arXiv subjects

Neeraj

Publications and source records attributed to Neeraj.

10 recordsLinked to original sources

arXiv2026

Multiplicity and transverse density as ordering variables of the $Λ/K^0_S$ ratio in oxygen-oxygen collisions at the LHC

Baryon-to-meson enhancements are commonly compared across collision systems at equal multiplicity. Using published data only, we test whether the $p_T$ dependence of the CMS $Λ/K^0_S$ ratio in minimum-bias O-O collisions at $\sqrt{s_{NN}}=5.36$ TeV can be obtained from CMS multiplicity-differential p-Pb and Pb-Pb measurements weighted with the O-O multiplicity distribution. The prediction from p-Pb collisions of equal multiplicity is harder than the data, with a tilt of $+0.35\pm0.04$ in $\ln(\mathrm{prediction}/\mathrm{data})$ versus $\ln p_T$, whereas that from peripheral Pb-Pb collisions is consistent with the data. In a Glauber model calibrated on O-O data, O-O and peripheral Pb-Pb collisions of equal multiplicity have similar transverse densities $ρ=\langle(dN_{ch}/dη)/S_\perp\rangle$, about half of that in p-Pb. Matching the p-Pb reference in $ρ$ instead of multiplicity reduces the tilt by about 60%, to $+0.14\pm0.04$, with nucleons as sources; the tension is fully removed ($-0.02\pm0.04$) only when sub-nucleonic structure is included (three constituents per nucleon), so the conclusion depends on the modeled size of high-multiplicity p-Pb collisions. The published blast-wave velocities of p-Pb and Pb-Pb collisions, which differ at equal multiplicity, are close at equal $ρ$. Multiplicity alone thus does not determine the $Λ/K^0_S$ shape across systems; the data are consistent with a radial boost governed by the transverse density, as expected from hydrodynamics. The same pattern is found in EPOS4, which has a hydrodynamic core, but not in PYTHIA Angantyr. For Ne-Ne collisions, which have the transverse density of O-O at equal multiplicity, the minimum-bias ratio is predicted to agree with O-O within 3-5%, as EPOS4 also predicts.

hep-ph↗

arXiv2026

A Coupled Source Description of Pseudorapidity Distributions from RHIC to LHC: Emergent $1/μ_B$ Scaling and Limiting Fragmentation

One of the most remarkable observations in heavy-ion collisions is the systematic regularity exhibited by pseudorapidity distributions of charged particles across collision energies. While single-source models fail at higher energies and independent multi-source approaches do not reproduce the central dip observed at LHC energies, a unified description across the full RHIC-LHC energy range remains elusive. These distributions from Au+Au collisions at RHIC ($\sqrt{s_{NN}}$ = 19.6--200 GeV) and Pb+Pb collisions at LHC ($\sqrt{s_{NN}}$ = 2.76--5.36 TeV) are analyzed using a novel parametrization based on coupled Gaussian sources where the interaction strength is quantified by parameter $λ$. This coupled two-source model captures the interaction between forward and backward sources through the medium formed in the collision. Remarkably, $λ$ exhibits empirical scaling behavior resembling $1/μ_B$, suggesting sensitivity to baryon stopping and the strongly-interacting medium. All fitting parameters follow systematic energy trends, with the peak-to-peak distance and chemical freeze-out temperature exhibiting identical exponential saturation patterns, indicating that geometric expansion and thermal evolution share a common underlying dynamics governed by QCD phase structure. Furthermore, the approach naturally preserves limiting fragmentation behavior across all energies, in contrast to independent source models that suggest its violation at LHC energies. Although the theoretical basis requires further investigation, these empirical correlations successfully unify charged particle production across nearly two orders of magnitude in collision energy, revealing fundamental connections to underlying collision dynamics.

hep-ph↗

arXiv2026

Midrapidity dip and limiting fragmentation of charged hadrons from RHIC to LHC energies

