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
Case Studies & Success Stories
Orphan-Chenab Stream Progenitor Mass Estimate
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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