Showing 1–14 of 14 results for author: Nardi, F

On the Codesign of Scientific Experiments and Industrial Systems

Authors: Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas, Tosin Adewumi, Lama Alkhaled, Lorenzo Arsini, Muhammad Awais, Maxim Borisyak, András Bóta, Florian Bury, Sascha Caron, James Carzon, Long Chen, Prakash C. Chhipa, Paul Christakopoulos, Jacopo De Piccoli, Andrea De Vita, Zlatan Dimitrov, Michele Doro, Luigi Favaro, Francesco Ferranti, Santiago Folgueras, Rihab Gargouri, Nicolas R. Gauger, Andrea Giammanco , et al. (62 additional authors not shown)

Abstract: The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this c… ▽ More The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems. △ Less

Submitted 27 March, 2026; originally announced March 2026.

Comments: 91 pages, 58 figures

Differentiable Surrogate for Detector Simulation and Design with Diffusion Models

Authors: Xuan Tung Nguyen, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Pietro Vischia, Federico Nardi, Muhammad Awais, Hamza Hanif, Shahzaib Abbas, Rukshak Kapoor

Abstract: In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps conditioned on key detector and beam parameters. The model employs efficient inference using Denoising Diffusion Implicit Model sampling and is pre-trained on GEANT4 simulations before being adapted to a new calorimeter geomet… ▽ More In this work, we present a conditional denoising-diffusion surrogate for electromagnetic calorimeter showers that is trained to generate high-fidelity energy-deposition maps conditioned on key detector and beam parameters. The model employs efficient inference using Denoising Diffusion Implicit Model sampling and is pre-trained on GEANT4 simulations before being adapted to a new calorimeter geometry through Low-Rank Adaptation, requiring only a small post-training dataset. We evaluate physically meaningful observables, including total deposited energy, energy-weighted radius, and shower dispersion, obtaining relative root mean square error values below 2% for representative high-energy cases. This is in line with state-of-the-art calorimeter surrogates which report comparable fidelity on high-level observables. Furthermore, we compare gradients of a reconstruction-based utility function with respect to design parameters between the surrogate and finite-difference references. The diffusion surrogate reproduces the qualitative structure and directional trends of the true utility landscape, providing usable sensitivities for gradient-based optimization. These results show that diffusion-based surrogates can accelerate simulation-driven detector design while enabling differentiable, gradient-informed analysis. △ Less

Submitted 27 March, 2026; v1 submitted 9 January, 2026; originally announced January 2026.

Comments: 43 pages, 21 figures

Journal ref: Mach. Learn.: Sci. Technol. 7 (2026) 025061

MuCol Milestone Report No. 7: Consolidated Parameters

Authors: Rebecca Taylor, Antoine Chancé, Dario Augusto Giove, Natalia Milas, Roberto Losito, Donatella Lucchesi, Chris Rogers, Lucio Rossi, Daniel Schulte, Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida, Chiara Aime, Avni Aksoy, Gian Luigi Alberghi, Simon Albright, Siobhan Alden, Luca Alfonso, Muhammad Ali, Anna Rita Altamura, Nicola Amapane, Kathleen Amm, David Amorim, Paolo Andreetto , et al. (437 additional authors not shown)

Abstract: This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass design. Additional details of the designs con… ▽ More This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Preliminary Parameters Report. Attention has been given to a high-level consistent set of baseline parameters throughout all systems of the complex, following a 10 TeV center-of-mass design. Additional details of the designs contributing to this baseline design are featured in the appendix. Likewise, explorative variations from this baseline set can be found in the appendix. The data is collected from a collaborative spreadsheet and transferred to overleaf. △ Less

Submitted 31 October, 2025; originally announced October 2025.

The Muon Collider

Authors: Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida, Chiara Aime', Avni Aksoy, Gian Luigi Alberghi, Siobhan Alden, Luca Alfonso, Muhammad Ali, Anna Rita Altamura, Nicola Amapane, Kathleen Amm, David Amorim, Paolo Andreetto, Fabio Anulli, Ludovica Aperio Bella, Rob Appleby, Artur Apresyan, Pouya Asadi, Mohammed Attia Mahmoud, Bernhard Auchmann, John Back, Anthony Badea, Kyu Jung Bae , et al. (433 additional authors not shown)

Abstract: Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an… ▽ More Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the Standard Model and unparalleled reach beyond it. It will be a paradigm-shifting tool for particle physics representing the first collider to combine the high-energy reach of a proton collider and the high precision of an electron-positron collider, yielding a physics potential significantly greater than the sum of its individual parts. A high-energy muon collider is the natural next step in the exploration of fundamental physics after the HL-LHC and a natural complement to a future low-energy Higgs factory. Such a facility would significantly broaden the scope of particle colliders, engaging the many frontiers of the high energy community. The last European Strategy for Particle Physics Update and later the Particle Physics Project Prioritisation Panel in the US requested a study of the muon collider, which is being carried on by the International Muon Collider Collaboration. In this comprehensive document we present the physics case, the state of the work on accelerator design and technology, and propose an R\&D project that can make the muon collider a reality. △ Less

Submitted 30 April, 2025; originally announced April 2025.

