8 papers
Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
Ben Moews, Gbenga Ibikunle
Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep lea…
On the road to percent accuracy II: calibration of the non-linear matter power spectrum for arbitrary cosmologies
Benjamin Giblin, Matteo Cataneo, Ben Moews +1
We introduce an emulator approach to predict the non-linear matter power spectrum for broad classes of beyond-CDM cosmologies, using only a suite of CDM -body simulations.…
Photometry of high-redshift blended galaxies using deep learning
Alexandre Boucaud, Marc Huertas-Company, Caroline Heneka +11
The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At su…
Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
Ben Moews, Joe Zuntz
We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel computing resources. G…
Stress testing the dark energy equation of state imprint on supernova data
Ben Moews, Rafael S. de Souza, Emille E. O. Ishida +4
This work determines the degree to which a standard Lambda-CDM analysis based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-d…
Lagged correlation-based deep learning for directional trend change prediction in financial time series
Ben Moews, J. Michael Herrmann, Gbenga Ibikunle
Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriousl…