de los Rios, M., Petac, M., Zaldivar, B., Bonaventura, N. R., Calore, F., & Iocco, F. (2023). Determining the dark matter distribution in simulated galaxies with deep learning. Mon. Not. Roy. Astron. Soc., 525(4), 6015–6035.
Abstract: We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris-TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass similar to 10(11)-10(13)M(circle dot) from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below approximate to 0.25 when using photometrical and spectroscopic information. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations.
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Manera, M., Scoccimarro, R., Percival, W. J., Samushia, L., McBride, C. K., Ross, A. J., et al. (2013). The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: a large sample of mock galaxy catalogues. Mon. Not. Roy. Astron. Soc., 428(2), 1036–1054.
Abstract: We present a fast method for producing mock galaxy catalogues that can be used to compute the covariance of large-scale clustering measurements and test analysis techniques. Our method populates a second-order Lagrangian perturbation theory (2LPT) matter field, where we calibrate masses of dark matter haloes by detailed comparisons with N-body simulations. We demonstrate that the clustering of haloes is recovered at similar to 10 per cent accuracy. We populate haloes with mock galaxies using a halo occupation distribution (HOD) prescription, which has been calibrated to reproduce the clustering measurements on scales between 30 and 80 h(-1) Mpc. We compare the sample covariance matrix from our mocks with analytic estimates, and discuss differences. We have used this method to make catalogues corresponding to Data Release 9 of the Baryon Oscillation Spectroscopic Survey (BOSS), producing 600 mock catalogues of the 'CMASS' galaxy sample. These mocks have enabled detailed tests of methods and errors, and have formed an integral part of companion analyses of these galaxy data.
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Moline, A., Sanchez-Conde, M. A., Palomares-Ruiz, S., & Prada, F. (2017). Characterization of subhalo structural properties and implications for dark matter annihilation signals. Mon. Not. Roy. Astron. Soc., 466(4), 4974–4990.
Abstract: A prediction of the standard Lambda cold dark matter cosmology is that dark matter (DM) haloes are teeming with numerous self-bound substructure or subhaloes. The precise properties of these subhaloes represent important probes of the underlying cosmological model. We use data from Via Lactea II and Exploring the Local Volume in Simulations N-body simulations to learn about the structure of subhaloes with masses 10(6)-10(11) h(-1) M circle dot. Thanks to a superb subhalo statistics, we study subhalo properties as a function of distance to host halo centre and subhalo mass, and provide a set of fits that accurately describe the subhalo structure. We also investigate the role of subhaloes on the search for DM annihilation. Previous work has shown that subhaloes are expected to boost the DM signal of their host haloes significantly. Yet, these works traditionally assumed that subhaloes exhibit similar structural properties than those of field haloes, while it is known that subhaloes are more concentrated. Building upon our N-body data analysis, we refine the substructure boost model of Sanchez-Conde & Prada (2014), and find boosts that are a factor 2-3 higher. We further refine the model to include unavoidable tidal stripping effects on the subhalo population. For field haloes, this introduces a moderate (similar to 20-30 per cent) suppression. Yet, for subhaloes like those hosting dwarf galaxy satellites, tidal stripping plays a critical role, the boost being at the level of a few tens of percent at most. We provide a parametrization of the boost for field haloes that can be safely applied over a wide halo mass range.
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Reid, B. A. et al, & de Putter, R. (2012). The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: measurements of the growth of structure and expansion rate at z=0.57 from anisotropic clustering. Mon. Not. Roy. Astron. Soc., 426(4), 2719–2737.
Abstract: We analyse the anisotropic clustering of massive galaxies from the Sloan Digital Sky Survey III Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 9 (DR9) sample, which consists of 264-283 galaxies in the redshift range 0.43 < z < 0.7 spanning 3275 deg(2). Both peculiar velocities and errors in the assumed redshiftdistance relation (AlcockPaczynski effect) generate correlations between clustering amplitude and orientation with respect to the line of sight. Together with the sharp baryon acoustic oscillation (BAO) standard ruler, our measurements of the broad-band shape of the monopole and quadrupole correlation functions simultaneously constrain the comoving angular diameter distance (2190 +/- 61 Mpc) to z = 0.57, the Hubble expansion rate at z = 0.57 (92.4 +/- 4.5 km s(-1) Mpc(-1)) and the growth rate of structure at that same redshift (d(sigma 8)/d ln a = 0.43 +/- 0.069). Our analysis provides the best current direct determination of both DA and H in galaxy clustering data using this technique. If we further assume a cold dark matter expansion history, our growth constraint tightens to d(sigma 8)/d ln a = 0.415 +/- 0.034. In combination with the cosmic microwave background, our measurements of D-A,H and d(sigma 8)/d ln a all separately require dark energy at z > 0.57, and when combined imply Omega(A) = 0.74 +/- 0.016, independent of the Universe's evolution at z < 0.57. All of these constraints assume scale-independent linear growth, and assume general relativity to compute both O(10 per cent) non-linear model corrections and our errors. In our companion paper, Samushia et al., we explore further cosmological implications of these observations.
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