The charged-hadron pseudorapidity density $\dndeta$ in central heavy-ion collisions has a midrapidity depression that deepens with collision energy. Whether it reflects structure in the rapidity density $\dndy$ or only the kinematics of the $y\toη$ transformation has mostly been argued with effective or model-based Jacobians. We test this without an event generator, with a $y\toη$ kernel built from measured identified-hadron spectra. In central Au+Au and Pb+Pb collisions from $\snn=19.6$~GeV to 5.36~TeV the data do not require a dip in $\dndy$: the decisive null-hypothesis family gives $p\ge\pMinAll$ in all variants. For dips up to one rapidity unit wide, the depth giving $p<0.05$ in half the pseudo-experiments is \rng{\dminLHClo}{\dminLHChi}\% at the LHC and \rng{\dminRHIClo}{\dminRHIChi}\% at 62.4--200~GeV. Generator closure tests show a false dip at top RHIC energy caused by the forward region. As the LHC data barely constrain the fragmentation region, an energy sum rule calibrated on RHIC data, with the RHIC shape of that region, gives best-fit LHC/RHIC ratios of \rng{\sLoE}{\sHiE}, \rng{\sLoF}{\sHiF} and \rng{\sLoG}{\sHiG} at 2.76, 5.02 and 5.36~TeV for three shape families, with 95\% intervals over the families of \rng{\sNFLoE}{\sNFHiE}, \rng{\sNFLoF}{\sNFHiF} and \sNFG{} that include unity, although at 2.76 and 5.02~TeV the fits prefer \rng{\pullLoEF}{\pullHiEF}$\,σ$ more energy than calibrated. Over plausible RHIC covariances and kernels the best-fit ratios span \rng{\sSystAllLo}{\sSystAllHi}, and single variants exclude unity at \systUnionAbs: limiting fragmentation is neither established nor excluded.

hep-ph↗

arXiv2026

System-size dependence of strangeness production from p+p to Pb+Pb: quantitative tests of the $K^+/π^+$ horn

The pronounced maximum ("horn") in the $K^+/π^+$ excitation function observed in central Pb+Pb collisions is well established feature of collision-energy dependence, but its dependence on system size remains poorly understood. We confront hadronic (SMASH) and partonic (PHSD) transport, Glauber core-corona, canonical-ensemble suppression, and the two-phase statistical model of the early stage (SMES) with $K^+/π^+$ data spanning the complete NA61/SHINE system-size ladder (p+p, Be+Be, Ar+Sc, Xe+La) together with NA49 Pb+Pb and STAR Au+Au, quantifying every comparison by $χ^2$. No single framework describes the full range: core-corona is preferred for intermediate systems, SMES with canonical strangeness conservation for heavy systems, and none reproduces the small-system data. Neither transport model generates the horn, with or without partonic degrees of freedom. The data exhibit a step-like enhancement of $K^+/π^+$ between Be+Be and Ar+Sc, quantified within the core-corona framework by a step from $\fcore \approx 0.22$ (Be+Be) to $\fcore \approx 0.58$ (Ar+Sc). The strangeness saturation factor $\gs$ rises in two distinct steps, separating geometric core formation from thermodynamic equilibration. The $(K^+/π^+)/(K^-/π^-)$ double ratio, which at fixed $\sqrts$ cancels the system-independent $μ_B$ contribution, shows a step between light and heavy systems at a global significance of $3.7σ$, providing evidence for genuine strangeness enhancement beyond the pair-production baseline. Preliminary Xe+La data favor core-corona ($\chindf = 5.8$) over SMASH ($\chindf = 23.3$); finalized spectra will sharpen the CC/SMES discrimination at the $27\%$ level.

hep-ph↗

arXiv2026

Evidence for differential kinetic freeze-out of the $ϕ(1020)$ meson in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV

In heavy-ion collisions, hadronic species with small interaction cross sections may decouple from the evolving fireball earlier than the bulk, yet quantitative evidence for this differential freeze-out has remained elusive. We report that the $ϕ(1020)$ meson does \emph{not} kinetically freeze out with the bulk hadrons in 0--5\% central Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV: a Boltzmann-Gibbs blast-wave contour analysis of ALICE $ϕ(1020)$ $p_{\rm T}$ transverse-momentum spectra shows that the bulk $π/K/p$ freeze-out point is excluded at $4.1σ$ ($Δχ^2 = 21.7$). Despite its proton-like mass, the $ϕ$ exhibits freeze-out parameters incompatible with those of the bulk hadrons, implying that the observed spectral hardening cannot be attributed solely to mass-dependent collective expansion. Instead, it is naturally explained by the OZI-suppressed $ϕ$-hadron interaction cross section which causes $ϕ$ to decouple earlier and probe a distinct freeze-out surface. The exclusion is robust under all systematic variations tested and is qualitatively reproduced by SMASH hadronic transport simulations. These findings establish the $ϕ$ meson as a clean probe of species-dependent hadronization, and provide quantitative evidence for a kinetic freeze-out hierarchy in ultra-relativistic heavy-ion collisions.

