arXiv subjects

Prakhar Bansal

Publications and source records attributed to Prakhar Bansal.

7 recordsLinked to original sources

arXiv2026

Implications of SARAS3 data for Coulomb-like interacting dark matter

The 21-cm signal from cosmic dawn is a potentially sensitive probe of interactions between dark matter (DM) and baryons. We investigate the implications of the SARAS3 non-detection in the 55.5-84.4 MHz band for Coulomb-like interacting DM (IDM). In contrast to earlier constraint analyses that focused primarily on baryon cooling, we model the interaction self-consistently by including both excess cooling of the gas and the suppression of structure formation, which delays the onset of star formation and hence suppresses the Ly$α$, X-ray, and ionizing backgrounds at early times. We perform a joint Bayesian fit of a global 21-cm signal model and a flexible foreground model to the SARAS3 antenna temperature, and find that the signal parameters remain weakly constrained after marginalizing over the foregrounds. The null result is nonetheless informative: the data disfavour deep absorption features within the observed band, with the strongest bound at $z = 23.6$ ($ν\approx 57.7$ MHz), where $T_{21} \gtrsim -277.6$ mK at $3σ$. Comparing the IDM and standard cold dark matter scenarios, we find no statistically significant preference for IDM (Bayes factor $B \approx 1.7$). While we do not constrain the strength of baryon-DM interactions, the SARAS3 non-detection places a meaningful upper bound on the amplitude of the global 21-cm signal in this class of models.

astro-ph.CO↗

arXiv2026

CMBComp: A Simple and Accurate Compressed CMB Likelihood for Dark Energy, Curvature, and Massive Neutrinos

We present CMBComp, a compact and accurate compressed cosmic microwave background (CMB) likelihood that captures the dominant geometric information of the full CMB likelihood derived from the combined SPT-3G D1 + ACT DR6 + Planck PR3 primary CMB anisotropy + Planck PR4/NPIPE CMB-lensing dataset, which we collectively refer to as SPA. The compression is fast to evaluate and trivial to implement in standard inference pipelines. We construct and validate it in five model spaces: the spatially flat cosmological-constant model ($Λ$CDM), and its dynamical-dark-energy ($w_0w_a$CDM), massive-neutrino ($νΛ$CDM), non-flat ($oΛ$CDM), and joint massive-neutrino--dynamical-dark-energy ($νw_0 w_a$CDM) extensions. Five compressed likelihoods are introduced, corresponding to two three-parameter compressions for the dark-energy sector (CMB-3 for $Λ$CDM and CMB-3w for $w_0w_a$CDM), two four-parameter compressions for the neutrino sector (CMB-4$ν$ for $νΛ$CDM and CMB-4$ν$w for $νw_0 w_a$CDM), and a four-parameter curvature compression (CMB-4k for $oΛ$CDM). Combining each compressed likelihood with the DESI DR2 baryon acoustic oscillation (BAO) data, we demonstrate that the resulting posteriors agree to high precision with those obtained from the corresponding full-CMB chains. CMBComp is therefore particularly well suited to cosmological inference for models that modify the late-time expansion history, enabling accurate CMB constraints to be incorporated into new analyses with minimal computational overhead and without reliance on a full Boltzmann-solver-based inference pipeline. The compressed likelihood files and example notebooks accompanying CMBComp are made publicly available at https://github.com/Amoghsriv/CMBComp.

astro-ph.CO↗

arXiv2026

Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation

Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite context windows, and weakly structured causal reasoning. This survey provides a unified account of augmentation strategies along a single axis: the degree of structured context supplied at inference time. We cover in-context learning and prompt engineering, Retrieval-Augmented Generation (RAG), GraphRAG, and CausalRAG. Beyond conceptual comparison, we provide a transparent literature-screening protocol, a claim-audit framework, and a structured cross-paper evidence synthesis that distinguishes higher-confidence findings from emerging results. The paper concludes with a deployment-oriented decision framework and concrete research priorities for trustworthy retrieval-augmented NLP.

