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NOBEL PRIZE 2026

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NSFDirectorate for Mathematical and Physical Sciences

The Emergence of Star Clusters: Insights from Artificial Intelligence

Completed
Institution
University of Massachusetts Amherst, MA
Principal investigator
Daniela Calzetti
Funding
$421K
Duration
2024–2026
Open portfolio

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Abstract

Galaxies transform gas into stars in a process known as star formation. Stars are most often born in clusters. Stellar Clusters can contain thousands of stars and are the main product of star formation in galaxies. The process of forming stars can last for millions of years. The investigators will observe a large number Stellar Clusters in nearby galaxies, with the goal of accurately estimating the time it takes for these Clusters to form. By comparing the environments of star formation with the estimated times of formation, the team will better explain the key physical mechanisms of star formation. To achieve this goal, the team will analyze data of nearby galaxies from the Atacama Large Millimeter Array and from the James Webb Space Telescope. The team will also integrate research experiences for teachers from local schools into the project. The teachers will be guided in creating science educational modules, which they will bring back to their classrooms. The aim is to inspire students from minority-dominated school districts into pursuing STEM careers. Molecular gas maps from ALMA and multi-wavelength images from JWST will yield thousands of gas clouds and dusty young star clusters, with their physical parameters, for a sample of 13 galaxies within the local 12 Mpc. A new Artificial Intelligence framework, “AI with Humans in the Loop”, will be employed to search for and label the young clusters, decreasing the effort for these tasks by 50-fold, thus providing a game-changing capability. The large collections of clouds and star clusters will be used to measure the timescales for clearing the natal gas and establish the nature of the feedback mechanism mostly responsible for the clearing. The range of physical parameters probed will enable the team to determine whether the timescales, and their physical origin, depend on cluster mass and/or gas pressure. The results from this project will inform models and simulations of galaxy evolution, of the interplay between galaxies and their surrounding medium, and of the conditions that enable galaxies to leak ionizing photons into the interstellar and intergalactic media. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research team

  • Daniela CalzettiPI
  • Subhransu MajiCo-PI

LOADING MOONBASE SCORE

Directorate for Mathematical and Physical SciencesArtificial Intelligence (AI)OBSERVATIONAL ASTRONOMYGALACTIC ASTRONOMY PROGRAMTHEORETICAL & COMPUTATIONAL ASTROPHYSICSSPECIAL PROGRAMS IN ASTRONOMYRET SUPP-Res Exp for Tchr SuppRESEARCH EXP FOR TEACHERSclustersgalaxiescloudsclearingstar formationstarsgasstar

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