78 citations · 326 across the 39 of their papers we have counts for
41 papers
Feedback-Conditional 3D Reconstruction of Cosmic Baryons with Flow Matching
Maryam Hussaini, Aizhan Akhmetzhanova, Carolina Cuesta-Lazaro +1
The complex relationship between galaxies and diffuse gas at large scales depends strongly on astrophysical feedback and cosmology. The dispersion measure (DM) of Fast Radio Bursts…
From starlight to dark matter: a stochastic interpolation approach to map dark matter from stellar density
Xiaowei Ou, Lina Necib, Carolina Cuesta-Lazaro +9
The dark matter halo profile in galaxies holds key information about the nature of dark matter and galaxy formation. Constraining the dark matter profile of galaxies beyond the Mil…
Unknown Unknowns: Model Misspecification in Machine Learning for Physics
Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held +1
Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the quest…
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking
Matt L. Wiemann, Lindsay M. Smith, Peter Melchior +4
Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of established science. We introduce Disc…
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna +5
Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-depende…
Augmenting representations with scientific papers
Nicolò Oreste Pinciroli Vago, Rocco Di Tella, Carolina Cuesta-Lázaro +3
Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical source…
BaryonBridge: Stochastic Interpolant Model for Fast Hydrodynamical Simulations
Benjamin Horowitz, Carolina Cuesta-Lazaro, Omar Yehia
Constructing a general-purpose framework for mapping between dark matter simulations and observable hydrodynamical simulation outputs is a long-standing problem in modern astrophys…
Hierarchical Simulation-Based Inference of Supernova Power Sources and their Physical Properties
Edgar P. Vidal, Alexander T. Gagliano, Carolina Cuesta-Lazaro
Time domain surveys such as the Vera C. Rubin Observatory are projected to annually discover millions of astronomical transients. This and complementary programs demand fast, autom…
Detecting Model Misspecification in Cosmology with Scale-Dependent Normalizing Flows
Aizhan Akhmetzhanova, Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma
Current and upcoming cosmological surveys will produce unprecedented amounts of high-dimensional data, which require complex high-fidelity forward simulations to accurately model b…
High-redshift Millennium and Astrid galaxies in effective field theory at the field level
James M. Sullivan, Carolina Cuesta-Lazaro, Mikhail M. Ivanov +6
Effective Field Theory (EFT) modeling is expected to be a useful tool in the era of future higher-redshift galaxy surveys such as DESI-II and Spec-S5 due to its robust description…
A theoretical approach to density-split clustering
Mathilde Pinon, Arnaud de Mattia, Étienne Burtin +4
We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlat…
Cosmological constraints from the Minkowski functionals of the BOSS CMASS galaxy sample
Wei Liu, Enrique Paillas, Carolina Cuesta-Lazaro +2
For the first time, we develop a simulation-based model for the Minkowski functionals (MFs) of large-scale structure, which allows us to extract the full information available from…
The Millennium and Astrid galaxies in effective field theory: comparison with galaxy-halo connection models at the field level
Mikhail M. Ivanov, Carolina Cuesta-Lazaro, Andrej Obuljen +8
Cosmological analyses of redshift space clustering data are primarily based on using luminous ``red'' galaxies (LRGs) and ``blue'' emission line galaxies (ELGs) to trace underlying…
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing
Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro +2
Efficiently processing structured point cloud data while preserving multiscale information is a key challenge across domains, from graphics to atomistic modeling. Using a curated d…
How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds
Tri Nguyen, Francisco Villaescusa-Navarro, Siddharth Mishra-Sharma +16
The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upc…
Full-shape analysis with simulation-based priors: cosmological parameters and the structure growth anomaly
Mikhail M. Ivanov, Andrej Obuljen, Carolina Cuesta-Lazaro +1
We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data analysis that combines effective field theory (EFT) on large s…
Emulation of modified gravity from CDM using conditional GANs
Yash Gondhalekar, Sownak Bose, Baojiu Li +1
A major aim of cosmological surveys is to test deviations from the standard CDM model, but the full scientific value of these surveys will only be realised through efficient sim…
Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform
Matthew Craigie, Peter L. Taylor, Yuan-Sen Ting +3
Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simula…
Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks in…
A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
2pt Collaboration, Elisabeth Krause, Yosuke Kobayashi +23
The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical a…
Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
Victoria Ono, Core Francisco Park, Nayantara Mudur +3
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to…
Full-shape analysis with simulation-based priors: constraints on single field inflation from BOSS
Mikhail M. Ivanov, Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma +2
Perturbative, or effective field theory (EFT)-based, full-shape analyses of galaxy clustering data involve ``nuisance parameters'' to capture various observational effects such as…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…