Making the leap I: Modelling the reconstructed lensing convergence PDF from cosmic shear with survey masks and systematics

The last few years have seen the development of a promising theoretical framework for statistics of the cosmic large-scale structure -- the theory of large deviations (LDT) for modelling weak-lensing one-point statistics in the mildly non-linear regime. The goal of this series of papers is to make the leap and lay out the steps to perform an actual data analysis with this theoretical tool. Building upon the LDT framework, in this work (Paper I) we demonstrate how to accurately model the Probability Distribution Function (PDF) of a reconstructed Kaiser-Squires convergence field under a realistic mask, that of the third data release of the Dark Energy Survey (DES). We also present how weak lensing systematics and higher-order lensing corrections due to intrinsic alignments, shear biases, photo-$z$ errors and baryonic feedback can be incorporated in the modelling of the reconstructed convergence PDF. In an upcoming work (Paper II) we will then demonstrate the robustness of our modelling through simulated likelihood analyses, the final step required before applying our method to actual data.Comment: 39 pages, 9 figures and 2 tables. Accepted for publication in JCAP, comments welcom

The integrated 3-point correlation function of cosmic shear

We present the integrated 3-point shear correlation function $i\zeta_{\pm}$ -- a higher-order statistic of the cosmic shear field -- which can be directly estimated in wide-area weak lensing surveys without measuring the full 3-point shear correlation function, making this a practical and complementary tool to 2-point statistics for weak lensing cosmology. We define it as the 1-point aperture mass statistic $M_{\mathrm{ap}}$ measured at different locations on the shear field correlated with the corresponding local 2-point shear correlation function $\xi_{\pm}$. Building upon existing work on the integrated bispectrum of the weak lensing convergence field, we present a theoretical framework for computing the integrated 3-point function in real space for any projected field within the flat-sky approximation and apply it to cosmic shear. Using analytical formulae for the non-linear matter power spectrum and bispectrum, we model $i\zeta_{\pm}$ and validate it on N-body simulations within the uncertainties expected from the sixth year cosmic shear data of the Dark Energy Survey. We also explore the Fisher information content of $i\zeta_{\pm}$ and perform a joint analysis with $\xi_{\pm}$ for two tomographic source redshift bins with realistic shape-noise to analyse its power in constraining cosmological parameters. We find that the joint analysis of $\xi_{\pm}$ and $i\zeta_{\pm}$ has the potential to considerably improve parameter constraints from $\xi_{\pm}$ alone, and can be particularly useful in improving the figure of merit of the dynamical dark energy equation of state parameters from cosmic shear data.Comment: Accepted for publication in MNRAS; v2 matches the accepted manuscript; 18 pages + appendi

Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

The integrated shear 3-point correlation function $\zeta_{\pm}$ measures the correlation between the local shear 2-point function $\xi_{\pm}$ and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, $\zeta_{\pm}$ can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using $\zeta_{\pm}$. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on $N$-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to $\xi_{\pm}$-only, adding $\zeta_{\pm}$ can lead to $\approx 10-25\%$ improvements on the constraints of parameters like $A_s$ (or $\sigma_8$) and $w_0$. We find no evidence in $\xi_{\pm} + \zeta_{\pm}$ constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where $\zeta_{\pm}$ is measured, and of the strategy to estimate the covariance matrix ($N$-body vs. lognormal). Our analysis solidifies the strong potential of the $\zeta_{\pm}$ statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data.Comment: 21 pages, 11 figures, 3 tables. Comments welcom

C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses

Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference and various other tasks. However, many models which make use of CNNs are criticized as "black boxes" due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN) -- a fusion of CNN architecture with the framework of cosmological N-point correlation functions (NPCFs). We demonstrate that the output of this model can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we are able to open the "black box" by quantitatively ranking different orders of the interpretable convolution outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and Log-normal random fields and relating its outputs to the analytical NPCFs describing the two fields. Furthermore, we exhibit the model's ability to distinguish different dark energy scenarios ($w_0=-0.95$ and $-1.05$) using N-body simulated weak lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool with the potential to extract physical insights in a robust and explainable way from observational data.Comment: 19 pages, 8 figures, 5 tables; Comments are welcome

