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Register a new userAstrometric Microlensing by Local Dark Matter Subhalos
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Authors
Adrienne L. Erickcek
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High-resolution N-body simulations of dark matter halos indicate that the Milky Way contains numerous subhalos. When a dark matter subhalo passes in front of a star, the light from that star will be deflected by gravitational lensing, leading to a small change in the stars apparent position. This astrometric microlensing signal depends on the inner density profile of the subhalo and can be greater than a few microarcseconds for an intermediate-mass subhalo (Mvir > 10000 solar masses) passing within arcseconds of a star. Current and near-future instruments could detect this signal, and we evaluate SIMs, Gaias, and ground-based telescopes potential as subhalo detectors. We develop a general formalism to calculate a subhalos astrometric lensing cross section over a wide range of masses and density profiles, and we calculate the lensing event rate by extrapolating the subhalo mass function predicted by simulations down to the subhalo masses potentially detectable with this technique. We find that, although the detectable event rates are predicted to be low on the basis of current simulations, lensing events may be observed if the central regions of dark matter subhalos are more dense than current models predict (>1 solar mass within 0.1 pc of the subhalo center). Furthermore, targeted astrometric observations can be used to confirm the presence of a nearby subhalo detected by gamma-ray emission. We show that, for sufficiently steep density profiles, ground-based adaptive optics astrometric techniques could be capable of detecting intermediate-mass subhalos at distances of hundreds of parsecs, while SIM could detect smaller and more distant subhalos.
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The fact that dark matter (DM), thus far, has revealed itself only on scales of galaxies and larger, again thrusts onto astrophysics the opportunity and the responsibility to confront the age old mystery What is the nature of matter? By deriving basic data on the nature of DM - e.g., mass of its particle(s), present mean temperature, distribution in galaxies and other structures in the universe, and capacity for dissipational collapse - we will be uncovering the properties of the dominant species of matter in the universe and significantly extending the standard models of particle physics. Determining the mass of the DM particle to an order of magnitude would help to sort out the particle family to which it (or they) belongs. Beyond mass, there are issues of stability. The DM particle may be unstable with a measurable half-life, or it may become unstable after absorbing a certain amount of energy from collisions. In both cases it would contribute to the present hot dark matter component. Some key parameters of DM can most accurately be measured in the very nearby universe because DM dominates the mass in the outer Milky Way (MW), in other galaxies in the Local Group, and in the Local Group in its entirety. The presence and amount of DM can be quantified by study of dynamical processes observable in fine detail within these entities. Precise measurements of 3-D velocities for stars, coherent star streams, and stars in satellite stellar systems out to the edge of the Galaxy can reveal what is the shape, orientation, density law, and lumpiness of the dark matter halo as well as what is the total mass of the Galaxy?
We investigate the possibility of applying machine learning techniques to images of strongly lensed galaxies to detect a low mass cut-off in the spectrum of dark matter sub-halos within the lens system. We generate lensed images of systems containing substructure in seven different categories corresponding to lower mass cut-offs ranging from $10^9M_odot$ down to $10^6M_odot$. We use convolutional neural networks to perform a multi-classification sorting of these images and see that the algorithm is able to correctly identify the lower mass cut-off within an order of magnitude to better than 93% accuracy.
We study the application of machine learning techniques for the detection of the astrometric signature of dark matter substructure. In this proof of principle a population of dark matter subhalos in the Milky Way will act as lenses for sources of extragalactic origin such as quasars. We train ResNet-18, a state-of-the-art convolutional neural network to classify angular velocity maps of a population of quasars into lensed and no lensed classes. We show that an SKA -like survey with extended operational baseline can be used to probe the substructure content of the Milky Way.
Dark matter may be in the form of non-baryonic structures such as compact subhalos and boson stars. Structures weighing between asteroid and solar masses may be discovered via gravitational microlensing, an astronomical probe that has in the past helped constrain the population of primordial black holes and baryonic MACHOs. We investigate the non-trivial effect of the size of and density distribution within these structures on the microlensing signal, and constrain their populations using the EROS-2 and OGLE-IV surveys. Structures larger than a solar radius are generally constrained more weakly than point-like lenses, but stronger constraints may be obtained for structures with mass distributions that give rise to caustic crossings or produce larger magnifications.
We describe the methodology to include nonlinear evolution, including tidal effects, in the computation of subhalo distribution properties in both cold (CDM) and warm (WDM) dark matter universes. Using semi-analytic modeling, we include effects from dynamical friction, tidal stripping, and tidal heating, allowing us to dynamically evolve the subhalo distribution. We calibrate our nonlinear evolution scheme to the CDM subhalo mass function in the Aquarius N-body simulation, producing a subhalo mass function within the range of simulations. We find tidal effects to be the dominant mechanism of nonlinear evolution in the subhalo population. Finally, we compute the subhalo mass function for $m_chi=1.5$ keV WDM including the effects of nonlinear evolution, and compare radial number densities and mass density profiles of subhalos in CDM and WDM models. We show that all three signatures differ between the two dark matter models, suggesting that probes of substructure may be able to differentiate between them.
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