arXiv subjects

Salman Habib

Publications and source records attributed to Salman Habib.

At least 19 recordsLinked to original sources

arXiv2026

OpenCosmo: Community Portal and Analysis Framework for Flagship Cosmological Simulations

Cosmology is a precision observational science, and large simulations are necessary components of many analyses. These simulations are computationally expensive and produce massive, complex datasets; sharing them widely -- to enable further explorations, comparison with observations, and communication with general audiences -- is crucial to realizing their scientific value. In this paper, we introduce the OpenCosmo project, which provides flexible access to, and analysis of, flagship cosmological simulations performed with HACC. A web-based portal (https://opencosmo.science) serves custom subsets -- halo catalogs, profiles, particles, galaxy catalogs, and lightcone catalogs and maps -- from simulations including the two-trillion-particle Frontier-E gravity-only run, Last Journey, Discovery, and a 64-member hydrodynamic suite. A companion Python toolkit analyzes the returned data and scales without modification from laptop-sized subsets to full simulations on supercomputers. OpenCosmo supports multiple levels of interaction, from browser-based search and download to programmatic and AI-agent-driven workflows, by integrating with existing high-performance computing and data infrastructure. Its architecture, built on Globus services, provides a scalable and adaptable framework that can be extended to other scientific domains seeking to couple data sharing with computational capability.

astro-ph.IM↗

arXiv2026

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2026

Learning to Decode Quantum LDPC Codes Via Belief Propagation

Belief-propagation (BP) decoding for quantum low-density parity-check (QLDPC) codes is appealing due to its low complexity, yet it often exhibits convergence issues due to quantum degeneracy and short cycles that exist in the Tanner graph. To overcome this challenge, this paper proposes a reinforcement-learning (RL) approach that learns (offline) how to decode QLDPC codes based on sequential decoding trajectories. The decoding is formulated as a Markov decision process with a local, syndrome-driven state representation of the underlying RL agent. To enable fast inference, critical for practical implementation, we incrementally update our RL-based QLDPC decoder using second-order neighborhoods that avoid global rescans. Simulation results on representative QLDPC codes demonstrate the superiority of the proposed RL-based QLDPC decoders in terms of performance and convergence speed when compared to flooding and random sequential schedules, while achieving performance competitive with state-of-the-art BP-based decoders at comparable complexity.

cs.IT↗

arXiv2026

CelloAI Benchmarks: Toward Repeatable Evaluation of AI Assistants

Large Language Models (LLM) are increasingly used for software development, yet existing benchmarks for LLM-based coding assistance do not reflect the constraints of High Energy Physics (HEP) and High Performance Computing (HPC) software. Code correctness must respect science constraints and changes must integrate into large, performance-critical codebases with complex dependencies and build systems. The primary contribution of this paper is the development of practical, repeatable benchmarks that quantify LLM performance on HEP/HPC-relevant tasks. We introduce three evaluation tracks -- code documentation benchmarks measure the ability of an LLM to generate Doxygen-style comments, code generation benchmarks evaluate end-to-end usability on representative GPU kernels, and graphical data analysis benchmarks evaluate vision-enabled LLMs. These benchmarks provide a unified framework for measuring progress in scientific coding assistance across documentation quality, code generation robustness, and multimodal validation analysis. By emphasizing repeatability, automated scoring, and domain-relevant failure modes, the suite enables fair comparisons of models and settings while supporting future work on methods that improve reliability for HEP/HPC software development.

