A5034486773- Galaxies: Formation, Evolution, Phenomena
- Cosmology and Gravitation Theories
- COVID-19 epidemiological studies
- Astronomy and Astrophysical Research
- Gaussian Processes and Bayesian Inference
- Computational Physics and Python Applications
- Dark Matter and Cosmic Phenomena
- Scientific Research and Discoveries
- Geophysics and Gravity Measurements
- Stellar, planetary, and galactic studies
- Youth Substance Use and School Attendance
- Child and Adolescent Health
- demographic modeling and climate adaptation
- Homelessness and Social Issues
- Viral Infections and Outbreaks Research
- Remote Sensing in Agriculture
- Markov Chains and Monte Carlo Methods
- Black Holes and Theoretical Physics
- Gamma-ray bursts and supernovae
- Statistical Methods and Inference
- Relativity and Gravitational Theory
- Vaccine Coverage and Hesitancy
- Astrophysics and Cosmic Phenomena
- SARS-CoV-2 and COVID-19 Research
- Distributed and Parallel Computing Systems
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions
2023-2025
Center for Astrophysics Harvard & Smithsonian
2023-2025
Massachusetts Institute of Technology
2023-2025
Durham University
2019-2023
Heidelberg University
2018
X-Ray image enhancement, along with many other medical processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust efficient inference. Since institutions often do not have resources process label large quantity usually needed for neural network training, we design small datasets, while achieving state-of-the-art results. Our...
ABSTRACT The dependence of galaxy clustering on local density provides an effective method for extracting non-Gaussian information from surveys. two-point correlation function (2PCF) a complete statistical description Gaussian field. However, the late-time field becomes due to non-linear gravitational evolution and higher-order summary statistics are required capture all its cosmological information. Using Fisher formalism based halo catalogues Quijote simulations, we explore possibility...
ABSTRACT We present a clustering analysis of the BOSS DR12 CMASS galaxy sample, combining measurements two-point correlation function and density-split down to scale $1 \, h^{-1}\, \text{Mpc}$. Our theoretical framework is based on emulators trained high-fidelity mock catalogues that forward model cosmological dependence statistics within an extended-ΛCDM framework, including redshift-space Alcock–Paczynski distortions. base-ΛCDM finds ωcdm = 0.1201 ± 0.0022, σ8 0.792 0.034, ns 0.970 0.018,...
Abstract Shortly after its discovery, General Relativity (GR) was applied to predict the behavior of our Universe on largest scales, and later became foundation modern cosmology. Its validity has been verified a range scales environments from Solar system merging black holes. However, experimental confirmations GR cosmological have so far lacked accuracy one would hope for — applications those being largely based extrapolation there sometimes questioned in shadow discovery unexpected cosmic...
ABSTRACT Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian the density field that is not readily captured by standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to non-linear regime has so far remained challenging. We present a simulation-based model able capture dependence full shape density-split (DSC) intra-halo...
We introduce a diffusion-based generative model to describe the distribution of galaxies in our Universe directly as collection points 3D space (coordinates) optionally with associated attributes (e.g., velocities and masses), without resorting binning or voxelization. The custom diffusion can be used both for emulation, reproducing essential summary statistics galaxy distribution, well inference, by computing conditional likelihood field. demonstrate first application massive dark matter...
Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can used to study relationship between DM density fields and galaxy distributions. However, this sensitive assumptions in cosmology astrophysical processes embedded models, remain uncertain many aspects. In work, we develop a diffusion generative model reconstruct from galaxies. The trained on CAMELS simulation...
ABSTRACT In this series of papers, we present an emulator-based halo model for the non-linear clustering galaxies in modified gravity cosmologies. first paper, emulators following properties: mass function, concentration–mass relation and halo-matter cross-correlation function. The are trained on data extracted from forge bridge suites N-body simulations, respectively, two (MG) theories: f(R) gravity, DGP model, varying three standard cosmological parameters Ωm0, H0, σ8, one MG parameter,...
