Machine Learning & AI

Leveraging the latest AI technologies

I have used machine / deep learning methods, and AI approaches more generally, in a number of different projects. These include direct application to observational data, to increase the accuracy and speed of predictions.

Most of my applications of these methods has, however, been to increase the efficiency and applicability of numerical simulations. Asd an example, in (Lovell et al., 2022) we used simple tree based regression methods to learn the galaxy-halo relationship in a series of zoom simulations, and then apply this back to a large parent only volume. This allowed us to predict clustering statistics over large volumes and dynamic ranges, but using the results of high fidelity hydrodynamic simulations.

Figure showing the frameowrk used in (Lovell et al., 2022) to learn the galaxy-halo relationship from a series of zooms, and apply to a larent dark-matter only parent simulation.

In (Lovell et al., 2023) we extended this framework using the CAMELS simulations combined with normalising flows, for probabilistic modelling of the galaxy-halo relationship. Finally, Maxwell Maltz, a student at the University of Sussex, has explored adding quantitative scatter to tree based predictions, to better recover the covariances in common distribution functions (Maltz et al., 2024).

I have also explored convolutional neural networks applied to spectra for star formation history recovery (Lovell et al., 2019). With collaborators I have worked on tree models applied to synthetic absorption spectra (Appleby et al., 2023), graph neural networks and symbolic regression for cosmological inference (Shao et al., 2023), dimensionality reduction techniques for representing SPS models (Lovell, 2021), and explored how to estimate generalization error (Acquaviva et al., 2020).

Simulation Based Inference approaches can be considered a branch of AI / deep learning, and I have done considerable work applying these methods to numerical simulations (Lovell et al., 2024).

References

2024

  1. arXiv
    First Light and Reionisation Epoch Simulations (FLARES) XVII: Learning the galaxy-halo connection at high redshifts
    Maxwell G. A. Maltz, Peter A. Thomas, Christoper C. Lovell, and 6 more authors
    Oct 2024
    arXiv:2410.24082
  2. arXiv
    Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
    Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho, and 9 more authors
    Nov 2024
    arXiv:2411.13960

2023

  1. ICML
    A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship
    Christopher C. Lovell, Sultan Hassan, Daniel Anglés-Alcázar, and 8 more authors
    ICML, Jul 2023
    Publication Title: arXiv e-prints ADS Bibcode: 2023arXiv230706967L
  2. MNRAS
    Mapping circumgalactic medium observations to theory using machine learning
    Sarah Appleby, Romeel Davé, Daniele Sorini, and 2 more authors
    MNRAS, Oct 2023
    Publisher: OUP ADS Bibcode: 2023MNRAS.525.1167A
  3. ApJ
    A Universal Equation to Predict Ωm from Halo and Galaxy Catalogs
    Helen Shao, Natalí S. M. Santi, Francisco Villaescusa-Navarro, and 14 more authors
    ApJ, Oct 2023
    Publisher: IOP ADS Bibcode: 2023ApJ...956..149S

2022

  1. MNRAS
    A machine learning approach to mapping baryons on to dark matter haloes using the EAGLE and C-EAGLE simulations
    Christopher C. Lovell, Stephen M. Wilkins, Peter A. Thomas, and 4 more authors
    MNRAS, Feb 2022
    ADS Bibcode: 2022MNRAS.509.5046L

2021

  1. A&C
    Sengi: A small, fast, interactive viewer for spectral outputs from stellar population synthesis models
    C. C. Lovell
    A&C, Jan 2021

2020

  1. NeurIPS
    Debunking Generalization Error or: How I Learned to Stop Worrying and Love My Training Set
    Viviana Acquaviva, Chistopher Lovell, and Emille Ishida
    NeurIPS, Nov 2020

2019

  1. MNRAS
    Learning the relationship between galaxies spectra and their star formation histories using convolutional neural networks and cosmological simulations
    Christopher C. Lovell, Viviana Acquaviva, Peter A. Thomas, and 3 more authors
    MNRAS, Dec 2019