arXiv subjects

Nhat-Minh Nguyen

Publications and source records attributed to Nhat-Minh Nguyen.

At least 19 recordsLinked to original sources

arXiv2026

From quantum fluctuations to galaxy power spectrum multipoles

These notes trace large-scale structure from primordial curvature perturbations generated by inflationary quantum fluctuations to galaxy power-spectrum multipoles. Three core lectures develop the linear matter power spectrum, spherical and anisotropic collapse, galaxy bias, redshift-space distortions, the Kaiser model, and multipole estimators with Gaussian covariance. The extension develops nonlinear bias and the one-loop effective field theory model used in full-shape analyses. Derivations are explicit; appendices collect longer calculations and solutions. The core lectures assume undergraduate-level cosmology; the extension assumes familiarity with perturbation theory.

astro-ph.CO↗

arXiv2026

Future of Artificial Intelligence for Science in Japan 2024 Community Report

This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three major physics domains: accelerator physics, cosmology and astrophysics, and neutrino physics. Although each domain has distinct scientific goals and experimental constraints, several common technical themes emerge: high-dimensional reconstruction, fast and accurate simulation, uncertainty propagation, simulation-to-data mismatch, anomaly detection, real-time decision-making, and shared infrastructure.

hep-ph↗

arXiv2026

How I stop worrying about non-universality and $b_\phi$: Constraining local $f_{\rm NL}$ with $b_\phi$ priors from HOD posteriors

Local primordial non-Gaussianity (local PNG) induces a scale-dependent contribution to galaxy clustering proportional to $f_{\rm NL}\,b_\phi$, where $f_{\rm NL}$ is the local PNG amplitude and $b_\phi$ encodes the galaxy response to a long-wavelength primordial potential perturbation. Uncertainty in $b_\phi$ is the dominant obstacle to precise, robust constraints on $f_{\rm NL}$ from galaxy surveys. We translate small-scale clustering constraints on the galaxy--halo connection into priors on $b_\phi$: sampling the posterior of a halo occupation distribution (HOD) model fit to the DESI EDR, we generate mocks from which we measure $b_\phi$ and construct its prior. Validating against additional mocks with different local PNG amplitudes, we show that the method recovers unbiased $f_{\rm NL}$, even in the presence of assembly bias.

astro-ph.CO↗

arXiv2026

Physics Is All You Need? A Case Study in Physicist-Supervised AI Development of Scientific Software

Are AI agents tools, co-authors, or researchers? We present a quantified case study ($N=1$): a physicist supervising an AI coding agent (Claude Code, Sonnet and Opus models) over 12 work days and 57 sessions to build CLAX-PT, a differentiable one-loop perturbation theory module in JAX. We documented and classified 15 supervision events by intervention level. The agent resolved ten autonomously by iterating against oracle tests. Two more by the physicist's domain knowledge. The three it could not -- all evaded oracle detection -- share a common property: the agent treated symptom reduction as root-cause resolution. It spent 33 of the 57 sessions adjusting coefficients within a code architecture that could not represent the target physics, and could not re-evaluate its CLASS-PT branch choice even when prompted to reconsider; only an injected physics concept (anisotropic BAO damping) triggered the redesign. Separately, the agent committed a calibrated correction that passed all oracle tests but corresponded to no quantity in the theory, predicting wrong values at any other cosmology. The fudge factor was caught and replaced within the same session. Three supervision practices proved critical for catching what oracle tests missed: testing at diverse parameter points beyond the fiducial calibration; shared changelogs that surfaced stalled exploration across sessions; and an explicit rule against unphysical numerical patches. In this case, supervision design, not model capability, determined whether the agent's output was trustworthy. Closing the gap would require agents that propose architectural alternatives rather than optimize within a given structure, and distinguish predictive adequacy from explanatory correctness -- capabilities not exhibited here, not obviously addressed by scaling alone. [Abridged.]

