arXiv subjects

Arghya Choudhury

Publications and source records attributed to Arghya Choudhury.

At least 19 recordsLinked to original sources

arXiv2026

Complementary probes of Bilinear RPV SUSY models with a wino-like LSP via Neutrino Oscillation and LHC

In this work, we explore the bilinear R-parity violating Supersymmetry model's parameter space by performing a Markov Chain Monte Carlo scan with neutrino oscillation data, Higgs mass and its coupling strengths, and flavor observables such as $B$-hadron decay branching ratios. From the allowed parameter space, we analyze the decay patterns of wino-like lighter charginos and lightest neutralinos and demonstrate how the branching ratios to different neutrino and charged lepton flavors depend on the neutrino mass hierarchy. Furthermore, we investigate the impact of current LHC bounds and projected future sensitivities from trilepton resonance searches on the allowed parameter space. We show that considering the branching ratio $\mathrm{Br}(\widetildeχ_1^{\pm} \to Zl^\pm; l= e,μ,τ) \sim$23\%, obtained at the best-fit point, the wino-like mass degenerate $\widetildeχ_1^{\pm}/\widetildeχ_1^0$ are excluded upto 565 GeV from LHC Run-II data. The projected exclusion reach with a similar branching ratio at High-Luminosity LHC (HL-LHC) is around 950 GeV. For a simplified scenario where $\widetildeχ_1^{\pm} / \widetildeχ_1^0$ decays via a $Z$ boson with branching ratios of 1\%, 50\%, and 100\%, wino masses can be excluded up to approximately $600~\mathrm{GeV}$, $1185~\mathrm{GeV}$, and $1350~\mathrm{GeV}$ respectively. Our analysis shows that the HL-LHC can probe a significant portion of the 1$σ$ allowed parameter space by neutrino oscillation measurements and other experimental constraints.

hep-ph↗

arXiv2026

Revisiting the Electroweakino Sector of the Baryon Number Violating MSSM at the HL-LHC with Deep Neural Networks

We study the projected sensitivity of direct electroweakino production $pp \to \tildeχ_1^{\pm} \tildeχ_2^0$ at the HL-LHC in a simplified framework with wino-like, mass degenerate $\tildeχ_1^{\pm}$ and $\tildeχ_2^0$, and a bino-like lightest neutralino $\tildeχ_1^0$, assuming R-parity violating~(RPV) through the baryon number violating $λ^{\prime \prime}_{112}u^c d^c d^c$ and $λ^{\prime \prime}_{113}u^c d^c b^c$ operators. We consider three channels with the $λ^{\prime \prime}_{112}u^c d^c d^c$ RPV operator: $Wh$ mediated $1\,\ell + 2\,b + \rm E{\!\!\!/}_T$, $Wh$ mediated $1\,\ell + (\geq 2\,j) + 2\, γ+ \rm E{\!\!\!/}_T$, and $WZ$ mediated $3\ell + (\geq 2 j) + \rm E{\!\!\!/}_T$. In each channel, we train benchmark-specific multi-layer perceptrons (MLPs), analogous to signal-region classifiers, on the four-momenta of the final state particles along with a small set of higher-level observables to distinguish the signal from the dominant SM backgrounds. We find that the HL-LHC will be able to probe winos up to $\sim 900~$GeV, $\sim 780~$GeV, and $\sim 880~$GeV in the $Wh$ mediated $1\,\ell + 2\,b + \rm E{\!\!\!/}_T$, $Wh$ mediated $1\,\ell + (\geq 2\,j) + 2\, γ+ \rm E{\!\!\!/}_T$, and $WZ$ mediated $3\ell + (\geq 2 j) + \rm E{\!\!\!/}_T$ channels, respectively, for $m_{\tildeχ_1^0} \sim 50~$GeV, in the presence of $λ^{\prime \prime}_{112}u^c d^c d^c$ couplings, at $2σ$ sensitivity. In case the $λ^{\prime \prime}_{113}u^c d^c b^c$ operator is solely switched on, the projected sensitivity for winos reach up to $\sim 700~$GeV for $Wh$ mediated $1\,\ell + (\geq 1\,b)\, + (\geq 1j)\, + 2\, γ+ \rm E{\!\!\!/}_T$ and $\sim 850~$GeV for the $WZ$ mediated $3\ell + (\geq 1 b) + \rm E{\!\!\!/}_T$ channel.