hep-ex↗

arXiv2026

Sequential BP-based Decoding of QLDPC Codes

Quantum low-density parity-check (QLDPC) codes are a leading approach to quantum error correction, yet conventional belief propagation (BP) decoders often perform poorly, primarily due to non-convergence exacerbated by stabilizer constraints, which induce short cycles and degeneracy. We propose two scheduling variants, sequential check node scheduling (SCNS) and sequential variable node scheduling (SVNS), that improve BP's error-correction ability by processing check nodes (CNs) or variable nodes (VNs), respectively, in a fixed order, stabilizing message updates and reducing stalls. We also employ this technique to an improved BP-variant called BP guided decimation (BPGD), where symbols are progressively fixed during decoding iterations. Here, we demonstrate that the sequential BPGD (SBPGD) decoder can further improve the convergence properties and performance of the decoder. On standard QLDPC benchmarks under a Pauli-X noise model, our sequential schedules are shown to lower the block error rate relative to conventional BP, and SBPGD outperforms BPGD while using significantly fewer decimation rounds, translating to lower computational cost. These results demonstrate that changing the update schedule, without altering the code, can improve both the reliability and efficiency of BP-based decoding for QLDPC codes. For the [[1922,50,16]] C2 hypergraph-product code with independent X errors, SVNS-BP surpasses BP-OSD-0 in error correction at roughly the same complexity as standard BP.

cs.IT↗

arXiv2026

Emulator-Based Inference of Cosmological Subgrid Models

The formation of structure in the Universe at large scales is dominated by gravity, with baryonic physics becoming significant at $\sim{\rm Mpc}$ scales. To capture the impact of baryonic physics, cosmological simulations must model gas dynamics and a host of relevant astrophysical processes. A recent extension of the Hardware/Hybrid Accelerated Cosmology Code (HACC) couples its gravity solver with a modern smoothed particle hydrodynamics method. This extension incorporates sub-resolution models for chemical enrichment, black hole and star formation, AGN kinetic and thermal feedback, supernova-driven feedback, galactic winds, and metal-line cooling. We present an inference framework based on high-fidelity emulators to aid in model calibration against observational targets, e.g., the galaxy stellar mass function, radial gas density profiles, and the cluster gas fraction. The emulators are trained on simulation suites comprising 64 boxes with side-length $128\,h^{-1}$Mpc and 16 boxes with side-length $256\,h^{-1}$Mpc with $2\times 512^3$ and $2\times 1024^3$ particles, respectively. Our analysis reveals two distinct AGN kinetic feedback modes -- a low-feedback mode yielding strong agreement with the observed radial gas density profiles of massive X-ray clusters, and a high-feedback mode providing a better fit to cluster gas fraction data, but systematically underestimating gas densities in inner regions.

astro-ph.GA↗

arXiv2025

The SPHEREx Satellite Mission

SPHEREx, a NASA explorer satellite launched on 11 March 2025, is carrying out the first all-sky near-infrared spectral survey. The satellite observes in 102 spectral bands from 0.75 to 5.0 um with a resolving power ranging from 35 to 130 in 6.2 arcsecond pixels. The observatory obtains a 5-sigma depth of 19.5 - 19.9 AB mag for 0.75 to 3.8 um and 17.8 - 18.8 AB mag for 3.8 to 5.0 um after mapping the full sky four times over two years. Scientifically, SPHEREx will produce a large galaxy redshift survey over the full sky, intended to constrain the amplitude of inflationary non-Gaussianity. The observations will produce two deep spectral maps near the ecliptic poles that will use intensity mapping to probe the evolution of galaxies over cosmic history. By mapping the depth of infrared absorption features over the Galactic plane, SPHEREx will comprehensively survey the abundance and composition of water and other biogenic ice species in the interstellar medium. The initial data are rapidly released in the form of spectral images to the public. The project will release specialized data products over the life of the mission as the surveys proceed. The science team will also produce specialized spectral catalogs on planet-bearing and low-mass stars, solar system objects, and galaxy clusters 3 years after launch. We describe the design of the instrument and spacecraft, which flow from the core science requirements. Finally, we present an initial evaluation of the in-flight performance and key characteristics.

