63 citations · 224 across the 12 of their papers we have counts for
12 papers
Dark Energy Survey Year 3 results: optimized CDM simulation-based inference with weak lensing map-level hybrid statistics
J. Williamson, T. L. Makinen, N. Porqueres +85
We present cosmological constraints from the Dark Energy Survey Year 3 (DES Y3) weak lensing data using hierarchical hybrid statistics within a Bayesian simulation-based inference…
Hybrid Summary Statistics
T. Lucas Makinen, Ce Sui, Benjamin D. Wandelt +2
We present a way to capture high-information posteriors from training sets that are sparsely sampled over the parameter space for robust simulation-based inference. In physical inf…
CHARM: Creating Halos with Auto-Regressive Multi-stage networks
Shivam Pandey, Chirag Modi, Benjamin D. Wandelt +7
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations…
Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference
Denise Lanzieri, Justine Zeghal, T. Lucas Makinen +3
Traditionally, weak lensing cosmological surveys have been analyzed using summary statistics motivated by their analytically tractable likelihoods, or by their ability to access hi…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs
T. Lucas Makinen, Justin Alsing, Benjamin D. Wandelt
Set-based learning is an essential component of modern deep learning and network science. Graph Neural Networks (GNNs) and their edge-free counterparts Deepsets have proven remarka…
Field-level inference of cosmic shear with intrinsic alignments and baryons
Natalia Porqueres, Alan Heavens, Daniel Mortlock +2
We construct a field-based Bayesian Hierarchical Model for cosmic shear that includes, for the first time, the important astrophysical systematics of intrinsic alignments and baryo…
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
T. Lucas Makinen, Tom Charnock, Pablo Lemos +3
We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter co…
Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields
T. Lucas Makinen, Tom Charnock, Justin Alsing +1
We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two case…
Water Production Rates from SOHO/SWAN Observations of Six comets: 2017-2020
Michael R. Combi, Terhi Mäkinen, Jean-Loup. Bertaux +3
The Solar Wind ANisotropies (SWAN) all-sky hydrogen Lyman-alpha camera on the SOlar and Heliosphere Observer (SOHO) satellite makes daily images of the entire sky to monitor the th…
Comet 21P/Giacobini-Zinner: Water Production Activity over 20 Years with SOHO/SWAN
Michael R. Combi, Terhi Mäkinen, Jean-Loup. Bertaux +3
In 1985 Comet 21P/Giacobini-Zinner was the first comet visited by a spacecraft, the International Cometary Explorer (ICE) satellite, several months before the armada of Halley spac…
deep21: a Deep Learning Method for 21cm Foreground Removal
T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro +4
We seek to remove foreground contaminants from 21cm intensity mapping observations. We demonstrate that a deep convolutional neural network (CNN) with a UNet architecture and three…