cs.CL↗

arXiv2026

Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Bench

Retrieval-Augmented Generation pipelines span a wide range of retrieval strategies that differ substantially in token cost and capability. Selecting the right strategy per query is a practical efficiency problem, yet no routing classifiers have been trained on RAGRouter-Bench \citep{wang2026ragrouterbench}, a recently released benchmark of $7,727$ queries spanning four knowledge domains, each annotated with one of three canonical query types: factual, reasoning, and summarization. We present the first systematic evaluation of lightweight classifier-based routing on this benchmark. Five classical classifiers are evaluated under three feature regimes, namely, TF-IDF, MiniLM sentence embeddings \citep{reimers2019sbert}, and hand-crafted structural features, yielding 15 classifier feature combinations. Our best configuration, TF-IDF with an SVM, achieves a macro-averaged F1 of $\mathbf{0.928}$ and an accuracy of $\mathbf{93.2\%}$, while simulating $\mathbf{28.1\%}$ token savings relative to always using the most expensive paradigm. Lexical TF-IDF features outperform semantic sentence embeddings by $3.1$ macro-F1 points, suggesting that surface keyword patterns are strong predictors of query-type complexity. Domain-level analysis reveals that medical queries are hardest to route and legal queries most tractable. These results establish a reproducible query-side baseline and highlight the gap that corpus-aware routing must close.

cs.IR↗

arXiv2026

On the Difficulties with Late-Time Solutions for the Hubble Tension

We explore the notion that cosmological models that modify the late-time expansion history cannot simultaneously fit the SH0ES collaboration's measurements of the Hubble constant, DESI baryon acoustic oscillations data, and Type Ia supernova distances. Adopting a few simple phenomenological models, we quantitatively demonstrate that a satisfactory fit with a model with late-time expansion history can only be achieved if one of the following is true: 1) there is a sharp step in the absolute magnitude of Type Ia supernovae at very low redshift, $z\sim 0.01$, or 2) the distance duality relation, $d_L(z)=(1+z)^2d_A(z)$, is broken. Both solutions are trivial in that they effectively decouple the calibrated SNIa measurements from other data, and this qualitatively agrees with previous work built on studying specific dark-energy models. We also identify a less effective class of late-time solutions with a transition at $z\simeq 0.15$ that lead to a more modest improvement in fit to the data than models with a very low-z transition. Our conclusions are largely unchanged when we include surface brightness fluctuation distance measurements, with their current systematic uncertainties, to our analysis. We finally illustrate our findings by studying a physical model which, when equipped with the ability to smoothly change the absolute magnitude of Type Ia supernovae, partially resolves the Hubble tension.

astro-ph.CO↗

arXiv2025

Expansion-history preferences of DESI DR2 and external data

We explore the origin of the preference of DESI DR2 baryon acoustic oscillation (BAO) measurements and external data from cosmic microwave background (CMB) and type Ia supernovae (SNIa) that dark energy behavior departs from that expected in the standard cosmological model with vacuum energy ($Λ$CDM). In our analysis, we allow a flexible scaling of the expansion rate with redshift that nevertheless allows reasonably tight constraints on the quantities of interest, and adopt and validate a simple yet accurate compression of the CMB data that allows us to constrain our phenomenological model of the expansion history. We find that data consistently show a preference for a 3-4 % increase in the expansion rate at $z\simeq 0.7$ relative to that predicted by the standard $Λ$CDM model, in excellent agreement with results from the less flexible $(w_0, w_a)$ parameterization which was used in previous analyses. Even though our model allows a departure from the best-fit $Λ$CDM model at zero redshift, we find no evidence for such a signal. We also find no evidence (at greater than 1$σ$ significance) for a departure of the expansion rate from the $Λ$CDM predictions at higher redshifts for any of the data combinations that we consider. Overall, our results strengthen the robustness of the findings using the combination of DESI, CMB, and SNIa data to dark-energy modeling assumptions.

astro-ph.CO↗

arXiv2018

Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions

Convolutional Neural Networks (CNNs) have demonstrated great results for the single-image super-resolution (SISR) problem. Currently, most CNN algorithms promote deep and computationally expensive models to solve SISR. However, we propose a novel SISR method that uses relatively less number of computations. On training, we get group convolutions that have unused connections removed. We have refined this system specifically for the task at hand by removing unnecessary modules from original CondenseNet. Further, a reconstruction network consisting of deconvolutional layers has been used in order to upscale to high resolution. All these steps significantly reduce the number of computations required at testing time. Along with this, bicubic upsampled input is added to the network output for easier learning. Our model is named SRCondenseNet. We evaluate the method using various benchmark datasets and show that it performs favourably against the state-of-the-art methods in terms of both accuracy and number of computations required.

cs.CV↗