Applying algorithmic differentiation (AD) to computer code is a challenging task. To this end, several tools have been developed over the last decade, which ease the application of AD. With MeDiPack, it is no longer a problem to handle all kinds of MPI communication. The recently released library OpDiLib provides out of the box AD capabilities for OpenMP parallel codes. Modern AD tools, like...
The recent increase in volume and complexity of astronomical data has fomented the use of machine learning techniques. However, the acquisition of labels in astronomy is, by construction, very expensive and time-consuming. In this context, experiment design tasks are aimed at optimizing the allocation of scarce labelling resources. The proper application of such methods will be crucial to...
Dark matter particles pervading our galactic halo could be directly detected by measuring their scattering off target nuclei or electrons in a suitable detector. The expected signal from this interaction is rare, demanding ultra-low background conditions, and small energy deposits below tens of keV would be produced, requiring low energy detection thresholds. Many different and complementary...
The Cosmic Microwave Background (CMB) is a radiation that reaches us from all the directions of the sky. It originated shortly after the Big Bang and is the oldest radiation that we can observe in the Universe, providing us with very valuable information about the early universe and how it evolved. CMB radiation is also polarized. In particular, the next big challenge in the CMB field is to...
Many acquired data are inherently relational: human or automated annotations create high value relational data. For example, a planet might belong to a stellar system which has a star of a certain type. This stellar system belongs to a galaxy which also has its own type (Fig.1).
Relational programming languages are the right tools to handle these strong structured data. Our...
Radiation therapy using protons or heavier ions is sensitive to range errors caused by misalignment of the patient, changes in patient anatomy, and uncertainties in treatment planning. It is therefore of the utmost importance to ensure treatment quality through range verification. Determining the position of the Bragg peak inside the patient can be done through various means such as prompt...
P-ONE is a planned cubic-kilometer-scale neutrino detector in the Pacific ocean. Similar to the successful IceCube Neutrino Observatory, P-ONE will measure high-energy astrophysical neutrinos to help characterize the nature of astrophysical accelerators. Using existing deep-sea infrastructure provided by Ocean Networks Canada (ONC), P-ONE will instrument the ocean with optical modules - which...
The Einstein Telescope (ET) is the future European terrestrial gravitational wave observatory based on laser interferometry. The project is currently in the preparatory phase after its integration in the EU ESFRI roadmap for large research infrastructures in 2021 and the official creation of the scientific Collaboration in 2022. A number of key design innovations are foreseen for ET like...
I will give a short introduction to (conditional) normalizing flows and discuss why they are essential to calculate "manageable" differentiable expectation values of continuous random variables. I will discuss some examples where this might be useful and end the talk with a github-package that combines some state of the art conditional normalizing flows to be used with minimal manual labor.
The recent MODE whitepaper*, proposes an end-to-end differential pipeline for the optimisation of detector designs directly with respect to the end goal of the experiment, rather than intermediate proxy targets. The TomOpt python package is the first concrete endeavour in attempting to realise such a pipeline, and aims to allow the optimisation of detectors for the purpose of muon tomography...
Muon Scattering Tomography (MST) has been through a fruitful period of development which led to many applications in various fields such as border controls, nuclear waste characterization, nuclear reactor monitoring and preventive maintenance of industrial facilities. Whatever the use case, MST detector conception aims at reaching the best performance that respects specific constraints,...
Muon tomography applications often require detection of material contrast. One example of such application is the detection of contraband at the border. Another example is the detection of steel rebars inside reinforced concrete blocks. Sensitivity of material discrimination depends on the detector configuration, exposure time and clutter. We explore how machine learning techniques can be used...
We investigate the problem of distinguishing materials by scattering tomography with an R implementation of the EM (Expectation Maximization) algorithm and we compare the results with those from the PoCA (Point of Closest Approach) method.
Several applications of scattering muon tomography require the estimation of a limited number of key parameters associated to a given sample. In this presentation we explore the use of the quantiles of the angular and spatial deviation distributions as the input to Deep Neural Networks regressing on the parameters of interest. We provide examples related to the measurement of the position of...
This presentation explores the possibility of using Generative Adversarial Neural Networks (GANN) in order to simulate the propagation of muons through material without using a complete simulation of the physical processes. In order to achieve this goal, Generative Adversarial Neural Networks have been used to simulate muon tomography data applied to the measurement of the thicknes of isolated...
Automatic Differentiation is a powerful technique to evaluate the derivative of a function specified by a computer program. Thanks to the ROOT interpreter, Cling, this technique is available in ROOT for computing gradients and Hessian matrices of multi-dimensional functions.
We will present the current integration of this tool in the ROOT Mathematical libraries for computing gradients of...
The IRIS-HEP Analysis Grand Challenge (AGC) seeks to build and test a fully representative HL-LHC analysis based around new analysis tools being developed within IRIS-HEP and by others. The size of the HL-LHC datasets is expected to require fully distributed analyses sometimes sourcing 100’s of TB of data or event, later in the HL-LHC, PB-sized datasets. This talk will give a brief overview of...
