Ptychographic imaging generates high-resolution datasets at the cost of heavy computational complexity, limiting its use in real-time experimental decision-making. In this cross-institutional effort, we introduce a hybrid edge-to-cloud workflow that delivers fast feedback for ptychography experiments by combining a modern synchrotron beamline at Diamond Light Source I13-1, featuring an...
Quartz Crystal Microbalance (QCM) sensors are renowned for their high sensitivity to mass changes, making them ideal for detecting environmental parameters such as relative humidity (RH) and ultraviolet (UV) radiation. In this work, we present an AI-driven, dual-sided coated QCM sensor integrated with advanced machine learning (ML) and implemented on a real-time hardware platform. This sensor...
Transformers excel at modeling correlations in LHC collisions but incur high costs from quadratic attention. We analyze the Particle Transformer using attention maps and pair correlations on the (η,ϕ) plane, revealing that Particle Transformer attention maps learn traditional jet substructure observables. To improve efficiency we benchmark linear attention variants on JetClass and find that...
Alpha Magnetic Spectrometer (AMS-02) is a precision high-energy cosmic-ray experiment consisting of Transition Radiation Detector (TRD), Silicon Tracker, Magnet, Time of Flight (ToF), and Ring Imaging Cherenkov Detector (RICH), Anti-Coincidence Counter (ACC), and Electromagnetic Calorimeter (ECAL) on the ISS operating since 2011, and has collected more than 240 billion cosmic-ray events. Among...
Small ($R<4\,\mathrm{R}_{\oplus}$), long-period ($30\,\mathrm{days}<P$) exoplanets with low equilibrium temperatures are an extremely interesting population, promising insights into planet formation, atmospheric chemistry and evolution, as well as habitability. However, for these planets, the current observing strategy of NASA's Transiting Exoplanet Survey Satellite (TESS) can only capture...
With the increasing size of the machine learning (ML) model and vast datasets, the foundation model has transformed how we apply ML to solve real-world problems. Multimodal language models like chatGPT and Llama have expanded their capability to specialized tasks with common pre-train. Similarly, in high-energy physics (HEP), common tasks in the analysis face recurring challenges that demand...
Since version 1.0, hls4ml has provided a oneAPI backend for Altera FPGAs, as an evolution of the backend that targeted Intel HLS. Some design choices will be presented here, including the use of pipes and task sequences to develop a dataflow-style architecture. The oneAPI framework, unlike the Intel HLS framework, also naturally supports an accelerator-style deployment. Using always-run...
We present the development of a machine learning (ML) based regulation system for third-order resonant beam extraction in the Mu2e experiment at Fermilab. Classical and ML-based controllers have been optimized using semi-analytic simulations and evaluated in terms of regulation performance and training efficiency. We compare several controller architectures and discuss the integration of...
Introduction
Accurate climate prediction hinges on the ability to resolve multi-scale turbulent dynamics in the atmosphere and oceans [1]. An important mechanism of energy exchange between the ocean and the atmosphere is mesoscale turbulence, which contains motions of length scale $\mathcal{O}$(100 km). Two-layer quasi-geostrophic (QG) simulations [2] are a popular technique for...
The rising popularity of large language models (LLMs) has led to a growing demand for efficient model deployment. In this context, the combination of post-training quantization (PTQ) and low-precision floating-point formats such as FP4, FP6 and FP8 has emerged as an important technique, allowing for rapid and accurate quantization with the ability to capture outlier values in LLMs without...
Modern development flows that use tooling for automated building, testing, and deployment of software are becoming the norm for large scale software and hardware projects. These flows offer quite a few advantages that make them desirable, but when attempting to implement them for projects that use FPGAs, some complications can arise when attempting to integrate them with traditional FPGA...
In preparation for the High Luminosity LHC (HL-LHC) run, the CMS experiment is developing a major upgrade of its Level-1 (L1) Trigger system, which will integrate high-granularity calorimeter data and real-time tracking using FPGA-based processors connected via a high-bandwidth optical network. A central challenge is the identification of electrons in a high pileup environment within strict...
