In this talk I will focus on the possibilities that arise from recent advances in the area of deep learning for physical simulations. In this context, especially the Navier-Stokes equations represent an interesting and challenging advection-diffusion PDE that poses a variety of challenges for deep learning methods.
In particular, I will focus on differentiable physics solvers from the...
From the first images of a black hole by Katie Bouman using Matplotlib to neuroscience research that motivated the development of the scikit-learn library, open source has now revolutionized the way we do science. The scikit-learn software has now been cited more than 50000 times in 10 years. It's the most used software by machine learning experts on kaggle. One considers that about 2 millions...
OpenAi’s GPT-3 language model has triggered a new generation of Machine Learning models. Leveraging transformers architectures at billion-size parameters trained on massive unlabeled datasets, these language models achieve new capabilities such as text generation, question answering, or even zero-shot learning - tasks the model has not been explicitly trained for. However, training these...
At the LHC, the full-simulation workflow requires a large fraction of the computing resources available for experiments. With the planned High Luminosity upgrade of the LHC, the amount of needed simulated datasets would even increase. Speeding up the simulation workflow is of crucial importance for the success of the HL-LHC program and Deep Learning is considered as a promising approach to...
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard GEANT4 are computationally intensive, and current deep generative architectures work on discretized, lower resolution versions of the detailed simulation.
The development of models that work at higher spatial...
Stochastic simulators are an indispensable tool in many branches of science. Often based on first principles, they deliver a series of samples whose distribution implicitly defines a probability measure to describe the phenomena of interest. However, the fidelity of these simulators is not always sufficient for all scientific purposes, necessitating the construction of ad-hoc corrections to...
The mass of the top quark is of paramount importance as it is a highly sensitive probe of the structure and stability of the Standard Model. The final state of a leptonically decaying top quark contains a neutrino. In collider physics the neutrino escapes detection, leaving only experimental proxies for its momentum in the plane transverse to the beam pipe and no information about its...
Machine learning tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced sensitivity to systematic uncertainties. Contrary to the traditional wisdom of constructing an...
New Physics Learning Machine (NPLM) is a novel machine-learning based strategy to detect multivariate data departures from the Standard Model predictions, with no prior bias on the nature of the new physics responsible for the discrepancy [[1][1], [2][2]]. The main idea behind the method is to build the log-likelihood-ratio hypothesis test by translating the problem of maximizing the...
The design of optimal test statistics is a key task in frequentist statistics and for a number of scenarios optimal test statistics such as the profile-likelihood ratio are known. By turning this argument around we can find the profile likelihood ratio even in likelihood-free cases, where only samples from a simulator are available, by optimizing a test statistic within those scenarios. We...
The Data Quality Monitoring (DQM) is in place to spot and diagnose particle physics data problems as promptly as possible to avoid data loss in the CMS experiment of CERN. Several studies have proposed to leverage the DQM automation using machine learning algorithms. However, only a few efforts explored temporal characteristics to underpin system monitoring automation of the CMS detectors via...
Agenda and zoom connection are dedicated to this seminar (different from the zoom room of the workshop)
AI is having a transformational impact for accelerating discovery. Massive volumes of scientific data, which are continuously growing due to tireless efforts from many scientific communities of discovery, are enabling data-driven AI methods to be developed at ever increasing scale and applied in novel ways to breakthrough bottlenecks in the scientific method, and to speed up the discovery...
Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter inference in this context can be challenging. We present a neural simulation-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability, which is unique among...
Learning To Discover event has taken place 19th to 29th April 2022.
Three themes have been selected based on the one hand, their interest for HEP, and the fact there is already a number of HEP teams working on it, on the other hand, their importance in the Machine Learning field : Representation Learning over Heterogeneous/Graph Data, Dealing with Uncertainties and Generative Models. A final...
The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in proton-proton collisions could result in semivisible jets of particles: collimated sprays of dark hadrons of which only some are detectable by particle collider experiments. The experimental signature is...
