arXiv subjects

Bhuvnesh Jain

Publications and source records attributed to Bhuvnesh Jain.

At least 19 recordsLinked to original sources

arXiv2026

Tidal alignment and tidal torquing modeling for the cosmic shear three-point correlation function and mass aperture skewness

We present a model for the intrinsic alignment contamination of the shear three-point correlation function and skewness of the mass aperture statistic using the tidal alignment and tidal torquing (TATT) formalism. We compute the intrinsic alignment bispectra components in terms of the TATT model parameters. We consider two effective field theory approaches in the literature, relate them to the TATT model parameters and an extension to TATT that includes the velocity-shear (VS) parameter. We compare the impact of changing between NLA, TATT, and TATT+VS on the theoretical computation of the 3PCF using the best fit parameters and tomographic redshift distributions from Dark Energy Survey Year 3. We find that the TATT model significantly impacts the skewed triangle configurations of the 3PCF. Additionally, including the higher-order effects from TATT can introduce opposite effects on the two-point function and on the mass aperture skewness, damping the signal of the former while boosting the signal of the latter. We argue that a joint 2PCF+3PCF analysis with the TATT model can help break the degeneracy between its model parameters and provide more robust constraints on both cosmology and intrinsic alignment amplitude parameters. We show that typical values of order unity for the intrinsic alignment parameters introduce differences of around $10\%$ between NLA and TATT predictions.

astro-ph.CO↗

arXiv2025

Vision Transformers for Cosmological Fields: Application to Weak Lensing Mass Maps

Weak gravitational lensing is a powerful probe of the universe's growth history. While traditional two-point statistics capture only the Gaussian features of the convergence field, deep learning methods such as convolutional neural networks (CNNs) have shown promise in extracting non-Gaussian information from small-scale, nonlinear structures. In this work, we evaluate the effectiveness of attention-based architectures, including variants of vision transformers (ViTs) and shifted window (Swin) transformers, in constraining the cosmological parameters $\Omega_m$ and $S_8$ from weak lensing mass maps. Using a simulation-based inference (SBI) framework, we compare transformer-based methods to CNNs. We also examine performance scaling with the number of available $N$-body simulations, highlighting the importance of pre-training for transformer architectures. We find that the Swin transformer performs significantly better than vanilla ViTs, especially with limited training data. Despite their higher representational capacity, the Figure of Merit for cosmology achieved by transformers is comparable to that of CNNs under realistic noise conditions.

astro-ph.CO↗

arXiv2025

The Speed of Gravity and the Fate of Dark Energy

On August 17$^{\rm th}$ 2017, observatories worldwide made a landmark detection: gravitational waves and light from a binary neutron star merger. This event revolutionized our understanding of astrophysics, cosmology, and gravitation. In this proceeding of the 2025 International Congress of Basic Science, we describe how it transformed our view of cosmic acceleration (dark energy). The near-simultaneous arrival of light and gravitational waves shows that their speeds agree to within one part in $10^{15}$, excluding large classes of modified gravity theories and interactions between dark energy and matter.

astro-ph.CO↗

arXiv2025

High-resolution weak lensing mass mapping from DES-Y3 data using diffusion-based prior

High-resolution mapping of cosmic mass distribution is essential for a variety of astrophysical applications including understanding cosmic structure formation, and galaxy formation and evolution. However dark matter is not directly observed and therefore we need advanced methods for solving inverse problems to reconstruct the underlying cosmic matter distribution. Here, we train a generative diffusion model and use it in the Diffusion Posterior Sampling (DPS) framework to reconstruct mass maps from Dark Energy Survey-Year 3 (DES-Y3) weak gravitational lensing data at high (1 arcminute) resolution. We show that the standard DPS results are biased, but they can be easily corrected by scaling the log-likelihood score during the diffusion process, yielding unbiased results with proper uncertainty quantification. The resulting mass maps reveal cosmic structures with enhanced detail, opening the door for improved astrophysical studies using the obtained mass maps.

