arXiv subjects

Moonzarin Reza

Publications and source records attributed to Moonzarin Reza.

3 recordsLinked to original sources

arXiv2025

FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology

Precision cosmology requires robust, data-driven methods that can handle complex survey systematics without closed-form likelihoods. Simulation-Based Inference (SBI) meets this need through forward simulations that encode complex survey characteristics. Previous SBI analyses in supernova cosmology used SALT2 light-curve parameters as summary statistics; however, SALT2's two-basis spectral template imposes rigid modeling assumptions, motivating a more flexible representation. We present the first application of Functional Principal Component Analysis (FPCA) light-curve parameters as summary statistics for SBI-based cosmological inference under a non-flat $\Lambda$CDM model. On identically generated simulations, FPCA+SBI yields constraints comparable to both SALT2-based SBI and explicit-likelihood analyses, while providing more robust constraints on out-of-domain simulations than SALT2. Applying a model trained on LSST-like light curves to a spectroscopically confirmed DES Year 5 supernova sample, we recover constraints consistent with those of the DES collaboration to within 0.12 $\sigma$ and 0.23 $\sigma$ in $\Omega_m$ and $\Omega_\Lambda$, respectively, establishing generalizability to real survey data. Introducing host-dependent systematics through a mass-dependent dust extinction law, we find that FPCA-based summary statistics implicitly encode them, suggesting dedicated host modeling may be unnecessary. Combined with its demonstrated effectiveness for photometric classification, FPCA offers a foundation for unified, data-driven pipelines that jointly perform supernova classification and cosmological inference for upcoming wide-field photometric surveys.

astro-ph.IM↗

arXiv2024

Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

We test the robustness of simulation-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets. We construct ``simulations'' using analytical models for the galaxy cluster halo mass function (HMF) and for the observed richness (number of observed member galaxies) to train and test the SBI method. We compare the SBI parameter posterior samples to those from an MCMC analysis that uses the same analytical models to construct predictions of the observed data vector. The two methods exhibit comparable performance, with reliable constraints derived for the primary cosmological parameters, ($Ω_m$ and $σ_8$), and richness-mass relation parameters. We also perform out-of-domain tests with observables constructed from galaxy cluster-sized halos in the Quijote simulations. Again, the SBI and MCMC results have comparable posteriors, with similar uncertainties and biases. Unsurprisingly, upon evaluating the SBI method on thousands of simulated data vectors that span the parameter space, SBI exhibits worsened posterior calibration metrics in the out-of-domain application. We note that such calibration tests with MCMC is less computationally feasible and highlight the potential use of SBI to stress-test limitations of analytical models, such as in the use for constructing models for inference with MCMC.

astro-ph.CO↗

arXiv2022

Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference

Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulation-based inference (SBI) approach to estimate cosmological constraints from a simplified galaxy cluster observation analysis. Using data generated from the Quijote simulation suite and analytical models, we train a machine learning algorithm to learn the probability function between cosmological parameters and the possible galaxy cluster observables. The posterior distribution of the cosmological parameters at a given observation is then obtained by sampling the predictions from the trained algorithm. Our results show that the SBI method can successfully recover the truth values of the cosmological parameters within the 2σ limit for this simplified galaxy cluster analysis, and acquires similar posterior constraints obtained with a likelihood-based Markov Chain Monte Carlo method, the current state-of the-art method used in similar cosmological studies.

astro-ph.CO↗