About

Vivian Miranda is an Assistant Professor at the C.N. Yang Institute for Theoretical Physics at Stony Brook University. Her research lies at the intersection of theoretical cosmology and data science, with a focus on understanding dark energy, inflation, and the fundamental physics driving cosmic acceleration. Her work combines theoretical modeling, numerical simulations, and machine learning to probe the nature of dark energy and the evolution of the universe. She leverages data from major cosmological surveys—including the Dark Energy Survey, the Rubin Observatory Legacy Survey of Space and Time (LSST), and the Nancy Grace Roman Space Telescope—to test fundamental physics through multi-probe analyses of weak lensing, galaxy clustering, and supernova observations. Miranda has developed novel statistical and AI-driven methods for cosmological inference, contributing to advances in how the field interprets tensions among key parameters such as the Hubble constant and matter density. Through her leadership in large collaborations and NASA science working groups, her work is shaping the next generation of precision cosmology.

Research topics

• Physics
• Astrophysics
• Theoretical physics
• Particle physics
• Quantum mechanics
• Astronomy
• Classical mechanics

Selected publications

• Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part III: Modeling The Next Generation Surveys

  ArXiv.org · 2025-05-28

  preprintOpen access

  Machine learning can accelerate cosmological inferences that involve many sequential evaluations of computationally expensive data vectors. Previous works in this series have examined how machine learning architectures impact emulator accuracy and training time for optical shear and galaxy clustering 2-point function. In this final manuscript, we explore neural network performance when emulating Cosmic Microwave Background temperature and polarization power spectra. We maximize the volume of applicability in the parameter space of our emulators within the standard $Λ$-cold-dark-matter model while ensuring that errors are below cosmic variance. Relative to standard multi-layer perceptron architectures, we find the dot-product-attention mechanism reduces the number of outliers among testing cosmologies, defined as the fraction of testing points with $Δχ^2 > 0.2$ relative to \textsc{CAMB} outputs, for a wide range of training set sizes. Such precision enables attention-based emulators to be directly applied to real data without requiring any additional correction via importance sampling. Combined with pre-processing techniques and optimized activation and loss functions, attention-based models can meet the precision criteria set by current and future CMB and lensing experiments. For each of Planck, Simons Observatory, CMB S4, and CMB HD, we find the fraction of outlier points to be less than $10\%$ with around $2\times10^5$ to $4\times10^5$ training data vectors. We further explore the applications of these methods to supernova distance, weak lensing, and galaxy clustering, as well as alternative architectures and pre-processing techniques.

  PublisherOA PDFDOI

• Modeling nonlinear scales for dynamical dark energy cosmologies with COLA

  arXiv (Cornell University) · 2025-10-16

  preprintOpen accessSenior author

  Upcoming galaxy surveys will bring a wealth of information about the clustering of matter, but modeling small-scale structure beyond $Λ$CDM remains computationally challenging. While accurate N-body emulators exist to model the matter power spectrum for $Λ$CDM and some limited extensions, it's unfeasible to generate N-body simulation suites for all candidate models. Motivated by recent hints of an evolving dark energy equation of state, we assess the viability of employing the COmoving Lagrangian Acceleration (COLA) method to generate simulation suites assuming the $w_0w_a$ dark energy model. We combine COLA simulations with an existing high-precision $Λ$CDM emulator to extend its predictions into new regions of parameter space. We assess the precision of our emulator at the level of the matter power spectrum, finding that our emulator can reproduce the nonlinear boosts from EuclidEmulator2 at less than $2\%$ error. Moreover, we perform an analysis of a simulated cosmic shear survey akin to the Legacy Survey of Space and Time (LSST) first year of observations, assessing the differences in parameter constraints between our COLA-based emulator and the benchmark emulator. We find our emulator to be in excellent agreement with the benchmark, achieving less than $0.3σ$ shifts in cosmological parameters. We compare our emulator's performance to a commonly used approach: assuming the $Λ$CDM boost can be employed for extended parameter spaces without modification. We find that our emulator yields a significantly smaller $Δχ^2$ distribution, parameter constraint biases, and a more accurate figure of merit compared to this second approach. Our results demonstrate that COLA emulators provide a computationally efficient path forward for modeling nonlinear structure in extended cosmologies, offering a practical alternative to full N-body suites.