hep-ph↗

arXiv2024

Semantic Search and Recommendation Algorithm

This paper introduces a new semantic search algorithm that uses Word2Vec and Annoy Index to improve the efficiency of information retrieval from large datasets. The proposed approach addresses the limitations of traditional search methods by offering enhanced speed, accuracy, and scalability. Testing on datasets up to 100GB demonstrates the method's effectiveness in processing vast amounts of data while maintaining high precision and performance.

cs.IR↗

arXiv2023

A Framework for Modeling, Analyzing, and Decision-Making in Disease Spread Dynamics and Medicine/Vaccine Distribution

The challenges posed by epidemics and pandemics are immense, especially if the causes are novel. This article introduces a versatile open-source simulation framework designed to model intricate dynamics of infectious diseases across diverse population centres. Taking inspiration from historical precedents such as the Spanish flu and COVID-19, and geographical economic theories such as Central place theory, the simulation integrates agent-based modelling to depict the movement and interactions of individuals within different settlement hierarchies. Additionally, the framework provides a tool for decision-makers to assess and strategize optimal distribution plans for limited resources like vaccines or cures as well as to impose mobility restrictions.

cs.MA↗

arXiv2021

Kink-like Solitons in Quantum Droplet

Solitonic excitations of the one-dimensional quantum droplets are obtained, which smoothly connect vacuum with the flat-top droplet, akin to compactons in classical liquids. These solitons are of the kink type, necessarily residing on a constant pedestal, determined by the mean-field repulsion and beyond mean field quantum correction and having exactly one-third of the uniform condensate amplitude. Akin to the kinks, the propagating modes occur in pairs and are phase-locked with the background. The lowest chemical potential and condensate amplitude at the flat-top boundary matches with the self-trapped quantum droplet. More general excitations of analogous kind are obtained through the Möbius transform, which connect the required solutions to elliptic functions in general.

cond-mat.quant-gas↗

arXiv2020

A Deep Learning Framework for COVID Outbreak Prediction

The outbreak of COVID-19 i.e. a variation of coronavirus, also known as novel corona virus causing respiratory disease is a big concern worldwide since the end of December 2019. As of September 12, 2020, it has turned into an epidemic outbreak with more than 29 million confirmed cases and around 1 million reported deaths worldwide. It has created an urgent need to monitor and forecast COVID-19 spread behavior to better control this spread. Among all the popular models for COVID-19 forecasting, statistical models are receiving much attention in media. However, statistical models are showing less accuracy for long term forecasting, as there is high level of uncertainty and required data is also not sufficiently available. In this paper, we propose a comparative analysis of deep learning models to forecast the COVID-19 outbreak as an alternative to statistical models. We propose a new Attention-based encoder-decoder model, named Attention-Long Short Term Memory (AttentionLSTM). LSTM based neural network layer architecture incorporates the idea of fine-grained attention mechanism i.e., attention on hidden state dimensions instead of hidden state vector itself, which is capable of highlighting the importance and contribution of each hidden state dimension. It helps in detection on crucial temporal information, resulting in a highly interpretable network. Additionally, we implement a learnable vector embedding for time. As, time in a vector representation can be easily added with many architectures. This vector representation is called Time2Vec. We have used COVID-19 data repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University to assess the proposed model's performance. The proposed model give superior forecasting accuracy compared to other existing methods.

cs.LG↗

arXiv2016

Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship

We study the multi-scale temporal correlations and causality connections between the New York Stock Exchange (NYSE) and Bombay Stock Exchange (BSE) monthly average closing price indexes for a period of 300 months, encompassing the time period of the liberalisation of the Indian economy and its gradual global exposure. In multi-scale analysis; clearly identifiable 1, 2 and 3 year non-stationary periodic modulations in NYSE and BSE have been observed, with NYSE commensurating changes in BSE at 3 years scale. Interestingly, at one year time scale, the two exchanges are phase locked only during the turbulent times, while at the scale of three year, in-phase nature is observed for a much longer time frame. The two year time period, having characteristics of both one and three year variations, acts as the transition regime. The normalised NYSE's stock value is found to Granger cause those of BSE, with a time lag of 9 months. Surprisingly, observed Granger causality of high frequency variations reveals BSE behaviour getting reflected in the NYSE index fluctuations, after a smaller time lag. This Janus faced relationship, shows that smaller stock exchanges may provide a natural setting for simulating market fluctuations of much bigger exchanges. This possibly arises due to the fact that high frequency fluctuations form an universal part of the financial time series, and are expected to exhibit similar characteristics in open market economies.

q-fin.CP↗