astro-ph.CO↗

arXiv2025

Baryonic Feedback across Halo Mass: Impact on the Matter Power Spectrum

Upcoming weak-lensing surveys will probe the matter distribution at a few percent level on nonlinear scales ($k>1\,{\rm h\,Mpc}^{-1}$) where baryonic feedback from galaxy formation modifies the clustering of matter. Using the IllustrisTNG hydrodynamical simulations, we quantify the mass and radial dependence of baryonic suppression of the matter power spectrum by selectively replacing matter around the center of halos out to a specified radius in the collisionless run with that around their full-physics counterparts. We find that group-scale halos with $\log M_{\rm 200m}/h^{-1}M_\odot \in[13, 14]$ dominate the suppression, contributing a large fraction of the total reduction in power at $k\sim2-30\,h\,{\rm Mpc}^{-1}$, with smaller suppression on either sides of this mass bin. Correctly reproducing the full suppression of the power spectrum requires accounting for matter redistribution (while enforcing mass conservation) beyond the virial radius of each halo. We show that the same group-scale regime produces the most detectable deviations in the weak gravitational lensing of background galaxies measured around foreground galaxy groups binned by mass or richness. Such a lensing signal could be a powerful observational test of feedback models together with SZ measurements. Our results motivate emulators that jointly predict the matter power spectrum and halo-matter cross-power spectrum including baryonic effects, enabling unbiased cosmological inference from small scales.

astro-ph.CO↗

arXiv2025

T-FIX: Text-Based Explanations with Features Interpretable to eXperts

As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror professional reasoning. Yet evaluating whether an LLM "thinks like an expert" remains difficult: existing approaches rely on per-example expert annotation, making them costly, hard to scale, and tied to a single notion of correct reasoning within each domain. To address this gap, we introduce T-FIX, a unified evaluation framework that operationalizes expert alignment as a desired attribute of LLM-generated explanations. T-FIX spans seven scientific tasks across three domains, with each task evaluated against expert-defined criteria that capture domain-grounded reasoning rather than generic explanation quality. Our framework enables automatic, personalizable evaluation of expert alignment that generalizes to unseen explanations without ongoing expert involvement. Code is available at https://github.com/BrachioLab/FIX-2/.

cs.CL↗

arXiv2025

Tests of Evolving Dark Energy with Geometric Probes of the Late-Time Universe

Recent results from the Dark Energy Spectroscopic Instrument (DESI) have shown a strong statistical preference for a time-evolving dark energy model over $\Lambda$CDM when combining BAO, CMB, and supernova (SN) data. We investigate the robustness of this conclusion by isolating geometric information in weak lensing measurements from the DES Year 3 survey and combining it with different datasets. We introduce a hyperparameter, $\Omega_{\rm m}^{\rm growth}$, to decouple the growth contribution from the lensing 2-point correlation and thus bypass the possible effect of the $\sigma_8$ tension in our analysis. We then combine with the late-time geometric probes provided by BAO and SN, along with CMB primary data. The preference for evolving dark energy is consistent with the DESI-DR2 findings: when combining BAO, primary CMB, and weak lensing data, the $w_0w_a$CDM is preferred at about the $3\sigma$ significance. However, when we add SN, the result is sensitive to the choice of data: if we leave out $z<0.1$ SN data in the analysis, as a test of the effect of inhomogeneous calibration, we obtain a statistical significance below $2\sigma$ for time evolving dark energy. Indeed, the high-z only SN data \textbf{lowers} the evidence for evolving dark energy in all the data combinations we have examined. This underscores the importance of improved SN samples at low redshift and of alternative data combinations. We show that cosmic shear measurements with LSST Year 1 data will provide comparable power to current SN data. We discuss other low-redshift probes provided by lensing and galaxy clustering to test for evolving dark energy.

