arXiv subjects

Nihar Ranjan Sahoo

Publications and source records attributed to Nihar Ranjan Sahoo.

At least 19 recordsLinked to original sources

arXiv2026

Probing QCD instantons using jet correlation observables in proton-proton collisions at the LHC

Discovery of instantons in colliders will provide experimental evidence for the topological properties of the QCD vacuum. In this work, we propose jet correlation observables that can unambiguously discriminate between instanton-induced processes and perturbative hard scattering events in $pp$ collisions at the LHC for a specific range of center-of-mass energies of the produced hadrons. By calculating the instanton sizes and their separations in 2+1 flavor QCD with physical quark masses, we provide a qualitative understanding of why the semiclassical methods reliably predict the cross-section of instanton-induced processes at the particular center-of-mass energies we are focusing on. Our proposal is directly applicable to future $ep$ measurements at the Electron-Ion Collider, offering a cleaner environment to probe instanton-induced processes.

hep-ph↗

arXiv2026

Discriminating QCD Compton and Quark-Antiquark Annihilation Processes in $γ$ + Jets Using Interpretable Machine Learning

We investigate how effectively final-state jet substructure can discriminate between QCD Compton and quark-antiquark annihilation processes from photon-jet production in $pp$ collisions at $\sqrt{s}=13$ TeV. Using infrared- and collinear-safe jet observables, multivariate classifiers -- boosted decision trees and multilayer perceptrons -- are trained on labeled quark- and gluon-initiated jets from dijet events and applied to photon-jet samples. Observables probing soft and wide-angle radiation, in particular jet multiplicity and jet girth, dominate the discrimination. The jet mass provides a complementary but weaker contribution, while the jet charge exhibits negligible discriminating power. A comparison of the two classifiers demonstrates that the achievable separation is limited primarily by QCD radiation effects rather than by classifier complexity. These findings quantify the extent to which information about the underlying hard process survives hadronization and realistic jet reconstruction, providing a physics-driven baseline for precision jet measurements in $pp$, $ep/$A, and heavy-ion collisions.

hep-ph↗

arXiv2025

Exploring the role of hyperbolicity in surface enhanced Raman sensing

A plasmonic nanostructure-based substrate, serving as a surface-enhanced Raman scattering (SERS) substrate, enhances the Raman scattering of molecules. By employing an electron beam lithography followed by our recently developed nano-electroplating protocol, a gold nanorod array SERS substrate can be fabricated to detect lower molecular analyte concentrations, such as Rhodamine 6G (R6G) solution. As the critical dimensions of the nanorod array decrease, they exhibit hyperbolic metamaterial (HMM) characteristics with anisotropic permittivity behavior. In our study, we fabricated two sets of nanorod arrays: one in the HMM regime (140 nm periodicity) and the other in the non-HMM regime (400 nm periodicity), aiming to evaluate the performance of each set based on R6G detection. The obtained results are compared and analyzed using COMSOL simulation and Raman mapping and the role of hyperbolicity is discussed.

physics.optics↗

arXiv2025

Mathematics Isn't Culture-Free: Probing Cultural Gaps via Entity and Scenario Perturbations

Although mathematics is often considered culturally neutral, the way mathematical problems are presented can carry implicit cultural context. Existing benchmarks like GSM8K are predominantly rooted in Western norms, including names, currencies, and everyday scenarios. In this work, we create culturally adapted variants of the GSM8K test set for five regions Africa, India, China, Korea, and Japan using prompt-based transformations followed by manual verification. We evaluate six large language models (LLMs), ranging from 8B to 72B parameters, across five prompting strategies to assess their robustness to cultural variation in math problem presentation. Our findings reveal a consistent performance gap: models perform best on the original US-centric dataset and comparatively worse on culturally adapted versions. However, models with reasoning capabilities are more resilient to these shifts, suggesting that deeper reasoning helps bridge cultural presentation gaps in mathematical tasks

