New machine learning methods for analyzing expanding stars in the Orion complex

Hannah Woodward; Elena D'Onghia; Kashika Mahajan; Moritz Münchmeyer; Cameren Swiggum; Joao Alves

doi:10.3847/25c2cfeb.e7358267

New machine learning methods for analyzing expanding stars in the Orion complex

Hannah Woodward, Elena D'Onghia, Kashika Mahajan, Moritz Münchmeyer, Cameren Swiggum, Joao Alves

Department of Astrophysics

Publications: Contribution to journal › Meeting abstract/Conference paper › Peer Reviewed

Abstract

In this poster we present results from applying new machine learning algorithms to stars in Orion. Using new data from Gaia DR3 and SDSS, positions, radial velocities, proper motions, parallaxes, and ages are assembled for available stars in the region. Stars in the sample are clustered into groups based on these characteristics. With current data, we are able to include more details than previous work, which will improve inferences about the past dynamics of stars in Orion.

Original language	English
Article number	101.07
Journal	Bulletin of the American Astronomical Society
Volume	56
Issue number	7
DOIs	https://doi.org/10.3847/25c2cfeb.e7358267
Publication status	Published - 1 Jul 2024
Event	244th Meeting of the American Astronomical Society - Monona Terrace Convention Center, Madison, Wisconsin, United States Duration: 9 Jun 2024 → 13 Jun 2025 https://aas.org/meetings/aas244

Austrian Fields of Science 2012

103003 Astronomy
103004 Astrophysics

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10.3847/25c2cfeb.e7358267Licence: CC BY 4.0

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title = "New machine learning methods for analyzing expanding stars in the Orion complex",

abstract = "In this poster we present results from applying new machine learning algorithms to stars in Orion. Using new data from Gaia DR3 and SDSS, positions, radial velocities, proper motions, parallaxes, and ages are assembled for available stars in the region. Stars in the sample are clustered into groups based on these characteristics. With current data, we are able to include more details than previous work, which will improve inferences about the past dynamics of stars in Orion.",

author = "Hannah Woodward and Elena D'Onghia and Kashika Mahajan and Moritz M{\"u}nchmeyer and Cameren Swiggum and Joao Alves",

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T1 - New machine learning methods for analyzing expanding stars in the Orion complex

AU - Woodward, Hannah

AU - D'Onghia, Elena

AU - Mahajan, Kashika

AU - Münchmeyer, Moritz

AU - Swiggum, Cameren

AU - Alves, Joao

PY - 2024/7/1

Y1 - 2024/7/1

N2 - In this poster we present results from applying new machine learning algorithms to stars in Orion. Using new data from Gaia DR3 and SDSS, positions, radial velocities, proper motions, parallaxes, and ages are assembled for available stars in the region. Stars in the sample are clustered into groups based on these characteristics. With current data, we are able to include more details than previous work, which will improve inferences about the past dynamics of stars in Orion.

AB - In this poster we present results from applying new machine learning algorithms to stars in Orion. Using new data from Gaia DR3 and SDSS, positions, radial velocities, proper motions, parallaxes, and ages are assembled for available stars in the region. Stars in the sample are clustered into groups based on these characteristics. With current data, we are able to include more details than previous work, which will improve inferences about the past dynamics of stars in Orion.

U2 - 10.3847/25c2cfeb.e7358267

DO - 10.3847/25c2cfeb.e7358267

M3 - Meeting abstract/Conference paper

SN - 0002-7537

VL - 56

JO - Bulletin of the American Astronomical Society

JF - Bulletin of the American Astronomical Society

IS - 7

M1 - 101.07

T2 - 244th Meeting of the American Astronomical Society

Y2 - 9 June 2024 through 13 June 2025

ER -

New machine learning methods for analyzing expanding stars in the Orion complex

Abstract

Austrian Fields of Science 2012

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