hep-ph↗

arXiv2025

Solving Navier-Stokes Equations Using Data-free Physics-Informed Neural Networks With Hard Boundary Conditions

In recent years, Physics-Informed Neural Networks (PINNs) have emerged as a powerful and robust framework for solving nonlinear differential equations across a wide range of scientific and engineering disciplines, including biology, geophysics, astrophysics and fluid dynamics. In the PINN framework, the governing partial differential equations, along with initial and boundary conditions, are encoded directly into the loss function, enabling the network to learn solutions that are consistent with the underlying physics. In this work, we employ the PINN framework to solve the dimensionless Navier-Stokes equations for three two-dimensional incompressible, steady, laminar flow problems without using any labeled data. The boundary and initial conditions are enforced in a hard manner, ensuring they are satisfied exactly rather than penalized during training. We validate the PINN predicted velocity profiles, drag coefficients and pressure profiles against the conventional computational fluid dynamics (CFD) simulations for moderate to high values of Reynolds number ($Re$). It is observed that the PINN predictions show good agreement with the CFD results at lower $Re$. We also extend our analysis to a transient condition and find that our method is equally capable of simulating complex time-dependent flow dynamics. To quantitatively assess the accuracy, we compute the $L_2$ normalized error, which lies in the range $\mathcal{O}(10^{-4})$ - $\mathcal{O}(10^{-1})$ for our chosen case studies.

physics.flu-dyn↗

arXiv2025

Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference

Some of the issues that make sampling parameter spaces of various beyond the Standard Model (BSM) scenarios computationally expensive are the high dimensionality of the input parameter space, complex likelihoods, and stringent experimental constraints. In this work, we explore likelihood-free approaches, leveraging neural network-aided Simulation-Based Inference (SBI) to alleviate this issue. We focus on three amortized SBI methods: Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE), and Neural Ratio Estimation (NRE) and perform a comparative analysis through the validation test known as the \textit{ Test of Accuracy with Random Points} (TARP), as well as through posterior sample efficiency and computational time. As an example, we focus on the scalar sector of the phenomenological minimal supersymmetric SM (pMSSM) and observe that the NPE method outperforms the others and generates correct posterior distributions of the parameters with a minimal number of samples. The efficacy of this framework is tested on 5 parameter pMSSM with Higgs and flavor physics data and its performance is compared with the MCMC method. We further add dark matter (DM) observables to make the task more challenging and consider a 9 parameter pMSSM. We observe that even though the efficiency factor drops, the amortized SBI method still produces faithful posterior distributions. SBI predicted points satisfying DM constraints are mostly bino-dominated upto $\sim$ 1.5 TeV, and are mostly wino-dominated within the 1.5 - 2 TeV range.

hep-ph↗

arXiv2025

Reconstructing Sparticle masses at the LHC using Generative Machine Learning

We explore a generative model framework to infer the masses of heavy particles from detector-level data over a broad parameter space. Our model combines a transformer-based detector encoder and a diffusion neural network. We first apply our model to a new physics scenario involving the pair production of wino-like chargino-neutralino, $pp \to \tildeχ_1^{\pm} \tildeχ_2^0$, in the $1\ell + 2γ+ jets$ channel at the high luminosity LHC~(HL-LHC). We find that our framework can achieve mass reconstruction efficiency of $\gtrsim 70\%$ for the lightest neutralino $\tildeχ_1^0$ and $\gtrsim 40\%$ for the second lightest neutralino $\tildeχ_2^0$, for a mass tolerance of $Δm = 30~$GeV, across the entire parameter space accessible at the HL-LHC. We further extend our analysis to a different scenario with $pp\to\tildeχ_1^{\pm}\tildeχ_1^{\mp}+\tildeχ_1^{\pm}\tildeχ_2^0$ pair production at the HL-LHC in the $4\ell+\rm E{\!\!\!/}_T$ channel, and for a fixed value of $m_{\tildeχ_2^0}$, we obtain reconstruction efficiencies $\gtrsim80\%$ over a wide range of $m_{\tildeχ_1^0}$ for $Δm = 30~$GeV.

