64 citations · 234 across the 20 of their papers we have counts for
20 papers
Morphology of Radio Sources in Representation Space
Nicolas Baron Perez, Marcus Brüggen, Luisa Lucie-Smith
Understanding radio source morphologies and their classification remains challenging. We previously developed a deep clustering method based on self-supervised learning to classify…
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler
We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of…
QUEST (Quasar Unsupervised Encoder and Synthesis Tool): A machine learning framework to generate quasar spectra
F. Guarneri, J. T. Schindler, R. A. Meyer +5
Quasars at the redshift frontier (z > 7.0) are fundamental probes of black hole (BH) growth and evolution but notoriously difficult to identify. At these redshifts, machine learnin…
Unveiling the small-scale web around galaxies with miniJPAS and DESI
Daniela Galárraga-Espinosa, Guinevere Kauffmann, Silvia Bonoli +23
We present the first statistical observational study detecting filaments in the immediate surroundings of galaxies, i.e. the local web of galaxies. Simulations predict that cold ga…
Cosmological feedback from a halo assembly perspective
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +6
The impact of feedback from galaxy formation on cosmological probes is typically quantified in terms of the suppression of the matter power spectrum in hydrodynamical compared to g…
CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum
Davide Piras, Laura Herold, Luisa Lucie-Smith +1
Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological mo…
Classification of Radio Sources Through Self-Supervised Learning
Nicolas Baron Perez, Marcus Brüggen, Gregor Kasieczka +1
The morphology of radio galaxies is indicative of their interaction with their surroundings, among other effects. Since modern radio surveys contain a large number of radio sources…
Cosmological Inference with Cosmic Voids and Neural Network Emulators
Kai Lehman, Nico Schuster, Luisa Lucie-Smith +3
Cosmic Voids are a promising probe of cosmology for spectroscopic galaxy surveys due to their unique response to cosmological parameters. Their combination with other probes promis…
Deep learning insights into non-universality in the halo mass function
Ningyuan Guo, Luisa Lucie-Smith, Hiranya V. Peiris +2
The abundance of dark matter haloes is a key cosmological probe in forthcoming galaxy surveys. The theoretical understanding of the halo mass function (HMF) is limited by our incom…
A deep learning model for the density profiles of subhaloes in IllustrisTNG
Luisa Lucie-Smith, Giulia Despali, Volker Springel
We present a machine-learning-based model for the total density profiles of subhaloes with masses in the IllustrisTNG100 simulation. T…
Explaining dark matter halo density profiles with neural networks
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen
We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in th…
Halo assembly bias from a deep learning model of halo formation
Luisa Lucie-Smith, Alexandre Barreira, Fabian Schmidt
We build a deep learning framework that connects the local formation process of dark matter halos to the halo bias. We train a convolutional neural network (CNN) to predict the fin…
A robust estimator of mutual information for deep learning interpretability
Davide Piras, Hiranya V. Peiris, Andrew Pontzen +3
We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI f…
Insights into the origin of halo mass profiles from machine learning
Luisa Lucie-Smith, Susmita Adhikari, Risa H. Wechsler
The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable ma…
Discovering the building blocks of dark matter halo density profiles with neural networks
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +3
The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network mod…
The causal effect of environment on halo mass and concentration
Corentin Cadiou, Andrew Pontzen, Hiranya V. Peiris +1
Understanding the impact of environment on the formation and evolution of dark matter halos and galaxies is a crucial open problem. Studying statistical correlations in large simul…
Deep learning insights into cosmological structure formation
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +2
The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a…
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
Brian Nord, Andrew J. Connolly, Jamie Kinney +35
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical mode…
An interpretable machine learning framework for dark matter halo formation
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen
We present a generalization of our recently proposed machine learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact…
Machine learning cosmological structure formation
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +1
We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and th…