cs.CL↗

arXiv2025

Open-DeBias: Toward Mitigating Open-Set Bias in Language Models

Large Language Models (LLMs) have achieved remarkable success on question answering (QA) tasks, yet they often encode harmful biases that compromise fairness and trustworthiness. Most existing bias mitigation approaches are restricted to predefined categories, limiting their ability to address novel or context-specific emergent biases. To bridge this gap, we tackle the novel problem of open-set bias detection and mitigation in text-based QA. We introduce OpenBiasBench, a comprehensive benchmark designed to evaluate biases across a wide range of categories and subgroups, encompassing both known and previously unseen biases. Additionally, we propose Open-DeBias, a novel, data-efficient, and parameter-efficient debiasing method that leverages adapter modules to mitigate existing social and stereotypical biases while generalizing to unseen ones. Compared to the state-of-the-art BMBI method, Open-DeBias improves QA accuracy on BBQ dataset by nearly $48\%$ on ambiguous subsets and $6\%$ on disambiguated ones, using adapters fine-tuned on just a small fraction of the training data. Remarkably, the same adapters, in a zero-shot transfer to Korean BBQ, achieve $84\%$ accuracy, demonstrating robust language-agnostic generalization. Through extensive evaluation, we also validate the effectiveness of Open-DeBias across a broad range of NLP tasks, including StereoSet and CrowS-Pairs, highlighting its robustness, multilingual strength, and suitability for general-purpose, open-domain bias mitigation. The project page is available at: https://sites.google.com/view/open-debias25

cs.CL↗

arXiv2025

BharatBBQ: A Multilingual Bias Benchmark for Question Answering in the Indian Context

Evaluating social biases in language models (LMs) is crucial for ensuring fairness and minimizing the reinforcement of harmful stereotypes in AI systems. Existing benchmarks, such as the Bias Benchmark for Question Answering (BBQ), primarily focus on Western contexts, limiting their applicability to the Indian context. To address this gap, we introduce BharatBBQ, a culturally adapted benchmark designed to assess biases in Hindi, English, Marathi, Bengali, Tamil, Telugu, Odia, and Assamese. BharatBBQ covers 13 social categories, including 3 intersectional groups, reflecting prevalent biases in the Indian sociocultural landscape. Our dataset contains 49,108 examples in one language that are expanded using translation and verification to 392,864 examples in eight different languages. We evaluate five multilingual LM families across zero and few-shot settings, analyzing their bias and stereotypical bias scores. Our findings highlight persistent biases across languages and social categories and often amplified biases in Indian languages compared to English, demonstrating the necessity of linguistically and culturally grounded benchmarks for bias evaluation.

cs.CL↗

arXiv2025

Jet Quenching in Heavy-Ion Collisions at RHIC and the LHC experiments

Jet quenching serves as a key probes of the Quark-Gluon Plasma (QGP) in heavy-ion collisions. This proceedings presents recent results from RHIC and LHC on jet energy loss, acoplanarity, and the flavour and path-length dependence of Parton energy loss, providing critical constraints on QGP properties and theoretical models. Upcoming data taking campaigns at RHIC and the LHC will offer enhanced precision and extended kinematic reach to further advance our understanding of jet-medium interactions.

nucl-ex↗

arXiv2024

Dynamics of Hot QCD Matter 2024 -- Hard Probes

The hot and dense QCD matter, known as the Quark-Gluon Plasma (QGP), is explored through heavy-ion collision experiments at the LHC and RHIC. Jets and heavy flavors, produced from the initial hard scattering, are used as hard probes to study the properties of the QGP. Recent experimental observations on jet quenching and heavy-flavor suppression have strengthened our understanding, allowing for fine-tuning of theoretical models in hard probes. The second conference, HOT QCD Matter 2024, was organized to bring the community together for discussions on key topics in the field. This article comprises 15 sections, each addressing various aspects of hard probes in relativistic heavy-ion collisions, offering a snapshot of current experimental observations and theoretical advancements. The article begins with a discussion on memory effects in the quantum evolution of quarkonia in the quark-gluon plasma, followed by an experimental review, new insights on jet quenching at RHIC and LHC, and concludes with a machine learning approach to heavy flavor production at the Large Hadron Collider.

