arXiv subjects

K. Suresh

Publications and source records attributed to K. Suresh.

9 recordsLinked to original sources

arXiv2024

First Measurement of Near- and Sub-Threshold $J/\psi$ Photoproduction off Nuclei

We report on the first measurement of $J/\psi$ photoproduction from nuclei in the photon energy range of $7$ to $10.8$ GeV, extending above and below the photoproduction threshold in the free proton of $\sim8.2$ GeV. The experiment used a tagged photon beam incident on deuterium, helium, and carbon, and the GlueX detector at Jefferson Lab to measure the semi-inclusive $A(\gamma,e^+e^-p)$ reaction with a dilepton invariant mass $M(e^+e^-)\sim m_{J/\psi}=3.1$ GeV. The incoherent $J/\psi$ photoproduction cross sections in the measured nuclei are extracted as a function of the incident photon energy, momentum transfer, and proton reconstructed missing light-cone momentum fraction. Comparisons with theoretical predictions assuming a dipole form factor allow extracting a gluonic radius for bound protons of $\sqrt{\langle r^2\rangle}=0.85\pm0.14$ fm. The data also suggest an excess of the measured cross section for sub-threshold production and for interactions with high missing light-cone momentum fraction protons. The measured enhancement can be explained by modified gluon structure for high-virtuality bound-protons.

nucl-ex↗

arXiv2024

AI-Assisted Detector Design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using Geant4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

physics.ins-det↗

arXiv2023

Search for axion-like particles through nuclear Primakoff production using the GlueX detector

We report on the results of the first search for the production of axion-like particles (ALP) via Primakoff production on nuclear targets using the GlueX detector. This search uses an integrated luminosity of 100 pb$^{-1}\cdot$nucleon on a $^{12}$C target, and explores the mass region of 200 < $m_a$ < 450 MeV via the decay $X\rightarrow\gamma\gamma$. This mass range is between the $\pi^0$ and $\eta$ masses, which enables the use of the measured $\eta$ production rate to obtain absolute bounds on the ALP production with reduced sensitivity to experimental luminosity and detection efficiency. We find no evidence for an ALP, consistent with previous searches in the quoted mass range, and present limits on the coupling on the scale of $O$(1 TeV). We further find that the ALP production limit we obtain is hindered by the peaking structure of the non-target-related dominant background in GlueX, which we treat by using data on $^4$He to estimate and subtract these backgrounds. We comment on how this search can be improved in a future higher-statistics dedicated measurement.

hep-ex↗

arXiv2023

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

physics.acc-ph↗

arXiv2022

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.

physics.ins-det↗

arXiv2017

Adapting the DMTCP Plugin Model for Checkpointing of Hardware Emulation

Checkpoint-restart is now a mature technology. It allows a user to save and later restore the state of a running process. The new plugin model for the upcoming version 3.0 of DMTCP (Distributed MultiThreaded Checkpointing) is described here. This plugin model allows a target application to disconnect from the hardware emulator at checkpoint time and then re-connect to a possibly different hardware emulator at the time of restart. The DMTCP plugin model is important in allowing three distinct parties to seamlessly inter-operate. The three parties are: the EDA designer, who is concerned with formal verification of a circuit design; the DMTCP developers, who are concerned with providing transparent checkpointing during the circuit emulation; and the hardware emulator vendor, who provides a plugin library that responds to checkpoint, restart, and other events. The new plugin model is an example of process-level virtualization: virtualization of external abstractions from within a process. This capability is motivated by scenarios for testing circuit models with the help of a hardware emulator. The plugin model enables a three-way collaboration: allowing a circuit designer and emulator vendor to each contribute separate proprietary plugins while sharing an open source software framework from the DMTCP developers. This provides a more flexible platform, where different fault injection models based on plugins can be designed within the DMTCP checkpointing framework. After initialization, one restarts from a checkpointed state under the control of the desired plugin. This restart saves the time spent in simulating the initialization phase, while enabling fault injection exactly at the region of interest. Upon restart, one can inject faults or otherwise modify the remainder of the simulation. The work concludes with a brief survey of checkpointing and process-level virtualization.

cs.OS↗

arXiv2014

Improved realization of canonical Chua's circuit with synthetic inductor using current feedback operational amplifiers

In this paper, we report an improved implementation of an inductorless third order autonomous canonical Chua's circuit. The active elements as well as the synthetic inductor employed in this circuit are designed using current feedback operational amplifiers (CFOAs). The reason for employing CFOAs is that they have better features such as high slew rate, high speed of operation, etc., which enable the circuit to operate at higher frequency ranges, when compared to the circuits designed using voltage operational amplifiers. In addition to this, the inclusion of CFOAs provide a buffered output which directly represent a state variable of the system. The Multisim simulations in the time and frequency domains confirm the theoretical estimates of the performance of the proposed circuit at high frequencies. It is also confirmed through hardware experiments.

nlin.CD↗

arXiv2011

Bubble doubling route to strange nonchaotic attractor in a quasiperiodically forced Chua's circuit

We have identified a novel mechanism for the birth of Strange Nonchaotic Attractor (SNA) in a quasiperiodically forced Chua's circuit. In this study the amplitude of one of the external driving forces is considered as the control parameter. By varying this control parameter, we find that bubbles appear in the strands of the torus. These bubbles start to double in number as the control parameter is increased. On increasing the parameter continuously, successive doubling of the bubbles occurs, leading to the birth of SNAs. We call this mechanism as the bubble doubling mechanism. The formation of SNA through this bubble doubling route is confirmed numerically, using Poincaré maps, maximal Lyapunov exponent and its variance and the distribution of finite-time Lyapunov exponents. Also a quantitative confirmation of the strange nonchaotic dynamics is carried out with the help of singular continuous spectrum analysis.

nlin.CD↗

arXiv2011

"Improved FCM algorithm for Clustering on Web Usage Mining"

In this paper we present clustering method is very sensitive to the initial center values, requirements on the data set too high, and cannot handle noisy data the proposal method is using information entropy to initialize the cluster centers and introduce weighting parameters to adjust the location of cluster centers and noise problems.The navigation datasets which are sequential in nature, Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using improved fuzzy c-means(FCM) clustering. Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. Web data Clusters are formed using on MSNBC web navigation dataset.

cs.IR↗