(no recording)
Procter & Gamble (P&G) is one of the oldest and largest “consumer goods” companies in the world. It is present in about 180 markets, with operations in 70 countries and almost 100 thousand employes. Machine Learning models created by the P&G Data Scientists support every aspect of this global business, from R&D, to shipment to marketing. The Data Science teams in the company...
A recent new branch of the, currently called AI, is the Topological Data Analysis (TDA). TDA was born as an extension of algebraic topology to discrete data and, therefore, is a combination of algebraic topology, geometry, statistics and computational methods. According to E. Munch TDA comprises “a collection of powerful tools that can quantify shape and structure in data in order to answer...
Interpretable models are a hot topic in neural network research. My talk will look on interpretability from the perspective of inverse problems, where one wants to infer backwards from observations to the hidden characteristics of a system. I will focus on three aspects: reliable uncertainty quantification, outlier detection, and disentanglement into meaningful features. It turns out that...
6 Years after first demonstration of Deep Learning in HEP, the LHC community has explored a broad range of applications aiming for better, cheaper, faster, and easier solutions that ultimately extend the physics reach of the experiments and over come HL-LHC computing challenges. I’ll present a snapshot of where the ATLAS experiment currently stands in adoption of Deep Learning and suggest...
With firm evidence of neutrino oscillation and measurements of mixing parameters, neutrino experiments are entering the high precision measurement era. The detector is becoming larger and denser to gain high statistics of measurements, and detector technologies evolve toward particle imaging, essentially a hi-resolution "camera", in order to capture every single detail of particles produced in...
Generative machine learning models have been successfully applied to many problems in particle physics, ranging from event generation to fast calorimeter simulation to many more. This indicates that generative models have the potential to become a mainstay in many simulation chains. However, one question that still remains is whether a generative model can have increased statistical precision...
Simulating detectors response is a crucial task in HEP experiments. Currently employed methods, such as Monte Carlo algorithms, provide high-fidelity results at a price of high computational cost, especially for dense detectors such as ZDC calorimeter in ALICE experiment. Multiple attempts are taken to reduce this burden, e.g. using generative approaches based on Generative Adversarial...
Deep learning simulations are known as computational heavy with the need of a lot of memory and bandwidth. A promising approach to make deep learning more efficient and to reduce its hardware workload is to quantize the parameters of the model to lower precision. This approach results in lower execution inference time, lower memory footprint and lower memory bandwidth.
We will research the...
Building on the recent success of deep learning algorithms, Generative Adversarial Networks (GANs) are exploited for modelling the response of the ATLAS detector calorimeter of different particle types; simulating calorimeter showers for photons, electrons and pions over a range of energies (between 256 MeV and 4 TeV) in the full detector $\eta$ range. The properties of showers in...
Generative Adversarial Networks are usually used to generate images similar to the provided training data. The 3DGAN introduced in Khattak et al 2019 has the ability to simulate data from High Energy Physics detectors where each shower is represented by a three dimensional image. To evaluate the results, the generated images were compared to Monte Carlo GEANT4 simulations in terms of physics...
NICA accelerator complex is currently being assembled in JINR (Dubna) to perform studies of heavy-ion collisions and explore new regions of the QCD phase diagram. Located at one of the two interaction points of the facility, the Multi-Purpose Detector (MPD) will utilize the Time-Projection Chamber (TPC) as the main tracker of the detector’s central barrel. TPC consists of a gas-filled...
Classifying particle types on the basis of detectors response is a fundamental task in the ALICE experiment. Methods currently employed in this job are based on linear classifiers which are built on Monte Carlo simulation data, due to lack of labels (pdg code) in case of production data and require manual fine tuning to match latter data set distribution. This calibration is performed by...
We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the simulator is stochastic. To address such cases,...
Design of new experiments, as well as upgrade of ongoing ones, is a
continuous process in the experimental high energy physics.
Frontier R&Ds are used to squeeze the maximum physics performance using cutting edge detector technologies.
The evaluation of physics performance for a particular configuration
includes sketching this configuration in Geant, simulating typical
signals and...
The Matrix Element Method (MEM) is a powerful method to extract information from measured events at collider experiments. Compared to multivariate techniques built on large sets of experimental data, the MEM does not rely on an examples-based learning phase but directly exploits our knowledge of the physics processes. This comes at a price, both in term of complexity and computing time since...
