arXiv subjects

Arijit Das

Publications and source records attributed to Arijit Das.

At least 19 recordsLinked to original sources

arXiv2026

Observables and conformal properties of dark matter admixed isentropic neutron stars

We construct an equation of state for isentropic dark-matter-admixed neutron stars (DMANS) with a hot core and relatively cold crust incorporating self-consistent temperature and DM density profiles for GeV-scale fermionic DM. We show that the enhancement of central stellar density due to DM accumulation, previously reported for cold neutron stars, remains robust. Substantial observable effects of DM accumulation arise only for sufficiently massive stellar configurations. Similar to earlier studies of cold NS, the speed of sound profile is shown to exhibit non-monotonic behavior for sufficiently large DM density in the core. We identify a competition between thermal effects due to nonzero values of entropy per baryon and softening effects of the dark sector which drive macroscopic properties and conformality indicators in opposite directions. This competition determines the onset of conformality near the stellar core and indicates that conformality signatures attributed to quark-matter in cold NS could be mimicked by DM admixture in isentropic stars.

hep-ph↗

arXiv2026

Adjoint-based gradient methods for inverse design in a multiple fragmentation model

We study an inverse design problem for the linear multiple fragmentation equation arising in particle dynamics. Our objective is to reconstruct an unknown initial size distribution that evolves, under a prescribed fragmentation law, into a desired size distribution at a specified final time. We first establish the existence of global mass-conserving solutions for a broad class of fragmentation kernels with unbounded rates, and subsequently prove the continuous dependence and uniqueness of these solutions under additional assumptions on the fragmentation kernels. We then formulate the inverse design problem as an optimal control problem constrained by the fragmentation dynamics and prove the existence of the optimal control problem. Also derive the corresponding continuous adjoint equation and propose a gradient-type iterative reconstruction method. For the numerical implementation, we develop finite volume schemes for both the forward and adjoint equations, including a weighted finite volume scheme designed to enhance mass conservation and accuracy. Two benchmark problems, involving linear and nonlinear fragmentation rates with known analytical solutions, are used to assess the accuracy and efficiency of the proposed approach and to compare the performance of the two discretizations in both forward simulations and inverse reconstructions.

math.OC↗

arXiv2025

An energy cascade finite volume scheme for a mixed 3- and 4-wave kinetic equation arising from the theory of finite-temperature trapped Bose gases

Building on recent developments in numerical schemes designed to capture energy cascades for 3-wave kinetic equations~\cite{das2024numerical, walton2022deep, walton2023numerical, walton2024numerical}, we construct in this work a finite-volume algorithm for a significantly more complex wave kinetic equation whose collision operator incorporates both 3-wave and 4-wave interactions. This model arises in the context of finite-temperature Bose-Einstein condensation. We establish theoretical properties of the proposed scheme, and our numerical experiments demonstrate that it successfully captures the energy cascade behavior predicted by the equation.

math.NA↗

arXiv2025

Fresh look at the LHC limits on vector leptoquarks

Vector leptoquarks (vLQs) are popular candidates for searching for physics beyond the Standard Model. In this paper, we present updated exclusion limits on various vLQ species, accounting for all the relevant production mechanisms at the LHC. In particular, we highlight the critical role of indirect production and its interference with the Standard Model Drell-Yan process. This interference can be constructive or destructive, depending on the specific quantum numbers of the vLQ, and significantly impacts the sensitivity of current searches. Furthermore, we demonstrate that including QCD-QED mixed pair production channels leads to a noticeable shift in model-independent mass limits. Additionally, we examine the validity of the full theory with vLQs and corresponding effective operators in the high mass regime. Overall, our analysis yields a substantial improvement in the exclusion limits on vLQs compared to the existing results in the literature.

hep-ph↗

arXiv2025

Effects of a Brueckner-Hartree-Fock-corrected effective mass on speed of sound, conformality, and observables of dark matter-admixed neutron stars

