arXiv subjects

Songshaptak De

Publications and source records attributed to Songshaptak De.

6 recordsLinked to original sources

arXiv2026

Probing Fermion-Portal Scalar Dark Matter through Charged Vector-Like Fermions at Future Muon Colliders

We revisit a minimal fermion-portal scalar dark matter model consisting of a real singlet scalar dark matter candidate and additional vector-like singlet and doublet charged fermions stabilized by a discrete $Z_2$ symmetry. In light of the latest dark matter direct-detection constraints, the conventional Higgs-portal interaction is severely restricted, motivating a detailed investigation of fermion-mediated dark matter annihilation channels. We perform a comprehensive analysis of the model parameter space by incorporating theoretical constraints from vacuum stability and perturbative unitarity, together with experimental bounds from relic density measurements, direct-detection experiments, Higgs invisible decay searches, lepton-flavor-violating processes, and anomalous magnetic moments. We show that the observed dark matter relic abundance can be successfully reproduced over a wide mass range through Yukawa-driven $t$- and $u$-annihilation and co-annihilation processes involving the new fermions, while remaining consistent with current direct-detection limits. Motivated by the viable parameter space, we investigate the discovery prospects of the lightest charged vector-like fermion at future muon colliders operating at center-of-mass energies of 3 TeV and 10 TeV. Focusing on the process $\mu^+\mu^- \to E_1^+E_1^- \to e^+e^- + \cancel{E}_T$, we perform a detector-level analysis including realistic Standard Model backgrounds. We demonstrate that the clean experimental environment of a muon collider provides excellent sensitivity to charged fermion masses extending into the multi-TeV regime, significantly improving the exploration prospects of this class of fermion-portal dark matter scenarios.

hep-ph↗

arXiv2026

Bayesian Learning of (n,p) Reaction Cross Sections with Quantified Uncertainties

Accurate neutron-induced $(n,p)$ reaction cross sections are essential for applications in nuclear energy, radionuclide production, materials studies, and nuclear astrophysics. However, experimental data remain sparse for many isotopes, and evaluated nuclear data libraries can show systematic deviations from available measurements. We develop a Bayesian neural network (BNN) residual learning model, denoted \texttt{BNN-R5}, to improve $(n,p)$ reaction cross-section predictions. The model uses five physically motivated nuclear descriptors and does not employ experimental or evaluated cross-section values as input features. Rather than predicting the cross sections directly, \texttt{BNN-R5} learns the log-space residual between the evaluated TENDL-2023 data and experimental measurements, thereby providing a data-driven correction to the evaluated library. The model is trained using stochastic variational inference, which provides predictive mean values together with Bayesian uncertainty estimates. Across a broad range of target nuclei, the corrected cross sections generally show improved agreement with experimental data and outperform the original TENDL-2023 evaluations. Feature-importance analysis using SHapley Additive exPlanations (SHAP) identifies the pairing term $\delta$ as the most influential descriptor, followed by the excitation-energy variable $\ln(\Delta E)$ and the neutron number $N$, while the proton number $Z$ has the smallest overall influence. These results demonstrate that Bayesian residual learning provides a robust and interpretable framework for improving evaluated nuclear data and predicting reaction cross sections in data-sparse regions of the nuclear chart.

nucl-th↗

arXiv2025

Unfolding quantum entanglement from $h\to ZZ^*\to jj\ell\ell$ at a muon collider

We explore the potential to study quantum entanglement through Bell-type inequalities in Higgs boson decays at a future muon collider. Our analysis focuses on the channel $\mu^+ \mu^- \to \nu \bar{\nu} h \to \nu \bar{\nu} ZZ^*$, with one $Z$ decaying to charged leptons and the other decaying hadronically into jets. We study the violation of the CGLMP inequality using the optimal Bell operator for the bipartite qutrit system from $h \to ZZ^*$. The entanglement measure $\mathcal{I}_3$ is constructed from spin-correlated angular observables of the $Z$ decay products. An unfolding method on the angular variables is applied to correct for hadronization and detector effects, recovering the advantage of the hadronic mode with higher event yield and reduced uncertainty. The study is performed at 1, 3, and 10 TeV centre-of-mass energies, assuming 10 ab$^{-1}$ integrated luminosity for each case. At 1 TeV, we use a boosted decision tree for signal isolation, while at higher energies, simple cut-based analyses are sufficient. We find clear Bell inequality violation with the expected values $\mathcal{I}_3 = 2.625 \pm 0.012$, $2.623 \pm 0.004$, and $2.582 \pm 0.010$ for the 1, 3, and 10 TeV machines, respectively. Overall, a strong level of entanglement close to the maximum achievable value of 2.9149 for a two-qutrit system can be measured with very small uncertainties due to the large event yield in the hadronic mode.