This talk contains 2 contributions:
1) Adaptive divergence for rapid adversarial optimization.
Adversarial Optimization provides a reliable, practical way to match two implicitly defined distributions, one of which is typically represented by a sample of real data, and the other is represented by a parameterized generator. Matching of the distributions is achieved by minimizing a...
Track finding is a critical and computationally expensive step of object reconstruction for the LHC detectors. The current method of track reconstruction is a physics-inspired Kalman Filter guided combinatorial search. This procedure is highly accurate but is sequential and thus scales poorly with increased luminosity like that planned for the HL-LHC. It is therefore necessary to consider new...
(due to slow internet connection : youtube video + recording of Q&A)
Secondary vertex finding is a crucial task for identifying jets containing heavy flavor hadron decays.
Bottom jets in particular have a very distinctive topology of 𝑏→𝑐→𝑠 decay which gives rise to two secondary vertices with high invariant mass and several associated charged tracks.
Existing secondary vertex finding...
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects...
We present Hit-reco model for denoising and region of interest selection on raw simulation data from ProtoDUNE experiment. ProtoDUNE detector is hosted by CERN and it aims to test and calibrate technologies for DUNE, a forthcoming experiment in neutrino physics. Hit-reco leverages deep learning algorithms to make the first step in the reconstruction workchain, which consists in converting...
An overarching issue of LHC experiments is the necessity to produce massive numbers of simulated collision events in very restricted regions of phase space. A commonly used approach to tackle the problem is the use of event weighting techniques where the selection cuts are replaced by event weights constructed from efficiency parametrizations. These techniques are however limited by the...
For many top quark measurements, it is essential to reconstruct the top quark from its decay products. For example, the top quark pair production process in the all-jets final state has six jets initiated from daughter partons and additional jets from initial/final state radiation. Due to the many possible permutations, it is very hard to assign jets to partons. We use a deep neural network...
Experimental measurements in high energy physics are primarily designed using the expert knowledge and intuition of the analysers, who define their background rejection cuts, control/signal regions and observables of interest based on their understanding of the physical processes involved. More recently, modern multivariate analysis techniques such as neural density estimation and boosted...
This talk will provide an introduction to the concept of over-parametrization in neural networks and the associated benefits that have been identified from the theoretical and empirical standpoints. It will then present the practice of pruning as both a practical engineering intervention to reduce model size and a scientific tool to investigate the behavior and trainability of compressed...
AutoDQM is an automated monitoring system which implements statistical tests and machine learning (ML) algorithms to compare data runs and flag anomalies for CMS data quality. It is used in conjunction with the existing Data Quality Monitoring (DQM) software to reduce the time and labor required of shifters during collision running by identifying anomalous behavior for further review from...
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will undergo an upgrade to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in luminosity, will yield many more detector hits (occupancy), and thus measurements will pose a challenge to track reconstruction algorithms being...
In High Energy Physics (HEP), calorimeter outputs play an essential role in understanding low distance processes occurring during particle collisions. Due to the complexity of underlying physics, the traditional Monte-Carlo simulation is computationally expensive, and thus, the HEP community has suggested Generative Adversarial Networks (GAN) for fast simulation. Meanwhile, it has also been...
SWAN (Service for Web-based ANalysis) is CERN’s general-purpose Jupyter notebook service. It offers a pre-configured, fully-fledged, and easy to use environment, integrating CERN-IT compute, GPU, storage, and analytics services, available at a simple mouse click. In this talk, we will describe the currently deployed SWAN service, as well as recent developments and service improvements that can...
Abstract
Machine Learning has been used in a wide array of areas and the necessity to make it faster while still maintaining the accuracy and validity of the results is a growing problem for data scientists. This work explores the Tensorflow distributed parallel strategy approach to effectively and efficiently run a Generative Adversarial Network, GAN, model [1] in a parallel environment,...
Experiments at HL-LHC and beyond will have ever higher read-out rate. It is then essential to explore new hardware paradigms for large scale computations. In this work we consider the Optical Processing Units (OPU) from LightOn, which compute random matrix multiplications on large datasets in an analog, fast and economic way, fostering faster machine learning results on a dataset of reduced...