astro-ph.IM↗

arXiv2025

Modeling Galaxy Formation in Cosmological Simulations with CRK-HACC

Self-consistently modeling baryonic effects in survey-scale cosmological simulations has become increasingly important as the diversity, precision, and statistical reach of modern observations continue to improve. The advent of exascale computing now enables a new generation of simulations that couple these physical processes across full-sky volumes with excellent statistical sampling of large-scale structure tracers such as galaxies, groups, and clusters. To support these efforts, we extend the CRK-HACC framework, a GPU-accelerated cosmological hydrodynamics code, with a suite of astrophysical subgrid models that simulate radiative cooling, star formation, stellar evolution, and AGN feedback within a numerically robust formulation optimized for scalability on modern exascale architectures. The models were selected and calibrated to reproduce observed galaxy stellar mass functions over the redshift range $0 < z < 2$ and cluster populations probed by cosmological surveys, capturing the large-scale baryonic evolution relevant for multi-wavelength, cross-correlated analyses. We describe the implementation and calibration of these models and demonstrate their consistency with observed galaxy population statistics and modern hydrodynamic simulations, establishing the baseline for exascale efforts that extend this framework to survey-scale volumes.

astro-ph.CO↗

arXiv2025

Benchmarking AI-evolved cosmological structure formation

The potential of deep learning-based image-to-image translations has recently attracted significant attention. One possible application of such a framework is as a fast, approximate alternative to cosmological simulations, which would be particularly useful in various contexts, including covariance studies, investigations of systematics, and cosmological parameter inference. To investigate different aspects of learning-based cosmological mappings, we choose two approaches for generating suitable cosmological matter fields as datasets: a simple analytical prescription provided by the Zel'dovich approximation, and a numerical N-body method using the Particle-Mesh approach. The evolution of structure formation is modeled using U-Net, a widely employed convolutional image translation framework. Because of the lack of a controlled methodology, validation of these learned mappings requires multiple benchmarks beyond simple visual comparisons and summary statistics. A comprehensive list of metrics is considered, including higher-order correlation functions, conservation laws, topological indicators, and statistical independence of density fields. We find that the U-Net approach performs well only for some of these physical metrics, and accuracy is worse at increasingly smaller scales, where the dynamic range in density is large. By introducing a custom density-weighted loss function during training, we demonstrate a significant improvement in the U-Net results at smaller scales. This study provides an example of how a family of physically motivated benchmarks can, in turn, be used to fine-tune optimization schemes -- such as the density-weighted loss used here -- to significantly enhance the accuracy of scientific machine learning approaches by focusing attention on relevant features.

astro-ph.CO↗

arXiv2025

Multi-modal Foundation Model for Cosmological Simulation Data

We present a multi-modal foundation model for astrophysical galaxy data, designed to map between simulation- and observation-based galactic features. Our encoder-only transformer flexibly ingests scalar quantities (e.g., redshifts, galaxy masses) and vectors (e.g., star formation histories, spectra), supporting multi-task training that includes within-modality reconstruction and cross-modality prediction. With a dynamic masking strategy, the model can query arbitrary galaxy properties from partial inputs -- including predicting spectra from redshift and mass, or estimating photometric redshifts from broadband magnitudes -- while also recovering missing segments within a modality. Trained on 185,000 simulated galaxies from a gigaparsec-scale Cosmology simulation, the model yields a 50% improvement in redshift estimation when combining LSST and SPHEREx photometry over LSST photometry alone, and a 63% improvement in stellar mass inference when combining late-time SFH with LSST photometry over early-time SFH with LSST photometry. The model demonstrates strong generalization across multi-modal tasks and lays the groundwork for future integration of higher-dimensional and structured data such as images, merger trees, and 3D fields. This approach provides a unified framework for connecting simulations and observations, advancing the development of generalizable astrophysical foundation models.