We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised Standard...
In this talk we present CURTAINs, a new data driven ML technique for constructing a background template on a resonant spectrum, for use in bump hunts in the search for new physics using a sliding window approach. By employing invertible neural networks to parametrise the distribution of side band data as a function of the resonant observable, we learn a transformation to map any data point...
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network [2], trained with object condensation [1], a graph segmentation technique. Through a single-shot approach, the...
With the approaching HL-LHC upgrade, the current endcap calorimeters of the CMS are to be replaced with the High Granularity Calorimeter (HGCAL). Most of the sensitive part of HGCAL will consist of approximately 25,000 silicon pad sensor wafers, each approximately 20 cm in diameter, covering a total area of more than 600 m$^2$ of silicon sensors. Electrical breakdowns have been observed during...
After discovering the last piece of the Standard Model (SM), the Higgs boson, experiments at the Large Hadron Collider (LHC) have been searching for hints of physics Beyond the SM (BSM) to yield insights into these phenomena. These searches have not yet produced any significant deviations from SM predictions. Identifying unexplored regions in experimental observable space (object pTs, MET,...
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for...
The development of faster simulation methods is one of the crucial tasks currently undertaken at CERN. A part of this process in the ALICE experiment is the deep learning-based simulation tool for the Zero Degree Calorimeters (ZDC).
Generative models such as GANs that are currently used in the fast simulation framework successfully replicate the results for input particles that produce...
Cosmological simulations use generative deep learning models to generate galaxy images that are indiscernible from real images. Such simulations allow for a precise modeling of competing cosmological models as well as realistic propagation effects that affect observations. We present a new stochastic contrastive conditional generative adversarial network (InfoSCC-GAN) with explorable latent...
A realistic detector simulation is extremely important in particle physics. However, the current methods are very inefficient computationally since large amounts of resources are required for the readout, storage and distribution of simulation data. Deep generative models allow for more effective fast simulation of this information. Nevertheless, generating detector responses is a highly...
Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied...
Discriminating quark and gluon jets is a long-standing topic in collider phenomenology. In this paper, we address this question using the Lund jet plane substructure technique introduced in recent years. We present two complementary approaches: one where the quark/gluon likelihood ratio is computed analytically, to single-logarithmic accuracy, in perturbative QCD, and one where the Lund...
Numerical evaluations of Feynman integrals often proceed via a deformation of the integration contour into the complex plane. While valid contours are easy to construct, the numerical precision for a multi-loop integral can depend critically on the chosen contour. We present methods to optimize this contour using a combination of optimized, global complex shifts and a normalizing flow. They...
Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely-used models of hadronization in event generators are based on physically-inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with Graphical...
In the recent years, a series of measurements in the observables $R_{K^{(*)}}$ and $R_{D^{(*)}}$ concerning the semileptonic decays of the $B$ mesons have shown hints of violations of Lepton Flavour Universality (LFU). An updated model-independent analysis of New Physics violating LFU, by using the Standard Model Effective Field Theory (SMEFT) Lagrangian with semileptonic dimension six...
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. By maximising the mutual information between the input and the output of both the encoder and the decoder, we are able to rediscover the loss terms usually found in adversarial autoencoders and generative adversarial networks, as well as various more...
Normalizing flows are exact likelihood models that have been useful in several applications in HEP. The use of these models is hampered by the dimension preserving nature of the transformations, which results in many parameters and makes the models unusable for some techniques. In this talk we introduce [funnels][1], a new family of dimension reducing exact likelihood models.
Funnel models...
The Geant4 detector simulation, using full particle tracking (FullSim), is usually the most accurate detector simulation used in HEP but it is computationally expensive. The cost of FullSim is amplified in highly segmented calorimeters where large fraction of the computations are performed to track the shower’s low-energy photons through the complex geometry. A method to limit the amount of...
