7 papers
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…
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…
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…
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…
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…
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…