MilkyWay@home

Business Profile

Core Value Proposition

MilkyWay@home provides a distributed volunteer computing platform (BOINC) that harnesses unused computing resources to build a highly accurate three-dimensional model of the Milky Way using Sloan Digital Sky Survey data, enabling research in astroinformatics and computer science.

Target Customer

Academic researchers in astronomy/astrophysics and computer science; volunteers who contribute computing resources; research institutions affiliated with Rensselaer Polytechnic Institute and partner universities.

Key Differentiator

Combines volunteer-driven distributed computing (BOINC) with astrophysical data analysis to tackle complex, data-intensive modeling of the Milky Way, including separate N-body simulations and data-fitting workflows, with a focus on dark matter and tidal streams.

Implementation Timeline

{"note":"Project progress and timelines referenced in site content (historical):","separation_completion":"The Separation project was expected to be complete in late-2013 to mid-2014.","n_body_status":"N-body project under development with plans to run tests and eventually crunch real data; goal to add GPU support in the future.","publications":"Publications listed span 2014–2022, reflecting ongoing research outputs from MilkyWay@home."}

Pain Points

1Requires enormous computational resources to model the Milky Way and its tidal streams using SDSS data.
2Computational tasks are fault-prone, heterogeneous, and asynchronous, requiring optimization methods resilient to distributed computing environments.
3Identifying and fitting tidal streams within noisy SDSS data is a complex, high-dimension optimization problem (maximum likelihood style) that benefits from large-scale collaborative computing.

Key Features

1BOINC-based platform to mobilize volunteered computing resources.
23D modeling of the Milky Way using Sloan Digital Sky Survey data.
3Multiple sub-projects: Separation/Stream Fit and N-body simulations.
4Parameter-space work units that drive iterative model fitting and likelihood evaluation.
5Publications and research outputs from funded academic collaborations.

Target Customer Industries

Decision Makers

1Assistant Professor of Computer Science
2Professor of Physics, Applied Physics, and Astronomy
3Professor of Computer Science
4Associate Professor of Computer Science
5Postdoctoral Researcher in Physics, Applied Physics, and Astronomy
6Graduate Research Assistant in Physics, Applied Physics, and Astronomy
7Graduate Research Assistant in Computer Science

Client Results

1Published results include an estimate of the mass and radial profile of the Orphan-Chenab Stream's dwarf galaxy progenitor using MilkyWay@home's N-body project (Mendelsohn et al., 2022). The inferred progenitor mass is roughly 2 x 10^7 solar masses with a mass-to-light ratio of 73.5, indicating approximately 98.6% dark matter; the majority of the mass, especially dark matter, resides in the tidal tails.

Competitive Advantages

1Leverages a large pool of volunteered computing power via BOINC, enabling large-scale simulations without a single centralized supercomputer.
2Integrates observational data (SDSS) with sophisticated modeling workflows (Separation and N-body) to study dark matter and Galactic structure.
3Designed to handle fault-prone, heterogeneous, asynchronous distributed computing, with optimization approaches cited (evolutionary/genetic algorithms, asynchronous Newton methods).
4Produces publishable scientific results and supports multiple lines of inquiry (separation of tidal streams, N-body galaxy interactions).

Key Benefits

1Access to large-scale computational resources via volunteers.

2Ability to perform detailed modeling of Milky Way structure and dark matter via multiple sub-projects.

3Generation of publishable scientific results and contributions to astronomical understanding.

Case Studies & Success Stories

Orphan-Chenab Stream Progenitor Mass Estimate

Case #1

Using MilkyWay@home's N-body project and SDSS/DEC data, researchers estimated the progenitor dwarf galaxy mass at ~2 x 10^7 solar masses with a mass-to-light ratio of 73.5 (~98.6% dark matter), highlighting the capability to extract physical properties from tidal debris and to assess model systematics and Galactic potential.

Additional Product Information

Product Description

MilkyWay@home is a distributed volunteer computing project that uses the BOINC platform to build detailed models of the Milky Way from SDSS data, including Separation/Stream Fit and N-body sub-projects, to advance astroinformatics and computational astrophysics.

Target Market

Academic researchers in astronomy/astrophysics and computer science, and volunteers who contribute computing cycles to science projects.

Unique Value Proposition

A citizen-science enabled, scalable modeling platform that combines SDSS observations with advanced computational methods (N-body and stream fitting) through volunteer computing to constrain the Milky Way’s structure and dark matter distribution.

Technical Requirements

{"BOINC platform for volunteer computing","Data from Sloan Digital Sky Survey (SDSS)","Sub-projects including Separation/Stream Fit and N-body simulations","Future plans for GPU support (as indicated in project updates)"}

Pricing Model

Not applicable; MilkyWay@home is a volunteer computing project funded by the National Science Foundation (NSF).

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