92 citations · 189 across the 14 of their papers we have counts for
14 papers
FAIR AI Models in High Energy Physics
Javier Duarte, Haoyang Li, Avik Roy +14
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientifi…
FAIR Principles for data and AI models in high energy physics research and education
Avik Roy
In recent years, digital object management practices to support findability, accessibility, interoperability, and reusability (FAIR) have begun to be adopted across a number of dat…
Deep Learning for the Matrix Element Method
Matthew Feickert, Mihir Katare, Mark Neubauer +1
Extracting scientific results from high-energy collider data involves the comparison of data collected from the experiments with synthetic data produced from computationally-intens…
FAIR for AI: An interdisciplinary and international community building perspective
E. A. Huerta, Ben Blaiszik, L. Catherine Brinson +21
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with th…
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
Ayush Khot, Mark S. Neubauer, Avik Roy
Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input…
Making Digital Objects FAIR in High Energy Physics: An Implementation for Universal FeynRules Output (UFO) Models
Mark S. Neubauer, Avik Roy, Zijun Wang
Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation o…
Explainable AI for High Energy Physics
Mark S. Neubauer, Avik Roy
Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to…
Non-resonant Diagrams for Single Production of Top and Bottom Partners
Avik Roy, Timothy Andeen
The search for Top and Bottom Partners is a major focus of analyses at both the ATLAS and CMS experiments. When singly produced, these vector-like partners of the Standard Model th…
Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks
Chandrajit Bajaj, Luke McLennan, Timothy Andeen +1
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the tra…
Modifications of the Page Curve from correlations within Hawking radiation
Mishkat Al Alvi, Mahbub Majumdar, Md. Abdul Matin +2
We investigate quantum correlations between successive steps of black hole evaporation and investigate whether they might resolve the black hole information paradox. 'Small' correc…
Tunneling across the quantum horizon does not resolve the information paradox
Avik Roy, Moinul Hossain Rahat
Parikh and Wilczek formulated Hawking radiation as quantum tunneling across the event horizon proving the spectrum to be nonthermal. These nonthermality factors emerging due to bac…
Does Considering Quantum Correlations Resolve the Information Paradox?
Avik Roy, Moinul Hossain Rahat, Mishkat Al Alvi +1
In this paper, we analyze whether quantum correlations between successive steps of evaporation can open any way to resolve the black hole information paradox. Recently a celebrated…
Comments on Information Erasure in Black Hole
Mishkat Al Alvi, Md. Abdul Matin, Moinul Hossain Rahat +2
We analyze the Kim, Lee & Lee model of information erasure by black holes and find contradictions with standard physical laws. We demonstrate that the erasure model leads to arbitr…
A Novel Parametric Bound for Information Retrieval from Black Hole Radiation
Avik Roy, Moinul Hossain Rahat, Mishkat Al Alvi +2
This paper has been withdrawn by author.