Machine Learning is increasingly used in many fields of HEP and will give its contribute in the upcoming High-Luminosity LHC (HL-LHC) program at CERN. The raising of data produced needs new approaches to train and use ML models. In this presentation we discuss the Machine Learning as a Service (MLaaS) infrastructure, that allows to read data directly in the ROOT format exploiting the...
Graph Neural Networks (GNN) are trainable functions that operate on a graph to learn latent graph attributes and to form a parameterized message-passing by which information is propagated across the graph, ultimately learning sophisticated graph attributes. Its application in the High Energy Physics grows rapidly in the past years, ranging from event reconstructions to data analyses, from...
With the emerging of more and more sophisticated machine learning models in high energy physics, optimising the parameters of the models (hyperparameters) is becoming more and more crucial in order to get the best performance for physics analysis. This requires a lot of computing resources. So far, many of the training results are worked out in a personal computer or a local institution...
The identification of heavy particles such as top quarks or vector bosons is one of the key issues at the Large Hadron Collider. In this talk, we introduce a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background QCD jets. We apply this framework to...
The important step of the analysis of HEP scattering processes is the optimization of the input space for multivariate technique. We propose general recipe how to form the set of low-level observables which are sensitive to the differences in hard scattering processes at the colliders. It will be demonstrated that without any sophisticated analysis of the kinematic properties one can achieve...
We present a new set of neural network architectures, Lorentz group covariant architectures for learning the kinematics and properties of complex systems of particles. The novel design of this network, called LGN (Lorentz Group Network), implements activations as vectors that transform according to arbitrary finite-dimensional representations of the underlying symmetry group that governs...
The measurement of the associated production of Higgs boson with a top-quark pair (ttH) at the LHC provides a direct determination of the Higgs-Top Yukawa interaction. The presence of a large number of objects in the final state makes the measurement very challenging. Multivariate Analysis methods such as Boosted Decision Trees (BDT) were used to enhance the analysis sensitivity. However, the...
Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark...
One of the goals of current particle physics research is to obtain evidence of physics beyond the Standard Model (BSM) at accelerators such as the Large Hadron Collider (LHC). The searches for new physics are often guided by BSM theories that depend on many unknown parameters, which makes testing their predictions computationally challenging. Bayesian neural networks (BNN) can map the...
We introduce a novel strategy for machine-learning-based predictive simulators, which can be trained in an unsupervised manner using observed data samples to learn a predictive model of the detector response and other difficult-to-model transformations. Particle physics detectors cannot directly probe fundamental particle collisions. Instead, statistical inference must be used to surmise...
Searching for rare physics processes requires a good understanding of the
backgrounds involved. This often requires large amounts of simulated data that
are computationally expensive to produce. The Belle II collaboration is planning
to collect 50 times the amount of data of its predecessor Belle. With the
increase in data volume the necessary volume of simulated data increases as
well....
The pixel detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background noise on track reconstruction for measured data is emulated for simulated data by a mixture of measured background noise and easily-simulated particle decays. This model...
Deep learn physics open dataset contains thousands of frames LARTPC detector data. The main problem of the dataset is semantic segmentation. This problem has been solved succesfully with modified version of U-Net, as well as graph-networks. The main difficulty of this problem lays within the sparcity of data (thin tracks inside pixels, or voxels) which make it difficult to feed classical...
Canonical particle flow algorithm tries to estimate neutral energy deposition in calorimeter by first performing matching between calorimeter deposits and track
direction and subsequently subtracting the track momenta from the matched cluster energy deposition.
We propose a Deep Learning based method for estimating the energy fraction of individual components for each cell of the...
Super-resolution algorithms are commonly used to enhance the granularity of an imaging system beyond what can be achieved using the measuring device.
We show the first application of super-resolution algorithms using deep learning-based methods for calorimeter reconstruction using a simplified geometry consisting of overlapping showers originated by charged and neutral pions events.
The...
Calorimetric cluster reconstruction can be performed using deep learning solutions from real-time computer vision by casting the detector readout as a two-dimensional image. The increased luminosity expected of Run III poses unprecedented challenges to shower reconstruction at LHCb. This work seeks to perform shower identification and energy regression under such conditions through both...