  PublisherOA PDFDOI

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

  Journal of Cosmology and Astroparticle Physics · 2025-02-01 · 54 citations

  articleOpen access

  Abstract Baryonic Acoustic Oscillation (BAO) data from the Dark Energy Spectroscopic Instrument (DESI), in combination with Cosmic Microwave Background (CMB) data and Type Ia Supernovae (SN) luminosity distances, suggests a dynamical evolution of the dark energy equation of state with a phantom phase ( w < -1) in the past when the so-called w 0 w a parametrization w ( a ) = w 0 + w a (1- a ) is assumed. In this work, we investigate more general dark energy models that also allow a phantom equation of state. We consider three cases: an equation of state with a transition feature, a model-agnostic equation of state with constant values in chosen redshift bins, and a k-essence model. Since the dark energy equation of state is correlated with neutrino masses, we reassess constraints on the neutrino mass sum focusing on the model-agnostic equation of state. We find that the combination of DESI BAO with Planck 2018 CMB data and SN data from Pantheon, Pantheon+, or Union3 is consistent with an oscillatory dark energy equation of state, while a monotonic behavior is preferred by the DESY5 SN data. Performing model comparison techniques, we find that the w 0 w a parametrization remains the simplest dark energy model that can provide a better fit to DESI BAO, CMB, and all SN datasets than ΛCDM. Constraints on the neutrino mass sum assuming dynamical dark energy are relaxed compared to ΛCDM and we show that these constraints are tighter in the model-agnostic case relative to w 0 w a model by 70%–90%.

  PublisherOA PDFDOI

• The CIELO Project: The Chemo-dynamical properties of gaLaxies and the cosmic web

  Conicet · 2025-01-10

  preprintOpen access

  The CIELO project introduces a novel set of chemo-dynamical zoom-in simulations designed to simultaneously resolve galaxies and their nearby environments. The initial conditions include a diverse range of cosmic structures, such as local groups, filaments, voids, and walls, allowing for a detailed exploration of galaxies within the broader context of the cosmic web. This study presents the initial conditions and characterizes the global properties of CIELO galaxies and their environments. It focuses on galaxies with stellar masses ranging from log [8,11] solar masses and examines key scaling relations, including the mass-size relation, the Tully-Fisher relation, and the mass-metallicity relation for both stars and star-forming gas. The DisPerSe algorithm was used to determine the positions of CIELO galaxies within the cosmic web, with a specific focus on the Pehuen haloes. The selection of local group volumes was guided by criteria based on the relative positions and velocities of the two primary galaxies. The Pehuen regions were chosen to map walls, filaments, and voids. Synthetic images in the SDSS i, r, and g bands were generated using the SKIRT radiative transfer code. Additionally, a dynamical decomposition was performed to classify galaxy morphologies into bulge, disc, and stellar halo components (abridged).

  PublisherDOI

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

  Physical review. D/Physical review. D. · 2025-06-12 · 3 citations

  article

  We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}^{\mathrm{geo}}$ and growth ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}^{\text{growth}}$ dark matter densities in the so-called split $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ parametrization. When trained with a few million samples, our emulator shows uniform accuracy across a wide range in an 18-dimensional parameter space. We provide a detailed comparison of three neural network designs, illustrating the importance of adopting state-of-the-art transformer blocks. Our study also details their performance when computing Bayesian evidence for cosmic shear on three fiducial cosmologies. The transformers-based emulator is always accurate within polychord's precision. As an application, we use our emulator to study the degeneracies between dark energy models and growth geometry split parametrizations. We find that the growth-geometry split remains a meaningful test of the smooth dark energy assumption.