Particle identification (PID) is an essential ingredient of many measurements performed by the ALICE Collaboration. The ALICE detectors provide PID information via complementary experimental techniques, allowing for the identification of particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c. The biggest challenge lies in combining the information from the...
The Time Projection Chamber (TPC) of the ALICE experiment at CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data acquisition at the highest interaction rates expected in Pb-Pb collisions. In the absence of a gating grid system, a significant amount of ions generated in the multiplication...
The CMS experiment at the Large Hadron Collider (LHC) at CERN adopts the LLP (Long-Lived Particle) Jet Algorithm, to search for new physics by tagging hadronic jets which stem from exotic long-lived particles. The LLP Jet Algorithm is a multiclass classifier based on a state-of-the-art Deep Neural Network (DNN). The jet tagging model’s forward inference stage employs 12 convolutional layers...
The large data rates at the LHC make it impossible to store every single observed interaction. Therefore we require an online trigger system to select relevant collisions. We propose an additional approach, where rather than compressing individual events, we compress the entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data...
We present a study on latency and resource requirements for deep learning algorithms to run on a typical High Level Trigger computing farm at a high-pT LHC experiment at CERN. As a benchmark, we consider convolutional and graph autoencoders, developed to perform real-time anomaly detection on all the events entering the High Level Trigger (HLT) stage. The benchmark dataset consists of...
We propose a signal-agnostic strategy to reject QCD jets and identify anomalous signatures in a High Level Trigger (HLT) system at the LHC. Soft unclustered energy patterns (SUEP) could be such a signal — predicted in models with strongly-coupled hidden valleys — primarily characterized by a nearly spherically-symmetric signature of an anomalously large number of soft charged particles, in...
The CMS experiment will be upgraded to maintain physics sensitivity and exploit the higher luminosity of the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks within the full outer silicon tracker volume as an input for the first time and new algorithms are being designed to make use of these tracks. One such algorithm is primary...
The CMS collaboration has chosen a novel High-Granularity Calorimeter (HGCAL) for the endcap regions as part of its planned upgrade for the high luminosity LHC. The high granularity of the detector is crucial for disentangling showers overlapped with high levels of pileup events (140 or more per bunch crossing at HL-LHC). But the reconstruction of the complex events and rejection of background...
With deep learning becoming very popular among LHC experiments, it is expected that speeding up the network training and optimization will soon be an issue. To this purpose, we are developing a dedicated tool at CMS, Neural Network Learning and Optimization library (NNLO). NNLO aims to support both widely known deep learning libraries Tensorflow and PyTorch. It should help engineers and...
Nowadays Machine Learning (ML) techniques are widely adopted in many areas of HEP and certainly will play a significant role also in the upcoming High-Luminosity LHC (HL-LHC) upgrade foreseen at CERN, when a huge amount of data will be produced by LHC and collected by the experiments, facing challenges at the exascale.
Here, we present a Machine Learning as a Service solution for HEP...
We present a study, based on supervised and unsupervised quantum machine learning algorithms, with the goal of proposing a new strategy for anomaly detection at the LHC. This study focuses on designing an algorithm capable of finding hidden patterns in the jet data. The algorithm is structured as a sequence of a classic and a quantum machine learning algorithm: the classic algorithm is the...
The LHCb experiment is currently undergoing its Upgrade I, which will allow it to collect data at a five-times larger instantaneous luminosity. In a decade from now, the Upgrade II of LHCb will prepare the experiment to face another ten-fold increase in instantaneous luminosity. Such an increase in event complexity will pose unprecedented challenges to the online-trigger system, for which a...
Graph Neural Networks (GNNs) have been shown to produce high accuracy performance on a variety of HEP tasks, including track reconstruction in the TrackML challenge, and tagging in jet physics. However, GNNs are less explored in applications with noisy, heterogeneous or ambiguous data. These elements are expected from ATLAS Inner Tracker (ITk) detector data, when it is reformulated as a graph....
