64 citations · 234 across the 20 of their papers we have counts for
6 papers · 1 filter
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…