We construct an equation of state describing cold and dense matter in the core of neutron stars which includes an admixture of fermionic dark matter and incorporates nucleon effective masses derived from the relativistic Brueckner-Hartree-Fock (BHF) many-body approach within a relativistic mean-field model. Such a BHF-informed mixed-model approach increases stellar compactness, with mass-radius configurations which are consistent with smaller, lighter pulsars. The model displays the expected non-monotonic behavior of sound speed hinted at by neutron star data, and is closer to the conformal bound at maximum mass. We find that the model displays tension with bounds on heavier pulsars, suggesting that the hypothesis of an aggregated dark component in neutron stars needs further critical study.

hep-ph↗

arXiv2024

TooLQit: Leptoquark Models and Limits

We introduce the leptoquark (LQ) toolkit, TooLQit, which includes leading-order FeynRules models for all types of LQs and a Python-based calculator, named CaLQ, to test if a set of parameter points are allowed by the LHC dilepton searches. The models include electroweak gauge interactions of the LQs and follow a set of intuitive notations. Currently, CaLQ can calculate the LHC limits on LQ ($S_1$ and $U_1$) couplings (one or more simultaneously) for any mass between $1$ and $5$ TeV using a $\chi^2$ method. In this manual for TooLQit, we describe the FeynRules models and discuss the techniques used in CaLQ. We outline the workflow to check parameter spaces of LQ models with an example. We show some illustrative scans for one- and multi-coupling scenarios for the $U_1$ vector LQ. The TooLQit code is available at https://github.com/rsrchtsm/TooLQit

hep-ph↗

arXiv2024

Numerical schemes for a fully nonlinear coagulation-fragmentation model coming from wave kinetic theory

This article introduces a novel numerical approach, based on Finite Volume Techniques, for studying fully nonlinear coagulation-fragmentation models, where both the coagulation and fragmentation components of the collision operator are nonlinear. The models come from $3-$wave kinetic equations, a pivotal framework in wave turbulence theory. Despite the importance of wave turbulence theory in physics and mechanics, there have been very few numerical schemes for $3-$wave kinetic equations, in which no ad-hoc additional assumptions are imposed on the evolution of the solutions, and the current manuscript provides one of the first of such schemes. To the best of our knowledge, this also is the first numerical scheme capable of accurately capturing the long-term asymptotic behavior of solutions to a fully nonlinear coagulation-fragmentation model that includes both forward and backward energy cascades. The scheme is implemented on some test problems, demonstrating strong alignment with theoretical predictions of energy cascade rates. We further introduce a weighted Finite Volume variant to ensure energy conservation across varying degrees of kernel homogeneity. Convergence and first-order consistency are established through theoretical analysis and verified by experimental convergence orders in test cases.

math.NA↗

arXiv2024

Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuning

Training LLMs presents significant memory challenges due to growing size of data, weights, and optimizer states. Techniques such as data and model parallelism, gradient checkpointing, and offloading strategies address this issue but are often infeasible due to hardware constraints. To mitigate memory usage, alternative methods like Parameter-Efficient-Fine-Tuning (PEFT) and GaLore approximate weights or optimizer states. PEFT methods, such as LoRA, have gained popularity for fine-tuning LLMs, though they require a full-rank warm start. In contrast, GaLore allows full-parameter learning while being more memory-efficient. This work introduces Natural GaLore, a simple drop in replacement for AdamW, which efficiently applies the inverse Empirical Fisher Information Matrix to low-rank gradients using Woodbury's Identity. We demonstrate that incorporating second-order information speeds up optimization significantly, especially when the iteration budget is limited. Empirical pretraining on 60M, 130M, 350M, and 1.1B parameter Llama models on C4 data demonstrate significantly lower perplexity over GaLore without additional memory overhead. By fine-tuning RoBERTa on the GLUE benchmark using Natural GaLore, we demonstrate significant reduction in gap 86.05% vs 86.28% for full-finetuning. Furthermore, fine-tuning the TinyLlama 1.1B model for function calling using the TinyAgent framework shows that Natural GaLore achieving 83.09% accuracy on the TinyAgent dataset, significantly outperforms 16-bit LoRA at 80.06% and even surpasses GPT4-Turbo by 4%, all while using 30% less memory. All code to reproduce the results are available at: https://github.com/selfsupervised-ai/Natural-GaLore.git

cs.LG↗

arXiv2024

Corrections to Hawking radiation from asteroid-mass primordial black holes: Numerical evaluation of dissipative effects