Climate change and agricultural adaptation in Sri Lanka: a review

Climate change is inevitable and will continue into the next century. Since the agricultural sector in Sri Lanka is one of the most vulnerable to climate change, a thorough understanding of climate transition is critical for formulating effective adaptation strategies. This paper provides an overview of the status of climate change and adaptation in the agricultural sector in Sri Lanka. The review clearly indicates that climate change is taking place in Sri Lanka in terms of rainfall variability and an increase in climate extremes and warming. A number of planned and reactive adaptation responses stemming from policy and farm-level decisions are reported. These adaptation efforts were fragmented and lacked a coherent connection to the national development policies and strategies. Research efforts are needed to develop and identify adaptation approaches and practices that are feasible for smallholder farmers, particularly in the dry zone where paddy and other food crops are predominately cultivated. To achieve the envisaged growth in the agricultural sector, rigorous efforts are necessary to mainstream climate change adaptation into national development policies and ensure that they are implemented at national, regional and local levels

The integrated 3-point correlation functions of cosmic shear and projected galaxy density fields

Mithilfe des schwachen Gravitationslinseneffekts versuchen Kosmologen, die Natur der dunklen Materie und dunklen Energie zu verstehen. Programme zur Auswertungen großskaliger Himmelsdurchmusterungen verfolgen dieses Ziel zurzeit hauptsächlich durch Messung und Analyse der 2-Punkt-Korrelationsfunktion (2PKF) des kosmischen Scherungsfelds, d.h. der geringfügigen Verzerrungen von Bildern von Hintergrundgalaxien durch gravitative Gezeitenfelder im Vordergrund. Die großskalige Struktur unseres Universums folgt jedoch keiner Gaußverteilung, und signifikante Anteile kosmologischer Informationen sind in höheren Momenten des Scherungsfeldes enthalten, welche nicht von diesen traditionellen 2-Punkt-Statistiken erfasst werden. Das zuverlässige Extrahieren dieser Information höherer Ordnung ist insbesondere daher erstrebenswert, weil sie Messungen kosmologischer Parameter stark verbessern kann. Im Laufe des letzten Jahrzehnts wurden eine Vielzahl von Statistiken höherer Ordnung vorgeschlagen, um höhere Momente des Scherungsfelds zu quantifizieren und zu nutzen. Solche Statistiken stehen aber in der Regel vor einer Reihe von Herausforderungen, insbesondere dem hohen numerischen Aufwand für ihre Schätzungen sowie schwerwiegenden Mängel in den theoretischen Modellen. Daher ist die Anwendung solcher Methoden bisher begrenzt geblieben. Um diese Hürden zu überwinden, entwickele und analysiere ich in dieser Arbeit eine neuartige Statistik höherer Ordnung des kosmischen Scherungsfelders namens integrierte 3-Punkt-Korrelationsfunktion (i3PKF) . Diese kann direkt anhand von kosmischem Scherungsdaten gemessen werden, indem lokale Messungen der Scherungs-2PKF mit der mittleren Scherungs-Aperturmasse innerhalb von lokalen sub-Volumen einer Himmelsdurchmusterung korreliert werden. Zusätzlich zu Details und Validierung dieser Methode präsentiere ich eine gemeinsame Analyse von Scherungs-i3PKF und den traditionellen Scherungs-2PKF in verblindeten Daten des drei-Jahres-Datensatzes des sogenannten Dark Energy Survey (DES). Unsere Ergebnisse zeigen, dass die von i3PKF zu 2PKF addierte Information signifikante Verbesserungen der Messungen kosmologischer Parameter ermöglicht, insbesondere eine Verbesserung von etwa 40% für den Zustandsgleichungsparameter der dunklen Energie, w0. Ermutigt von diesen Ergebnissen analysiere ich auch das Potential der integrierten 3-Punkt-Kreuzkorrelationen zwischen kosmischem Scherungs- und den Dichtefeldern. Damit erweitere ich das weitgenutzte Schema der 3x2-Punkt-Korrelationsfunktionen von Scherungs- und Galaxiendichtefeld auf ein praktisches Schema höherer Ordnung: die integrierten 6x3-Punkt-Korrelationsfunktionen. Damit können nicht nur kosmologische, sondern auch Parameter der Beziehung von Materie- und Galaxiendichte um 20-40% genauer bestimmt werden. Diese Ergebnisse motivieren daher zukünftige Anwendungen der Galaxie-Scherung integrierten 3PKF in Bedobachtungsdaten. Neben der integrierten 3PKF präsentiere ich auch meine Beteiligung an Fortschritten bei der theoretischen Modellierung einer anderen Statistik, welche ebenfalls in der Lage ist, einen umfassenderen Blick auf die großskalige Struktur des kosmischen Dichtefeldes zu werfen als die 2PKF: die gemeinsame Wahrscheinlichkeitsdichtefunktion des lokalen Materiedichtekontrasts und des Galaxiendichtekontrasts. Auch diese Analysemethode kann nicht nur kosmologische Parameter einschränken, sondern