astro-ph.CO↗

arXiv2025

Cosmology from a joint analysis of second and third order shear statistics with Subaru Hyper Suprime-Cam Year 3 data

We present a joint cosmological analysis of the two-point correlation function and the aperture-mass skewness measured from the Year 3 data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-Y3). The aperture-mass skewness is a compressed representation of three-point shear information, designed to capture non-Gaussian features while keeping the data vector computationally tractable. We find that including the aperture-mass skewness improves the $S_8$-$\Omega_m$ figure of merit by 80% compared to the 2PCF-only case, primarily due to the breaking of degeneracies. Our joint analysis yields a constraint of $S_8=0.736\pm0.020$, which is slightly lower than the two-point-only result and increases the tension with Planck 2018 to 3.2$\sigma$ in the $S_8$-$\Omega_m$ plane. The two- and three-point statistics are found to be internally consistent across redshift bins and angular scales, and we detect no significant intrinsic alignment signal. We also explore extensions to the $w$CDM model and find no evidence for deviations from a cosmological constant. This work demonstrates the feasibility and scientific value of incorporating third-order shear statistics into weak lensing cosmology and provides a practical pathway for similar analyses in future Stage-IV surveys such as LSST, Euclid, and Roman.

astro-ph.CO↗

arXiv2025

Reconstructing the shape of the non-linear matter power spectrum using CMB lensing and cosmic shear

We reconstruct the non-linear matter power spectrum $P(k)$ using a joint analysis of gravitational lensing of the cosmic microwave background (CMB) and lensing of galaxies. This reconstruction is motivated by the $S_8$ tension between early-universe CMB predictions and late-time observables. We use CMB lensing data from the Atacama Cosmology Telescope DR6 and cosmic shear data from the Dark Energy Survey (DES) Y3 release to perform a gravity-only (i.e. no baryonic feedback) fit to $P(k)$ in bins of wave-number, within $\rm{\Lambda CDM}$. We find that with DES cosmic shear data alone, $P(k)$ departs from the early-universe CMB prediction on all scales. The joint fit with CMB lensing is consistent on large scales $k<0.2 \;{\rm Mpc}^{-1}$ but shows a $\sim 2 \sigma$ deviation from scale-independence when extending to $k = 10 \;h/\mathrm{Mpc}$. We compare our agnostic $P(k)$ reconstruction to baryonic feedback models and non-standard dark matter models: reasonable variations of both scenarios can recover the shape and amplitude of the suppression. We discuss the advances needed to disentangle these physical effects with a full mapping of $P(k,z)$.

astro-ph.CO↗

arXiv2025

Diffusion-based mass map reconstruction from weak lensing data

Diffusion models have been used in cosmological applications as a generative model for fast simulations and to reconstruct underlying cosmological fields or astrophysical images from noisy data. These two tasks are often treated as separate: diffusion models trained for one purpose do not generalize to perform the other task. In this paper, we develop a single diffusion model that can be used for both tasks. By using the Diffusion Posterior Sampling (DPS) approach, we use a diffusion model trained to simulate weak lensing maps for the inverse problem of reconstructing mass maps from noisy weak lensing data. We find that the standard DPS method leads to biased inference but we correct this bias by down weighting the likelihood term at early sampling time steps of the diffusion. Our method give us a way to reconstruct accurate high-resolution (sub-arcminute) mass maps that have the correct power spectrum and a range of non-Gaussian summary statistics. We discuss several applications enabled by the computational efficiency and accuracy of our model. These include generation of simulation quality mass maps, aiding covariance estimation for higher order statistics, and for finding filaments, voids and clusters from noisy lensing shear data.

astro-ph.CO↗

arXiv2024

Fast Generation of Weak Lensing Maps with Analytical Point Transformation Functions