hep-ph↗

arXiv2025

Generalized Quantum Hadamard Test for Machine Learning

Quantum machine learning models are designed for performing learning tasks. Some quantum classifier models are proposed to assign classes of inputs based on fidelity measurements. Quantum Hadamard test is a well-known quantum algorithm for computing these fidelities. However, the basic requirement for deploying the quantum Hadamard test maps input space to L2-normalize vector space. Consequently, computed fidelities correspond to cosine similarities in mapped input space. We propose a quantum Hadamard test with the additional capability to compute the inner product in bounded input space, which refers to the Generalized Quantum Hadamard test. It incorporates not only L2-normalization of input space but also other standardization methods, such as Min-max normalization. This capability is raised due to different quantum feature mapping and unitary evolution of the mapped quantum state. We discuss the quantum circuital implementation of our algorithm and establish this circuit design through numerical simulation. Our circuital architecture is efficient in terms of computational complexities. We show the application of our algorithm by integrating it with two classical machine learning models: Logistic regression binary classifier and Centroid-based binary classifier and solve four classification problems over two public-benchmark datasets and two artificial datasets.

quant-ph↗

arXiv2025

Markov Chain Monte Carlo analysis to probe trilinear $R$-parity violating SUSY scenarios and possible LHC signatures

In this article, we probe the trilinear $R$-parity violating (RPV) supersymmetric (SUSY) scenarios with specific nonzero interactions in the light of neutrino oscillation, Higgs, and flavor observables. We attempt to fit the set of observables using a state-of-the-art Markov Chain Monte Carlo (MCMC) setup and study its impact on the model parameter space. Our main objective is to constrain the trilinear couplings individually, along with some other SUSY parameters relevant to the observables. We present the constrained parameter regions in the form of marginalized posterior distributions on different two-dimensional parameter planes. We perform our analyses with two different scenarios characterized by our choices for the lightest SUSY particle (LSP), bino, and stop. Our results indicate that the lepton number violating trilinear couplings $λ_{i33}$ ($i$=1,2) and $λ_{j33}^{\prime}$ ($j$=1,2,3) can be at most of the order of $10^{-4}$ or even smaller while $\tanβ$ is restricted to below 15 even when $3σ$ allowed regions are considered. We further comment on the possible LHC signatures of these LSPs focusing on and around the best-fit regions.

hep-ph↗

arXiv2025

Status of R-parity violating SUSY

In this article, we discuss various phenomenological implications of possible R-parity violating (RPV) supersymmetric scenarios. In this context, the implications of both bilinear and trilinear RPV terms are reviewed from the viewpoint of neutrino physics, anomalous muon magnetic moment, different flavor observables, and collider physics. Apart from discussing the distinctive phenomenological implications of the RPV scenarios, we also survey the updated results from different studies to highlight the present status of the RPV couplings.

hep-ph↗

arXiv2025

Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree (BDT) algorithm has been a cornerstone of the high energy physics for analyzing event triggering, particle identification, jet tagging, object reconstruction, event classification, and other related tasks for quite some time. This article presents a comprehensive overview of research conducted by both HEP experimental and phenomenological groups that utilize decision tree algorithms in the context of the Standard Model and Supersymmetry (SUSY). We also summarize the basic concept of machine learning and decision tree algorithm along with the working principle of \texttt{Random Forest}, \texttt{AdaBoost} and two gradient boosting frameworks, such as \texttt{XGBoost}, and \texttt{LightGBM}. Using a case study of electroweakino productions at the high luminosity LHC, we demonstrate how these algorithms lead to improvement in the search sensitivity compared to traditional cut-based methods in both compressed and non-compressed R-parity conserving SUSY scenarios. The effect of different hyperparameters and their optimization, feature importance study using SHapley values are also discussed in detail.