  PublisherDOI

• Uniting the Observed Dynamical Dark Energy Preference with the Discrepancies in Ω_<i>m</i> and <i>H</i> ₀ across Cosmological Probes

  The Astrophysical Journal Letters · 2025-04-09 · 15 citations

  articleOpen access

  Abstract Recent results from Type Ia supernovae, baryon acoustic oscillations (BAOs), and the cosmic microwave background (CMB) indicate (1) potentially discrepant measurements of the matter density Ω m and Hubble constant H 0 in the ΛCDM model when analyzed individually and (2) hint of dynamical dark energy in a w 0 w a CDM model when data are combined in a joint analysis. We examine whether underlying dynamical dark energy cosmologies favored by data would result in biases in Ω m and H 0 for each probe when analyzed individually under ΛCDM. We generate mock data sets in w 0 w a CDM cosmologies, fit the individual probes under the ΛCDM model, and find that expected biases in Ω m are ∼0.03. Notably, the Ω m differences between probes are consistent with values observed in real data sets. We also observe that mock DESI-BAO data sets generated in the w 0 w a CDM cosmologies will lead to a biased measurement of H 0 higher by ∼1.2 km s −1 Mpc −1 when fitted under ΛCDM, appearing to mildly improve the Hubble tension, but as the true underlying H 0 is lower, the tension is in fact worsened. We find that the Ω m discrepancies, the high BAO H 0 relative to the CMB, and the joint dynamical dark energy signal are all related effects that could be explained simultaneously with either new physics or new systematics. While it is possible to unite many of the discrepancies seen in recent analyses along a single axis, our results underscore the importance of understanding systematic differences in data sets, as they have unique impacts in different cosmological parameter spaces.

  PublisherDOI

• Cosmology Constraints from Type Ia Supernova Simulations of the Nancy Grace Roman Space Telescope Strategy Recommended by the High Latitude Time Domain Survey Definition Committee

  ArXiv.org · 2025-06-04

  preprintOpen access

  Within the next few years, the upcoming Nancy Grace Roman Space Telescope will be gathering data for the High Latitude Time Domain Survey (HLTDS) that will be used to significantly improve the Type Ia supernova measurement of the dark energy equation of state parameters w0 and wa. Here we generate a catalog-level simulation of the in-guide strategy recommended by the HLTDS definition committee, and determine dark energy parameter constraints using a detailed analysis that includes light curve fitting, photometric redshifts and classification, BEAMS formalism, systematic uncertainties, and cosmology fitting. After analysis and selection requirements, the sample includes 10,000 Roman SNe Ia that we combine with 4,400 events from LSST. The resulting dark energy figure of merit is well above the NASA mission requirement of 326, with the caveat that SN Ia model training systematics have not been included.

  PublisherOA PDFDOI

• Cosmology Constraints from Type Ia Supernova Simulations of the Nancy Grace Roman Space Telescope Strategy Recommended by the High-Latitude Time Domain Survey Definition Committee

  The Astrophysical Journal · 2025-10-29 · 3 citations

  articleOpen access

  Abstract Within the next few years, the upcoming Nancy Grace Roman Space Telescope will be gathering data for the High-Latitude Time Domain Survey (HLTDS) that will be used to significantly improve the Type Ia supernova measurement of the dark energy equation of state parameters w 0 and w a . Here we generate a catalog-level simulation of the in-guide strategy recommended by the HLTDS definition committee, and determine dark energy parameter constraints using a detailed analysis that includes light curve fitting, photometric redshifts and classification, BEAMS formalism, systematic uncertainties, and cosmology fitting. After analysis and selection requirements, the sample includes ∼10,000 Roman SNe Ia that we combine with ∼4400 events from LSST. The resulting dark energy figure of merit is well above the NASA mission requirement of 326, with the caveat that SN Ia model training systematics have not been included.