Primordial black holes (PBHs) are theorized objects that may make up some - or all - of the dark matter in the universe. At the lowest allowed masses, Hawking radiation (in the form of photons or electrons and positrons) is the primary tool to search for PBHs. This paper is part of an ongoing series in which we aim to calculate the $O(\alpha)$ corrections to Hawking radiation from asteroid-mass primordial black holes, based on a perturbative quantum electrodymanics (QED) calculation on Schwarzschild background. Silva et. al. (2023) divided the corrections into dissipative and conservative parts; this work focuses on the numerical computation of the dissipative $O(\alpha)$ corrections to the photon spectrum. We generate spectra for primordial black holes of mass $M=1$-$8 \times 10^{21} m_{\rm planck}$. This calculation confirms the expectation that at low energies, the inner bremsstrahlung radiation is the dominant contribution to the Hawking radiation spectrum. At high energies, the main $O(\alpha)$ effect is a suppression of the photon spectrum due to pair production (emitted $\gamma\rightarrow e^+e^-$), but this is small compared to the overall spectrum. We compare the low-energy tail in our curved spacetime QED calculation to several approximation schemes in the literature, and find deviations that could have important implications for constraints from Hawking radiation on primordial black holes as dark matter.

gr-qc↗

arXiv2024

Corrections to Hawking radiation from asteroid-mass primordial black holes: description of the stochastic charge effect in quantum electrodynamics

Hawking radiation sets stringent constraints on Primordial Black Holes (PBHs) as a dark matter candidate in the $M \sim 10^{16} \ \mathrm{g}$ regime based on the evaporation products such as photons, electrons, and positrons. This motivates the need for rigorous modeling of the Hawking emission spectrum. Using semi-classical arguments, Page [Phys. Rev. D 16, 2402 (1977)] showed that the emission of electrons and positrons is altered due to the black hole acquiring an equal and opposite charge to the emitted particle. The Poisson fluctuations of emitted particles cause the charge $Z|e|$ to random walk, but since acquisition of charge increases the probability of the black hole emitting another charged particle of the same sign, the walk is biased toward $Z=0$, and $P(Z)$ approaches an equilibrium probability distribution with finite variance $\langle Z^2\rangle$. This paper explores how this ``stochastic charge'' phenomenon arises from quantum electrodynamics (QED) on a Schwarzschild spacetime. We prove that (except for a small Fermi blocking term) the semi-classical variance $\langle Z^2 \rangle$ agrees with the variance of a quantum operator $\langle \hat{\cal Z}^2 \rangle$, where $\hat{\cal Z}$ may be thought of as an ``atomic number'' that includes the black hole as well as charge near it (weighted by a factor of $2M/r$). In QED, the fluctuations in $\hat{\cal Z}$ do not arise from the black hole itself (whose charge remains fixed), but rather as a collective effect in the Hawking-emitted particles mediated by the long-range electromagnetic interaction. We find the rms charge $\langle Z^2\rangle^{1/2}$ asymptotes to 3.44 at small PBH masses $M \lesssim 2\times 10^{16}\,$g, declining to 2.42 at $M=5.2\times 10^{17}\,$g.

