10 papers
Cosmo-Learn: code for learning cosmology using different methods and mock data
Reginald Christian Bernardo, Daniela Grandón, Jackson Levi Said +3
We present cosmo_learn, an open-source python-based software package designed to simulate cosmological data and perform data-driven inference using a range of modern statistical an…
Stochastic problems in pulsar timing
Reginald Christian Bernardo
Langevin stochastic differential equations provide a dynamical description of pulsar timing noise and gravitational wave background (GWB) signals. They are also central to state sp…
Simulating a Gaussian stochastic gravitational wave background signal in pulsar timing arrays
Reginald Christian Bernardo, Kin-Wang Ng
We revisit the theoretical modeling and simulation of a Gaussian stochastic gravitational wave background (SGWB) signal in a pulsar timing array (PTA). We show that the correlation…
Genetic algorithm demystified for cosmological parameter estimation
Reginald Christian Bernardo, Yun Chen
Genetic algorithm (GA) belongs to a class of nature-inspired evolutionary algorithms that leverage concepts from natural selection to perform optimization tasks. In cosmology, the…
Nature-inspired optimization, the Philippine Eagle, and cosmological parameter estimation
Reginald Christian Bernardo, Erika Antonette Enriquez, Renier Mendoza +2
Precise and accurate estimation of cosmological parameters is crucial for understanding the Universe's dynamics and addressing cosmological tensions. In this methods paper, we expl…
The Topology of Rayleigh-Levy Flights in Two Dimensions
Reginald Christian Bernardo, Stephen Appleby, Francis Bernardeau +1
Rayleigh-Lévy flights are simplified cosmological tools which capture certain essential statistical properties of the cosmic density field, including hierarchical structures in hi…