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Periodicity significance testing with null-signal templates: reassessment of PTF's SMBH binary candidates
Jakob Robnik, Adrian E. Bayer, Maria Charisi +3
Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic s…
Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo
Minas Karamanis, Uroš Seljak
Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accur…
Sequential Kalman Tuning of the -preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems
Richard D. P. Grumitt, Minas Karamanis, Uroš Seljak
Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when appl…
A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks
Divij Sharma, Biwei Dai, Uros Seljak
Weak Lensing (WL) surveys are reaching unprecedented depths, enabling the investigation of very small angular scales. At these scales, nonlinear gravitational effects lead to highe…
A field-level emulator for modeling baryonic effects across hydrodynamic simulations
Divij Sharma, Biwei Dai, Francisco Villaescusa-Navarro +1
We develop a new and simple method to model baryonic effects at the field level relevant for weak lensing analyses. We analyze thousands of state-of-the-art hydrodynamic simulation…