Formation of a stellar stream, shown in the orbital plane (distances are given in kiloparsecs from the Galactic centre). As the progenitor — a star cluster or dwarf galaxy (blue) — orbits the Milky Way, stars are gradually stripped away by our galaxy's gravity and spread out along the orbit, building up the two thin arms of the stream over time. © MPA
Although dark matter makes up most of the matter in the universe, what it is made of remains one of the biggest open questions in physics. One indirect clue to its particle nature is how clumpy it is on small scales, such as in dwarf galaxies and smaller. The smallest of these clumps are associated with few or no stars and cannot be seen directly; however, their gravity can perturb stellar streams, thin trails of stars that act as sensitive probes. MPA scientists have now demonstrated that analysing both the location and the movement of stellar stream's stars can pinpoint the scale at which dark matter stops clumping several times more precisely, achieving a level of sensitivity comparable to the most advanced methods currently available.
At the largest scales, a simple model of dark matter works well: a cold, slow-moving substance whose gravity pulls ordinary matter together to form galaxies. While this leading model accounts for much of what telescopes and observatories observe, it remains silent on a fundamental question: what is dark matter? There are many competing answers, ranging from massive elementary particles to small black holes and ultra-light wave-like particles, all of which reproduce the same large-scale universe. The models diverge on small scales: some predict that dark matter continues to gather into ever-smaller clumps, while others predict a cutoff, a minimum size below which clumps simply fail to form. Finding where this cutoff lies would provide a crucial clue to the identity of dark matter.
The problem is that the smallest clumps cannot gather enough ordinary matter to form stars, so they are essentially invisible to us. Their only trace is gravitational. The Milky Way offers a natural detector here. Over cosmic time, it has grown by absorbing many smaller star clusters and dwarf galaxies. As one of these is gradually pulled apart by our galaxy's gravity, its stars spread out along its orbit to form a long, narrow stream. As the stars move along the same orbit, the stream remains dynamically cold. This makes it highly sensitive to small disturbances: when a clump of dark matter passes nearby, its gravity shifts the stars slightly, leaving an imprint. The GD-1 stream is one of the most striking examples, displaying small features and gaps that cannot easily be explained by a smooth dark matter halo.
Most early studies modelled these clumps individually, interpreting a feature such as a gap as the mark of a single passing object. However, if low-mass clumps are as abundant as the leading model predicts, a stream is continuously perturbed by a whole population of them, with their effects overlapping. Consequently, the focus shifted to describing the clumps collectively. However, many population-level methods still resolve each encounter and sum them up, which becomes prohibitively expensive at low masses, where encounters are most numerous and competing dark matter models differ most.
The new study by MPA researchers Noemi Anau Montel and Fabian Schmidt avoids resolving encounters at all. It represents the entire population as a statistical pattern of density fluctuations at each scale. This field imparted many small velocity changes to the stars, which built up gradually rather than arriving as one sharp deflection. The cost no longer increases with the number of clumps, and any dark matter model can be tested by substituting a different pattern. Additionally, the model quantifies how sensitively the stream's appearance responds to a small change in any dark matter property. This enables the new framework to predict with forecasts, before the data is available, how accurately a future observation could measure each property.
The main advance comes from the motions of the stars. Earlier analyses relied mainly on the density of the stream, i.e. how the stars are spaced along its length. However, the same perturbations are also known to leave a pattern in the stars' velocities, affecting both their motion across the sky and their motion towards or away from us. The new study incorporates kinematic information into the forecast and demonstrates that using the motions of the stars, as well as their positions, improves the measurement of the cutoff scale by a factor of three to five. Specifically, the spacing of the stars alone locates the cutoff to within a factor of about ten, whereas adding the motions narrows it to a factor of roughly two. Even better, the constraints improve for an older stream that has been perturbed for a longer period of time.
These numbers are forecasts, not measurements. Nevertheless, the implication is significant: a single, accurately measured stream could constrain dark matter's behaviour on small scales as well as today's leading methods, such as the gravitational lensing of distant quasars and the counting of small satellite galaxies in the Milky Way (see also this press release from 2025). Because a stream is a purely local, purely gravitational probe, its sources of error are independent of these methods, offering a valuable cross-check.
The required data are now becoming available from the precise positions of the Gaia satellite, the velocity measurements of the DESI survey, and dedicated instruments such as the VIA Project. However, two challenges remain: separating the perturbations caused by visible structures, such as gas clouds and the galactic bar, from those caused by dark matter; and handling the rare close passes of the largest clumps, which lie outside the weak accumulating regime discussed here.
