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Register a new userDark Matter Substructure Detection Using Spatially Resolved Spectroscopy of Lensed Dusty Galaxies
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We investigate how strong lensing of dusty, star-forming galaxies by foreground galaxies can be used as a probe of dark matter halo substructure. We find that spatially resolved spectroscopy of lensed sources allows dramatic improvements to measurements of lens parameters. In particular we find that modeling of the full, three-dimensional (angular position and radial velocity) data can significantly facilitate substructure detection, increasing the sensitivity of observables to lower mass subhalos. We carry out simulations of lensed dusty sources observed by early ALMA (Cycle 1) and use a Fisher matrix analysis to study the parameter degeneracies and mass detection limits of this method. We find that, even with conservative assumptions, it is possible to detect galactic dark matter subhalos of ~ 10^8 M_{odot} with high significance in most lensed DSFGs. Specifically, we find that in typical DSFG lenses, there is a ~ 55 % probability of detecting a substructure with M>10^8 M_{odot} with more than 5 sigma detection significance in each lens, if the abundance of substructure is consistent with previous lensing results. The full ALMA array, with its significantly enhanced sensitivity and resolution, should improve these estimates considerably. Given the sample of ~100 lenses provided by surveys like the South Pole Telescope, our understanding of dark matter substructure in typical galaxy halos is poised to improve dramatically over the next few years.
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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 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.
We report the detection of a dark substructure through direct gravitational imaging - undetected in the HST-ACS F814W image - in the gravitational lens galaxy of SLACS SDSSJ0946+1006 (the Double Einstein Ring). The detection is based on a Bayesian grid reconstruction of the two-dimensional surface density of the galaxy inside an annulus around its Einstein radius (few kpc). [...] We confirm this detection by modeling the system including a parametric mass model with a tidally truncated pseudo-Jaffe density profile; in that case the substructure mass is M_sub=(3.51+-0.15)x10^9 Msun, located at (-0.651+-0.038,1.040+-0.034), precisely where also the surface density map shows a strong convergence peak. [...] We set a lower limit of (M/L)_V}>=120 (Msun/L}_V,sun (3-sigma) inside a sphere of 0.3 kpc centred on the substructure (r_tidal=1.1kpc). The result is robust under substantial changes in the model and the data-set (e.g. PSF, pixel number and scale, source and potential regularization, rotations and galaxy subtraction). Despite being at the limits of detectability, it can therefore not be attributed to obvious systematic effects. Our detection implies a dark matter mass fraction at the radius of the inner Einstein ring of f_CDM=2.15^{+2.05}_{-1.25} percent (68 percent C.L) in the mass range 4x10^6 Msun to 4x10^9 Msun assuming alpha=1.9+-0.1 (with dN/dm ~ m^-alpha). Assuming a flat prior on alpha, between 1.0 and 3.0, increases this to f_CDM=2.56^{+3.26}_{-1.50} percent (68 percent C.L). The likelihood ratio is 0.51 between our best value (f_CDM=0.0215) and that from simulations (f_sim=0.003). Hence the inferred mass fraction, admittedly based on a single lens system, is large but still consistent with predictions. [...]
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.
Water ($rm H_{2}O$), one of the most ubiquitous molecules in the universe, has bright millimeter-wave emission lines easily observed at high-redshift with the current generation of instruments. The low excitation transition of $rm H_{2}O$, p$-$$rm H_{2}O$(202 $-$ 111) ($ u_{rest}$ = 987.927 GHz) is known to trace the far-infrared (FIR) radiation field independent of the presence of active galactic nuclei (AGN) over many orders-of-magnitude in FIR luminosity (L$_{rm FIR}$). This indicates that this transition arises mainly due to star formation. In this paper, we present spatially ($sim$0.5 arcsec corresponding to $sim$1 kiloparsec) and spectrally resolved ($sim$100 kms$^{-1}$) observations of p$-$$rm H_{2}O$(202 $-$ 111) in a sample of four strong gravitationally lensed high-redshift galaxies with the Atacama Large Millimeter/submillimeter Array (ALMA). In addition to increasing the sample of luminous ($ > $ $10^{12}$L$_{odot}$) galaxies observed with $rm H_{2}O$, this paper examines the L$_{rm H_{2}O}$/L$_{rm FIR}$ relation on resolved scales for the first time at high-redshift. We find that L$_{rm H_{2}O}$ is correlated with L$_{rm FIR}$ on both global and resolved kiloparsec scales within the galaxy in starbursts and AGN with average L$_{rm H_{2}O}$/L$_{rm FIR}$ =$2.76^{+2.15}_{-1.21}times10^{-5}$. We find that the scatter in the observed L$_{rm H_{2}O}$/L$_{rm FIR}$ relation does not obviously correlate with the effective temperature of the dust spectral energy distribution (SED) or the molecular gas surface density. This is a first step in developing p$-$$rm H_{2}O$(202 $-$ 111) as a resolved star formation rate (SFR) calibrator.
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