DETECTION OF LENSING SUBSTRUCTURE USING ALMA OBSERVATIONS OF THE DUSTY GALAXY SDP.81

By Y. Hezaveh, N. Dalal, D. Marrone, Yao-Yuan Mao, W. Morningstar, D. Wen, R. Blandford, J. Carlstrom, C. Fassnacht, G. Holder and 6 more. 2016

215 citations recorded by the source corpus.

We study the abundance of substructure in the matter density near galaxies using ALMA Science Verification observations of the strong lensing system SDP.81. We present a method to measure the abundance of subhalos around galaxies using interferometric observations of gravitational lenses. Using simulated ALMA observations we explore the effects of various systematics, including antenna phase errors and source priors, and show how such errors may be measured or marginalized. We apply our formalism to ALMA observations of SDP.81. We find evidence for the presence of a M = 108.96±0.12M⊙ subhalo near one of the images, with a significance of 6.9σ in a joint fit to data from bands 6 and 7; the effect of the subhalo is also detected in both bands individually. We also derive constraints on the abundance of dark matter (DM) subhalos down to M ∼ 2 × 107M⊙, pushing down to the mass regime of the smallest detected satellites in the Local Group, where there are significant discrepancies between the observed population of luminous galaxies and predicted DM subhalos. We find hints of additional substructure, warranting further study using the full SDP.81 data set (including, for example, the spectroscopic imaging of the lensed carbon monoxide emission). We compare the results of this search to the predictions of ΛCDM halos, and find that given current uncertainties in the host halo properties of SDP.81, our measurements of substructure are consistent with theoretical expectations. Observations of larger samples of gravitational lenses with ALMA should be able to improve the constraints on the abundance of galactic substructure.

Subfields: Astronomy and Astrophysics, Instrumentation, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/8d5b0ee5bcdeca0f45e41b337d95d1b05e5fb87a

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Citations in the corpus

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019, methodology): “…density estimation techniques require reducing the dimensionality of x with summary statistics v ( x ), for instance based on power spectra (Hezaveh et al. 2016a; Cyr-Racine et al. 2016; Diaz Rivero et al. 2018; Chatterjee & Koop-mans 2018; D´ıaz Rivero et al. 2018; Cyr-Racine et al. 2019;…”
Small-Scale Challenges to the ΛCDM Paradigm (2017, methodology): “The discovery of additional lens systems, and the enhanced resolution and sensitivity of ALMA in its completed configuration, promise to reveal lower-mass substructure, perhaps down to scales similar to Local Group satellites but at cosmological distances and in very different host galaxies.”
The number and size of subhalo-induced gaps in stellar streams (2016, unknown): “109 M (see e.g. Vegetti et al. 2010, 2012; Hezaveh et al. 2016).”
Not so lumpy after all: modelling the depletion of dark matter subhaloes by Milky Way-like galaxies (2017, background): “This is particularly relevant for searches for dark subhalos via lensing anomalies (e.g. Vegetti et al. 2010; MacLeod et al. 2013; Nierenberg et al. 2014; Hezaveh et al. 2016).”
Ultralight scalars as cosmological dark matter (2016, background): “The strongest current limits come from ALMA observations of highredshift star-forming galaxies; at present these are only sensitive to sub-halo masses & 10(9)M [106], but technical advances should reduce this limit to the interesting range.”