hep-ph↗

arXiv2024

Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY

Probing higgsinos remains a challenge at the LHC owing to their small production cross-sections and the complexity of the decay modes of the nearly mass degenerate higgsino states. The existing limits on higgsino mass are much weaker compared to its bino and wino counterparts. This leaves a large chunk of sub-TeV supersymmetric parameter space unexplored so far. In this work, we explore the possibility of probing higgsino masses in the 400 - 1000 GeV range. We consider a simplified supersymmetric scenario where R-Parity is violated through a baryon number violating trilinear coupling. We adopt a machine learning-based top tagger to tag the boosted top jets originating from higgsinos, and for our collider analysis, we use a BDT classifier to discriminate signal over SM backgrounds. We construct two signal regions characterized by at least one top jet and different multiplicities of $b$-jets and light jets. Combining the statistical significance obtained from the two signal regions, we show that higgsino mass as high as 925 GeV can be probed at the high luminosity LHC.

hep-ph↗

arXiv2024

Bilinear R-parity violating supersymmetry under the light of neutrino oscillation, higgs and flavor data

In this work, we explore a well motivated beyond the Standard Model scenario, namely, R-parity violating Supersymmetry, in the context of light neutrino masses and mixing. We assume that the R-parity is only broken by the lepton number violating bilinear term. We try to fit two non-zero neutrino mass square differences and three mixing angle values obtained from the global $χ^2$ analysis of neutrino oscillation data. We have also taken into account the updated data of the standard model (SM) Higgs mass and its coupling strengths with other SM particles from LHC Run-II along with low energy flavor violating constraints like rare b-hadron decays. We have used a Markov Chain Monte Carlo (MCMC) analysis to constrain the new physics parameter space. While doing so, we ensure that all the existing collider constraints are duly taken into account. Through our analysis, we have derived the most stringent constraints possible to date with existing data on the 9 bilinear R-parity violating parameters along with $μ$ and $\tanβ$. We further explore the possibility of explaining the anomalous muon~(g~-~2) measurement staying within the parameter space allowed by neutrino, Higgs and flavor data while satisfying the collider constraints as well. We find that there still remains a small sub-TeV parameter space where the required excess can be obtained.

hep-ph↗

arXiv2023

Slepton searches in the trilinear RPV SUSY scenarios at the HL-LHC and HE-LHC

In this work we have studied a multi-lepton final state arising from sneutrino and left-handed slepton production at the high luminosity and high energy LHC in the context of R-parity violating supersymmetry when only the lepton number violating $λ_{121}$ and/or $λ_{122}$ couplings are non-zero. We have taken into account both pair production and associated production of the three generations of left-handed sleptons and sneutrinos, which are assumed to be mass degenerate. The lightest supersymmetric particle is assumed to be bino and it decays via the R-parity violating couplings into light leptons and neutrinos. Our final state has a large lepton multiplicity, $N_{l}\geq 4~(l=e,~μ)$. We perform both cut-based and machine learning based analyses for comparison. We present our results in the bino-slepton/sneutrino mass plane in terms of exclusion and discovery reach at the LHC. Following our analysis, the slepton mass can be discovered upto $\sim$ 1.54 TeV and excluded upto $\sim$ 1.87 TeV at the high luminosity LHC while these ranges go upto $\sim$ 2.46 TeV and $\sim$ 3.06 TeV respectively at the high energy LHC.

hep-ph↗

arXiv2023

Improving sensitivity of trilinear RPV SUSY searches using machine learning at the LHC

In this work, we have explored the sensitivity of multilepton final states in probing the gaugino sector of R-parity violating supersymmetric scenario with specific lepton number violating trilinear couplings ($λ_{ijk}$) being non-zero. The gaugino spectrum is such that the charged leptons in the final state can arise from the R-parity violating decays of the lightest supersymmetric particle (LSP) as well as R-parity conserving decays of the next-to-LSP (NLSP). Apart from a detailed cut-based analysis, we have also performed a machine learning-based analysis using boosted decision tree algorithm which provides much better sensitivity. In the scenarios with non-zero $λ_{121}$ and/or $λ_{122}$ couplings, the LSP pair in the final states decays to $4l~(l = e, μ) + \rm E{\!\!\!/}_T$ final states with $100\%$ branching ratio. We have shown that under this circumstance, a final state with $\ge 4l$ has the highest sensitivity in probing the gaugino masses. We also discuss how the sensitivity can change in the presence of $τ$ lepton(s) in the final state due to other choices of trilinear couplings. We present our results through the estimation of the discovery and exclusion contours in the gaugino mass plane for both the HL-LHC and the HE-LHC. For $λ_{121}$ and/or $λ_{122}$ nonzero scenario, the projected 2$σ$ exclusion limit on NLSP masses reaches upto 2.37 TeV and 4 TeV for the HL-LHC and the HE-LHC respectively by using a machine learning based algorithm. We obtain an enhancement of $\sim$ 380 (190) GeV in the projected 2$σ$ exclusion limit on the NLSP masses at the 27 (14) TeV LHC. Considering the same final state ($N_l \geq 4$) for $λ_{133}$ and/or $λ_{233}$ non-zero scenario, we find that the corresponding 2$σ$ projected limits are $\sim$ 1.97 TeV and $\sim$ 3.25 TeV for the HL-LHC and HE-LHC respectively.

hep-ph↗

arXiv2021

Electroweakino searches at the HL-LHC in the baryon number violating MSSM

The projected reach of direct electroweakino searches at the HL-LHC ($\sqrt{s}=14~{\rm TeV}, ~3000~{\rm fb^{-1}}$ LHC) in the framework of simplified models with R-parity violating (RPV) operators: $λ_{112}^{\prime \prime}u^{c}d^{c}s^{c}$ and $λ_{113}^{\prime\prime}u^{c}d^{c}b^{c}$, is studied. Four different analysis channels are chosen: $Wh$ mediated $1l+2b+jets+\rm E{\!\!\!/}_T$, $Wh$ mediated $1l+2γ+jets+\rm E{\!\!\!/}_T$, $WZ$ mediated $3l+jets+\rm E{\!\!\!/}_T$ and $WZ$ mediated $3l+2b+jets+\rm E{\!\!\!/}_T$ and the projected exclusion/discovery reach of direct wino searches in these channels is analyzed by performing a detailed cut based collider analysis. The projected exclusion contour reaches up to $600-700~{\rm GeV}$ for a massless bino-like $χ_{1}^{0}$ from searches in the $Wh$ mediated $1l+2b+jets+\rm E{\!\!\!/}_T$, $Wh$ mediated $1l+2γ+jets+\rm E{\!\!\!/}_T$ and $WZ$ mediated $3l+jets+\rm E{\!\!\!/}_T$ channels, while the $WZ$ mediated $3l+2b+jets+\rm E{\!\!\!/}_T$ search channel is found to have a projected exclusion reach up to $600~{\rm GeV}$ for $150~{\rm GeV} < M_{χ_{1}^{0}} < 250~{\rm GeV}$. The baryon number violating simplified scenario considered in this work is found to furnish a weaker projected reach (typically by a factor of $\sim 1/2$) than the R-parity conserving (RPC) case. The projected reach at the HL-LHC in these four channels is also recast for realistic benchmark scenarios.

hep-ph↗

arXiv2019

Current status of MSSM Higgs sector with LHC 13 TeV data

ATLAS and CMS collaborations have reported the results on the Higgs search analyzing $\sim 36$ fb$^{-1}$ data from Run-II of LHC at 13 TeV. In this work, we study the Higgs sector of the phenomenological Minimal Supersymmetric Standard Model, in light of the recent Higgs data, by studying separately the impact of Run-I and Run-II data. One of the major impacts of the new data on the parameter space comes from the direct searches of neutral CP-even and CP-odd heavy Higgses ($H$ and $A$, respectively) in the $H/A \to τ^{+} τ^{-}$ channel which disfavours high $\tanβ$ regions more efficiently than Run-I data. Secondly, we show that the latest result of the rare radiative decay of $B$ meson imposes a slightly stronger constraint on low $\tan β$ and low $M_A$ region of the parameter space, as compared to its previous measurement. Further, we find that in a global fit Run-II light Higgs signal strength data is almost comparable in strength with the corresponding Run-I data. Finally, we discuss scenarios with the Heavy Higgs boson decaying into electroweakinos and third generation squarks and sleptons.

