| CosmoPower: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys A Spurio Mancini, D Piras, J Alsing, B Joachimi, MP Hobson Monthly Notices of the Royal Astronomical Society 511 (2), 1771-1788, 2022 | 104 | 2022 |
| The mass dependence of dark matter halo alignments with large-scale structure D Piras, B Joachimi, BM Schäfer, M Bonamigo, S Hilbert, E van Uitert Monthly Notices of the Royal Astronomical Society 474 (1), 1165-1175, 2018 | 39 | 2018 |
| Discovering the building blocks of dark matter halo density profiles with neural networks L Lucie-Smith, HV Peiris, A Pontzen, B Nord, J Thiyagalingam, D Piras Physical Review D 105 (10), 103533, 2022 | 10 | 2022 |
| Accelerating Bayesian microseismic event location with deep learning A Spurio Mancini, D Piras, AMG Ferreira, MP Hobson, B Joachimi Solid Earth Discussions 2021, 1-36, 2021 | 10 | 2021 |
| A robust estimator of mutual information for deep learning interpretability D Piras, HV Peiris, A Pontzen, L Lucie-Smith, N Guo, B Nord Machine Learning: Science and Technology 4 (2), 025006, 2023 | 9 | 2023 |
| Representation Learning for High-Dimensional Data Collection under Local Differential Privacy A Mansbridge, G Barbour, D Piras, M Murray, C Frye, I Feige, D Barber arXiv preprint arXiv:2010.12464, 2020 | 7* | 2020 |
| Fast and realistic large-scale structure from machine-learning-augmented random field simulations D Piras, B Joachimi, F Villaescusa-Navarro Monthly Notices of the Royal Astronomical Society 520 (1), 668-683, 2023 | 6 | 2023 |
| CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators D Piras, AS Mancini The Open Journal of Astrophysics 6 (arXiv:2305.06347), 2023 | 5 | 2023 |
| Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism D Piras, A Spurio Mancini, AMG Ferreira, B Joachimi, MP Hobson Geophysical Journal International 232 (2), 1219-1235, 2023 | 2 | 2023 |
| A representation learning approach to probe for dynamical dark energy in matter power spectra D Piras, L Lombriser arXiv preprint arXiv:2310.10717, 2023 | 1 | 2023 |
| Toward Machine-learning-based Metastudies: Applications to Cosmological Parameters T Crossland, P Stenetorp, D Kawata, S Riedel, TD Kitching, A Deshpande, ... The Astrophysical Journal Supplement Series 269 (2), 34, 2023 | | 2023 |
| Towards Bayesian Full-Waveform Source Inversion using Simulation-Based Inference A Saoulis, A Ferreira, B Joachimi, A Spurio Mancini, D Piras EGU General Assembly Conference Abstracts, EGU-7939, 2023 | | 2023 |
| PhD thesis: Accelerating inference in cosmology and seismology with generative models D Piras UCL (University College London), 2022 | | 2022 |
| Master thesis: Measuring the slope of the intrinsic alignment amplitude as a function of mass using simulation and real data D Piras | | 2017 |
| Bachelor thesis: Testing phenomenological Dark Matter models using the Fermi LAT data D Piras | | 2015 |
Benjamin JoachimiProfessor of Astrophysics, University College LondonVerified email at ucl.ac.uk
