Project:	Accelerating models for the extragalactic background light in preparation for SPHEREx (JPL AO No. 15241)
Disciplines:	Astrophysics, Statistics
Mentor:	Tzu-Ching Chang, (JPL), tzu-ching.chang@jpl.nasa.gov, Phone: (626) 298-5446
Background:	The extragalactic background light (EBL), i.e., diffuse emission originating from beyond our own galaxy, encodes the growth of galaxies and black holes across all of cosmic history. Previous efforts to measure the EBL, ranging from sounding rockets to the analysis of archival Hubble data, have placed interesting constraints on models of star formation and yielded tantalizing clues of 'extra' stellar mass, either in a diffuse reservoir surrounding galaxies at low redshifts or perhaps a new population of very distant galaxies forming during the Epoch of Reionization. Given the limited spectral coverage of these datasets, which cover just a few broad photometric bands around 1 micron, their interpretation remains especially challenging. NASA's SPHEREx mission, to launch in early 2025, will map the entire sky in ~100 narrow near-infrared bands from 0.75-5 microns, yielding an unprecedented new EBL dataset with which to constrain the properties of galaxies across cosmic history.
Description:	The SPHEREx EBL team is currently developing a suite of models that will be used to interpret data from its deep field survey. Among them is a semi-empirical model of galaxy formation that can be used to generate predictions for the EBL power spectrum -- a key statistical measure of the EBL targeted by SPHEREx -- directly in a "halo model" framework, which side-steps the computationally-demanding approach of generating model universes at the map level. These halo models, though relatively cheap individually, have ~40-60 free parameters and so must be run ~millions of times in order explore the entire parameter space adequately. As a result, using these models to fit SPHEREx power spectra directly and constrain the model's parameters remains a non-trivial computational problem. The goal of this project is to develop an emulator for our models, which takes as input a large database of models and learns how to translate model parameters directly into summary statistics like the EBL power spectrum. Such an emulator could dramatically accelerate parameter inference and so allow us to afford additional complexity in the models (e.g., more source populations, parameters, physical processes), and potentially become an integral piece of the SPHEREx "component separation" pipeline.
References:	https://spherex.caltech.edu, https://arxiv.org/abs/1412.4872, https://github.com/mirochaj/ares, https://arxiv.org/abs/1808.05964,
Student Requirements:	Knowledge of Cosmology and Astrophysics. Skilled in Python coding. Experience with emulation and/or machine learning desirable but not required.
Location / Safety:	Project building and/or room locations: . Student will need special safety training: .
Programs:	This AO can be done under the following programs:   Program    Available To        SURF@JPL    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.