Dark Matter in the Milky Way - how to find it using Gaia and other - PowerPoint PPT Presentation

Dark Matter in the Milky Way - how to find it using Gaia and other surveys Paul McMillan Surveys For All, 1st February 2016

Why do we care? On the biggest scales, the Λ CDM model works

Why do we care? On the scale of individual galaxies, agreement is less obvious. Things are more complicated, we have to consider the baryons! We should look at the galaxy we can study closest (an individual galaxy)

Dark Matter detection Particle physicists hope to find dark matter as it passes through the Earth (and their detector). Cold lump of germanium How many particles should they expect to pass through per second? Important piece of information: what’s the local dark matter density? Thing hits other thing = Nobel prize

How can we learn The only way we’ve detected dark matter is through it’s gravitational effects We can find the dark matter density by finding the gravitational potential (and subtracting off the baryonic bit) r 2 Φ = 4 π G ( ρ b + ρ DM ) Only equation in this talk

We have so many surveys of the Milky Way’s stars But also: Gaia footprint And others

So we need to learn ứ MW from the observed stars Too Measure the acceleration? small Need to use observed positions and velocities only How do we relate that to the potential?

We need a dynamical model: f(x,v) given a potential Sometimes this is just HI rotation curve of M33 that a given tracer is on a (near) circular orbit (e.g. HI gas, masers: Rubin 1980; Bosma 1978; Zigmanovic 2016) (possibly sign of an alien megastructure)

But generally we have to approximate that we’re not seeing the Milky Way at a special time – that the stellar dynamics are in equilibrium x x If we just let the stars go… x ✓ So: the probability of a star having a given position & velocity f(x,v) doesn’t depend on whereabouts it is on its orbit. It can only depend on things that are constants (e.g. energy)

How do we do that? Fortunately, we’ve found suitable constants (inspired by Solar System dynamics) – action variables ( J ). They’re not easy to find in Galactic potentials, but once you’ve worked out how to do that (and we have)… (with thanks to Spirograph™)

How do we do that? Fortunately, we’ve found suitable constants (inspired by Solar System dynamics) – action variables ( J ). They’re not easy to find in Galactic potentials, but once you’ve worked out how to do that (and we have)… (with thanks to Spirograph™)

Finding ứ MW from stellar surveys Find maximum likelihood on discrete data (for each star parallax, μ ,v r ) Compare the best fitting f(J) in each ứ . Fortunately we checked whether this was feasible with pseudo-data before diving in to real data (McMillan & Binney 2013)… Find J directly Orbit library N.B. change of scale Error bars: numerical uncertainty N.B. Orbit library (Schwarzschild modeling) is standard for external galaxies

And we already know lots about the Milky Way We have observations of cold gas that is on near circular orbits in the Milky Way We can measure the proper motion of (and distance to) Sgr A* We roughly know the structure - bulge, halo, disc(s) with ~known scale lengths There are existing constraints on the Milky Way’s mass from other dynamical modeling (you don’t have to do everything yourself!) The true Φ MW has to satisfy all of these constraints

Putting it together We combined these approaches to analyse RAVE survey data 1. For a given DM halo - demand potential fits known constraints. This ứ will have some vertical disc density profile at the Sun 2. Fit f(J) to (binned) kinematics of RAVE giants, which predicts a different disc density profile. 3. Iterate until these two vertical profiles agree with each other 4. Compare to vertical density profile from literature (Juric et al 2008, 0.7<r-i<0.8) (Piffl, Binney, McMillan, & RAVE 2014)

Local dark matter We’re left with effectively two free parameters for the potential: Local DM density & halo flattening. For spherical halo: ρ DM, ¤ = 0.0126 M ¤ /pc 3 = 0.48 GeV/cm 3 Note that statistical error bars are tiny ( ~ 0.4%)

With systematic uncertainties and varying halo flattening Where q is axis ratio of q DM halo, and α = 0.89 1

Compare to recent results (compiled by Read 2014) Our result Largest component of the uncertainty is the systematic uncertainty in the distance scale (affects density profile & velocities) We need Gaia!

Conclusions If you want to know where the dark matter is, you have to find the gravitational potential To do that for the Milky Way, you need a good model. We have good models (using action variables), and we’ve already used them to analyse Milky Way data. I can’t wait to work with Gaia data!