Vivian Miranda

Assistant Professor

Stony Brook University

United States

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Vivian Miranda is an Assistant Professor at Stony Brook University in the United States. Her research focuses on cosmology, particularly in the areas of dark energy, galaxy bias, and the application of neural networks in astrophysical data analysis. She has contributed to recent publications on multiprobe data vectors and the dynamics of scalar fields, emphasizing the interplay between observational data and theoretical frameworks in understanding cosmic phenomena.

Articles (20)

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Collaborators (9)

Hans Arnold Winther

Associate professor

NORWAY

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Xiao Fang

University of Arizona

UNITED STATES

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Felipe Andrade Oliveira

University of Zurich

SWITZERLAND

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Jessica Muir

Assistant Professor

University of Cincinnati

UNITED STATES

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Seshadri Nadathur

University College London

UNITED KINGDOM

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Santiago Avila

Institut de Física d'Altes Energies

SPAIN

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Arya Farahi

Assistant Professor

University of Texas at Austin

UNITED STATES

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Pablo Lemos

University College London

UNITED KINGDOM

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Elisabeth Krause

Associate Professor

University of Arizona

UNITED STATES

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Vivian Miranda

Research Interests

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About

Articles (20)

Mitigation of nonlinear galaxy bias with a theoretical-error likelihood

Attention-based neural network emulators for multiprobe data vectors. I. Forecasting the growth-geometry split

Attention-based neural network emulators for multiprobe data vectors. II. Assessing tension metrics

Uniting the Observed Dynamical Dark Energy Preference with the Discrepancies in Ω m and H 0 across Cosmological Probes

Investigating late-time dark energy and massive neutrinos in light of DESI Y1 BAO

Modeling neutrino-induced scale-dependent galaxy clustering for photometric galaxy surveys

Early dark energy constraints with late-time expansion marginalization

Constraining baryonic physics with DES Y1 and Planck data: Combining galaxy clustering, weak lensing, and CMB lensing

Quantifying scalar field dynamics with DESI 2024 Y1 BAO measurements

Modeling nonlinear scales with the comoving Lagrangian acceleration method: Preparing for LSST Y1

Sound horizon independent constraints on early dark energy: the role of supernova data

Cosmology of dark energy radiation

Dark Energy Survey Year 3 Results

Growth and geometry split in light of the DES-Y3 survey

Cosmology from weak lensing, galaxy clustering, CMB lensing, and tSZ – I. 10 × 2pt modelling methodology

Growth and geometry split in light of the DES-Y3 survey

Accelerating cosmological inference with Gaussian processes and neural networks – an application to LSST Y1 weak lensing and galaxy clustering

Cosmic shear in harmonic space from the Dark Energy Survey Year 1 Data: compatibility with configuration space results

Interpreting internal consistency of DES measurements

Mitigating baryonic effects with a theoretical error covariance

Collaborators (9)

Hans Arnold Winther

Xiao Fang

Felipe Andrade Oliveira

Jessica Muir

Seshadri Nadathur

Santiago Avila

Arya Farahi

Pablo Lemos

Elisabeth Krause

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