular Cloud (TMC-1). Rapid changes in the field of machine learning have provided new tools in optimizing this pipeline, including improved vector representations. Furthermore, the addition of new detections since the original model allows for a retrospective analysis of model performance, in addition to new data for the model. This study revisits TMC-1, investigating both effects of an increased detection inventory on the model and changes to various portions of the pipeline, yielding a significant improvement in column density predictions. Through these comparisons, we attempt to derive insight into the ultimate effectiveness of these models, as well as their current limitations and words of caution in their use and interpretation. Finally, we provide suggestions for future machine learning of interstellar sources. |
| format | Article |
| id | doaj-art-87f0e1506de143deb260fda6eee17143 |
| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-87f0e1506de143deb260fda6eee171432025-08-20T02:26:28ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01985112310.3847/1538-4357/adc80bExploring Effects of Modified Machine Learning Pipelines of Astrochemical InventoriesHannah Toru Shay0https://orcid.org/0000-0002-8721-0988Haley N. Scolati1https://orcid.org/0000-0002-8505-4459Gabi Wenzel2https://orcid.org/0000-0002-0332-2641Kin Long Kelvin Lee3https://orcid.org/0000-0002-1903-9242Aravindh N. Marimuthu4https://orcid.org/0000-0001-5444-6401Brett A. McGuire5https://orcid.org/0000-0003-1254-4817Department of Chemistry, Massachusetts Institute of Technology , Cambridge, MA 02139, USA ; hannahts@mit.edu, brettmc@mit.eduNational Radio Astronomy Observatory , Charlottesville, VA 22903, USA; Department of Chemistry, University of Virginia , Charlottesville, VA 22903, USADepartment of Chemistry, Massachusetts Institute of Technology , Cambridge, MA 02139, USA ; hannahts@mit.edu, brettmc@mit.eduIntel Labs , Intel Corporation, 2111 NE 25th Ave., Hillsboro, OR 97124, USADepartment of Chemistry, Massachusetts Institute of Technology , Cambridge, MA 02139, USA ; hannahts@mit.edu, brettmc@mit.eduDepartment of Chemistry, Massachusetts Institute of Technology , Cambridge, MA 02139, USA ; hannahts@mit.edu, brettmc@mit.edu; National Radio Astronomy Observatory , Charlottesville, VA 22903, USAMachine learning pipelines for astrochemical inventories have been introduced as a useful addition to the astrochemist toolbox, having first been used to model and predict column densities in the Taurus Molecular Cloud (TMC-1). Rapid changes in the field of machine learning have provided new tools in optimizing this pipeline, including improved vector representations. Furthermore, the addition of new detections since the original model allows for a retrospective analysis of model performance, in addition to new data for the model. This study revisits TMC-1, investigating both effects of an increased detection inventory on the model and changes to various portions of the pipeline, yielding a significant improvement in column density predictions. Through these comparisons, we attempt to derive insight into the ultimate effectiveness of these models, as well as their current limitations and words of caution in their use and interpretation. Finally, we provide suggestions for future machine learning of interstellar sources.https://doi.org/10.3847/1538-4357/adc80bAstrochemistryInterdisciplinary astronomyInterstellar molecules |
| spellingShingle | Hannah Toru Shay Haley N. Scolati Gabi Wenzel Kin Long Kelvin Lee Aravindh N. Marimuthu Brett A. McGuire Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories The Astrophysical Journal Astrochemistry Interdisciplinary astronomy Interstellar molecules |
| title | Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories |
| title_full | Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories |
| title_fullStr | Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories |
| title_full_unstemmed | Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories |
| title_short | Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories |
| title_sort | exploring effects of modified machine learning pipelines of astrochemical inventories |
| topic | Astrochemistry Interdisciplinary astronomy Interstellar molecules |
| url | https://doi.org/10.3847/1538-4357/adc80b |
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