From 47551c976c743bdd9d5169f27bf9caebaf5a9b3c Mon Sep 17 00:00:00 2001 From: Ismael Mendoza Date: Sat, 6 Jul 2024 14:56:47 +0200 Subject: [PATCH] all the projects --- projects.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/projects.md b/projects.md index 7c3d9f3..6aefea1 100644 --- a/projects.md +++ b/projects.md @@ -6,9 +6,17 @@ permalink: /projects/ You can find details about what I have worked on most recently below. -## Statistcal framework for Galaxy-Halo Connection on N-body Simulations +## Shear Inference with Hamiltonian Monte Carlo -- I developed [MultiCAM](https://github.com/ismael-mendoza/multicam), a multi-variable extension to conditional abundance matching (CAM) that can be used to connect properties +- We leverage JAX-GalSim to Michael Schneider's importance sampling [approach](https://arxiv.org/abs/1411.2608) to develop a new Bayesian pipeline for cosmic shear inference. + +## Differentiable Forward Models of Galaxy Light Profiles + +- We developed [JAX-GalSim](https://github.com/GalSim-developers/JAX-GalSim), a GPU-accelerated and differentiable version of [GalSim](https://github.com/GalSim-developers/GalSim), which is currently under active development. + +## Statistical framework for Galaxy-Halo Connection on N-body Simulations + +- We developed [MultiCAM](https://github.com/ismael-mendoza/multicam), a multi-variable extension to conditional abundance matching (CAM) that can be used to connect properties of dark matter haloes with properties of galaxies.

@@ -17,9 +25,9 @@ of dark matter haloes with properties of galaxies. *Above is a comparison of the correlation strength between predictions of MultiCAM and CAM, where MultiCAM can use the full mass accretion history (MAH) of a dark matter haloes as features for prediction.* -## Machine Learning models for mitigating the galaxy-galaxy blending problem in cosmology +## Machine Learning models for mitigating the galaxy-galaxy blending problem in cosmological surveys -- I developed [BLISS](https://github.com/prob-ml/bliss) a machine learning model for probablistic inference of galaxy properties specifically targeted at blended galaxy fields. +- We developed [BLISS](https://github.com/prob-ml/bliss) a machine learning model for probablistic inference of galaxy properties specifically targeted at blended galaxy fields.

bliss @@ -29,7 +37,7 @@ of dark matter haloes with properties of galaxies. ## Framework for evaluating galaxy deblending algorithms -- I developed [BTK](https://github.com/LSSTDESC/BlendingToolKit) a software tool for simulating galaxy blends and consistent comparing galaxy deblenders based onv various metrics. +- We developed [BTK](https://github.com/LSSTDESC/BlendingToolKit) a software tool for simulating galaxy blends and consistent comparing galaxy deblenders based onv various metrics.

btk