Parameter inference and model comparison using theoretical predictions from noisy simulations

When inferring unknown parameters or comparing different models, data must be compared to underlying theory. Even if a model has no closed-form solution to derive summary statistics, it is often still possible to simulate mock data in order to generate theoretical predictions. For realistic simulations of noisy data, this is identical to drawing realizations of the data from a likelihood distribution. Though the estimated summary statistic from simulated data vectors may be unbiased, the estimator has variance which should be accounted for. We show how to correct the likelihood in the presence of an estimated summary statistic by marginalizing over the true summary statistic in the framework of a Bayesian hierarchical model. For Gaussian likelihoods where the covariance must also be estimated from simulations, we present an alteration to the Sellentin-Heavens corrected likelihood. We show that excluding the proposed correction leads to an incorrect estimate of the Bayesian evidence with JLA data. The correction is highly relevant for cosmological inference that relies on simulated data for theory (e.g. weak lensing peak statistics and simulated power spectra) and can reduce the number of simulations required.Comment: 9 pages, 6 figures, published by MNRAS. Changes: matches published version, added Bayesian hierarchical interpretation and probabilistic graphical mode

CRISPR/Cas9‐mediated genome editing: from basic research to translational medicine

The recent development of the CRISPR/Cas9 system as an efficient and accessible programmable genome-editing tool has revolutionized basic science research. CRISPR/Cas9 system-based technologies have armed researchers with new powerful tools to unveil the impact of genetics on disease development by enabling the creation of precise cellular and animal models of human diseases. The therapeutic potential of these technologies is tremendous, particularly in gene therapy, in which a patient-specific mutation is genetically corrected in order to treat human diseases that are untreatable with conventional therapies. However, the translation of CRISPR/Cas9 into the clinics will be challenging, since we still need to improve the efficiency, specificity and delivery of this technology. In this review, we focus on several in vitro, in vivo and ex vivo applications of the CRISPR/Cas9 system in human disease-focused research, explore the potential of this technology in translational medicine and discuss some of the major challenges for its future use in patients.Portuguese Foundation for Science and Technology: UID/BIM/04773/2013 1334 Spanish Ministry of Science, Innovation and Universities RTI2018-094629-B-I00 Portuguese Foundation for Science and Technology SFRH/BPD/100434/2014 European Union (EU) 748585 LPCC-NRS/Terry Fox grantsinfo:eu-repo/semantics/publishedVersio

Radio Galaxy Detection in the Visibility Domain

We explore a new Bayesian method of detecting galaxies from radio interferometric data of the faint sky. Working in the Fourier domain, we fit a single, parameterised galaxy model to simulated visibility data of star-forming galaxies. The resulting multimodal posterior distribution is then sampled using a multimodal nested sampling algorithm such as MultiNest. For each galaxy, we construct parameter estimates for the position, flux, scale-length and ellipticities from the posterior samples. We first test our approach on simulated SKA1-MID visibility data of up to 100 galaxies in the field of view, considering a typical weak lensing survey regime (SNR $\ge 10$) where 98% of the input galaxies are detected with no spurious source detections. We then explore the low SNR regime, finding our approach reliable in galaxy detection and providing in particular high accuracy in positional estimates down to SNR $\sim 5$. The presented method does not require transformation of visibilities to the image domain, and requires no prior knowledge of the number of galaxies in the field of view, thus could become a useful tool for constructing accurate radio galaxy catalogs in the future.Comment: 11 pages, 11 figures. Accepted for publication in MNRA

Cross correlation surveys with the Square Kilometre Array

By the time that the first phase of the Square Kilometre Array is deployed it will be able to perform state of the art Large Scale Structure (LSS) as well as Weak Gravitational Lensing (WGL) measurements of the distribution of matter in the Universe. In this chapter we concentrate on the synergies that result from cross-correlating these different SKA data products as well as external correlation with the weak lensing measurements available from CMB missions. We show that the Dark Energy figures of merit obtained individually from WGL/LSS measurements and their independent combination is significantly increased when their full cross-correlations are taken into account. This is due to the increased knowledge of galaxy bias as a function of redshift as well as the extra information from the different cosmological dependences of the cross-correlations. We show that the cross-correlation between a spectroscopic LSS sample and a weak lensing sample with photometric redshifts can calibrate these same photometric redshifts, and their scatter, to high accuracy by modelling them as nuisance parameters and fitting them simultaneously cosmology. Finally we show that Modified Gravity parameters are greatly constrained by this cross-correlations because weak lensing and redshift space distortions (from the LSS survey) break strong degeneracies in common parameterisations of modified gravity.Comment: 12 pages, 3 figures. This article is part of the 'Cosmology Chapter, Advancing Astrophysics with the SKA (AASKA14) Conference, Giardini Naxos (Italy), June 9th-13th 2014

PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks

In this paper we introduce PkANN, a freely available software package for interpolating the non-linear matter power spectrum, constructed using Artificial Neural Networks (ANNs). Previously, using Halofit to calculate matter power spectrum, we demonstrated that ANNs can make extremely quick and accurate predictions of the power spectrum. Now, using a suite of 6380 N-body simulations spanning 580 cosmologies, we train ANNs to predict the power spectrum over the cosmological parameter space spanning $3\sigma$ confidence level (CL) around the concordance cosmology. When presented with a set of cosmological parameters ($\Omega_{\rm m} h^2, \Omega_{\rm b} h^2, n_s, w, \sigma_8, \sum m_\nu$ and redshift $z$), the trained ANN interpolates the power spectrum for $z\leq2$ at sub-per cent accuracy for modes up to $k\leq0.9\,h\textrm{Mpc}^{-1}$. PkANN is faster than computationally expensive N-body simulations, yet provides a worst-case error $<1$ per cent fit to the non-linear matter power spectrum deduced through N-body simulations. The overall precision of PkANN is set by the accuracy of our N-body simulations, at 5 per cent level for cosmological models with $\sum m_\nu<0.5$ eV for all redshifts $z\leq2$. For models with $\sum m_\nu>0.5$ eV, predictions are expected to be at 5 (10) per cent level for redshifts $z>1$ ($z\leq1$). The PkANN interpolator may be freely downloaded from http://zuserver2.star.ucl.ac.uk/~fba/PkANNComment: 21 pages, 14 figures, 2 table