nucl-ex↗

arXiv2024

Evaluating Dialect Robustness of Language Models via Conversation Understanding

With an evergrowing number of LLMs reporting superlative performance for English, their ability to perform equitably for different dialects of English ($\textit{i.e.}$, dialect robustness) needs to be ascertained. Specifically, we use English language (US English or Indian English) conversations between humans who play the word-guessing game of 'taboo'. We formulate two evaluative tasks: target word prediction (TWP) ($\textit{i.e.}$, predict the masked target word in a conversation) and target word selection (TWS) ($\textit{i.e.}$, select the most likely masked target word in a conversation, from among a set of candidate words). Extending MD3, an existing dialectic dataset of taboo-playing conversations, we introduce M-MD3, a target-word-masked version of MD3 with the en-US and en-IN subsets. We create two subsets: en-MV (where en-US is transformed to include dialectal information) and en-TR (where dialectal information is removed from en-IN). We evaluate one open-source (Llama3) and two closed-source (GPT-4/3.5) LLMs. LLMs perform significantly better for US English than Indian English for both TWP and TWS tasks, for all settings, exhibiting marginalisation against the Indian dialect of English. While GPT-based models perform the best, the comparatively smaller models work more equitably after fine-tuning. Our error analysis shows that the LLMs can understand the dialect better after fine-tuning using dialectal data. Our evaluation methodology exhibits a novel way to examine attributes of language models using pre-existing dialogue datasets.

cs.CL↗

arXiv2024

Twisted hyperbolic van der Waals crystals for full Stokes mid-infrared polarization detection

Investigating the polarization properties of light in the mid-infrared (mid-IR) spectrum is crucial for molecular sensing, biomedical diagnostics, and IR imaging system technologies. Traditional methods, limited by bulky size and intricate fabrication, utilize large rotating optics for full Stokes polarization detection, impeding miniaturization and accuracy. Van der Waals materials (vdW) based devices can address these challenges due to their lithography-free fabrication, ease of integration with chip-scale platforms and room-temperature operation. This study introduces a chip-integrated polarimeter device leveraging the in-plane biaxial hyperbolic vdW crystal properties for mid-infrared light manipulation. The spatial division measurement scheme incorporates six meticulously designed linear and circular polarization filters, achieving high extinction ratios exceeding 30 dB and transmittance surpassing 50%, with fabrication tolerance of film thickness up to 100 nm. The proposed device represents a significant advancement in polarimetric detection, providing a compact, cost-effective solution and opens new avenues for on-chip mid-IR polarimetric detection in next-generation ultra-compact optical systems.

physics.optics↗

arXiv2024

IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context

The pervasive influence of social biases in language data has sparked the need for benchmark datasets that capture and evaluate these biases in Large Language Models (LLMs). Existing efforts predominantly focus on English language and the Western context, leaving a void for a reliable dataset that encapsulates India's unique socio-cultural nuances. To bridge this gap, we introduce IndiBias, a comprehensive benchmarking dataset designed specifically for evaluating social biases in the Indian context. We filter and translate the existing CrowS-Pairs dataset to create a benchmark dataset suited to the Indian context in Hindi language. Additionally, we leverage LLMs including ChatGPT and InstructGPT to augment our dataset with diverse societal biases and stereotypes prevalent in India. The included bias dimensions encompass gender, religion, caste, age, region, physical appearance, and occupation. We also build a resource to address intersectional biases along three intersectional dimensions. Our dataset contains 800 sentence pairs and 300 tuples for bias measurement across different demographics. The dataset is available in English and Hindi, providing a size comparable to existing benchmark datasets. Furthermore, using IndiBias we compare ten different language models on multiple bias measurement metrics. We observed that the language models exhibit more bias across a majority of the intersectional groups.

cs.CL↗

arXiv2023

Exceptional point sensing via energy loss profile in a non-Hermitian system

The heightened sensitivity observed in non-Hermitian systems at exceptional points (EPs) has garnered significant attention. Typical EP sensor implementations rely on precise measurements of spectra and importantly, for real time sensing measurements, the EP condition ceases to hold as the perturbation increases over time, thereby preventing the use of high sensitivity at the EP point. In this work, we present an new approach to EP sensing which goes beyond these two traditional constraints. Firstly, instead of measuring the spectra, our scheme of EP based sensing is based on the observation of decay length of the optical mode in finite size gratings, which is validated via coupled mode theory as well as full wave electrodynamic simulations. Secondly, for larger perturbation strengths, the EP is spectrally shifted instead of being destroyed -- this spectral shift of the EP is calibrated and using this look-up table, we propose continuous real time detection by varying the excitation laser wavelength. As a proof of principle of our technique, we present an application to the sensing of coronavirus particles, which shows unprecedented limit of detection. These findings will contribute to the expanding field of exceptional point based sensing technologies for real time applications beyond spectral measurements.