PADME experiment at LNF-INFN is devoted to the search for the associate production of new light particles using accelerated positrons which annihilate in a thin active diamond target.
The core of the experiment is an electromagnetic calorimeter made of 616 BGO crystals which is dedicated to the measurement of the energy and the position of the final state photons.
The high beam particle...
The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level...
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the QCD phase diagram at high net-baryon density ($µ_{B} > 400$ MeV) in the energy range of $\sqrt{s_{NN}}$ = 2.7−4.9 GeV. Precise determination of dense baryonic matter properties requires multi-differential measurements of strange hadron yields, both for most copiously produced kaons and $\Lambda$ as well as for rare...
A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models. Nevertheless, the universality of QCD suggests that a large amount of the information learnt in the training is common to different physical signals and experimental setups. In this article, we explore the use of transfer...
Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging. The JetClass dataset consists of 100 M jets, about two orders of magnitude...
We explore a possible avenue for detecting Dark Showers that manifest as Soft Unclustered Energy Patterns (SUEP) in the detector with the use of supervised machine learning techniques and transfer learning. We employ a ResNet model based on Convolutional Neural Networks (CNNs) to classify events. Additionally, a robust, data-driven background estimation technique is embedded into the model...
We investigate the potential of graph neural networks in unsupervised search for new physics signatures in the extremely challenging environment at the L1 at the Large Hadron Collider (LHC). On a dataset mimicking the hardware-level trigger input, we demonstrate that graph autoencoders can significantly enhance new physics contributions. Moreover, we implement the graph autoencoder on FPGA to...
Calorimetric muon energy estimation in high-energy physics is an example of a likelihood-free inference (LFI) problem, where simulators that implicitly encode the likelihood function are used to mimic complex interactions at different configurations of the parameters. Recently, Kieseler et al. (2022) exploited simulated measurements from a dense, finely segmented calorimeter to infer the true...
Muon tomography is a useful imaging technique for studying volumes of interest by examining the scattering and absorption of cosmic muons which pass through them. Inferring properties of the volumes, however, is challenging since muons will scatter many times within the volume, the detectors involved have finite resolution, and each muon only ever traverses a sub-potion of the whole...
The currently used identification of electrons in ATLAS uses a likelihood approach without considering correlations between the input variables. In this talk we introduce the next generation identification algorithm using the same input information but with a deep neural network in order to extract more information from the input variables and substantially improve the rejection of fake...
Particle therapy using protons or heavy ions is a relatively new cancer treatment modality which has acquired increasing popularity in the last decade, due to its potential in reducing undesired dose to the nearby healthy tissues, with respect to conventional radiotherapy. However, current clinical treatment planning based on computed tomography suffers from modest range uncertainties due to...
Reconstructing the type and energy of isolated pions from the ATLAS calorimeters is a key step in the hadronic reconstruction. The baseline methods for local hadronic calibration were optimized early in the lifetime of the ATLAS experiment. Recently, image-based deep learning techniques demonstrated significant improvements over the performance over these traditional techniques. We present an...
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors. A graph neural network (GNN) model, known as the machine-learned particle-flow (MLPF) algorithm, has been developed to substitute the rule-based PF algorithm (https://arxiv.org/abs/2101.08578),...
I will discuss a new concept in anomaly detection based on (1) tagging of objects to detect "exotic" ones (2) search for anomalous features by comparing the untagged and tagged samples. Both (1) and (2) are done with ML tools, either supervised or non-supervised. In the current implementation for multi-pronged jets, tagging is supervised and the search for features is unsupervised.
Jets are collimated sprays of hadrons produced in high energy collisions. Jets play an important role in many searches for new physics, and provide an experimental window into the real time dynamics of hadronization, namely the confinement of asymptotically free quarks and gluons into hadrons. With the advent of a new class of theoretical observables, so called energy correlators, that probe...