astro-ph.GA↗

arXiv2025

A Gigaparsec-Scale Hydrodynamic Volume Reconstructed with Deep Learning

The next generation of cosmological spectroscopic sky surveys will probe the distribution of matter across several Gigaparsecs (Gpc) or many billion light-years. In order to leverage the rich data in these new maps to gain a better understanding of the physics that shapes the large-scale structure of the cosmos, observed matter distributions must be compared to simulated mock skies. Small mock skies can be produced using precise, physics-driven hydrodynamical simulations. However, the need to capture small, kpc-scale density fluctuations in the intergalactic medium (IGM) places tight restrictions on the necessary minimum resolution of these simulations. Even on the most powerful supercomputers, it is impossible to run simulations of such high resolution in volumes comparable to what will be probed by future surveys, due to the vast quantity of data needed to store such a simulation in computer memory. However, it is possible to represent the essential features of these high-resolution simulations using orders of magnitude less memory. We present a hybrid approach that employs a physics-driven hydrodynamical simulation at a much lower-than-necessary resolution, followed by a data-driven, deep-learning Enhancement. This hybrid approach allows us to produce hydrodynamic mock skies that accurately capture small, kpc-scale features in the IGM but which span hundreds of Megaparsecs. We have produced such a volume which is roughly one Gigaparsec in diameter and examine its relevant large-scale statistical features, emphasizing certain properties that could not be captured by previous smaller simulations. We present this hydrodynamic volume as well as a companion n-body dark matter simulation and halo catalog which we are making publically available to the community for use in calibrating data pipelines for upcoming survey analyses.

astro-ph.CO↗

arXiv2025

Cosmological Hydrodynamics at Exascale: A Trillion-Particle Leap in Capability

Resolving the most fundamental questions in cosmology requires simulations that match the scale, fidelity, and physical complexity demanded by next-generation sky surveys. To achieve the realism needed for this critical scientific partnership, detailed gas dynamics, along with a host of astrophysical effects, must be treated self-consistently with gravity for end-to-end modeling of structure formation. As an important step on this roadmap, exascale computing enables simulations that span survey-scale volumes while incorporating key subgrid processes that shape complex cosmic structures. We present results from CRK-HACC, a cosmological hydrodynamics code built for the extreme scalability requirements set by modern cosmological surveys. Using separation-of-scale techniques, GPU-resident tree solvers, in situ analysis pipelines, and multi-tiered I/O, CRK-HACC executed Frontier-E: a four trillion particle full-sky simulation, over an order of magnitude larger than previous efforts. The run achieved 513.1 PFLOPs peak performance, processing 46.6 billion particles per second and writing more than 100 PB of data in just over one week of runtime.

cs.DC↗

arXiv2025

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics

Next-generation High Energy Physics (HEP) experiments will generate unprecedented data volumes, necessitating High Performance Computing (HPC) integration alongside traditional high-throughput computing. However, HPC adoption in HEP is hindered by the challenge of porting legacy software to heterogeneous architectures and the sparse documentation of these complex scientific codebases. We present CelloAI, a locally hosted coding assistant that leverages Large Language Models (LLMs) with retrieval-augmented generation (RAG) to support HEP code documentation and generation. This local deployment ensures data privacy, eliminates recurring costs and provides access to large context windows without external dependencies. CelloAI addresses two primary use cases, code documentation and code generation, through specialized components. For code documentation, the assistant provides: (a) Doxygen style comment generation for all functions and classes by retrieving relevant information from RAG sources (papers, posters, presentations), (b) file-level summary generation, and (c) an interactive chatbot for code comprehension queries. For code generation, CelloAI employs syntax-aware chunking strategies that preserve syntactic boundaries during embedding, improving retrieval accuracy in large codebases. The system integrates callgraph knowledge to maintain dependency awareness during code modifications and provides AI-generated suggestions for performance optimization and accurate refactoring. We evaluate CelloAI using real-world HEP applications from ATLAS, CMS, and DUNE experiments, comparing different embedding models for code retrieval effectiveness. Our results demonstrate the AI assistant's capability to enhance code understanding and support reliable code generation while maintaining the transparency and safety requirements essential for scientific computing environments.

cs.SE↗

arXiv2025

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise

We propose SPOF (Stabilization of Perturbed Loss Function), a differentially private training mechanism intended for multi-user local differential privacy (LDP). SPOF perturbs a stabilized Taylor expanded polynomial approximation of a model's training loss function, where each user's data is privatized by calibrated noise added to the coefficients of the polynomial. Unlike gradient-based mechanisms such as differentially private stochastic gradient descent (DP-SGD), SPOF does not require injecting noise into the gradients of the loss function, which improves both computational efficiency and stability. This formulation naturally supports simultaneous privacy guarantees across all users. Moreover, SPOF exhibits robustness to environmental noise during training, maintaining stable performance even when user inputs are corrupted. We compare SPOF with a multi-user extension of DP-SGD, evaluating both methods in a wireless body area network (WBAN) scenario involving heterogeneous user data and stochastic channel noise from body sensors. Our results show that SPOF achieves, on average, up to 3.5% higher reconstruction accuracy and reduces mean training time by up to 57.2% compared to DP-SGD, demonstrating superior privacy-utility trade-offs in multi-user environments.

