A5043299159- Galaxies: Formation, Evolution, Phenomena
- Astronomy and Astrophysical Research
- Statistical and numerical algorithms
- Gamma-ray bursts and supernovae
- Advanced Vision and Imaging
- Gaussian Processes and Bayesian Inference
- Computer Graphics and Visualization Techniques
- Scientific Research and Discoveries
- Statistics Education and Methodologies
- Geological Modeling and Analysis
- Stellar, planetary, and galactic studies
- Astronomical Observations and Instrumentation
- Biblical Studies and Interpretation
- Historical Astronomy and Related Studies
- Adaptive optics and wavefront sensing
- Remote Sensing in Agriculture
- Advanced Statistical Methods and Models
- Historical and Architectural Studies
- Geophysics and Gravity Measurements
- Cosmology and Gravitation Theories
- Methane Hydrates and Related Phenomena
- demographic modeling and climate adaptation
- Algorithms and Data Compression
- Radio Astronomy Observations and Technology
University College London
2020-2023
We present a simulation-based inference (SBI) cosmological analysis of cosmic shear two-point statistics from the fourth weak gravitational lensing data release ESO Kilo-Degree Survey (KiDS-1000). KiDS-SBI efficiently performs non-Limber projection matter power spectrum via Levin 's method and constructs log-normal random fields on curved sky for arbitrary cosmologies, including effective prescriptions intrinsic alignments baryonic feedback. The forward model samples realistic galaxy...
We present the methodology for a joint cosmological analysis of weak gravitational lensing from fourth data release ESO Kilo-Degree Survey (KiDS-1000) and galaxy clustering partially overlapping Baryon Oscillation Spectroscopic (BOSS) 2-degree Field Lensing (2dFLenS). Cross-correlations between BOSS 2dFLenS positions source ellipticities have been incorporated into analysis, necessitating development hybrid model non-linear scales that blends perturbative non-perturbative approaches, an...
ABSTRACT Gravitational lensing magnification modifies the observed spatial distribution of galaxies and can severely bias cosmological probes large-scale structure if not accurately modelled. Standard approaches to modelling this may be applicable in practice as many galaxy samples have complex, often implicit, selection functions. We propose test a procedure quantify induced clustering galaxy–galaxy (GGL) signals subject function beyond simple flux limit. The method employs realistic mock...
ABSTRACT The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared analytic models in a Gaussian likelihood with pre-computed covariance. To overcome the idealizing assumptions about form of and complexity inherent approach, we investigate simulation-based (SBI), which learns as probability density parameterized by neural network. We construct suites simulated statistics, exactly distributed for validation purposes, most recent...
We present a simulation-based inference (SBI) cosmological analysis of cosmic shear two-point statistics from the fourth weak gravitational lensing data release ESO Kilo-Degree Survey (KiDS-1000). KiDS-SBI efficiently performs non-Limber projection matter power spectrum via Levin's method, and constructs log-normal random fields on curved sky for arbitrary cosmologies, including effective prescriptions intrinsic alignments baryonic feedback. The forward model samples realistic galaxy...
Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about large-scale structure (LSS) Universe thanks to high number density and extensive depth data. However, complicated selection effects in wide deep surveys can potentially cause significant bias angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure 2PCF newly published KiDS-Legacy sample. Given an r-band $5σ$ magnitude limit $24.8$...
The stellar-to-halo mass relation (SHMR) embodies the joint evolution of galaxies and their host dark matter halos. However, is poorly constrained at sub-galactic masses, because stellar emission from such objects so faint. it possible to directly detect halos along line sight a strong gravitational lens, when they perturb one its multiple images. Space telescopes including Euclid, CSST, Roman will soon discover millions galaxy-galaxy lensing systems. We simulate Euclid-like imaging typical...
We constrain the luminosity and redshift dependence of intrinsic alignment (IA) a nearly volume-limited sample luminous red galaxies selected from fourth public data release Kilo-Degree Survey (KiDS-1000). To measure shapes galaxies, we used two complementary algorithms, finding consistent IA measurements for overlapping galaxy sample. The global significance detection across our independent samples, with favoured method shape estimation, is $\sim10.7\sigma$. find no significant signal in...
We present GLASS, the Generator for Large Scale Structure, a new code simulation of galaxy surveys cosmology, which iteratively builds light cone with matter, galaxies, and weak gravitational lensing signals as sequence nested shells. This allows us to create deep realistic simulations at high angular resolution on standard computer hardware low resource consumption. GLASS also introduces technique generate transformations Gaussian random fields (including lognormal) essentially arbitrary...
We introduce OneCovariance, an open-source software designed to accurately compute covariance matrices for arbitrary set of two-point summary statistics across a variety large-scale structure tracers. Utilising the halo model, we estimate statistical properties matter and biased tracer fields, incorporating all Gaussian, non-Gaussian, super-sample terms. The flexible configuration permits user-specific parameters, such as complexity survey geometry, occupation distribution employed define...
We explore the enhanced self-calibration of photometric galaxy redshift distributions, $n(z)$, through combination up to six two-point functions. Our $\rm 3\times2pt$ configuration is comprised shear, spectroscopic clustering, and spectroscopic-photometric galaxy-galaxy lensing (GGL). further include cross-clustering; GGL; auto-clustering, using shear sample as density tracer. perform simulated likelihood forecasts cosmological nuisance parameter constraints for Stage-III- Stage-IV-like...
Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about large-scale structure (LSS) Universe thanks to large number density and extensive depth data. However, complicated selection effects in wide deep surveys will potentially cause significant bias angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure 2PCF newly published KiDS-Legacy sample. Given an $r$-band $5\sigma$ magnitude limit...
We present GLASS, the Generator for Large Scale Structure, a new code simulation of galaxy surveys cosmology, which iteratively builds light cone with matter, galaxies, and weak gravitational lensing signals as sequence nested shells. This allows us to create deep realistic simulations at high angular resolution on standard computer hardware low resource consumption. GLASS also introduces technique generate transformations Gaussian random fields (including lognormal) essentially arbitrary...
We present GLASS, the Generator for Large Scale Structure, a new code simulation of galaxy surveys cosmology, which iteratively builds light cone with matter, galaxies, and weak gravitational lensing signals as sequence nested shells. This allows us to create deep realistic simulations at high angular resolution on standard computer hardware low resource consumption. GLASS also introduces technique generate transformations Gaussian random fields (including lognormal) essentially arbitrary...
The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared analytic models in a Gaussian likelihood with pre-computed covariance. To overcome the idealising assumptions about form of and complexity inherent approach, we investigate simulation-based (SBI), which learns as probability density parameterised by neural network. We construct suites simulated, exactly Gaussian-distributed vectors for most recent Kilo-Degree Survey (KiDS)...