Finding Potential Detections of Dust in Protoplanetary Disk Winds. Using Machine Learning to Filter, Classify and Present ALMA data

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dc.contributor.authorFagrell, Peter
dc.contributor.authorKollberg, Jens
dc.contributor.authorRasmussen, Elias
dc.contributor.authorRedmo Axelsson, Erik
dc.contributor.authorSvensson, Oskar
dc.contributor.authorYbring, Alexander
dc.contributor.departmentChalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskapsv
dc.contributor.departmentChalmers University of Technology / Department of Space, Earth and Environmenten
dc.contributor.examinerThomasson, Magnus
dc.contributor.supervisorBjerkeli, Per
dc.contributor.supervisorToribio, Maria Carmen
dc.date.accessioned2023-06-21T06:43:01Z
dc.date.available2023-06-21T06:43:01Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractStar formation begins when clouds of dust and gas starts to collapse under the force of their own gravity. Eventually, a protoplanetary disk is formed in the central region. Because of magnetic fields, gravity and rotation, material is ejected through jets and winds that sweep up gas from the surrounding envelope and form an outflow. These outflows remove angular momentum which permits a star to form in the center. In the disk, small grains eventually starts to clump together to become planets. Whether lifting of dust via winds affect the planet formation process is yet not understood, but it is a research topic (e.g. Pascucci et al., 2022) where this project could contribute. The project aims to automatically extract images from the Atacama Large Millime ter/submillimeter Array (ALMA) Science Archive that indicates dust lifting from disks via the aforementioned winds, with the use of a Convolutional Neural Network (CNN). Additionally, the network should be flexible enough to be applied to other, similar problems. With the help of ALminer observations are fetched and then fed to the CNN to filter out objects of interest. Due to a low number of known observa tions that could possibly depict dust in the wind, images are augmented to produce sufficient training data. During training of the CNN, a portion of the training data is reserved for determining the accuracy of the network. Finally, it is applied to a part of the ALMA archive chosen through keywords and presents images it labels as positive. The methods mentioned led to an extraction of several observations. Prediction of labels was performed by the CNN with high accuracy and it can, with modification, act as a general model for other astronomical phenomena. In conclusion, a publicly available and free tool was created that can support researchers in their collection of data from the ALMA archive, minimizing manual labor and advancing the studies of the universe.
dc.identifier.coursecodeseex16
dc.identifier.urihttp://hdl.handle.net/20.500.12380/306335
dc.language.isoeng
dc.setspec.uppsokLifeEarthScience
dc.subjectProtoplanetary Disk, Outflows, Star, Winds, ALMA, CNN, ALminer
dc.titleFinding Potential Detections of Dust in Protoplanetary Disk Winds. Using Machine Learning to Filter, Classify and Present ALMA data
dc.type.degreeExamensarbete för kandidatexamensv
dc.type.degreeBachelor Thesisen
dc.type.uppsokM2
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