As detector technologies improve, the increase in resolution, number of channels and overall size create immense bandwidth challenges for the data acquisition system, long data center compute times and growing data storage costs. Much of the raw data does not contain useful information and can be significantly reduced with veto and compression systems as well as online analysis.
The...
The High Granularity Calorimeter (HGCAL) is a new subdetector of the CMS experiment in development as part of the upgrades for the High Luminosity LHC. The HGCAL readout system includes the Endcap Trigger Concentrator (ECON-T) ASIC, responsible for algorithmically reducing the immense data volume associated with the trigger patch of this six-million channel “imaging” calorimeter. To...
Over the past several years, machine learning algorithms at the Large Hadron Collider have become increasingly more prevalent. Because of their highly parallelized design, Machine Learning-based algorithms can be sped up dramatically when using coprocessors, such as GPUs. With increasing computational demands coming from future LHC upgrades, there is a need to enhance the overall...
Particle tracking plays a crucial role in many particle physics experiments, e.g, the Large Hadron Collider. Yet, it is also one of the most time-consuming components in the whole particle reconstruction chain. The Exa.TrkX group has developed in recent years a promising and well-performed machine-learning-based pipeline that carries out the track finding, which is the most computationally...
One of the products of the cooperation between the hls4ml and FINN groups is Quantized ONNX (QONNX), a simple but flexible method to represent uniform quantization in ONNX. Its goal is to provide a high-level representation that can be targeted by training frameworks while minimizing reliance on implementation-specific details. It should also be lightweight, only adding a small number of...
This work proposes a novel reconfigurable architecture for reducing the latency of JEDI-net, a Graph Neural Network (GNN) based algorithm for jet tagging in particle physics, which achieves state-of-the-art accuracy. Accelerating JEDI-net is challenging since low latency is requited to potentially deploy the network on the online event selection systems at the CERN Large Hadron Collider. This...
Efficient quantum control is necessary for practical quantum computing implementations with current technologies. However, conventional algorithms for determining optimal control parameters are computationally expensive, mainly excluding them from use outside of the simulation. Furthermore, existing hardware solutions structured as lookup tables are imprecise and costly. A more efficient...
The High Luminosity LHC provides a challenging environment for fast trigger algorithms; increased numbers of proton-proton interactions per collision will introduce more background energy in the detectors making triggering on interesting physics signatures more challenging. To help mitigate the effect of this higher background the highest energy interaction in an event can be found and other...
Charged particle tracking is important in high-energy particle physics. For CERN Large Hadron Collider (LHC), tracking algorithms are used to identify the trajectories of charged particles created in the collisions. The existing tracking algorithms are typically based on the combinatorial Kalman filter where the complexity increases quadratically with the number of hits. The poor scalability...
Neutrino detectors, such as the Deep Underground Neutrino Experiment (DUNE) "far detector" are usually located deep underground in order to filter background noise. These detectors can be used to observe supernova neutrinos, and serve as a trigger to direct other observers to capture the supernova evolution early for multi-messenger astronomy. The neutrino detectors need to point the other...
The discovery of the optical counterpart, along with the gravitational waves from GW170817, of the first binary neutron star merger, opened up a new era for multi-messenger astrophysics. The optical counterpart, designated as a kilonova (KN), has immense potential to reveal the nature of compact binary merging systems. Ejecta properties from the merging system provide important information...
Gravitational wave (GW) detectors such as advanced LIGO, advanced Virgo, and KAGRA are high-precision instruments that record the strain signals from transient astrophysical sources such as merging binary black holes. The sensitivities of these detectors are often limited by instrumental and environmental noise that couple non-linearly to the GW strain. Noise regression algorithms running as...
Imaging nanoscale dynamics and response in materials requires imaging techniques with high spatial and temporal resolution. To meet this need, various scanning-probe spectroscopic imaging modes have emerged to understand electrochemical and ionic mobility and dynamics, ferroelectric switching dynamics, and dynamics mechanical responses of materials under external perturbations. These...
DeepClean is the technique using deep learning to clean the environmental noises in the gravitational wave strain data. The signals from the witness censors recording the environmental noises are used to produce the noises coupled to the strain data. After training the DeepClean model, the online cleaning in low latency is conducted by the Inference-as-a-Service model. The plans of...
In the high luminosity LHC (HL-LHC) era, the CMS detector will be subject to an unprecedented level of simultaneous proton-proton interactions (pile-up) that complicate the reconstruction process. Mitigation of the effects of pile-up is of prime importance. In preparation for this, the detector will be upgraded, providing more granularity and more information than we have had before. In...
The upcoming sPHENIX experiment, scheduled to start data taking at the Brookhaven National Laboratory (BNL) Relativistic Heavy Ion Collider in 2023, and the future Electron-Ion Collider (EIC) experiments will employ sophisticated state-of-the-art, high-rate detectors to study high energy heavy ion and electron-ion collisions, respectively. The resulting large volumes of raw data far exceed...
High-speed cameras have broadly been used to monitor plasma-wall interactions and to study spatial features of the plasma edge inside magnetic confinement fusion experiments. Depending on plasma parameters and photon energy sensitivity, a 2D imaging system can also be used to track the phase and amplitude of long-wavelength instability modes [1]. Such cameras can be used in devices where there...
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent...
The deployment of fast ML models for on-detector inference is rapidly growing but faces key issues. One such issue is the difference between the training environment and the “real-world” environment in deployment giving unknown errors in inference. Examples of this include training a model on an abundance of well understood simulated data but deploying it on a real and imperfect detector or on...