Nonlinear cosmological fields like galaxy density and lensing convergence can be approximately related to Gaussian fields via analytic point transforms. The lognormal transform (LN) has been widely used and is a simple example of a function that relates nonlinear fields to Gaussian fields. We consider more accurate General Point-Transformed Gaussian (GPTG) functions for such a mapping and apply them to convergence maps. We show that we can create maps that preserve the LN's ability to exactly match any desired power spectrum but go beyond LN by significantly improving the accuracy of the probability distribution function (PDF). With the aid of symbolic regression, we find a remarkably accurate GPTG function for convergence maps: its higher-order moments, scattering wavelet transform, Minkowski functionals, and peak counts match those of N-body simulations to the statistical uncertainty expected from tomographic lensing maps of the Rubin LSST 10 years survey. Our five-parameter function performs 2 to 5$\times$ better than the lognormal. We restrict our study to scales above about 7 arcmin; baryonic feedback alters the mass distribution on smaller scales. We demonstrate that the GPTG can robustly emulate variations in cosmological parameters due to the simplicity of the analytic transform. This opens up several possible applications, such as field-level inference, rapid covariance estimation, and other uses based on the generation of arbitrarily many maps with laptop-level computation capability.

astro-ph.CO↗

arXiv2024

Bound on forecasting skill for models of North Atlantic tropical cyclone counts

Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find that this bound is already saturated by a simple linear model that explains roughly 50 percent of the annual variance using three climate indices: El Ni\~no Southern Oscillation (ENSO), average sea surface temperature (SST) in the main development region (MDR) of the North Atlantic and the North Atlantic oscillation (NAO) atmospheric circulation index (Kozar et al 2012). As expected under the bound, increased model complexity does not help: we demonstrate that allowing for quadratic and interaction terms, or using an Elastic Net to forecast TC counts using global SST maps, produces no detectable increase in skill. We provide evidence that observed TC counts are consistent with a Poisson process, limiting possible improvements in TC modeling by relaxing the Poisson assumption.

physics.ao-ph↗

arXiv2024

The FIX Benchmark: Extracting Features Interpretable to eXperts

Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available. However, this is often not the case for high-dimensional data, and it can be hard even for domain experts to mathematically specify which features are important. Can we instead automatically extract collections or groups of features that are aligned with expert knowledge? To address this gap, we present FIX (Features Interpretable to eXperts), a benchmark for measuring how well a collection of features aligns with expert knowledge. In collaboration with domain experts, we propose FIXScore, a unified expert alignment measure applicable to diverse real-world settings across cosmology, psychology, and medicine domains in vision, language, and time series data modalities. With FIXScore, we find that popular feature-based explanation methods have poor alignment with expert-specified knowledge, highlighting the need for new methods that can better identify features interpretable to experts.

cs.LG↗

arXiv2024

Dimensionality Reduction Techniques for Statistical Inference in Cosmology

We explore linear and non-linear dimensionality reduction techniques for statistical inference of parameters in cosmology. Given the importance of compressing the increasingly complex data vectors used in cosmology, we address questions that impact the constraining power achieved, such as: Are currently used methods effectively lossless? Under what conditions do nonlinear methods, typically based on neural nets, outperform linear methods? Through theoretical analysis and experiments with simulated weak lensing data vectors we compare three standard linear methods and neural network based methods. We propose two linear methods that outperform all others while using less computational resources: a variation of the MOPED algorithm we call e-MOPED and an adaptation of Canonical Correlation Analysis (CCA), which is a method new to cosmology but well known in statistics. Both e-MOPED and CCA utilize simulations spanning the full parameter space, and rely on the sensitivity of the data vector to the parameters of interest. The gains we obtain are significant compared to compression methods used in the literature: up to 30% in the Figure of Merit for $\Omega_m$ and $S_8$ in a realistic Simulation Based Inference analysis that includes statistical and systematic errors. We also recommend two modifications that improve the performance of all methods: First, include components in the compressed data vector that may not target the key parameters but still enhance the constraints on due to their correlations. The gain is significant, above 20% in the Figure of Merit. Second, compress Gaussian and non-Gaussian statistics separately -- we include two summary statistics of each type in our analysis.

