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Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
Authors:
Max Aehle,
Lorenzo Arsini,
R. Belén Barreiro,
Anastasios Belias,
Florian Bury,
Susana Cebrian,
Alexander Demin,
Jennet Dickinson,
Julien Donini,
Tommaso Dorigo,
Michele Doro,
Nicolas R. Gauger,
Andrea Giammanco,
Lindsey Gray,
Borja S. González,
Verena Kain,
Jan Kieseler,
Lisa Kusch,
Marcus Liwicki,
Gernot Maier,
Federico Nardi,
Fedor Ratnikov,
Ryan Roussel,
Roberto Ruiz de Austri,
Fredrik Sandin
, et al. (5 additional authors not shown)
Abstract:
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the ext…
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In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the extraction of inference on the parameters of interest of a measuring instrument -- along with the careful specification of a utility function well aligned with the end goals of the experiment.
Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism.
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Submitted 30 September, 2023;
originally announced October 2023.
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Predicting Properties of Oxide Glasses Using Informed Neural Networks
Authors:
Gregor Maier,
Jan Hamaekers,
Dominik-Sergio Martilotti,
Benedikt Ziebarth
Abstract:
Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and…
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Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young's modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models.
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Submitted 6 February, 2024; v1 submitted 18 August, 2023;
originally announced August 2023.
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Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Authors:
Tommaso Dorigo,
Andrea Giammanco,
Pietro Vischia,
Max Aehle,
Mateusz Bawaj,
Alexey Boldyrev,
Pablo de Castro Manzano,
Denis Derkach,
Julien Donini,
Auralee Edelen,
Federica Fanzago,
Nicolas R. Gauger,
Christian Glaser,
Atılım G. Baydin,
Lukas Heinrich,
Ralf Keidel,
Jan Kieseler,
Claudius Krause,
Maxime Lagrange,
Max Lamparth,
Lukas Layer,
Gernot Maier,
Federico Nardi,
Helge E. S. Pettersen,
Alberto Ramos
, et al. (11 additional authors not shown)
Abstract:
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massi…
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The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.
In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.
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Submitted 22 March, 2022;
originally announced March 2022.
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Very High-Energy Gamma-Ray Follow-Up Program Using Neutrino Triggers from IceCube
Authors:
IceCube Collaboration,
M. G. Aartsen,
K. Abraham,
M. Ackermann,
J. Adams,
J. A. Aguilar,
M. Ahlers,
M. Ahrens,
D. Altmann,
K. Andeen,
T. Anderson,
I. Ansseau,
G. Anton,
M. Archinger,
C. Arguelles,
J. Auffenberg,
S. Axani,
X. Bai,
S. W. Barwick,
V. Baum,
R. Bay,
J. J. Beatty,
J. Becker-Tjus,
K. -H. Becker,
S. BenZvi
, et al. (519 additional authors not shown)
Abstract:
We describe and report the status of a neutrino-triggered program in IceCube that generates real-time alerts for gamma-ray follow-up observations by atmospheric-Cherenkov telescopes (MAGIC and VERITAS). While IceCube is capable of monitoring the whole sky continuously, high-energy gamma-ray telescopes have restricted fields of view and in general are unlikely to be observing a potential neutrino-f…
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We describe and report the status of a neutrino-triggered program in IceCube that generates real-time alerts for gamma-ray follow-up observations by atmospheric-Cherenkov telescopes (MAGIC and VERITAS). While IceCube is capable of monitoring the whole sky continuously, high-energy gamma-ray telescopes have restricted fields of view and in general are unlikely to be observing a potential neutrino-flaring source at the time such neutrinos are recorded. The use of neutrino-triggered alerts thus aims at increasing the availability of simultaneous multi-messenger data during potential neutrino flaring activity, which can increase the discovery potential and constrain the phenomenological interpretation of the high-energy emission of selected source classes (e.g. blazars). The requirements of a fast and stable online analysis of potential neutrino signals and its operation are presented, along with first results of the program operating between 14 March 2012 and 31 December 2015.
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Submitted 12 November, 2016; v1 submitted 6 October, 2016;
originally announced October 2016.
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Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy
Authors:
Xuefei He,
Chuong Vinh Nguyen,
Mrinalini Pratap,
Yujie Zheng,
Yi Wang,
David R. Nisbet,
Richard J Williams,
Melanie Rug,
Alexander G. Maier,
Woei Ming Lee
Abstract:
Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image…
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Automated label-free quantitative imaging of biological samples can greatly benefit high throughput diseases diagnosis. Digital holographic microscopy (DHM) is a powerful quantitative label-free imaging tool that retrieves structural details of cellular samples non-invasively. In off-axis DHM, a proper spatial filtering window in Fourier space is crucial to the quality of reconstructed phase image. Here we describe a region-recognition approach that combines shape recognition with an iterative thresholding to extracts the optimal shape of frequency components. The region recognition technique offers fully automated adaptive filtering that can operate with a variety of samples and imaging conditions. When imaging through optically scattering biological hydrogel matrix, the technique surpasses previous histogram thresholding techniques without requiring any manual intervention. Finally, we automate the extraction of the statistical difference of optical height between malaria parasite infected and uninfected red blood cells. The method described here pave way to greater autonomy in automated DHM imaging for imaging live cell in thick cell cultures.
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Submitted 15 July, 2016;
originally announced July 2016.