Comments: 406 pages, supplementary report to the European Strategy for Particle Physics - 2026 update

Hadron Identification Prospects With Granular Calorimeters

Authors: Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph willmore

Abstract: In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and positive ka… ▽ More In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and positive kaons at 100 GeV. The analysis focuses on the impact of calorimeter granularity by progressively merging detector cells and extracting features like energy deposition patterns andtiming information. Two machine learning approaches, XGBoost and fully connected deep neural networks, were employed to assess the classification performance across particle pairs. The results indicate that fine segmentation improves particle discrimination, with higher granularity yielding more detailed characterization of energy showers. Additionally, the results highlight the importance of shower radius, energy fractions, and timing variables in distinguishing particle types. The XGBoost model demonstrated computational efficiency and interpretability advantages over deep learning for tabular data structures, while achieving similar classification performance. This motivates further work required to combine high- and low-level feature analysis, e.g., using convolutional and graph-based neural networks, and extending the study to a broader range of particle energies and types. △ Less

Submitted 15 February, 2025; originally announced February 2025.

Journal ref: Particles 2025, 8, 58

End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters

Authors: Kylian Schmidt, Nikhil Kota, Jan Kieseler, Andrea De Vita, Markus Klute, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Joseph Willmore, Pietro Vischia

Abstract: Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the $\textit{end-to-end}$ AI Detector Optimization framework (AIDO) that leverages a diffusion model as a surrogate for the full simul… ▽ More Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the $\textit{end-to-end}$ AI Detector Optimization framework (AIDO) that leverages a diffusion model as a surrogate for the full simulation and reconstruction chain, enabling gradient-based design exploration in both continuous and discrete parameter spaces. Although this framework is applicable to a broad range of detectors, we illustrate its power using the specific example of a sampling calorimeter, focusing on charged pions and photons as representative incident particles. Our results demonstrate that the diffusion model effectively captures critical performance metrics for calorimeter design, guiding the automatic search for layer arrangement and material composition that aligns with known calorimeter principles. The success of this proof-of-concept study provides a foundation for future applications of end-to-end optimization to more complex detector systems, offering a promising path toward systematically exploring the vast design space in next-generation experiments. △ Less

Submitted 3 March, 2025; v1 submitted 4 February, 2025; originally announced February 2025.

Comments: 15 pages, 9 figures, submitted to MDPI particles

MuCol Milestone Report No. 5: Preliminary Parameters

Authors: Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida, Chiara Aimé, Avni Aksoy, Gian Luigi Alberghi, Siobhan Alden, Luca Alfonso, Nicola Amapane, David Amorim, Paolo Andreetto, Fabio Anulli, Rob Appleby, Artur Apresyan, Pouya Asadi, Mohammed Attia Mahmoud, Bernhard Auchmann, John Back, Anthony Badea, Kyu Jung Bae, E. J. Bahng, Lorenzo Balconi, Fabrice Balli, Laura Bandiera , et al. (369 additional authors not shown)

Abstract: This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power… ▽ More This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf. △ Less

Submitted 5 November, 2024; originally announced November 2024.

Interim report for the International Muon Collider Collaboration (IMCC)

Authors: C. Accettura, S. Adrian, R. Agarwal, C. Ahdida, C. Aimé, A. Aksoy, G. L. Alberghi, S. Alden, N. Amapane, D. Amorim, P. Andreetto, F. Anulli, R. Appleby, A. Apresyan, P. Asadi, M. Attia Mahmoud, B. Auchmann, J. Back, A. Badea, K. J. Bae, E. J. Bahng, L. Balconi, F. Balli, L. Bandiera, C. Barbagallo , et al. (362 additional authors not shown)

Abstract: The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accele… ▽ More The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider. △ Less

Submitted 28 January, 2025; v1 submitted 17 July, 2024; originally announced July 2024.

Comments: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

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… ▽ More 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. △ Less

Submitted 30 September, 2023; originally announced October 2023.

Comments: 70 pages, 17 figures. To be submitted to journal

TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography

Authors: Giles C. Strong, Maxime Lagrange, Aitor Orio, Anna Bordignon, Florian Bury, Tommaso Dorigo, Andrea Giammanco, Mariam Heikal, Jan Kieseler, Max Lamparth, Pablo Martínez Ruíz del Árbol, Federico Nardi, Pietro Vischia, Haitham Zaraket

Abstract: We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In d… ▽ More We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In doing so, we provide the first demonstration of end-to-end-differentiable and inference-aware optimisation of particle physics instruments. We study the performance of the software on a relevant benchmark scenario and discuss its potential applications. Our code is available on Github. △ Less

Submitted 7 November, 2024; v1 submitted 25 September, 2023; originally announced September 2023.

