148 citations · 410 across the 9 of their papers we have counts for
4 papers · 1 filter
Constraining strongly coupled new physics from cosmic rays with machine learning techniques
Peter Schichtel, Michael Spannowsky, Philip Waite
Cosmic rays interacting with the atmosphere allow for the probing of fundamental interactions at ultra-high energies. We thus obtain limits on strongly coupled new physics models v…
Adversarially-trained autoencoders for robust unsupervised new physics searches
Andrew Blance, Michael Spannowsky, Philip Waite
Machine learning techniques in particle physics are most powerful when they are trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias on…
Mapping the shape of the scalar potential with gravitational waves
Mikael Chala, Valentin V. Khoze, Michael Spannowsky +1
We study the dependence of the observable stochastic gravitational wave background induced by a first-order phase transition on the global properties of the scalar effective potent…
Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
Maria Laura Piscopo, Michael Spannowsky, Philip Waite
Starting from the observation that artificial neural networks are uniquely suited to solving optimisation problems, and most physics problems can be cast as an optimisation task, w…