astro-ph.CO↗

arXiv2024

Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis

The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. In this survey paper, we address the topic of online learning in the context of haphazard inputs, explicitly foregoing such an assumption. We discuss, classify, evaluate, and compare the methodologies that are adept at modeling haphazard inputs, additionally providing the corresponding code implementations and their carbon footprint. Moreover, we classify the datasets related to the field of haphazard inputs and introduce evaluation metrics specifically designed for datasets exhibiting imbalance. The code of each methodology can be found at https://github.com/Rohit102497/HaphazardInputsReview

cs.LG↗

arXiv2024

Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying

In today's digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer. Plus, it talks about how making more accurate tools to spot cyberbullying will be really helpful in the future. Our paper introduces a deep learning-based ap-proach, primarily employing BERT and BiLSTM architectures, to effective-ly address cyberbullying. This approach is designed to analyse large vol-umes of posts and predict potential instances of cyberbullying in online spaces. Our results demonstrate the superiority of the hateBERT model, an extension of BERT focused on hate speech detection, among the five mod-els, achieving an accuracy rate of 89.16%. This research is a significant con-tribution to "Computational Intelligence for Social Transformation," prom-ising a safer and more inclusive digital landscape.

cs.LG↗

arXiv2024

Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond

Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter is the main objective of the present work. Substantial improvement is shown in the result. The main features of the present work are: a) proposing completely generalized algorithm which is able to generate models for any twitter handle and any financial component, b) predicting the time window for a tweets effect on a stock price c) analyzing the effect of multiple twitter handles for predicting the trend. A detailed survey is done to find out the latest work in recent years in the similar field, find the research gap, and collect the required data for analysis and prediction. State-of-the-art algorithm is proposed and complete implementation with environment is given. An insightful trend of the result improvement considering the NLP analysis of twitter data on financial market components is shown. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future. The socio-economic impact of the present work is discussed in conclusion.

cs.CE↗

arXiv2024

Analysis and Detection of Multilingual Hate Speech Using Transformer Based Deep Learning

Hate speech is harmful content that directly attacks or promotes hatred against members of groups or individuals based on actual or perceived aspects of identity, such as racism, religion, or sexual orientation. This can affect social life on social media platforms as hateful content shared through social media can harm both individuals and communities. As the prevalence of hate speech increases online, the demand for automated detection as an NLP task is increasing. In this work, the proposed method is using transformer-based model to detect hate speech in social media, like twitter, Facebook, WhatsApp, Instagram, etc. The proposed model is independent of languages and has been tested on Italian, English, German, Bengali. The Gold standard datasets were collected from renowned researcher Zeerak Talat, Sara Tonelli, Melanie Siegel, and Rezaul Karim. The success rate of the proposed model for hate speech detection is higher than the existing baseline and state-of-the-art models with accuracy in Bengali dataset is 89%, in English: 91%, in German dataset 91% and in Italian dataset it is 77%. The proposed algorithm shows substantial improvement to the benchmark method.

cs.CL↗

arXiv2023

Fresh look at the LHC limits on scalar leptoquarks

The scalar-leptoquark (sLQ) parameter space is well explored experimentally. The direct pair production searches at the LHC have excluded light sLQs almost model agnostically, and the high-$p_{\rm T}$ dilepton tail data have put strong bounds on the leptoquark-quark-lepton Yukawa couplings for a wide range of sLQ masses. However, these do not show the complete picture. Previously, Mandal \emph{et al.} [Single productions of colored particles at the LHC: An example with scalar leptoquarks, \href{https://doi.org/10.1007/JHEP07(2015)028}{J. High Energy Phys. 07 (2015) 028}] showed how the dilepton-dijet data from the pair production searches could give strong limits on these couplings. This was possible by including the single-production contribution to the dilepton-dijet signal. In this paper, we take a fresh look at the LHC limits on all sLQs by following the same principle and combine all significant contributions -- from pair and single productions, $t$-channel sLQ exchange and its interference with the Standard Model background -- to the $\mu\mu jj$ final state and recast the limits. We notice that the sLQ exchange and its interference with the background processes play significant roles in the limits. The $\mu\mu jj$-recast limits are comparable to or, in some cases, significantly better than the currently known limits (from high-$p_{\rm T}$ dilepton data and direct searches), i.e., the LHC data rules out more parameter space than what is considered in the current literature. For the first time, we also show how including the QED processes can noticeably improve the sLQ mass exclusion limits from the QCD-only limits.

