A. Gandrakota
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View article: Sensor Co-design for $\textit{smartpixels}$
Sensor Co-design for $\textit{smartpixels}$ Open
Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel…
View article: Realtime Anomaly Detection at the L1 Trigger of CMS Experiment
Realtime Anomaly Detection at the L1 Trigger of CMS Experiment Open
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whe…
View article: Interpreting Transformers for Jet Tagging
Interpreting Transformers for Jet Tagging Open
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer (Pa…
View article: Interpreting and Accelerating Transformers for Jet Tagging
Interpreting and Accelerating Transformers for Jet Tagging Open
Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based…
View article: Robust anomaly detection for particle physics using multi-background representation learning
Robust anomaly detection for particle physics using multi-background representation learning Open
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection (AD) f…
View article: Smart Pixel Sensors for the HL-LHC
Smart Pixel Sensors for the HL-LHC Open
Large-scale particle physics experiments produce tens of terabytes of data every second. Innovative methods to manage the data rate at the HL-LHC, which expects to operate at 10x the luminosity of what the LHC was initially designed for, a…
View article: Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning Open
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection for hi…
View article: Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder Open
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We …
View article: Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation Open
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models wi…
View article: Search for New Resonances at CMS and ATLAS
Search for New Resonances at CMS and ATLAS Open
of the SM bosons will be collimated into a smaller area such that they are clustered within a single large cone jet. Dedicated reconstruction techniques are used to distinguish the merged decay products of W, Z and H bosons produced with h…
View article: Physics Community Needs, Tools, and Resources for Machine Learning
Physics Community Needs, Tools, and Resources for Machine Learning Open
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community r…
View article: Physics Community Needs, Tools, and Resources for Machine Learning
Physics Community Needs, Tools, and Resources for Machine Learning Open
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community r…
View article: Model selection and signal extraction using Gaussian Process regression
Model selection and signal extraction using Gaussian Process regression Open
We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally prese…
View article: Higgs Physics at the HL-LHC and HE-LHC
Higgs Physics at the HL-LHC and HE-LHC Open
The discovery of the Higgs boson in 2012, by the ATLAS and CMS experiments, was a success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a landscape of possibilities in the study of Higgs boson propertie…