5 papers
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics
Sebastian Springer, Andre Scaffidi, Maximilian Autenrieth +4
Detecting localized differences between two samples is a central task in scientific data analysis, required for the identification of signal events, regime changes, or model mismat…
Rotary Masked Autoencoders are Versatile Learners
Uros Zivanovic, Serafina Di Gioia, Andre Scaffidi +3
Applying Transformers to irregular time-series typically requires specializations to their baseline architecture, which can result in additional computational overhead and increase…
Primordial non-Gaussianity -- Fast simulations and persistent summary statistics
Juan Calles, Gabriella Contardo, Jorge Noreña +3
We investigate the sensitivity of topological and traditional summary statistics to primordial non-Gaussianity (PNG) using two suites of simulations. First, we introduce a new simu…
Cosmology with Persistent Homology: Parameter Inference via Machine Learning
Juan Calles, Jacky H. T. Yip, Gabriella Contardo +3
Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pi…
On the effects of parameters on galaxy properties in CAMELS and the predictability of
Gabriella Contardo, Roberto Trotta, Serafina Di Gioia +2
Recent analyses of cosmological hydrodynamic simulations from CAMELS have shown that machine learning models can predict the parameter describing the total matter content of the un…