cs.AI↗

arXiv2026

Multi-tracers, multi-surveys: a joint Fisher analysis of DESI+PFS

Marginalizing over roughly 12 effective-field-theory (EFT) nuisance parameters per tracer per redshift bin is a dominant systematic cost in full-shape galaxy power spectrum analyses. Simulation-based priors (SBP) tighten these parameters but rely on N-body simulations and halo-occupation-distribution (HOD) models. We propose a multi-tracer Fisher analysis as a model-independent alternative: cross-spectra between galaxy populations calibrate EFT bias and stochastic parameters from data alone, through two channels -- within a survey and across overlapping surveys -- combined in a volume-partitioned joint Fisher. We forecast across the $14{,}000\;\mathrm{deg}^2$ Dark Energy Spectroscopic Instrument (DESI) footprint, including the $\sim\!1{,}200\;\mathrm{deg}^2$ Prime Focus Spectrograph (PFS) overlap at $z\in[0.6,1.6]$ with up to 4 tracers (PFS-ELG, DESI-ELG, DESI-LRG, DESI-QSO). The internal-DESI channel (LRG, ELG, and QSO over the full footprint) provides most of the gain, improving $\sigma(f\sigma_8)$ by 33%, $\sigma(M_\nu)$ by 80%, and $\sigma(\Omega_m)$ by 49% over a single-tracer broad-prior baseline at $k_{\rm max}=0.20\,h\,\mathrm{Mpc}^{-1}$. Adding the PFS$\,\times\,$DESI overlap further tightens these by 9%, 24%, and 9%, respectively, after marginalizing over residual cross-population stochasticity. A parameter-importance decomposition shows that the dominant driver is calibration of the $b_1\sigma_8$ prior, tightened from a flat prior to $\sigma\approx 0.13$, which breaks the $b_1\sigma_8$--$f\sigma_8$ degeneracy of single-tracer analyses. The multi-tracer multi-survey approach targets the same $b_1$ calibration as SBPs, using observed cross-spectra rather than HOD mocks as a model-independent check on SBP-driven $b_1\sigma_8$ shifts. The framework extends to any number of overlapping spectroscopic surveys.

astro-ph.CO↗

arXiv2026

Galaxy sizes as complementary (zero-)bias tracers of local primordial non-Gaussianity

The scale-dependent bias in halo and galaxy power spectra is a key signature of local primordial non-Gaussianity (local PNG), with PNG sensitivity scaling as $b_\phi/b_1$ -- the ratio of their responses to long-wavelength primordial potential $b_\phi$ and late-time density fluctuations $b_1$. For number density fluctuations, these responses are closely tied by the universality relation, limiting the achievable ratio. We show that size density fluctuations strongly violate this relation, thus evading the limit. For galaxy-mass halos, sizes have a vanishingly small density response but a sizable, negative local PNG response, implying an effective $b_\phi/b_1$ that is large in magnitude and opposite in sign to that of number counts. This makes galaxy sizes complementary probes of local PNG from the same galaxy sample, without any sample split. For a DESI-like survey, a multi-tracer analysis combining galaxy numbers and sizes improves the local-PNG detection significance by a factor of $\sim\!3.6$. Due to the sign flip, the number-size cross power spectrum further provides a handle on systematics in the event of a detection.

astro-ph.CO↗

arXiv2026

The Price of Universal Temporal Reachability

Dynamic networks are graphs in which edges are available only at specific time instants, modeling connections that change over time. The dynamic network creation game studies this setting as a strategic interaction where each vertex represents a player. Players can add or remove time-labeled edges in order to minimize their personal cost. This cost has two components: a construction cost, calculated as the number of time instants during which a player maintains edges multiplied by a constant $\alpha$, and a communication cost, defined as the average distance to all other vertices in the network. Communication occurs through temporal paths, which are sequences of adjacent edges with strictly increasing time labels and no repeated vertices. We show for the shortest distance (minimizing the number of edges) that the price of anarchy can be proportional to the number of vertices, contrasting the constant price conjectured for static networks.

cs.DS↗

arXiv2025

Reconstructing the local density field with combined convolutional and point cloud architecture

We construct a neural network to perform regression on the local dark-matter density field given line-of-sight peculiar velocities of dark-matter halos, biased tracers of the dark matter field. Our architecture combines a convolutional U-Net with a point-cloud DeepSets. This combination enables efficient use of small-scale information and improves reconstruction quality relative to a U-Net-only approach. Specifically, our hybrid network recovers both clustering amplitudes and phases better than the U-Net on small scales.

astro-ph.CO↗

arXiv2025

Late-time growth weakly affects the significance of high-redshift massive galaxies