ABSTRACT We present mglens, a large series of modified gravity lensing simulations tailored for cosmic shear data analyses and forecasts in which cosmological parameters are varied simultaneously. Based on the forge bridgeN-body simulation suites presented companion papers, we construct 100 × 5000 deg2 mock Stage-IV from two 4D Latin hypercubes that sample gravitational f(R) nDGP gravity, respectively. These then used to validate our inference analysis pipeline based power spectrum,...
The current standard cosmological model is constructed within the framework of general relativity with a constant $\Lambda$, which often associated dark energy, and phenomenologically explains accelerated cosmic expansion. Understanding nature energy one most appealing questions in achieving self-consistent physical at scales. Modification could potentially provide more natural solution to growth structure sensitive constraining gravity models. In this paper, we aim concise introductory...
For the first time, we develop a simulation-based model for Minkowski functionals (MFs) of large-scale structure, which allows us to extract full information available from MFs (including both Gaussian and non-Gaussian part), apply it BOSS DR12 CMASS galaxy sample. Our is based on high-fidelity mock catalogs constructed \textsc{Abacus}\textsc{Summit} simulations using halo occupation distribution (HOD) framework, include redshift-space distortions Alcock-Paczynski distortions, incorporate...
We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlation between regions and tracer field. show these functions can be expressed terms of two-point probability function (PDF) field, or equivalently, its bias function. derive predictions using three levels approximation PDF: a bivariate Gaussian distribution, shifted log-normal prediction based Large Deviation Theory...
ABSTRACT In this series of papers, we present a simulation-based model for the non-linear clustering galaxies based on separate modelling in real space and velocity statistics. first paper, an emulator real-space correlation function galaxies, whereas real-to-redshift mapping statistics is presented second paper. Here, show that neural network galaxy trained data extracted from dark quest suite N-body simulations achieves sub-per cent accuracies scales 1 < r 30 $h^{-1} \,...
We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data that combines effective field theory (EFT) on large scales and numerical simulations small scales. The core ingredient of our prior density EFT parameters we extract from a suite 10500 based halo occupation distribution (HOD) model. measure field-level forward model, enables us cancel cosmic variance. On side, develop new efficient calculate transfer functions using...
Abstract Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative involving regression or classification problems. The cornerstone of modern cosmology the ability generate predictions for observed astrophysical fields from theory constrain physical observations using these predictions. This work uses a single diffusion model address interlinked objectives—as surrogate emulator...
To understand the nature of accelerated expansion Universe, we need to combine constraints on rate and growth structure. The is usually extracted from three dimensional galaxy maps by exploiting effects peculiar motions clustering. However, theoretical models probability distribution function (PDF) pairwise velocities are not accurate enough small scales reduce error predictions level required match precision expected for measurements future surveys. Here, improve modelling velocity using...
We introduce J une , an open-source framework for the detailed simulation of epidemics on basis social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled groups various sizes properties, such as households, schools workplaces, other activities using mixing matrices. provides suite flexible parametrizations that describe infectious diseases, how...
We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part the RAMP initiative. JUNE requires substantial computational resources to evaluate, making calibration general uncertainty analysis extremely challenging. describe employ quantification approaches Bayes linear emulation history matching mimic perform global parameter search, hence identifying regions space that produce acceptable matches observed data, demonstrating...
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities leaving world's most vulnerable populations affected. Given their density available infrastructure, refugee internally displaced person (IDP) settlements can be particularly susceptible disease spread. In this paper we present an agent-based modeling approach simulating in IDP under various non-pharmaceutical intervention...
ABSTRACT The coming generation of galaxy surveys will provide measurements clustering with unprecedented accuracy and data size, which allow us to test cosmological models at much higher precision than achievable previously. This means that we must have more accurate theoretical predictions compare future observational data. As a first step towards modelling the redshift space distortions (RSD) small-scale in modified gravity (MG) cosmologies, investigate validity so-called Skew-T (ST)...