A5042258127- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Astrophysics and Cosmic Phenomena
- Radiation Detection and Scintillator Technologies
- Dark Matter and Cosmic Phenomena
- High-Energy Particle Collisions Research
- Graphite, nuclear technology, radiation studies
- Neutrino Physics Research
- Network Security and Intrusion Detection
- Muon and positron interactions and applications
- Particle accelerators and beam dynamics
- Privacy-Preserving Technologies in Data
- Fusion materials and technologies
- Nuclear reactor physics and engineering
- CCD and CMOS Imaging Sensors
- Scientific Computing and Data Management
- Information and Cyber Security
- Radiation Effects in Electronics
Fermi National Accelerator Laboratory
2022-2024
Indian Institute of Technology Kanpur
2023
Tata Institute of Fundamental Research
2021-2023
Aligarh Muslim University
2021-2023
Dibrugarh University
2021-2023
Indian Institute of Technology Indore
2023
National Research Centre for Grapes
2022
Goethe University Frankfurt
2021
Membership Inference Attacks (MIAs) have emerged as a valuable framework for evaluating privacy leakage by machine learning models. Score-based MIAs are distinguished, in particular, their ability to exploit the confidence scores that model generates particular inputs. Existing score-based implicitly assume adversary has access target model's hyperparameters, which can be used train shadow models attack. In this work, we demonstrate knowledge of hyperparameters is not prerequisite MIA...
Abstract We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is on Graph Neural Networks (GNNs) and directly analyzes hits in each HGCAL endcap. ML algorithm trained to predict clusters originating from same incident particle by labeling with cluster index. impose simple criteria assess whether associated as prediction are matched those resulting any particular individual particles....
High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate 40\,MHz, these detectors create massive amounts data. Signal processing that handles data incoming those and intelligently reduces within pixelated region detector \textit{at rate} will enhance performance enable analyses not currently possible. Using shape charge clusters deposited in array small pixels, physical properties traversing can be extracted with...
The GRAPES-3 experiment consists of a densely packed array 400 plastic scintillator detectors and large area (560 $m^{2}$) muon telescope. It measures cosmic rays in an energy range several TeV to over 10 PeV with trigger efficiency more than 90$\%$ for proton primaries above 40 TeV, providing substantial overlap space based direct experiments. records the particle density arrival time shower secondaries, which were used estimate parameters. telescope is dedicated recording component shower....
Here, we present the updated results on cosmic ray energy spectrum and composition analysis from GRAPES-3 experiment over range of 50 TeV to 1000 since ICRC 2021. The simulation showers was performed using post-LHC high hadronic interaction model QGSJetII-04 low FLUKA. A detailed GEANT4 muon telescope performed. obtained by fitting simulated multiplicity distributions for proton, helium, nitrogen, aluminium, iron primaries observed data. connects direct measurements with a fairly good...
The Gamma Ray Astronomy at PeV EnergieS phase-3 (GRAPES-3) experiment is located Ooty in India (11.4$^{\circ}$ N, 76.7$^{\circ}$ E and 2200 m above m.s.l.). It consists of a densely packed array 400 plastic scintillator detectors large area muon telescope (560 m$^{2}$). measures cosmic rays from several TeV to over 10 energies providing substantial overlap with direct experiments while covering the knee region. Shower parameters are reconstructed by fitting observed particle densities NKG...
We apply a state-of-the-art membership inference attack (MIA) to systematically test the practical privacy vulnerability of fine-tuning large image classification models.We focus on understanding properties data sets and samples that make them vulnerable inference. In terms set properties, we find strong power law dependence between number examples per class in MIA vulnerability, as measured by true positive rate at low false rate. For an individual sample, gradients end training are...
The large area muon telescope of GRAPES-3 has been operating continuously for more than two decades with a DAQ which several limitations. At present, this is in the process being upgraded FPGA based system. new system designed to be triggerless and capable recording every hit from 3712 proportional counters along time-stamp (10 ns accuracy) significantly expanded physics horizon experiment. This feature allows detection muons beyond nominal zenith range current ($\theta$$<$45$^\circ$)....
model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) develop a ML empowered system that collects streamed BLM data and additional machine readings infer in real-time, which generated beam loss.
We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining uniform proton beam intensity delivery in Muon to Electron Conversion Experiment (Mu2e) Fermi National Accelerator Laboratory (Fermilab). Our primary objective is regulate spill process ensure consistent profile, with ultimate goal creating an automated controller capable providing real-time feedback and calibration Spill Regulation System (SRS) parameters on millisecond...