Comments: V3: Updated to published version; 29 pages content + appendix + refs

Journal ref: Mach. Learn.: Sci. Technol. 5 035002 (2024)

Towards a Muon Collider

Authors: Carlotta Accettura, Dean Adams, Rohit Agarwal, Claudia Ahdida, Chiara Aimè, Nicola Amapane, David Amorim, Paolo Andreetto, Fabio Anulli, Robert Appleby, Artur Apresyan, Aram Apyan, Sergey Arsenyev, Pouya Asadi, Mohammed Attia Mahmoud, Aleksandr Azatov, John Back, Lorenzo Balconi, Laura Bandiera, Roger Barlow, Nazar Bartosik, Emanuela Barzi, Fabian Batsch, Matteo Bauce, J. Scott Berg , et al. (272 additional authors not shown)

Abstract: A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders desi… ▽ More A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work. △ Less

Submitted 27 November, 2023; v1 submitted 15 March, 2023; originally announced March 2023.

Comments: 118 pages, 103 figures

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… ▽ More 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. △ Less

Submitted 22 March, 2022; originally announced March 2022.

Comments: 109 pages, 32 figures. To be submitted to Reviews in Physics

A Muon Collider Facility for Physics Discovery

Authors: D. Stratakis, N. Mokhov, M. Palmer, N. Pastrone, T. Raubenheimer, C. Rogers, D. Schulte, V. Shiltsev, J. Tang, A. Yamamoto, C. Aimè, M. A. Mahmoud, N. Bartosik, E. Barzi, A. Bersani, A. Bertolin, M. Bonesini, B. Caiffi, M. Casarsa, M. G. Catanesi, A. Cerri, C. Curatolo, M. Dam, H. Damerau, E. De Matteis , et al. (44 additional authors not shown)

Abstract: Muon colliders provide a unique route to deliver high energy collisions that enable discovery searches and precision measurements to extend our understanding of the fundamental laws of physics. The muon collider design aims to deliver physics reach at the highest energies with costs, power consumption and on a time scale that may prove favorable relative to other proposed facilities. In this conte… ▽ More Muon colliders provide a unique route to deliver high energy collisions that enable discovery searches and precision measurements to extend our understanding of the fundamental laws of physics. The muon collider design aims to deliver physics reach at the highest energies with costs, power consumption and on a time scale that may prove favorable relative to other proposed facilities. In this context, a new international collaboration has formed to further extend the design concepts and performance studies of such a machine. This effort is focused on delivering the elements of a $\sim$10 TeV center of mass (CM) energy design to explore the physics energy frontier. The path to such a machine may pass through lower energy options. Currently a 3 TeV CM stage is considered. Other energy stages could also be explored, e.g. an s-channel Higgs Factory operating at 125 GeV CM. We describe the status of the R&D and design effort towards such a machine and lay out a plan to bring these concepts to maturity as a tool for the high energy physics community. △ Less

Submitted 15 March, 2022; originally announced March 2022.

Comments: 23 pages, 3 figures. arXiv admin note: text overlap with arXiv:2201.07895

Promising Technologies and R&D Directions for the Future Muon Collider Detectors

Authors: Sergo Jindariani, Federico Meloni, Nadia Pastrone, Chiara Aimè, Nazar Bartosik, Emanuela Barzi, Alessandro Bertolin, Alessandro Braghieri, Laura Buonincontri, Simone Calzaferri, Massimo Casarsa, Maria Gabriella Catanesi, Alessandro Cerri, Grigorios Chachamis, Anna Colaleo, Camilla Curatolo, Giacomo Da Molin, Jean-Pierre Delahaye, Biagio Di Micco, Tommaso Dorigo, Filippo Errico, Davide Fiorina, Alessio Gianelle, Carlo Giraldin, John Hauptman , et al. (36 additional authors not shown)

Abstract: Among the post-LHC generation of particle accelerators, the muon collider represents a unique machine with capability to provide very high energy leptonic collisions and to open the path to a vast and mostly unexplored physics programme. However, on the experimental side, such great physics potential is accompanied by unprecedented technological challenges, due to the fact that muons are unstable… ▽ More Among the post-LHC generation of particle accelerators, the muon collider represents a unique machine with capability to provide very high energy leptonic collisions and to open the path to a vast and mostly unexplored physics programme. However, on the experimental side, such great physics potential is accompanied by unprecedented technological challenges, due to the fact that muons are unstable particles. Their decay products interact with the machine elements and produce an intense flux of background particles that eventually reach the detector and may degrade its performance. In this paper, we present technologies that have a potential to match the challenging specifications of a muon collider detector and outline a path forward for the future R&D efforts. △ Less

Submitted 14 March, 2022; originally announced March 2022.

Comments: Contribution to Snowmass 2021, 27 pages, 15 figures