physics.optics↗

arXiv2023

Polaritons in photonic hypercrystals of van der Waals materials

In-plane Hyperbolic Phonon polaritons (HPhPs) are quasiparticles formed via coupling of photons and optical phonons in in-plane hyperbolic materials and offer unique applications in sensing, thermal emitters and high resolution imaging. However, the large momentum mismatch between photons and these in-plane HPhPs has restricted their technological potential as most experimental demonstration rely on sophisticated and expensive near field detection schemes. In this work, using the example of $α-$MoO$_3$, we demonstrate that by constructing photonic hypercrystals of this material, one can not only excite these in-plane HPhPs in the far field but also tune the far field response via twisting the hypercrystal lattice with respect the lattice of $α-$MoO$_3$. Our findings will pave the way for the development of practical in-plane HPhP devices as well as provide access to new fundamental physics of such materials via conventional and well developed far field measurement techniques.

physics.optics↗

arXiv2023

STAR Experimental Highlights at Hard Probes 2023

We highlight the STAR experiment's hard probes results, including jets and heavy flavor production in heavy-ion collisions to study the properties of the quark-gluon plasma. Various jet-substructure observables in proton-proton collisions are presented to explore both perturbative and non-perturbative regimes of quantum chromodynamics. Finally, we discuss the STAR experiment's hard probes physics program for ongoing data taking.

nucl-ex↗

arXiv2023

Cumulants of net-strangeness multiplicity distributions at energies available at the BNL Relativistic Heavy Ion Collider

The higher-order cumulants of net-proton number, net-charge, and net-strangeness multiplicity distributions are widely studied to search for the quantum-chromodynamics critical point and extract the chemical freeze-out parameters in heavy-ion collisions. In this context, the event-by-event fluctuations of the net-strangeness multiplicity distributions play important roles in extracting the chemical freeze-out parameter in the strangeness sector. Due to having difficulties in detecting all strange hadrons event by event, the kaon ($K$) and lambda ($Λ$) particles serve as a proxy for the strangeness-related observables in heavy-ion collisions. We have studied the net-$K$, net-$Λ$, and net-($K$ + $Λ$) multiplicity distributions and calculated their different order of cumulants using the ultrarelativistic quantum molecular dynamics model and hadron resonance gas calculation. To adequately account for the net-strangeness cumulants, it has been found that the inclusion of resonance decay contributions in $K$ and $Λ$ is necessary.

nucl-th↗

arXiv2023

Few-Shot Domain Adaptation for Low Light RAW Image Enhancement

Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain's enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images.

cs.CV↗

arXiv2022

A universal and stable metasurface for photonic quasi bound state in continuum coupled with two dimensional semiconductors

Strong coupling of excitons to optical cavity modes is of immense importance to understanding the fundamental physics of quantum electrodynamics at the nanoscale as well as for practical applications in quantum information technologies. There have been several attempts at achieving strong coupling between excitons in two dimensional semiconductors such as transition metal dichalcogenides (TMDCs) and photonic quasi-bound states in the continuum (BICs). We identify two gaps in the platforms for achieving strong coupling between TMDC excitons and photonic quasi-BICs: firstly, in the studies so far, different cavity architectures have been employed for coupling to different TMDCs. This would mean that typically, the fabrication process flow for the cavities will need to be modified as one moves from one TMDC to the other, which can limit the technological progress in the field. Secondly, there has been no discussion of the impact of fabrication imperfections in the studies on strong coupling of these subsystems so far. In this work, we address these two questions by optimizing a cavity with the same architecture which can couple to the four typical TMDCs (MoS$_2$, WS$_2$, MoSe$_2$, WSe$_2$) and perform a detailed investigation on the fabrication tolerance of the associated photonic quasi-BICs and their impact on strong coupling.

physics.optics↗

arXiv2022

Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.

nucl-th↗