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We present a method to reconstruct the initial conditions of the universe using observed galaxy positions and luminosities under the assumption that the luminosities can be calibrated with weak lensing to give the mean halo mass. Our method relies on following the gradients of forward model and since the standard way to identify halos is non-differentiable and results in a discrete sample of objects, we propose a framework to model the halo position and mass field starting from the non-linear matter field using Neural Networks. We evaluate the performance of our model with multiple metrics. Our model is more than $95%$ correlated with the halo-mass fields up to $ksim 0.7 {rm h/Mpc}$ and significantly reduces the stochasticity over the Poisson shot noise. We develop a data likelihood model that takes our modeling error and intrinsic scatter in the halo mass-light relation into account and show that a displaced log-normal model is a good approximation to it. We optimize over the corresponding loss function to reconstruct the initial density field and develop an annealing procedure to speed up and improve the convergence. We apply the method to halo number densities of $bar{n} = 2.5times 10^{-4} -10^{-3}({rm h/Mpc})^3$, typical of current and future redshift surveys, and recover a Gaussian initial density field, mapping all the higher order information in the data into the power spectrum. We show that our reconstruction improves over the standard reconstruction. For baryonic acoustic oscillations (BAO) the gains are relatively modest because BAO is dominated by large scales where standard reconstruction suffices. We improve upon it by $sim 15-20%$ in terms of error on BAO peak as estimated by Fisher analysis at $z=0$. We expect larger gains will be achieved when applying this method to the broadband linear power spectrum reconstruction on smaller scales.
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
We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through cosmological simula
Perturbative quantities, such as the growth rate ($f$) and index ($gamma$), are powerful tools to distinguish different dark energy models or modified gravity theories even if they produce the same cosmic expansion history. In this work, without any
We present models representing the scattering of quasar radiation off free electrons and dust grains in geometries that approximate the structure of quasar host galaxies. We show that, for reasonable assumptions, scattering alone can easily produce r
Many scenarios of physics beyond the standard model predict new light, weakly coupled degrees of freedom, populated in the early universe and remaining as cosmic relics today. Due to their high abundances, these relics can significantly affect the ev