The search for dark matter and other new physics at the Large Hadron Collider (LHC) involves enormous data collection. Due to this, a high-level trigger system (HLT) must decide which data to keep for long-term storage while maintaining high throughput and on the order of millisecond latency. A central part of the HLT is 3D clustering of low-level detector measurements in the calorimeter. In...
We present our design experience of a prototype System-on-Chip (SoC) for machine learning applications that run in a cryogenic environment to evaluate the performance of the digital backend flow. We combined two established open-source projects (ESP and HLS4ML) into a new system-level design flow to build and program the SoC. In the modular tile-based architecture, we integrated a low-power...
In the as-as-service paradigm, we offload coprocessors to servers to run dedicated algorithms at high rates. The use of as-a-service allows us to balance computation loads leading to a dynamically resource-efficient system. Furthermore, as-a-service enables the integration of new types of coprocessors easily and quickly. In this talk, we present next generation studies using as-a-service...
The Alpha Magnetic Spectrometer (AMS-02) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. One of these, the Electromagnetic Calorimeter (ECAL), is used to measure the energy of cosmic-ray electrons and positrons and to differentiate these particles from cosmic-ray protons up to TeV energy.
We present a new deep learning...
At the LHC, the FPGA-based real-time data filter system that rapidly decides which collision events to record, known as the level-1 trigger, requires small models because of the low latency budget and other computing resource constraints. To enhance the sensitivity to unknown new physics, we want to put generic anomaly detection algorithms into the trigger. Past research suggests that graph...
Convolutional neural networks (CNN) have been widely applied in a tremendous of applications that involve image processing, including particle physics. Deepcalo is a package designed for developing CNNs using ATLAS data at CERN, targeting tasks like energy regression of electrons and photons. Although it has been shown that CNNs used in Deepcalo can handle the task smoothly, the extensive...
While applications of deep learning (DL) to gravitational wave (GW) physics are becoming increasingly common, very few have reached the maturity to be deployed in truly automated services. This is symptomatic of a larger gap between the existing tool sets for both GW physics and DL, neither of which has historically been developed or optimized for use with the other. This has led to suboptimal...
Applications of machine learning (ML) are growing by the day for many unique
and challenging scientific applications. However, a crucial challenge facing
these applications is their need for ultra low-latency and on-detector ML
capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled
with the rapid advances in scientific instrumentation that is resulting in
growing...
Anomaly Detection algorithms, when used as triggering mechanisms in experiments like the LHC, can help make data collection more precise by predominantly capturing events on interest. To ensure the triggering events are of interest, these detection algorithms should be robust against nuisance kinematic variables and detector conditions. To achieve this robustness, popular detection models,...
Neural networks have been shown to be helpful in identifying events of interest in particle physics. However, to be used for live trigger decisions, they must meet demandingly low latencies and resource utilization for deployment on Field Programmable Gate Arrays (FPGAs). HAWQ-V3, a Hessian-based quantization-aware training framework, and hls4ml, an FPGA firmware implementation package,...
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection...
Crossfield Technology LLC is teaming with Fermi National Accelerator Laboratory (Fermilab) for a Department of Energy Small Business Innovation Research (SBIR) Phase II program to develop a framework that remotely updates weights and biases in a High Level Synthesis (HLS) for machine learning (HLS4ML) IP core running on an Arria 10 SoC FPGA. The framework can also capture the inputs and...
We want to use the WaveNet model for the detection of the gravitational wave signals from Core-collapse supernovas. The model is trained by the 3-D simulated core-collapse supernova waveforms injected into the background of Advanced LIGO detectors. The goal is to increase the efficiency of the model training and the hyperparameter tuning.
The High Granularity Calorimeter (HGCAL) is part of the High Luminosity upgrade of the CMS detector at the Large Hadron Collider (HL-LHC). For the trigger primitive generation of the 6 million channels in this detector, data compression at the front end may be accomplished by using deep-learning techniques using an on-ASICs network. The Endcap Trigger Concentrator (ECON-T) ASIC foresees an...
The High-Luminosity LHC (HL-LHC) is expected to reach a peak instantaneous luminosity of $ 7.5 \times 10^{34} \mathrm{cm}^{−2} \mathrm{s} ^{−1}$ at a center-of-mass energy of $\sqrt{s} = 14 \mathrm{TeV}$. This leads to an extremely high density environment with up to 200 interactions per proton-proton bunch crossing. Under these conditions, event reconstruction represents a major challenge for...
Ptychography is a technique for imaging an object through reconstruction of the diffraction of coherent photons. Through measuring these diffraction patterns across the whole of the object, small scale structures can be reconstructed. In-pixel detectors used for these measurements, the maximum frame rate is often limited by the rate at which data can be transferred off of the device. In...
The rise of robotics, automation and the creation of various levels of abstraction have by now enabled automated experiments on a range of scientific instruments ranging from chemical robots for molecular synthesis, to electron and scanning probe microscopes that can be programmed to enable automated and autonomous experiments with a view towards physics discovery.
In this talk, I will...
In our study of (usually chaotic) Dynamical Systems, we invented a method we call Data Driven Forecasting, or DDF, that can take observed data, recreate an approximate form to the original model with a sum of radial basis functions, and rapidly forecast the future behavior of the system. This method is faster than 4th Order Runge-Kutta, so even if the user has knowledge of the data sets...