  PublisherDOI

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

  Physical review. D/Physical review. D. · 2025-06-12 · 4 citations

  article

  The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge to analyzing theoretical models of cosmology. Additionally, new mitigation techniques of baryonic effects, intrinsic alignment, and other systematic effects will inevitably introduce more parameters, slowing down the convergence of Bayesian analyses. In this scenario, machine-learning-based accelerators are a promising solution, capable of reducing the computational costs and execution time of such tools by order of thousands. Yet, they have not been able to provide accurate predictions over the wide prior ranges in parameter space adopted by Stage III/IV collaborations in studies employing real-space two-point correlation functions. This paper offers a leap in this direction by carefully investigating the modern transformer-based neural network (NN) architectures in realistic simulated Rubin Observatory year one cosmic shear $\mathrm{\ensuremath{\Lambda}}$ cold dark matter inferences. Building on the framework introduced in Part I, we generalize the transformer block and incorporate additional layer types to develop a more versatile architecture. We present a scalable method to efficiently generate an extensive training dataset that significantly exceeds the scope of prior volumes considered in Part I, while still meeting strict accuracy standards. Through our meticulous architecture comparison and comprehensive hyperparameter optimization, we establish that the attention-based architecture performs an order of magnitude better in accuracy than widely adopted NN designs. Finally, we test and apply our emulators to calibrate tension metrics.

  PublisherDOI

• Mitigation of nonlinear galaxy bias with a theoretical-error likelihood

  Journal of Cosmology and Astroparticle Physics · 2025-04-01 · 1 citations

  articleSenior author

  Abstract Stage-IV galaxy surveys will measure correlations at small cosmological scales with high signal-to-noise ratio. One of the main challenges of extracting information from small scales is devising accurate models, as well as characterizing the theoretical uncertainties associated with any given model. In this work, we explore the mitigation of theoretical uncertainty due to nonlinear galaxy bias in the context of photometric 2×2-point analyses. We consider linear galaxy bias as the fiducial model and derive the contribution to the covariance matrix induced by neglected higher-order bias. We construct a covariance matrix for the theoretical error in galaxy clustering and galaxy-galaxy lensing using simulation-based relations that connect higher-order parameters to linear bias. To test this mitigation model, we apply the modified likelihood to 2×2-point analyses based on two sets of mock data vectors: (1) simulated data vectors, constructed from those same relations between bias parameters, and (2) data vectors based on the AbacusSummit simulation suite. We then compare the performance of the theoretical-error approach to the commonly employed scale cuts methodology. We find most theoretical-error configurations yield results equivalent to the scale cuts in terms of precision and accuracy, and in some cases, especially with the first data set, they produce significantly stronger bounds on cosmological parameters. These results are independent of the maximum scale k max in the analysis with theoretical error. We notice the relative performance of the theoretical-error approach depends mostly on the choice of the covariance-matrix diagonal. The scenarios where linear bias supplemented by theoretical error is unable to recover unbiased cosmology, which are mainly observed with the second data set, are connected to inadequate modeling of the gg-gκ covariance of theoretical error. This form of cross-probe covariance has not been considered in previous works. We additionally highlight a sensitivity of the construction to off-diagonal correlations of theoretical error. In view of its removing the ambiguity in the choice of k max , as well as the possibility of attaining higher precision than the usual scale cuts, we consider this method to be promising for analyses of LSS in upcoming photometric galaxy surveys.

  PublisherDOI

Frequent coauthors

• L. N. da Costa

  Laboratório Interinstitucional de e-Astronomia

  37 shared

• D. L. Burke

  35 shared

• E. Krause

  34 shared

• D. Gruen

  33 shared

• T. F. Eifler

  32 shared

• E. Bertin

  Orange (France)

  32 shared

• F. Menanteau

  University of Illinois Urbana-Champaign

  31 shared

• M. A. G. Maia

  Laboratório Interinstitucional de e-Astronomia

  30 shared

Education

• Ph.D., Astronomy and Astrophysics

  University of Chicago

  2015

• Master, Physics

  Universidade Federal do Rio de Janeiro Editora UFRJ

  2010

Awards & honors

• Stony Brook Trustees Faculty Award, 2025
• Ben Barres Fellowship Award, 2021
• Leona Woods Distinguished Postdoctoral Lectureship, 2019