Recent observations by the James Webb Space Telescope have revealed massive galaxies at very high redshift ($z\simeq 7-15$). The question of whether the existence of such galaxies is expected in the corresponding JWST surveys has received a lot of attention, though the answer straddles areas of cosmology and complex astrophysical details of high-redshift galaxy formation. The growth rate of density fluctuations determines the amplitude of overdensities that collapse to form galaxies. Late-time modifications of growth, combined with measurements at both $z\sim 1$ from large-scale structure and $z\sim 1000$ from the cosmic microwave background, affect the predictions for the abundance of first galaxies in the universe. In this paper, we point out that the late-time growth rate of structure affects the statistical significance of high-redshift, high-mass objects very weakly. Consequently, if the existence and abundance of these objects are confirmed to be unexpected, the variations in the late-time growth history are unlikely to explain these anomalies.

astro-ph.CO↗

arXiv2024

How much information can be extracted from galaxy clustering at the field level?

We present optimal Bayesian field-level cosmological constraints from nonlinear tracers of the large-scale structure, specifically the amplitude $σ_8$ of linear matter fluctuations inferred from rest-frame simulated dark matter halos in a comoving volume of $8\,(h^{-1}\mathrm{Gpc})^3$. Our constraint on $σ_8$ is entirely due to nonlinear information, and obtained by explicitly sampling the initial conditions along with bias and noise parameters via a Lagrangian EFT-based forward model, LEFTfield. The comparison with a simulation-based inference analysis employing the power spectrum and bispectrum -- likewise using the LEFTfield forward model -- shows that, when including precisely the same modes of the same data up to $k_{\mathrm{max}}= 0.10\,h\,\mathrm{Mpc}^{-1}$ ($0.12\,h\,\mathrm{Mpc}^{-1}$), the field-level approach yields a factor of 3.5 (5.2) improvement on the $σ_8$ constraint, from 20.0% to 5.7% (17.0% to 3.3%). This study provides direct insights into cosmological information encoded in galaxy clustering beyond low-order $n$-point functions.

astro-ph.CO↗

arXiv2024

Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood

The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for likelihood-free Bayesian inference. This method assumes that some summary statistics are normally distributed, which can be incorrect in many applications. We propose a transformation, called the Wasserstein Gaussianization transformation, that uses a Wasserstein gradient flow to approximately transform the distribution of the summary statistics into a Gaussian distribution. BSL also implicitly requires compatibility between simulated summary statistics under the working model and the observed summary statistics. A robust BSL variant which achieves this has been developed in the recent literature. We combine the Wasserstein Gaussianization transformation with robust BSL, and an efficient Variational Bayes procedure for posterior approximation, to develop a highly efficient and reliable approximate Bayesian inference method for likelihood-free problems.

stat.CO↗

arXiv2024

A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics

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 analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $\Lambda$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented.

astro-ph.CO↗

arXiv2024

Exploring the Growth Index $γ_L$: Insights from Different CMB Dataset Combinations and Approaches

In this study we investigate the growth index $γ_L$, which characterizes the growth of linear matter perturbations, while analysing different cosmological datasets. We compare the approaches implemented by two different patches of the cosmological solver CAMB: MGCAMB and CAMB_GammaPrime_Growth. In our analysis we uncover a deviation of the growth index from its expected $Λ$CDM value of $0.55$ when utilizing the Planck dataset, both in the MGCAMB case and in the CAMB_GammaPrime_Growth case, but in opposite directions. This deviation is accompanied by a change in the direction of correlations with derived cosmological parameters. However, the incorporation of CMB lensing data helps reconcile $γ_L$ with its $Λ$CDM value in both cases. Conversely, the alternative ground-based telescopes ACT and SPT consistently yield growth index values in agreement with $γ_L=0.55$. We conclude that the presence of the A$_{\mathrm{lens}}$ problem in the Planck dataset contributes to the observed deviations, underscoring the importance of additional datasets in resolving these discrepancies.

astro-ph.CO↗

arXiv2023

Sweeping Horndeski Canvas: New Growth-Rate Parameterization for Modified-Gravity Theories