auch detaillierte Informationen über die Verbindung zwischen der unsichtbaren dunklen Materie und dem beobachteten Galaxiendichtefeld extrahieren. Die Beiträge dieser Arbeit ebnen somit den Weg für eine effiziente und effektive Ausnutzung der nicht-Gaußschen Information in aktuellen und zukünftigen Himmelsdurchmusterungen.A central goal of weak gravitational lensing cosmology is to understand the nature of dark matter and dark energy. The key program of ongoing lensing surveys involves 2-point correlation function (2PCF) analyses of the cosmic shear field, minute distortions of background galaxy images by intervening foreground large-scale structure (LSS) of our Universe, to constrain cosmological parameters. However, the LSS is non-Gaussian distributed with important information in its higher-order moments, not captured by these traditional 2-point statistics. Reliably extracting this higher-order information can therefore enable tighter cosmological parameter constraints. Over the past decade, a plethora of higher-order statistics have hence been proposed to harness this information from lensing data, but several challenges including computationally expensive estimation, significant deficiencies in theoretical models etc. have kept their analyses rather few and standalone. To overcome these hurdles, in this thesis, I have developed and analysed a novel weak lensing higher-order statistic called the integrated 3-point correlation function (i3PCF) which can be directly measured on cosmic shear data by correlating the local measurements of shear 2PCF with the mean lensing aperture mass signal within patches on the sky-survey. In addition to modelling and validating this statistic, me and my collaborators have also performed a joint analysis of i3PCF alongside the traditional shear 2PCF in the blinded cosmic shear Year 3 dataset of the Dark Energy Survey. Our results show that the addition of i3PCF to 2PCF yields significant tightening of cosmological parameter constraints, specifically 40% improvement on the dark energy equation of state parameter, w0. Encouraged by these promising results I have also proposed the integrated 3-point cross-correlations between cosmic shear and the foreground galaxy density fields thus extending the popular 3x2-point galaxy-shear correlation functions to the practical higher-order framework: integrated 6x3-point correlations which have the potential to bring further 20-40% improvements on not only cosmological but also on galaxy bias parameters which describe the connection between the invisible dark matter and the observed galaxy density field that traces it. These results therefore motivate future applications of the galaxy-shear integrated 3PCFs in real data. In addition to the integrated 3PCF, I also present my contributions to advances in the theoretical modelling of another statistic capable of obtaining a more comprehensive view of the LSS than 2PCFs: the joint probability density function of local matter and galaxy density fluctuations which can not only constrain cosmological parameters but also extract detailed information about the galaxy bias parameters. The contributions of this thesis will thus pave the way for a efficient and effective utilisation of the non-Gaussian information from current and upcoming galaxy imaging sky-surveys

The PDF perspective on the tracer-matter connection: Lagrangian bias and non-Poissonian shot noise

International audienceWe study the connection of matter density and its tracers from the probability density function (PDF) perspective. One aspect of this connection is the conditional expectation value 〈δ_tracer|δ_m〉 when averaging both tracer and matter density over some scale. We present a new way to incorporate a Lagrangian bias expansion of this expectation value into standard frameworks for modelling the PDF of density fluctuations and counts-in-cells statistics. Using N-body simulations and mock galaxy catalogues we confirm the accuracy of this expansion and compare it to the more commonly used Eulerian parametrization. For haloes hosting typical luminous red galaxies, the Lagrangian model provides a significantly better description of 〈δ_tracer|δ_m〉 at second order in perturbations. A second aspect of the matter-tracer connection is shot-noise, i.e. the scatter of tracer density around 〈δ_tracer|δ_m〉. It is well known that this noise can be significantly non-Poissonian and we validate the performance of a more general, two-parameter shot-noise model for different tracers and simulations. Both parts of our analysis are meant to pave the way for forthcoming applications to survey data

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN

Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein–protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques