activity
20222026
most citedDeep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations

10 citations · 12 across the 14 of their papers we have counts for

collaborators

14 papers

physics.ins-det2026

A New High-Intensity Source for Ultracold Neutrons

K. Abe, S. Ahmed, B. Algohi +78

The TRIUMF UltraCold Advanced Neutron (TUCAN) collaboration has completed a new superthermal source for ultracold neutrons (UCNs) at TRIUMF. Neutrons produced in a spallation targe…

hep-ph2026

Higher-dimensional operators and Polyakov loop in hot Scalar QED from the heat kernel

Siddhartha Bandyopadhyay, Joydeep Chakrabortty, Debmalya Dey +2

Using the finite-temperature heat kernel method, we compute the gauge-invariant effective Lagrangian up to dimension-six for massive hot scalar QED. We propose two complementary me…

quant-ph2026

Magic Secret Sharing: Threshold Control of Quantum Computational Power via GHZ Entanglement

Soumyojyoti Dutta, Tushar

We introduce Magic Secret Sharing (MSS), a quantum cryptographic primitive in which the secret is the computational capability of a quantum state rather than its classical descript…

physics.ins-det2026

Performance of the TUCAN Magnetically Shielded Room

S. Ahmed, B. Algohi, D. Anthony +64

The TRIUMF Ultracold Advanced Neutron (TUCAN) collaboration has commissioned a large magnetically shielded room to be used for measuring the neutron electric dipole moment (nEDM) t…

hep-th2026

Background Fields Meet the Heat Kernel: Gauge Invariance and RGEs without diagrams

Debanjan Balui, Joydeep Chakrabortty, Christoph Englert +2

We introduce a new method that exploits the combination of the Heat Kernel (HK) and Background Field Method to compute gauge-invariant and gauge parameter-independent quantities su…

quant-ph2026

A Phase-Space Geometric Measure of Magic in Qubit Systems

Soumyojyoti Dutta, Tushar

Magic -- the resource enabling quantum computational advantage beyond stabilizer circuits -- has a clean phase-space characterization in odd prime dimensions that qubits notoriousl…

physics.ins-det2025

Ultracold Neutron Guide-Coating Facility at U.Winnipeg

T. Hepworth, A. Zahra, B. Algohi +72

We report the construction and commissioning of a new ultracold neutron (UCN) guide-coating facility at the University of Winnipeg. The facility employs pulsed laser deposition (PL…

quant-ph2025

Zero-Error Nash Equilibrium: Harnessing Nonlocal Correlation in Incomplete Information Games

Ambuj, Tushar, Siddharth R. Pandey +4

Claude Shannon's zero-error communication paradigm reshaped our understanding of fault-tolerant information transfer. Here, we adapt this notion into game theory with incomplete in…

nucl-ex2025

Initial results of the TRIUMF ultracold advanced neutron source

B. Algohi, D. Anthony, L. Barrón-Palos +83

We report the first results on ultracold neutron production from a new spallation-driven superfluid He (He-II) source at TRIUMF, which is being prepared for a new, precise meas…

physics.ins-det2025

Neutron EDM Experiment with an Advanced Ultracold Neutron Source at TRIUMF

T. Higuchi, B. Algohi, D. Anthony +67

The TRIUMF Ultracold Advanced Neutron (TUCAN) collaboration has been developing a high-intensity ultracold neutron (UCN) source aimed at searching for the neutron electric dipole m…

cs.CE2024

FUsion-based ConstitutivE model (FuCe): Towards model-data augmentation in constitutive modelling

Tushar, Sawan Kumar, Souvik Chakraborty

Constitutive modelling is crucial for engineering design and simulations to accurately describe material behavior. However, traditional phenomenological models often struggle to ca…

math.NA2024

Harnessing physics-informed operators for high-dimensional reliability analysis problems

N Navaneeth, Tushar, Souvik Chakraborty

Reliability analysis is a formidable task, particularly in systems with a large number of stochastic parameters. Conventional methods for quantifying reliability often rely on exte…

cs.LG2023★ 2 cited

DPA-WNO: A gray box model for a class of stochastic mechanics problem

Tushar, Souvik Chakraborty

The well-known governing physics in science and engineering is often based on certain assumptions and approximations. Therefore, analyses and designs carried out based on these equ…

stat.ML2022★ 10 cited

Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations

Tushar, Souvik Chakraborty

We propose a novel gray-box modeling algorithm for physical systems governed by stochastic differential equations (SDE). The proposed approach, referred to as the Deep Physics Corr…