We propose and numerically validate a new fitting formula that is sufficiently accurate to model the growth of structure in Horndeski theories of modified gravity for upcoming Stage IV and V large-scale structure surveys. Based on an analysis of more than 18,000 Horndeski models and adopting the popular parameterization of the growth rate $f(z) = Ω_{M}(z)^γ$, we generalize the constant growth index $γ$ to a two-parameter redshift-dependent quantity, $γ(z)$, that more accurately fits these models. We demonstrate that the functional form $γ(z)=γ_0+γ_1z^2 / (1+z)$ improves the median $χ^2$ of the fit to viable Horndeski models by a factor of $\sim40$ relative to that of a constant $γ$, and is sufficient to obtain unbiased results even for precise measurements expected in Stage IV and V surveys. Finally, we constrain the parameters of the new fitting formula using current cosmological data.

astro-ph.CO↗

arXiv2023

Evidence for suppression of structure growth in the concordance cosmological model

We present evidence for a suppressed growth rate of large-scale structure during the dark-energy dominated era. Modeling the growth rate of perturbations with the ``growth index'' $γ$, we find that current cosmological data strongly prefer a higher growth index than the value $γ=0.55$ predicted by general relativity in a flat $Λ$CDM cosmology. Both the cosmic microwave background data from Planck and the large-scale structure data from weak lensing, galaxy clustering, and cosmic velocities separately favor growth suppression. When combined, they yield $γ=0.633^{+0.025}_{-0.024}$, excluding $γ=0.55$ at a statistical significance of 3.7$σ$. The combination of $fσ_8$ and Planck measurements prefers an even higher growth index of $γ=0.639^{+0.024}_{-0.025}$, corresponding to a 4.2$σ$-tension with the concordance model. In Planck data, the suppressed growth rate offsets the preference for nonzero curvature and fits the data equally well as the latter model. A higher $γ$ leads to a higher matter fluctuation amplitude $S_8$ inferred from galaxy clustering and weak lensing measurements, and a lower $S_8$ from Planck data, effectively resolving the $S_8$ tension.

astro-ph.CO↗

arXiv2023

Consistency tests of field level inference with the EFT likelihood

Analyzing the clustering of galaxies at the field level in principle promises access to all the cosmological information available. Given this incentive, in this paper we investigate the performance of field-based forward modeling approach to galaxy clustering using the effective field theory (EFT) framework of large-scale structure (LSS). We do so by applying this formalism to a set of consistency and convergence tests on synthetic datasets. We explore the high-dimensional joint posterior of LSS initial conditions by combining Hamiltonian Monte Carlo sampling for the field of initial conditions, and slice sampling for cosmology and model parameters. We adopt the Lagrangian perturbation theory forward model from [1], up to second order, for the forward model of biased tracers. We specifically include model mis-specifications in our synthetic datasets within the EFT framework. We achieve this by generating synthetic data at a higher cutoff scale $Λ_0$, which controls which Fourier modes enter the EFT likelihood evaluation, than the cutoff $Λ$ used in the inference. In the presence of model mis-specifications, we find that the EFT framework still allows for robust, unbiased joint inference of a) cosmological parameters - specifically, the scaling amplitude of the initial conditions - b) the initial conditions themselves, and c) the bias and noise parameters. In addition, we show that in the purely linear case, where the posterior is analytically tractable, our samplers fully explore the posterior surface. We also demonstrate convergence in the cases of nonlinear forward models. Our findings serve as a confirmation of the EFT field-based forward model framework developed in [2-7], and as another step towards field-level cosmological analyses of real galaxy surveys.

astro-ph.CO↗

arXiv2022

MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities

Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphs' structures. MANDO extracts customized metapaths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score.

cs.SE↗

arXiv2022

Field-level inference of galaxy intrinsic alignment from the SDSS-III BOSS survey

As a large-scale overdensity collapses, it affects the orientation and shape of galaxies that form, by exerting tidal shear along their axes. Therefore, the shapes of elliptical galaxies align with the tidal field of cosmic structures. This intrinsic alignment provides insights into galaxy formation and the primordial universe, complements late-time cosmological probes and constitutes a significant systematic effect for weak gravitational lensing observations. In the present study, we provide constraints on the linear alignment model using a fully Bayesian field-level approach, using galaxy shape measurements from the SDSS-III BOSS LOWZ sample and three-dimensional tidal fields constrained with the LOWZ and CMASS galaxy samples of the SDSS-III BOSS survey. We find 4$σ$ evidence of intrinsic alignment, with an amplitude of $A_I=2.9 \pm 0.7$ at 20$h^{-1}\;\mathrm{Mpc}$.

astro-ph.CO↗