We present forecasts on the detectability of Ultra-light axion-like particles (ULAP) from future 21cm radio observations around the epoch of reionization (EoR). We show that the axion as the dominant dark matter component has a significant impact on
the reionization history due to the suppression of small scale density perturbations in the early universe. This behavior depends strongly on the mass of the axion particle. Using numerical simulations of the brightness temperature field of neutral hydrogen over a large redshift range, we construct a suite of training data. This data is used to train a convolutional neural network that can build a connection between the spatial structures of the brightness temperature field and the input axion mass directly. We construct mock observations of the future Square Kilometer Array survey, SKA1-Low, and find that even in the presence of realistic noise and resolution constraints, the network is still able to predict the input axion mass. We find that the axion mass can be recovered over a wide mass range with a precision of approximately 20%, and as the whole DM contribution, the axion can be detected using SKA1-Low at 68% if the axion mass is $M_X<1.86 times10^{-20}$eV although this can decrease to $M_X<5.25 times10^{-21}$eV if we relax our assumptions on the astrophysical modeling by treating those astrophysical parameters as nuisance parameters.
We present an algorithm for the fast computation of the general $N$-point spatial correlation functions of any discrete point set embedded within an Euclidean space of $mathbb{R}^n$. Utilizing the concepts of kd-trees and graph databases, we describe
how to count all possible $N$-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths $<r_{max}$. Through bench-marking we show the computational advantage of our new graph based algorithm over more traditional methods. We show that all 3-point configurations up to and beyond the Baryon Acoustic Oscillation scale ($sim$200 Mpc in physical units) can be performed on current SDSS data in reasonable time. Finally we present the first measurements of the 4-point correlation function of $sim$0.5 million SDSS galaxies over the redshift range $0.43<z<0.7$.
We perform an anisotropic clustering analysis of 1,133,326 galaxies from the Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS) Data Release (DR) 12 covering the redshift range $0.15<z<0.69$. The geometrical distortion
s of the galaxy positions, caused by incorrect cosmological model assumptions, are captured in the anisotropic two-point correlation function on scales 6 -- 40 $h^{-1}rm Mpc$. The redshift evolution of this anisotropic clustering is used to place constraints on the cosmological parameters. We improve the methodology of Li et al. 2016, to enable efficient exploration of high dimensional cosmological parameter spaces, and apply it to the Chevallier-Polarski-Linder parametrization of dark energy, $w=w_0+w_a{z}/({1+z})$. In combination with the CMB, BAO, SNIa and $H_0$ from Cepheid data, we obtain $Omega_m = 0.301 pm 0.008, w_0 = -1.042 pm 0.067, $ and $w_a = -0.07 pm 0.29$ (68.3% CL). Adding our new AP measurements to the aforementioned results reduces the error bars by $sim$30 -- 40% and improves the dark energy figure of merit by a factor of $sim$2. We check the robustness of the results using realistic mock galaxy catalogues.
We present measurements of the spatial clustering statistics in redshift space of various scalar field modified gravity simulations. We utilise the two-point and the three-point correlation functions to quantify the spatial distribution of dark matte
r halos within these simulations and thus discern between the models. We compare $Lambda$CDM simulations to various modified gravity scenarios and find consistency with previous work in terms of 2-point statistics in real and redshift-space. However using higher order statistics such as the three-point correlation function in redshift space we find significant deviations from $Lambda$CDM hinting that higher order statistics may prove to be a useful tool in the hunt for deviations from General Relativity.
The cosmic distance can be precisely determined using a `standard ruler imprinted by primordial baryon acoustic oscillation (hereafter BAO) in the early Universe. The BAO at the targeted epoch is observed by analysing galaxy clustering in redshift sp
ace (hereafter RSD) for which a theoretical formulation is not yet fully understood, and thus makes this methodology unsatisfactory. The BAO analysis following a full RSD modelling is contaminated by systematic uncertainties due to a non-linear smearing effects such as non-linear corrections and by random viral velocity of galaxies. However, the BAO can be probed independently of RSD contamination using the BAO peak positions located in the 2D anisotropic correlation function. A new methodology is presented to measure peak positions, to test whether it is also contaminated by the same systematics in RSD, and to provide the radial and transverse cosmic distances determined by the 2D BAO peak positions. We find that in our model independent anisotropic clustering analysis we can obtain about $2%$ and $5%$ constraints on $D_A$ and $H^{-1}$ respectively with current BOSS data, which is competitive with other analysis.
We present a new method for dealing with geometrical selection effects in galaxy surveys while using a multifractal framework. The power of multifractal analysis lies in its connection to higher order moments, in that it not only probes clustering on
different scales but also different densities. Therefore any incompleteness issues must be correctly addressed before blindly applying this technique to real survey data.
