Search Results for author: Gianfranco Bertone

Found 6 papers, 3 papers with code

Towards constraining warm dark matter with stellar streams through neural simulation-based inference

1 code implementation30 Nov 2020 Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, Gilles Louppe

A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent contraints on the mass function of dark matter subhaloes, which in turn can be used to constrain the mass of the dark matter particle.

Bayesian Inference

Detecting dark matter around black holes with gravitational waves: Effects of dark-matter dynamics on the gravitational waveform

1 code implementation28 Feb 2020 Bradley J. Kavanagh, David A. Nichols, Gianfranco Bertone, Daniele Gaggero

A dark matter overdensity around a black hole may significantly alter the dynamics of the black hole's merger with another compact object.

General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology

Evidence of a population of dark subhalos from Gaia and Pan-STARRS observations of the GD-1 stream

no code implementations6 Nov 2019 Nilanjan Banik, Jo Bovy, Gianfranco Bertone, Denis Erkal, T. J. L. de Boer

New data from the $\textit{Gaia}$ satellite, when combined with accurate photometry from the Pan-STARRS survey, allow us to accurately estimate the properties of the GD-1 stream.

Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics

Primordial Black Holes as Silver Bullets for New Physics at the Weak Scale

1 code implementation3 May 2019 Gianfranco Bertone, Adam Coogan, Daniele Gaggero, Bradley J. Kavanagh, Christoph Weniger

Observational constraints on gamma rays produced by the annihilation of weakly interacting massive particles around primordial black holes (PBHs) imply that these two classes of Dark Matter candidates cannot coexist.

High Energy Physics - Phenomenology High Energy Astrophysical Phenomena

Accelerating the BSM interpretation of LHC data with machine learning

no code implementations8 Nov 2016 Gianfranco Bertone, Marc Peter Deisenroth, Jong Soo Kim, Sebastian Liem, Roberto Ruiz de Austri, Max Welling

The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators.

BIG-bench Machine Learning

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