The LHCb experiment at CERN operates a fully software-based first-level trigger that processes 30 million collision events per second, with a data throughput of 4 TB/s. Real-time tracking—reconstructing particle trajectories from raw detector hits—is essential for selecting the most interesting events, but must be performed under tight latency and throughput constraints.
A key bottleneck in...
The CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) project aims to detect anomalous physics signatures without bias from theoretical models in proton-proton collisions at the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider. CICADA identifies anomalies in low-level calorimeter trigger data using a convolutional autoencoder, whose behavior is...
The Large Hadron Collider (LHC) will soon undergo a high-luminosity (HL) upgrade to improve future searches for new particles and to measure particle properties with increased precision. The upgrade is expected to provide a dataset ten times larger than the one currently available by the end of its data-taking period. The increased beam intensity will also increase the number of simultaneous...
The inverse design of photonic surfaces produced by high-throughput femtosecond laser processing is limited by a strongly non-linear, many-to-one mapping from laser parameters (power, speed, hatch spacing) to the resulting optical spectrum. Tandem Neural Networks (TNNs) mitigate this ill-posedness by pairing a forward surrogate with a separately trained inverse network, but they still rely on...
Charge particle track reconstruction is the foundation of the collider experiments. Yet, it's also the most computationally expensive part of the particle reconstruction. The innovation in tracking reconstruction using graph neural networks (GNNs) has demonstrated a promising capability to address the computing challenges posed by the High-Luminosity LHC (HL-LHC) with Machine learning....
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions.
Our method allows assessing the robustness of ML models to such effects as a function of...
Benchmarks are a cornerstone of modern
machine learning practice, providing standardized eval-
uations that enable reproducibility, comparison, and
scientific progress. Yet, as AI systems — particularly
deep learning models — become increasingly dynamic,
traditional static benchmarking approaches are losing
their relevance. Models rapidly evolve in architecture,
scale, and capability;...
We present NomAD (Nanosecond Anomaly Detection), a real-time anomaly detection algorithm designed for the ATLAS Level-1 Topological (L1Topo) trigger using unsupervised machine learning. The algorithm combines a Variational Autoencoder (VAE) with Boosted Decision Tree (BDT) regression to compress and distill deep learning inference into a firmware-compatible format for FPGAs. Trained on 2024...
The escalating demand for data processing in particle physics research has spurred the exploration of novel technologies to enhance the efficiency and speed of calculations. This study presents the development of an implementation of MADGRAPH, a widely used tool in particle collision simulations, to FPGA using High-Level Synthesis. This research presents a proof of concept limited to a single,...
As the era of the High-Luminosity Large Hadron Collider (HL-LHC) approaches, the GPU-accelerated High-Level Trigger (HLT) of the CMS experiment faces a stringent requirement to reduce the Level-1 readout stream from 100 kHz to 5 kHz, a twenty-fold decrease essential to adhere to archival bandwidth constraints [[1][1]], [[2][2]]. Meeting this demand necessitates highly efficient real-time...
Simulating relativistic orbital dynamics around Schwarzschild black holes is essential for understanding general relativity and astrophysical phenomena like precession. Traditional numerical solvers face difficulty while dealing with noisy or sparse data, necessitating data-driven approaches. We develop a Scientific Machine Learning (SciML) framework to model orbital trajectories and...
We investigate the application of state space models (SSMs) to a diverse set of scientific time series tasks. In particular, we benchmark the performance of SSMs against a set of baseline neural networks across three domains: magnet quench prediction, gravitational wave signal classification (LIGO), and neural phase estimation. Our analysis evaluates both computational efficiency—quantified by...
Pak choi (Brassica rapa subsp. chinensis) is a leafy green vegetable widely cultivated in vertical urban farming systems due to its rapid growth and high yield under compact, hydroponic setups. However, even in these controlled environments, crops remain susceptible to various diseases. Among the most common threats are fungal infections such as Alternaria leaf spot and powdery mildew, and...
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the
development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have
solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks,...
Graph Neural Networks (GNNs) have become promising candidates for particle reconstruction and identification in high-energy physics, but their computational complexity makes them challenging to deploy in real-time data processing pipelines. In the next-generation LHCb calorimeter, detector hits—characterized by energy, position, and timing—can be naturally encoded as node features, with...
