Conveners
3.4 Foundation models and related techniques
- Ik Siong Heng (University of Glasgow)
Flavour-tagging, the identification of jets originating from b and c quarks, is a critical component of the physics programme of the ATLAS experiment. Current flavour-tagging algorithms rely on the outputs of “low level” taggers, which are a mixture of manually optimised, physically informed algorithms and machine learning models. A new approach, instead uses a single machine learning model...
Our primary objective is to achieve a pioneering measurement of the challenging $gg\rightarrow ZH$ process in Large Hadron Collider (LHC) data to extract new physics contributions in the context of the Standard Model Effective Field Theory (SMEFT) framework. By leveraging the power of multi-head attention mechanism within Transformer encoders, we developed an innovative approach to efficiency...
Generative networks are promising tools for fast event generation for the LHC, yet struggle to meet the required precision when scaling up to particles in the final state. We employ the flexibility of autoregressive transformers to tackle this challenge, focusing on Z and top quark pair production with additional jets. We demonstrate the use of classifiers in combination with the...
Particle track reconstruction is a fundamental aspect of experimental analysis in high-energy particle physics. Conventional methodologies for track reconstruction are suboptimal in terms of efficiency in anticipation of the High Luminosity phase of the Large Hadron Collider. This has motivated researchers to explore the latest developments in deep learning for their scalability and potential...
Supervised learning has been used successfully for jet classification and to predict a range of jet properties, such as mass and energy. Each model learns to encode jet features, resulting in a representation that is tailored to its specific task. But could the common elements underlying such tasks be combined in a single foundation model to extract features generically? To address this...
The Advanced Virgo interferometer is a complex machine constantly monitored by a vast array of sensors, producing the auxiliary channels datastream. Many analytical tools aid in the task of navigating the information cointained in the $\sim 10^5$ channels, but the limitations of the linear algorithms can hinder their capabilities of correctly assessing the health of the instrument. In this...
In 2015, the first gravitational wave from a binary black hole merger was detected and since then, Ligo-Virgo-Kagra have observed many binary black hole mergers. However, identifying these cosmic events is computationally expensive. Therefore, fast data analysis will be essential in order to make future gravitational-wave observations a success. Template banks are used to identify potential...
In the LHCb experiment, during Run2, more than 90% of the computing resources available to the Collaboration were used for detector simulation. The detector and trigger upgrades introduced for Run3 allow to collect larger datasets that, in turn, will require larger simulated samples. Despite the use of a variety of fast simulation options, the demands for simulations will far exceed the...
With metallic-magnetic calorimeters (MMCs) - like the maXs-detector series developed within this collaboration - promising new tools for high precision x-ray spectroscopy application have become available. Because of their unique working principles, MMCs combine several advantages over conventional energy- and wavelength-dispersive photon detectors. They can reach spectral resolving powers of...
Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning techniques. In this talk, we introduce a new architecture for jet tagging: the particle dual attention transformer (P-DAT). This novel transformer architecture stands out by concurrently capturing both global and local...
