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To set useful limits on the abundance of small-scale dark matter halos (subhalos) in a galaxy scale, we have carried out mid-infrared imaging and integral-field spectroscopy for a sample of quadruple lens systems showing anomalous flux ratios. These observations using Subaru have been successful for distinguishing millilensing by subhalos from microlensing by stars. Current status for our lensing analysis of dark matter substructure is reported.
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Spatially resolved spectroscopic data from the CIRPASS integral field unit (IFU) on Gemini are used to measure the gravitational lensing of the 4-image quasar Q2237+0305 on different size scales. A method for measuring the substructure present in the lens using observations at multiple wavelengths is demonstrated to be very effective and independent of many of the degeneracies inherent in previous methods. The magnification ratios of the QSOs narrow line region (NLR) and broad line region (BLR) are measured and found to be disagree with each other and with the published radio and mid-infrared magnification ratios. The disagreement between the BLR ratios and the radio/mid-infrared ratios is interpreted as microlensing by stars in the lens galaxy of the BLR The disagreement between the radio/mid-infrared ratios and the NLR ratios is interpreted as a signature of substructure on a larger scale, possibly the missing small scale structure predicted by the standard cold dark matter (CDM) model. Certain combinations of the radial profile and the substructure surface densities are ruled out using extensive lensing simulations. A substructure mass scale as large as 10^8 M is strongly disfavored while 10^4 M is too small if the radio and mid-infrared emission regions have the expected sizes of ~10 pc. The standard elliptical isothermal lens mass profile is not compatible with a substructure surface density of < 280 M/pc^2 at the 95% confidence level. This is 4-7% of the galaxys surface density (depending on which image position is used to evaluate this). The required substructure surface density at the required mass scale is high in comparison with the present expectations within the CDM model.
We consider three extensions of the Navarro, Frenk and White (NFW) profile and investigate the intrinsic degeneracies among the density profile parameters on the gravitational lensing effect of satellite galaxies on highly magnified Einstein rings. In particular, we find that the gravitational imaging technique can be used to exclude specific regions of the considered parameter space, and therefore, models that predict a large number of satellites in those regions. By comparing the lensing degeneracy with the intrinsic density profile degeneracies, we show that theoretical predictions based on fits that are dominated by the density profile at larger radii may significantly over- or underestimate the number of satellites that are detectable with gravitational lensing. Finally, using the previously reported detection of a satellite in the gravitational lens system JVAS B1938+666 as an example, we derive for this detected satellite values of r_max and v_max that are, for each considered profile, consistent within 1sigma with the parameters found for the luminous dwarf satellites of the Milky Way and with a mass density slope gamma < 1.6. We also find that the mass of the satellite within the Einstein radius as measured using gravitational lensing is stable against assumptions on the substructure profile. In the future thanks to the increased angular resolution of very long baseline interferometry at radio wavelengths and of the E-ELT in the optical we will be able to set tighter constraints on the number of allowed substructure profiles.
The analysis of optical images of galaxy-galaxy strong gravitational lensing systems can provide important information about the distribution of dark matter at small scales. However, the modeling and statistical analysis of these images is extraordinarily complex, bringing together source image and main lens reconstruction, hyper-parameter optimization, and the marginalization over small-scale structure realizations. We present here a new analysis pipeline that tackles these diverse challenges by bringing together many recent machine learning developments in one coherent approach, including variational inference, Gaussian processes, differentiable probabilistic programming, and neural likelihood-to-evidence ratio estimation. Our pipeline enables: (a) fast reconstruction of the source image and lens mass distribution, (b) variational estimation of uncertainties, (c) efficient optimization of source regularization and other hyperparameters, and (d) marginalization over stochastic model components like the distribution of substructure. We present here preliminary results that demonstrate the validity of our approach.
We present a new algorithm for identifying the substructure within simulated dark matter haloes. The method is an extension of that proposed by Tormen et al. (2004) and Giocoli et al. (2008a), which identifies a subhalo as a group of self-bound particles that prior to being accreted by the main progenitor of the host halo belonged to one and the same progenitor halo (hereafter satellite). However, this definition does not account for the fact that these satellite haloes themselves may also have substructure, which thus gives rise to sub-subhaloes, etc. Our new algorithm identifies substructures at all levels of this hierarchy, and we use it to determine the mass function of all substructure (counting sub-haloes, sub-subhaloes, etc.). On average, haloes which formed more recently tend to have a larger mass fraction in substructure and to be less concentrated than average haloes of the same mass. We provide quantitative fits to these correlations. Even though our algorithm is very different from that of Gao et al. (2004), we too find that the subhalo mass function per unit mass at redshift z = 0 is universal. This universality extends to any redshift only if one accounts for the fact that host haloes of a given mass are less concentrated at higher redshifts, and concentration and substructure abundance are anti-correlated. This universality allows a simple parametrization of the subhalo mass function integrated over all host halo masses, at any given time. We provide analytic fits to this function which should be useful in halo model analyses which equate galaxies with halo substructure when interpreting clustering in large sky surveys. Finally, we discuss systematic differences in the subhalo mass function that arise from different definitions of (host) halo mass.
Multiresolution analysis is applied to the problem of halo identification in cosmological N-body simulations. The procedure makes use of a discrete wavelet transform known as the algorithme a trous and segmentation analysis. It has the ability to find subhalos in the dense regions of a parent halo and can discern the multiple levels of substructure expected in the hierarchical clustering scenario. As an illustration, a 500,000 particle dark matter halo is analyzed and over 600 subhalos are found. Statistical properties of the subhalo population are discussed.
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