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Register a new userDark Matter Subhalos, Strong Lensing and Machine Learning
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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.
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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.
A defining prediction of the cold dark matter (CDM) cosmological model is the existence of a very large population of low-mass haloes. This population is absent in models in which the dark matter particle is warm (WDM). These alternatives can, in principle, be distinguished observationally because halos along the line-of-sight can perturb galaxy-galaxy strong gravitational lenses. Furthermore, the WDM particle mass could be deduced because the cut-off in their halo mass function depends on the mass of the particle. We systematically explore the detectability of low-mass haloes in WDM models by simulating and fitting mock lensed images. Contrary to previous studies, we find that halos are harder to detect when they are either behind or in front of the lens. Furthermore, we find that the perturbing effect of haloes increases with their concentration: detectable haloes are systematically high-concentration haloes, and accounting for the scatter in the mass-concentration relation boosts the expected number of detections by as much as an order of magnitude. Haloes have lower concentration for lower particle masses and this further suppresses the number of detectable haloes beyond the reduction arising from the lower halo abundances alone. Taking these effects into account can make lensing constraints on the value of the mass function cut-off at least an order of magnitude more stringent than previously appreciated.
We study shapes and alignments of 45 dark matter (DM) haloes and their brightest cluster galaxies (BCGs) using a sample of 39 massive clusters from Hubble Frontier Field (HFF), Cluster Lensing And Supernova survey with Hubble (CLASH), and Reionization Lensing Cluster Survey (RELICS). We measure shapes of the DM haloes by strong gravitational lensing, whereas BCG shapes are derived from their light profiles in Hubble Space Telescope images. Our measurements from a large sample of massive clusters presented here provide new constraints on dark matter and cluster astrophysics. We find that DM haloes are on average highly elongated with the mean ellipticity of $0.482pm 0.028$, and position angles of major axes of DM haloes and their BCGs tend to be aligned well with the mean value of alignment angles of $22.2pm 3.9$ deg. We find that DM haloes in our sample are on average more elongated than their BCGs with the mean difference of their ellipticities of $0.11pm 0.03$. In contrast, the Horizon-AGN cosmological hydrodynamical simulation predicts on average similar ellipticities between DM haloes and their central galaxies. While such a difference between the observations and the simulation may well be explained by the difference of their halo mass scales, other possibilities include the bias inherent to strong lensing measurements, limited knowledge of baryon physics, or a limitation of cold dark matter.
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.
We assess how much unused strong lensing information is available in the deep emph{Hubble Space Telescope} imaging and VLT/MUSE spectroscopy of the emph{Frontier Field} clusters. As a pilot study, we analyse galaxy cluster MACS,J0416.1-2403 ($z$$=$$0.397$, $M(R<200,{rm kpc})$$=$$1.6$$times$$10^{14}msun$), which has 141 multiple images with spectroscopic redshifts. We find that many additional parameters in a cluster mass model can be constrained, and that adding even small amounts of extra freedom to a model can dramatically improve its figures of merit. We use this information to constrain the distribution of dark matter around cluster member galaxies, simultaneously with the clusters large-scale mass distribution. We find tentative evidence that some galaxies dark matter has surprisingly similar ellipticity to their stars (unlike in the field, where it is more spherical), but that its orientation is often misaligned. When non-coincident dark matter and baryonic halos are allowed, the model improves by 35%. This technique may provide a new way to investigate the processes and timescales on which dark matter is stripped from galaxies as they fall into a massive cluster. Our preliminary conclusions will be made more robust by analysing the remaining five emph{Frontier Field} clusters.
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