astro-ph.CO↗

arXiv2024

Galaxy cluster profiles: A Gaussian mixture model approach to halo miscentering

Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center. Departure of the assumed cluster center from the true halo center bias the resultant profile measurements, an effect known as miscentering bias. Currently, miscentering is typically modeled in stacked profiles of clusters with a two parameter model. We use an alternate approach in which the profiles of individual clusters are used with the corresponding likelihood computed using a Gaussian mixture model. We test the approach using halos and the corresponding subhalo profiles from the IllustrisTNG hydrodynamic simulations. We obtain significantly improved estimates of the miscentering parameters for both 3D and projected 2D profiles relevant for imaging surveys. We discuss applications to upcoming cosmological surveys.

astro-ph.IM↗

arXiv2024

At First Sight: Zero-Shot Classification of Astronomical Images with Large Multimodal Models

Vision-Language multimodal Models (VLMs) offer the possibility for zero-shot classification in astronomy: i.e. classification via natural language prompts, with no training. We investigate two models, GPT-4o and LLaVA-NeXT, for zero-shot classification of low-surface brightness galaxies and artifacts, as well as morphological classification of galaxies. We show that with natural language prompts these models achieved significant accuracy (above 80 percent typically) without additional training/fine tuning. We discuss areas that require improvement, especially for LLaVA-NeXT, which is an open source model. Our findings aim to motivate the astronomical community to consider VLMs as a powerful tool for both research and pedagogy, with the prospect that future custom-built or fine-tuned models could perform better.

astro-ph.IM↗

arXiv2024

Late Time Modification of Structure Growth and the S8 Tension

The $S_8$ tension between low-redshift galaxy surveys and the primary CMB signals a possible breakdown of the $Λ$CDM model. Recently differing results have been obtained using low-redshift galaxy surveys and the higher redshifts probed by CMB lensing, motivating a possible time-dependent modification to the growth of structure. We investigate a simple phenomenological model in which the growth of structure deviates from the $Λ$CDM prediction at late times, in particular as a simple function of the dark energy density. Fitting to galaxy lensing, CMB lensing, BAO, and Supernovae datasets, we find significant evidence - 2.5 - 3$σ$, depending on analysis choices - for a non-zero value of the parameter quantifying a deviation from $Λ$CDM. The preferred model, which has a slower growth of structure below $z\sim 1$, improves the joint fit to the data over $Λ$CDM. While the overall fit is improved, there is weak evidence for galaxy and CMB lensing favoring different changes in the growth of structure.

astro-ph.CO↗

arXiv2024

Improving Convolutional Neural Networks for Cosmological Fields with Random Permutation

Convolutional Neural Networks (CNNs) have recently been applied to cosmological fields -- weak lensing mass maps and galaxy maps. However, cosmological maps differ in several ways from the vast majority of images that CNNs have been tested on: they are stochastic, typically low signal-to-noise per pixel, and with correlations on all scales. Further, the cosmology goal is a regression problem aimed at inferring posteriors on parameters that must be unbiased. We explore simple CNN architectures and present a novel approach of regularization and data augmentation to improve its performance for lensing mass maps. We find robust improvement by using a mixture of pooling and shuffling of the pixels in the deep layers. The random permutation regularizes the network in the low signal-to-noise regime and effectively augments the existing data. We use simulation-based inference (SBI) to show that the model outperforms CNN designs in the literature. We find a 30% improvement in the constraints of the $S_8$ parameter for simulated Stage-III surveys, including systematic uncertainties such as intrinsic alignments. We explore various statistical errors corresponding to next-generation surveys and find comparable improvements. We expect that our approach will have applications to other cosmological fields as well, such as galaxy maps or 21-cm maps.

astro-ph.CO↗