hep-ph↗

arXiv2018

Scope of strongly self-interacting thermal WIMPs in a minimal $U(1)_D$ extension and its future prospects

In this work we have considered a minimal extension of Standard Model by a local $U(1)$ gauge group in order to accommodate a stable (fermionic) Dark Matter (DM) candidate. We have focussed on parameter regions where DM possesses adequate self interaction, owing to the presence of a light scalar mediator (the dark Higgs), alleviating some of the tensions in the small-scale structures. We have studied the scenario in the light of a variety of data, mostly from dark matter direct searches, collider searches and flavour physics experiments, with an attempt to constrain the interactions of the standard model (SM) particles with the ones in the Dark Sector (DS). Assuming a small gauge kinetic mixing parameter, we find that for rather heavy DM %$\gtrsim \mathcal{O}(1-10)\,\, {\rm GeV}$%, the most stringent bound on the mixing angle of the Dark Higgs with the SM Higgs boson comes from dark matter direct detection experiments, while for lighter DM, LHC constraints become more relevant. Note that, due to the presence of very light mediators the usual realisation of direct detection constraints in terms of momentum independent cross sections had to be reevaluated for our scenario. In addition, we find that the smallness of the relevant portal couplings, as dictated by data, critically suppress the viability of DM production by the standard "freeze-out" mechanism in such simplified scenarios. In particular, the viable DM masses are $\lesssim \mathcal{O}(2)$ GeV $i.e.$ in the regions where direct detection limits tend to become weak. For heavier DM with large self-interactions, we hence conclude that non-thermal production mechanisms are favoured. Lastly, future collider reach of such a simplified scenario has also been studied in detail.

hep-ph↗

arXiv2017

Impact of LHC data on muon $g-2$ solutions in vector-like extension of the Constrained MSSM

The long-standing discrepancy between the experimental determination by the Muon $g-2$ Collaboration at Brookhaven and the Standard Model predictions for the anomalous magnetic moment of the muon cannot be explained within simple unified framework like the Constrained Minimal Supersymmetric Standard Model, but it can within its extension with vector-like fermions. In this paper we consider a model with an additional vector-like $5+\bar{5}$ pair of $SU(5)$. Within this model we first identify its parameter space that is consistent with the current discrepancy and show that this implies the lighter chargino mass in the range of $700-1200$ GeV. We examine how it is affected by constraints from electroweak sparticle search at the LHC based on 13 TeV search with 36.1 ${fb}^{-1}$ integrated luminosity. We show that null trilepton signal searches coming from chargino-neutralino pair production significantly constrains the allowed parameter space except when the chargino-neutralino mass difference is relatively small, below about 10 GeV. Next we consider the expected impact of the New Muon $g-2$ experiment at Fermilab with its projected sensitivity reach of $7\,σ$ and, assuming it confirms the current discrepancy, show that the remaining parameter space of the considered model will be in strong tension with the current LHC limits.

hep-ph↗

arXiv2017

Muon g-2 and related phenomenology in constrained vector-like extensions of the MSSM

We analyze two minimal supersymmetric constrained models with low-energy vector-like matter preserving gauge coupling unification. In one we add to the MSSM spectrum a pair of 5-plets of SU(5), in the other a pair of 10-plets. We show that the muon g-2 anomaly can be explained in these models while retaining perturbativity up to the unification scale, satisfying electroweak and flavor precision tests and current LHC data. We examine also some related phenomenological features of the models, including Higgs mass, fine-tuning, dark matter and several LHC signatures. We stress that, at least for the 5-plet model, the parameter space consistent with g-2 is entirely in reach of the LHC with a moderate increase in luminosity with respect the current data set.

hep-ph↗