cs.LG↗

arXiv2025

Teaching LLMs to Speak Spectroscopy

Pre-trained Large Language Models (LLMs) have revolutionized text processing, yet adapting Transformer-based neural networks to non-textual scientific modalities typically requires specialized architectures and extensive computational resources. We demonstrate that LLaMA-3.1-8B can be efficiently repurposed to predict galaxy redshifts from spectroscopic data through Low-Rank Adaptation (LoRA), achieving competitive performance while preserving its linguistic capabilities. Using only 16 GPU-hours and adapting 0.04% of model parameters, our approach achieves a mean absolute error of 0.04 in redshift prediction while retaining over 85% of performance on AstroBench and 89% on general QA tasks from eval-harness. This minimal-effort adaptation--requiring only simple standard fine-tuning APIs--lowers barriers to entry for domain scientists and enables integrated agentic workflows where a single model handles both spectroscopic data for quantitative analysis and natural language for reasoning.

astro-ph.IM↗

arXiv2025

DESC DC2 Data Release Note

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created a 300 deg$^2$ simulated survey as part of an effort called Data Challenge 2 (DC2). The DC2 simulated sky survey, in six optical bands with observations following a reference LSST observing cadence, was processed with the LSST Science Pipelines (19.0.0). In this Note, we describe the public data release of the resulting object catalogs for the coadded images of five years of simulated observations along with associated truth catalogs. We include a brief description of the major features of the available data sets. To enable convenient access to the data products, we have developed a web portal connected to Globus data services. We describe how to access the data and provide example Jupyter Notebooks in Python to aid first interactions with the data. We welcome feedback and questions about the data release via a GitHub repository.

astro-ph.CO↗

arXiv2025

Sequential Interval Passing for Compressed Sensing

The reconstruction of sparse signals from a limited set of measurements poses a significant challenge as it necessitates a solution to an underdetermined system of linear equations. Compressed sensing (CS) deals with sparse signal reconstruction using techniques such as linear programming (LP) and iterative message passing schemes. The interval passing algorithm (IPA) is an attractive CS approach due to its low complexity when compared to LP. In this paper, we propose a sequential IPA that is inspired by sequential belief propagation decoding of low-density-parity-check (LDPC) codes used for forward error correction in channel coding. In the sequential setting, each check node (CN) in the Tanner graph of an LDPC measurement matrix is scheduled one at a time in every iteration, as opposed to the standard ``flooding'' interval passing approach in which all CNs are scheduled at once per iteration. The sequential scheme offers a significantly lower message passing complexity compared to flooding IPA on average, and for some measurement matrix and signal sparsity, a complexity reduction of 36% is achieved. We show both analytically and numerically that the reconstruction accuracy of the IPA is not compromised by adopting our sequential scheduling approach.

cs.IT↗

arXiv2024

Bayesian "Deep" Process Convolutions: An Application in Cosmology

The nonlinear matter power spectrum in cosmology describes how matter density fluctuations vary with scale in the universe, providing critical insights into large-scale structure formation. The matter power spectrum includes both smooth regions and highly oscillatory features. Cosmologists rely on noisy, multi-resolution realizations of large N-body simulations to study these phenomena, which require appropriate smoothing techniques to learn about underlying structures. We introduce a Bayesian Deep Process Convolution (DPC) model that flexibly adapts its smoothness parameter across the input space, enabling it to capture both smooth and variable structure within a single framework. The DPC model leverages common patterns across related functions to improve estimation in regions with sparse data. Compared to existing methods, the DPC model offers superior accuracy and uncertainty quantification in simulated data, and qualitatively superior performance with the cosmological data. This methodology will be useful in cosmology and other fields requiring flexible modeling of smooth nonstationary surfaces.

astro-ph.CO↗