How Can We Find The Speed Of Dark Matter?

  • Speed of dark matter can be calculated by measuring the speed of oldest stars in the Galaxy. 
  • Astrophysicists have used numerical simulations of Milky Way’s formation to accurately measure the local speed distribution of dark matter particles. 

A vast amount of matter in the universe is invisible. Scientists have deduced the existence of such particles, called dark matters, by analyzing its gravitational influence on other particles and stars within galaxies. Till date, we’ve learned that these dark matters were moving slowly in the early universe (when galaxies formed).

We’ve now several advanced telescopes regularly making detailed maps of dark matter’s location. But what we don’t know is how fast they actually move. So far, scientists have used simple theoretical ideas to estimate the characteristic speed of dark matter.

However, an international team of astrophysicists has shed light on this problem from a different perspective. They used numerical simulations to identify Galaxy’s old stars that share the similar characteristic speed as the dark matters, thus putting a new viewpoint into the dark side of our universe.

Weakly Interacting Massive Particle

A theoretically predicted elementary particle, known as weakly interacting massive particle (WIMP) is a longstanding dark matter candidate particle with mass anywhere from tens to hundreds of times that of a proton.

In the search of unusual scattering of WIMP off atomic nuclei, tons of direct-detection experiments from all around the world are gathering data. Although, all of these experiments have achieved nothing, modern searches are setting decent bounds on the energy of WIMP-nucleus interaction.

Speed Of Dark MatterImage credit: Markos Kay/ Quanta Magazine

These scattering events between a dark matter particle and a nucleus is based on the interaction strength, the cross section of scattering. It’s also based on the characteristic speed and density of the dark matter in the Solar System.

Many studies have revealed the mean density of the dark matter near the Sun, which is around 0.01 solar masses per cubic parsec. However, measuring dark matter’s speed distribution has been one the challenging tasks.

This speed distribution shows how these mysterious matters assemble to create a Galaxy. The direct-detection methods consider (nothing more than an educated guess) speed distribution as a Maxwell-Boltzmann distribution. It’s just like air molecules moving in a room.

Numerical Simulations of Dark Matter’s Speed Distribution

Researchers have used numerical simulations of Milky Way’s formation to accurately measure the local speed distribution of dark matter.

Actually, these simulations contains a large volume of visible and dark matter that span scales much bigger than the Milky Way’s size. Objects that have similar structure and mass to our Galaxy are found within this giant volume.

zoom-in simulation of the Milky WayZoom-in simulation of Milky Way

The left image shows the density of stars – least dense in blue and densest in yellow color. The middle image shows the distribution of the oldest stars, while the right one shows the distribution of dark matter in the same galaxy, simulated in the same coordinate system. The center of the galaxy contains the highest number of stars. Compared to distribution of all stars, dark matter distribution is more spherical and extended. 

By zooming these Milky-Way-like objects and re-simulating them at much higher spatial resolution, following the examination of dark matter’s behavior near the Sun, researchers found out that there are significant differences between Maxwell-Boltzmann distribution and local dark matter speed distribution. There are more slow-moving particles and fewer fast moving particles.

Reference: Physical Review Letters | doi:10.1103/PhysRevLett.120.041102 


The astrophysicists utilized a zoom-in simulation of the Milky Way galaxy, which accounts for electromagnetic interactions between gas and stars, and gravitational physics of both visible and dark matter.

These simulations feature two types of stars – younger ones with heavier elements (larger than helium) than the Sun, and older stars with fewer heavy elements than the Sun.

As you might have guessed by now, they calculated the speed distribution of the dark matter, as well as both star types. What they discovered is the speed distribution of the galaxy’s oldest stars matches that of the dark matter particles.

Therefore, they concluded that one can accurately measure the speeds of the dark matter by measuring the oldest stars’ speeds. This might be related to the fact that both dark matter and old stars have existed since the initial stage of the Galaxy’s formation, and have almost same equilibrium distribution.

More specifically, the velocities of local dark matter particles can be deduced from the stellar halo observation by the Sloan Digital Sky Survey within 4 kiloparsec (13048 light-years) of the Sun. The standard halo model differs from this empirical distribution in many ways and it indicates that bounds on the spin-independent scattering cross section may be weakened for dark matter particles with masses less than about 10 Giga electron Volt.

What’s Next?

There is still a lot to do. As far as simulation is concerned, it’s quite essential to implement more detailed nature of the electromagnetic interactions between gas and stars. So far, data on a small subset of stars has been used. The near-complete dataset will include more than a billion stars.

Read: How Strong Are Black Holes? | Precise Measurement of Magnetic Field

Also, it’s important to examine more simulated galaxies to get the precise measurement of speed distributions. Furthermore, with improved simulations and new data, including GAIA survey, these types of studies will become extremely important in the journey to uncover the nature of dark matter particles.

Written by
Varun Kumar

I am a professional technology and business research analyst with more than a decade of experience in the field. My main areas of expertise include software technologies, business strategies, competitive analysis, and staying up-to-date with market trends.

I hold a Master's degree in computer science from GGSIPU University. If you'd like to learn more about my latest projects and insights, please don't hesitate to reach out to me via email at [email protected].

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