At the largest scales, a simple model of dark matter works well: a cold, slow-moving substance whose gravity pulls ordinary matter together to form galaxies. While this leading model accounts for much of what telescopes and observatories observe, it remains silent on a fundamental question: what is dark matter? There are many competing answers, ranging from massive elementary particles to small black holes and ultra-light wave-like particles, all of which reproduce the same large-scale universe. The models diverge on small scales: some predict that dark matter continues to gather into ever-smaller clumps, while others predict a cutoff, a minimum size below which clumps simply fail to form. Finding where this cutoff lies would provide a crucial clue to the identity of dark matter.
The problem is that the smallest clumps cannot gather enough ordinary matter to form stars, so they are essentially invisible to us. Their only trace is gravitational. The Milky Way offers a natural detector here. Over cosmic time, it has grown by absorbing many smaller star clusters and dwarf galaxies. As one of these is gradually pulled apart by our galaxy's gravity, its stars spread out along its orbit to form a long, narrow stream. As the stars move along the same orbit, the stream remains dynamically cold. This makes it highly sensitive to small disturbances: when a clump of dark matter passes nearby, its gravity shifts the stars slightly, leaving an imprint. The GD-1 stream is one of the most striking examples, displaying small features and gaps that cannot easily be explained by a smooth dark matter halo.
Most early studies modelled these clumps individually, interpreting a feature such as a gap as the mark of a single passing object. However, if low-mass clumps are as abundant as the leading model predicts, a stream is continuously perturbed by a whole population of them, with their effects overlapping. Consequently, the focus shifted to describing the clumps collectively. However, many population-level methods still resolve each encounter and sum them up, which becomes prohibitively expensive at low masses, where encounters are most numerous and competing dark matter models differ most.
The new study by MPA researchers Noemi Anau Montel and Fabian Schmidt avoids resolving encounters at all. It represents the entire population as a statistical pattern of density fluctuations at each scale. This field imparted many small velocity changes to the stars, which built up gradually rather than arriving as one sharp deflection. The cost no longer increases with the number of clumps, and any dark matter model can be tested by substituting a different pattern. Additionally, the model quantifies how sensitively the stream's appearance responds to a small change in any dark matter property. This enables the new framework to predict with forecasts, before the data is available, how accurately a future observation could measure each property.
The main advance comes from the motions of the stars. Earlier analyses relied mainly on the density of the stream, i.e. how the stars are spaced along its length. However, the same perturbations are also known to leave a pattern in the stars' velocities, affecting both their motion across the sky and their motion towards or away from us. The new study incorporates kinematic information into the forecast and demonstrates that using the motions of the stars, as well as their positions, improves the measurement of the cutoff scale by a factor of three to five. Specifically, the spacing of the stars alone locates the cutoff to within a factor of about ten, whereas adding the motions narrows it to a factor of roughly two. Even better, the constraints improve for an older stream that has been perturbed for a longer period of time.
These numbers are forecasts, not measurements. Nevertheless, the implication is significant: a single, accurately measured stream could constrain dark matter's behaviour on small scales as well as today's leading methods, such as the gravitational lensing of distant quasars and the counting of small satellite galaxies in the Milky Way (see also this press release from 2025). Because a stream is a purely local, purely gravitational probe, its sources of error are independent of these methods, offering a valuable cross-check.
The required data are now becoming available from the precise positions of the Gaia satellite, the velocity measurements of the DESI survey, and dedicated instruments such as the VIA Project. However, two challenges remain: separating the perturbations caused by visible structures, such as gas clouds and the galactic bar, from those caused by dark matter; and handling the rare close passes of the largest clumps, which lie outside the weak accumulating regime discussed here.
Source: Max Planck Institute for Astrophysics/Research Highlights
Authors:
Dr. Noemi Anau Montel
Tel: 2215
noemiam@mpa-garching.mpg.de
Dr. Fabian Schmidt
Scientific Staff
Member of the works council, Representative of the Scientific Coworkers
Tel: 2274
fschmidt@mpa-garching.mpg.de
Original publication:
Noemi Anau Montel, Fabian Schmidt
A differentiable forward model for weakly perturbed stellar streams: substructure forecasts from density and kinematics spectra
submitted
Source