High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in computer vision, even machine learning assisted object reconstruction algorithms in particle physics almost exclusively predict properties on an object-by-object basis.
Traditional approaches...
In this talk I will present an unsupervised clustering (UCluster) method where a neural network is used to reduce the dimensionality of the data, while preserving the event information. The reduced representation is then clustered to a k-means friendly space with a suitable loss function. I will show how this idea can be used to unsupervised multi-class classification and anomaly detection.
Waveform analysis is a crucial first step in the data processing pipeline for any particle physics experiment. Its accuracy, therefore, can limit the overall analysis performance although waveform analyses often face a variety of challenges, for example: overlapping ‘pile-up’ pulses, noise, non-linearities, floating baselines. Historically, many experiments have viewed template fitting as...
Open Data are a crucial cornerstone of science. Using Open Data brings benefits such as direct access to cutting edge research, tools to promote public understanding of science and training for scientists of the future. This talk will describe the enormous potential of ATLAS Open Data and how it’s used for training, courses and tutorials in machine learning, from undergraduate and postgraduate...
We present the first application of adaptive machine learning to the identification of anomalies in a data set of non-periodic time series. The method follows an active learning strategy where highly informative objects are selected to be labelled. This new information is subsequently used to improve the machine learning model, allowing its accuracy to evolve with the addition of human...
We introduce a generative adversarial network for analyzing the dark matter distribution of a dwarf spheroidal galaxy.
The mock data generator for dwarf spheroidal galaxies in the spherically symmetric case has three functional parameters: the number density of stars, the density of dark matter, and velocity anisotropy.
The generator will be adversarially trained on a mock dataset, which...
The simulation of the passage of particles through the LHC detectors occupies already more than a third of the available computing resources and it's predicted to exceed them after 2026, for the example of the ATLAS detector. Significant portion of the most prevalent simulation toolkit, Geant4, is spent to explore the geometry of the detector volume in order to calculate a particle instance...
In this talk, we investigate the use of Generative Adversarial Networks (GANs) and a new architecture -- the Bounded Information Bottleneck Autoencoder (Bib-AE) -- for modeling electromagnetic showers in the central region of the Silicon-Tungsten calorimeter of the proposed International Large Detector. An accurate simulation of differential distributions including for the first time the shape...
The PyTorch just-in-time (jit) compiler is a powerful tool for optimizing and serializing neural network models. However, its range is limited by the subset of the python language that it is restricted to and the number of tensor operations implemented in C++. These limitations were a major blocker to using graph neural networks implemented in the geometric deep learning (GDL) library PyTorch...
The data rate may surge after some planned upgrades for the high-luminosity Large Hadron Collider (LHC) and accelerator-based neutrino experiments. Since there is no enough storage to save all of the data, there is a challenging demand to process and filter billions of events in real-time. Machine learning algorithms are becoming increasingly prevalent in the particle reconstruction pipeline....
With the wide use of deep learning in HEP analyses, answering questions beyond the classification performance becomes increasingly important. One crucial aspect is ensuring the robustness of classifier outputs against other observables - typically an invariant mass. Superior performance in decorrelation was so far achieved by adversarial training. We show that a simple additive term in the...
Abstract
Invariance of learned representations of neural networks against certain sensitive attributes of the input data is a desirable trait in many modern-day applications of machine learning, such as precision measurements in experimental high-energy physics. We propose to use the ability of variational autoencoders to learn a disentangled latent representation to achieve the desired...
A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures. Such structures could result in a false signal when the background is estimated from data using sideband methods. A variety of techniques have been developed to construct classifiers which are independent from the resonant feature (often a mass)....
A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere. One of the prototypical examples for these methods is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum. A significant challenge to methods that...
n this talk we present a new algorithm called `Anomaly Awareness’ (AA) to search for physics beyond the standard model (BSM). By making the algorithm aware of the presence of a range of different anomalies, we improve its capability to detect anomalous events, even those it had not been exposed to. As an example, we apply this method to a boosted jet topology for BSM searches at LHC and use it...
A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this work, we aim to search for new signals that appear as deviations from known Standard Model physics in high-dimensional particle physics data. To do this, we determine whether there is any statistically significant difference between the distribution of Standard...