hep-ph↗

arXiv2022

Corrections to Hawking Radiation from Asteroid Mass Primordial Black Holes: I. Formalism of Dissipative Interactions in Quantum Electrodynamics

Primordial black holes (PBHs) within the mass range $10^{17} - 10^{22}$ g are a favorable candidate for describing the all of the dark matter content. Towards the lower end of this mass range, the Hawking temperature, $T_{\rm H}$, of these PBHs is $T_{\rm H} \gtrsim 100$ keV, allowing for the creation of electron -- positron pairs; thus making their Hawking radiation a useful constraint for most current and future MeV surveys. This motivates the need for realistic and rigorous accounts of the distribution and dynamics of emitted particles from Hawking radiation in order to properly model detected signals from high energy observations. This is the first in a series of papers to account for the $\mathcal{O}(\alpha)$ correction to the Hawking radiation spectrum. We begin by the usual canonical quantization of the photon and spinor (electron/positron) fields on the Schwarzschild geometry. Then we compute the correction to the rate of emission by standard time dependent perturbation theory from the interaction Hamiltonian. We conclude with the analytic expression for the dissipative correction, i.e. corrections due to the creation and annihilation of electron/positrons in the plasma.

gr-qc↗

arXiv2020

Improvement of electronic Governance and mobile Governance in Multilingual Countries with Digital Etymology using Sanskrit Grammar

With huge improvement of digital connectivity (Wifi,3G,4G) and digital devices access to internet has reached in the remotest corners now a days. Rural people can easily access web or apps from PDAs, laptops, smartphones etc. This is an opportunity of the Government to reach to the citizen in large number, get their feedback, associate them in policy decision with e governance without deploying huge man, material or resourses. But the Government of multilingual countries face a lot of problem in successful implementation of Government to Citizen (G2C) and Citizen to Government (C2G) governance as the rural people tend and prefer to interact in their native languages. Presenting equal experience over web or app to different language group of speakers is a real challenge. In this research we have sorted out the problems faced by Indo Aryan speaking netizens which is in general also applicable to any language family groups or subgroups. Then we have tried to give probable solutions using Etymology. Etymology is used to correlate the words using their ROOT forms. In 5th century BC Panini wrote Astadhyayi where he depicted sutras or rules -- how a word is changed according to person,tense,gender,number etc. Later this book was followed in Western countries also to derive their grammar of comparatively new languages. We have trained our system for automatic root extraction from the surface level or morphed form of words using Panian Gramatical rules. We have tested our system over 10000 bengali Verbs and extracted the root form with 98% accuracy. We are now working to extend the program to successfully lemmatize any words of any language and correlate them by applying those rule sets in Artificial Neural Network.

cs.CY↗

arXiv2020

Automatic Extraction of Bengali Root Verbs using Paninian Grammar

In this research work, we have proposed an algorithm based on supervised learning methodology to extract the root forms of the Bengali verbs using the grammatical rules proposed by Panini [1] in Ashtadhyayi. This methodology can be applied for the languages which are derived from Sanskrit. The proposed system has been developed based on tense, person and morphological inflections of the verbs to find their root forms. The work has been executed in two phases: first, the surface level forms or inflected forms of the verbs have been classified into a certain number of groups of similar tense and person. For this task, a standard pattern, available in Bengali language has been used. Next, a set of rules have been applied to extract the root form from the surface level forms of a verb. The system has been tested on 10000 verbs collected from the Bengali text corpus developed in the TDIL project of the Govt. of India. The accuracy of the output has been achieved 98% which is verified by a linguistic expert. Root verb identification is a key step in semantic searching, multi-sentence search query processing, understanding the meaning of a language, disambiguation of word sense, classification of the sentences etc.

cs.CL↗