arXiv subjects

Abrar Ahmed

Publications and source records attributed to Abrar Ahmed.

4 recordsLinked to original sources

arXiv2022

Electron-Neutrino Scattering a Strong Aspirant for Precision Measurements

This work focuses on the study of electron and neutrino scattering in the frame work of physics beyond the standard model (SM) called new physics (NP). Both Model Independent (MI) and Model Depen-dent (MD) ways are used to constrain NP. R-parity violating Supersymmetry ( /Rp SUSY) Model is used to perform MD analysis, where the scattering cross-section is infuenced by new s-bosons. For MI way non-standard neutrino intections (NSI) are used where there is no need of introducing any new particle. LSND and LAMPF-E225 data is used to identify the physically allowed and forbidden regions for nonunivarsal NSI parameters and nonunivarsal SUSY parameters. Similarly, CHARM-II, BNL-COL and BNL-E734 experimental is used to explore allowed and forbidden regions and limits are established. Furthermore, we establish a relationship between MI and MD coupling parameters.

hep-ph↗

arXiv2021

Study of Nonstandard Interactions in Rare Decays of Hyprons with Missing Energy

We study rare decays of hyperons involving di-neutrinos in the final state in the standard model and compare them with other models. It is claimed that the branching ratio calculated in this article are 2 times the values of 331 model and exceptionally large than the previously calculated values. We explore the nonstandard neutrino interactions (NSI) and constrain NSIs free parameter with these decays. We obtain stringent bounds on of O(0.01). We show that branching ratios (Br) could be in the range of BES III if constraints are O(0.3).

hep-ph↗

arXiv2020

Drop spreading dynamics with a liquid needle drop deposition technique

This paper represents a theoretical and an experimental study of the spreading dynamics of a liquid droplet, generated by a needle free deposition system called the liquid needle droplet deposition technique. This technique utilizes a continuous liquid jet generated from a pressurized dosing system which generates a liquid drop on a substrate to be characterized by optical contact angle measurements. Although many studies have explored the theoretical modelling of the droplet spreading scenario, a theoretical model representing the spreading dynamics of a droplet, generated by the jet impact and continuous addition of liquid mass, is yet to be addressed. In this study, we developed a theoretical model based on the overall energy balance approach which enables us to study on the physics of variation of droplet spreading under surrounding medium of various viscosities. The numerical solution of the non-linear ordinary differential equation has provided us the opportunity to comment on the variation of droplet spreading, as a function of Weber number ($We$), Reynolds number ($Re$) and Bond number ($Bo$) ranging from 0.5-3, 75-150, and 0.001-0.3, respectively. We have also presented a liquid jet impact model in order to predict the initial droplet diameter as an initial condition for the proposed governing equation. The model has been verified further with the experimental measurements and reasonable agreement has been observed. Experimental observations and theoretical investigations also highlight the precision, repeatability and wide range of the applicability of liquid needle drop deposition technique.

physics.flu-dyn↗

arXiv2017

One-Shot Concept Learning by Simulating Evolutionary Instinct Development

Object recognition has become a crucial part of machine learning and computer vision recently. The current approach to object recognition involves Deep Learning and uses Convolutional Neural Networks to learn the pixel patterns of the objects implicitly through backpropagation. However, CNNs require thousands of examples in order to generalize successfully and often require heavy computing resources for training. This is considered rather sluggish when compared to the human ability to generalize and learn new categories given just a single example. Additionally, CNNs make it difficult to explicitly programmatically modify or intuitively interpret their learned representations. We propose a computational model that can successfully learn an object category from as few as one example and allows its learning style to be tailored explicitly to a scenario. Our model decomposes each image into two attributes: shape and color distribution. We then use a Bayesian criterion to probabilistically determine the likelihood of each category. The model takes each factor into account based on importance and calculates the conditional probability of the object belonging to each learned category. Our model is not only applicable to visual scenarios, but can also be implemented in a broader and more practical scope of situations such as Natural Language Processing as well as other places where it is possible to retrieve and construct individual attributes. Because the only condition our model presents is the ability to retrieve and construct individual attributes such as shape and color, it can be applied to essentially any class of visual objects.

cs.CV↗