hep-ph↗

arXiv2024

Jet Substructure Analysis for Distinguishing Left- and Right-Handed Couplings of Heavy Neutrino in $W'$ Decay at the HL-LHC

The search for heavy $W'$ bosons in their decay modes to a lepton and a heavy neutrino offers a promising avenue for probing new physics beyond the Standard Model. This work focuses on such a signature with an energetic lepton plus a fat jet, originating from the heavy neutrino and containing a lepton. We have employed the jet substructure techniques to isolate the embedded lepton as a subjet of the fat jet. The Lepton Subjet Fraction ($LSF$) and Lepton Mass Drop ($LMD$) variables constructed from the lepton subjet help in separating the signal region from the background. We further study the polarization properties of the $W'$ coupling to the lepton and heavy neutrino through the decay products of the neutrino. Instead of relying on a specific model, we employ generic couplings and explore the discrimination power. Jet substructure-based angular variables $z_\ell$, $z_\theta$, and $z_k$ are combined to form BDT scores to obtain better separation power between left-chiral ($V-A$) and right-chiral ($V+A$) coupling configurations. By using $CL_s$ type profile likelihood estimator, we could achieve $\sim$ 2$\sigma$ $-$ 3$\sigma$ significance of excluding one coupling configuration in favour of the other.

hep-ph↗

arXiv2023

Deep learning techniques for Imaging Air Cherenkov Telescopes

Very High Energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an observational window into the acceleration mechanisms of extreme astrophysical environments. One of the major challenges at Imaging Air Cherenkov Telescopes (IACTs) designed to look for VHE gamma rays, is the separation of air showers initiated by CCRs which form a background to gamma ray searches. Two other less well studied problems at IACTs are a) the classification of different primary nuclei among the CCR events and b) identification of anomalous events initiated by Beyond Standard Model particles that could give rise to shower signatures which differ from the standard images of either gamma rays or CCR showers. The problems of categorizing the primary particle that initiates a shower image, or the problem of tagging anomalous shower events in a model independent way, are problems that are well suited to a machine learning (ML) approach. Traditional studies that have explored gamma ray/CCR separation have used a multivariate analysis based on derived shower properties, which contains significantly reduced information about the shower. In our work, we address the problems outlined above by using ML architectures trained on full simulated shower images, as opposed to training on just a few derived shower properties. We illustrate the techniques of binary and multi-category classification using convolutional neural networks, and we also pioneer the use of autoencoders for anomaly detection at VHE gamma ray experiments. As a case study, we apply our techniques to the H.E.S.S. experiment. However, the real strength of the techniques that we broach here in the context of VHE gamma ray observatories, is that these methods can be applied broadly to any other IACT, such as the upcoming Cherenkov Telescope Array (CTA), or can even be suitably adapted to CCR experiments.

astro-ph.IM↗

arXiv2020

Measuring the polarization of boosted, hadronic $W$ bosons with jet substructure observables

In this work, we present a new technique to measure the longitudinal and transverse polarization fractions of hadronic decays of boosted $W$ bosons. We introduce a new jet substructure observable denoted as $p_\theta$, which is a proxy constructed purely out of subjet energies for the parton level decay polar angle of the $W$ boson in its rest-frame. The distribution of this observable is sensitive to the polarization of $W$ bosons and can therefore be used to reconstruct the $W$ polarization in a way that is independent of the production process -- assuming Standard Model (SM) rules governing decays. We argue that this proxy variable has lower reconstruction errors as compared to the other proxies that have been used by the experimental collaborations, especially for large boosts of the $W$-boson. As a test case, we study the efficacy of our technique on vector boson scattering (VBS) processes at the high luminosity Large Hadron Collider. We find that with only SM production channels, measuring the longitudinal polarization fraction is likely to be challenging even with 10 ab$^{-1}$ of data. We suggest further strategies and scenarios that may improve the prospects of measurement of the hadronic $W$ polarization fraction.

hep-ph↗