In the ideal world, we describe our models with recognizable mathematical expressions and directly fit those models to large data sample with high performance. It turns out that this can be done with a CAS, using its symbolic expression trees as template to computational back-ends like JAX. The CAS can in fact further simplify the expression tree, which results in speed-ups in the numerical...
The Electron Ion Collider (EIC), the future ultimate machine to study the strong force, is a large-scale experiment with an integrated detector that covers the central, far-forward, and far-backward regions. EIC is utilizing AI starting from the design phase in order to deal with compute intensive simulations and a design made by multiple sub-detectors --- each characterized by multiple design...
Background: The aim of our work is to build a Deep Learning algorithm capable of generating the distribution of absorbed dose by a medium interacting with a given particle beam.
Such an algorithm can provide a precise and faster alternative to a Monte Carlo (MC) simulation, which are currently employed in the optimisation process of radio therapy treatment (RT) planning.
A faster dose data...
As the first step in a wide-ranging study to determine the capabilities of
fine-grained calorimeters to identify different hadrons within dense showers, we show how to extract all the information about all intermediate processes taking place within the development of complex hadron showers produced by simulation in GEANT4.
The recent MODE whitepaper*, proposes an end-to-end differential pipeline for the optimization of detector designs directly with respect to the end goal of the experiment, rather than intermediate proxy targets. The TomOpt python package is the first concrete step in attempting to realize such a pipeline, and aims to allow the optimisation of detectors for the purpose of muon tomography with...
Derivatives, mainly in the form of gradients and Hessians, are ubiquitous in machine learning and Bayesian inference. Automatic differentiation (AD) techniques transform a program into a derivative (adjoint) program, which is run to compute the gradient.
Traditionally, most AD systems have been high level, and unable to extract good performance on scalar code or loops modifying memory....
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the
QCD phase diagram at high net-baryon density (μB > 500 MeV) with heavy-ion
collisions in the energy range of √sNN = 2.7−4.9 GeV. Precise determination of dense
baryonic matter properties requires multi-differential measurements of strange
hadron yields, both for the most copiously produced K0s and Λ as well as for...
ACTAR is an active-target TPC optimized for the study of nuclear reactions produced by low-intensity beams, such as radioactive beams. In this detector, the gas used to track charged particles within the chamber is at the same time used as a target for the incoming beam. Reconstructing the tracks left by ions is a challenging task, and two different reconstruction algorithms are compared in...
High energy physics experiments essentially rely on the simulation data used for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed.
t has been shown that deep learning techniques especially Generative Adversarial Networks may be used to reproduce detector...
LHCb ECAL optimization is a good use case for the generic problem of comprehensive optimization of the complex physics detector. Pipeline-based approach for LHCb ECAL optimization is established and used to scan parameter space for desired subspace which met needs of the LHCb experiment. Parameters for the calorimeter optimization include technology, granularity, Moliere Radius, timing...
Proton computed tomography (pCT) is a medical imaging modality with the potential to improve the accuracy of treatment planning for proton-beam radiotherapy. It produces a three-dimensional image of the relative stopping power (RSP) distribution inside an object, given a list of positions and directions of protons before and after passing through the object, along with the corresponding energy...
The synthesization of fast reverse- and forward-mode gradients is the key to many algorithms in scientific computing such as meta-learning, optimization, uncertainty quantification, stability analysis, machine learning-accelerated simulations, and end-to-end learning. Enzyme is an automatic differentiation tool which in difference to other tools is built into the LLVM compiler toolchain to be...
One of the key difficulties in making HEP differentiable is the highly stochastic and discrete nature of both simulation and reconstruction. While not directly differentiable, gradients of expectation values of stochastic simulator output can be estimated using probabilistic programming and score functions. In this talk I will demonstrate score function based optimization of material maps on...
Several new experiments that looks for millicharged particle has been proposed recently, Milliqan is an international collaboration that is now installed and under upgrade at point 5 @ CMS at CERN for a second run. SUBMET is a new proposal that is expected to be installed at JPARC. The common denominator of the two proposals is the simplicity of the construction. Muons are the main background...
Automatic Differentiation (AD) techniques allows to determine the
Taylor expansion of any deterministic function. The generalization of
these techniques to stochastic problems is not trivial. In this work we explore two approaches to extend the ideas of AD to Monte Carlo processes, one based on reweighting (importance sampling) and another one based on the ideas from the lattice field theory...
The Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC) is undergoing an extensive Phase II upgrade program to prepare for the challenging conditions of the High-Luminosity LHC (HL-LHC). As part of this program, a novel endcap calorimeter that uses almost 6M Silicon and Scintillator sensors is foreseen. These sensors will sample the electromagnetic and hadronic...
