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

Software engineers integrate third-party components into their applications. The resulting software supply chain is vulnerable. To reduce the attack surface, we can verify the origin of components (provenance) before adding them. Cryptographic signatures enable this. This article describes traditional signing, its challenges, and changes introduced by next generation signing platforms

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered supply chain security techniques, Comment: Published in the proceedings of the 1st ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses (SCORED) 2022

Sub-GeV dark matter (DM) particles produced via thermal freeze-out evade many of the strong constraints on heavier DM candidates but at the same time face a multitude of new constraints from laboratory experiments, astrophysical observations and cosmological data. In this work we combine all of these constraints in order to perform frequentist and Bayesian global analyses of fermionic and scalar sub-GeV DM coupled to a dark photon with kinetic mixing. For fermionic DM, we find viable parameter regions close to the dark photon resonance, which expand significantly when including a particle-antiparticle asymmetry. For scalar DM, the velocity-dependent annihilation cross section evades the strongest constraints even in the symmetric case. Using Bayesian model comparison, we show that both asymmetric fermionic DM and symmetric scalar DM are preferred over symmetric fermionic DM due to the reduced fine-tuning penalty. Finally, we explore the discovery prospects of near-future experiments both in the full parameter space and for specific benchmark points. We find that the most commonly used benchmark scenarios are already in tension with existing constraints and propose a new benchmark point that can be targeted with future searches., Comment: 59 pages, 5 tables, 24 figures

Cyclotron radiation emission spectroscopy (CRES) is a modern technique for high-precision energy spectroscopy, in which the energy of a charged particle in a magnetic field is measured via the frequency of the emitted cyclotron radiation. The He6-CRES collaboration aims to use CRES to probe beyond the standard model physics at the TeV scale by performing high-resolution and low-background beta-decay spectroscopy of ${}^6\textrm{He}$ and ${}^{19}\textrm{Ne}$. Having demonstrated the first observation of individual, high-energy (0.1 -- 2.5 MeV) positrons and electrons via their cyclotron radiation, the experiment provides a novel window into the radiation of relativistic charged particles in a waveguide via the time-derivative (slope) of the cyclotron radiation frequency, $\mathrm{d}f_\textrm{c}/\mathrm{d}t$. We show that analytic predictions for the total cyclotron radiation power emitted by a charged particle in circular and rectangular waveguides are approximately consistent with the Larmor formula, each scaling with the Lorentz factor of the underlying $e^\pm$ as $\gamma^4$. This hypothesis is corroborated with experimental CRES slope data., Comment: 20 pages, 5 figures

Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Reusing DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in reusing DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, including conceptual (e.g., reusing based on a research paper), adaptation (e.g., re-using by building on an existing implementation), and deployment (e.g., direct re-use on a new device). We outline possible advances that would improve each kind of re-use., Comment: Proceedings of the IEEE John Vincent Atanasoff Symposium on Modern Computing (JVA'23) 2023

The beam response to an external periodic excitation delivers relevant information about the optics, tune distribution and stability of a circulating beam in a storage ring. In this contribution the horizontal beam response to the excitation (transfer function) under conditions typical for slow extraction is presented for a coasting beam. The resulting spectrum exhibits a splitting behaviour. The single particle dynamics is discussed and an interpretation based on simulation results is presented.

Many software applications incorporate open-source third-party packages distributed by public package registries. Guaranteeing authorship along this supply chain is a challenge. Package maintainers can guarantee package authorship through software signing. However, it is unclear how common this practice is, and whether the resulting signatures are created properly. Prior work has provided raw data on registry signing practices, but only measured single platforms, did not consider quality, did not consider time, and did not assess factors that may influence signing. We do not have up-to-date measurements of signing practices nor do we know the quality of existing signatures. Furthermore, we lack a comprehensive understanding of factors that influence signing adoption. This study addresses this gap. We provide measurements across three kinds of package registries: traditional software (Maven, PyPI), container images (DockerHub), and machine learning models (Hugging Face). For each registry, we describe the nature of the signed artifacts as well as the current quantity and quality of signatures. Then, we examine longitudinal trends in signing practices. Finally, we use a quasi-experiment to estimate the effect that various factors had on software signing practices. To summarize our findings: (1) mandating signature adoption improves the quantity of signatures; (2) providing dedicated tooling improves the quality of signing; (3) getting started is the hard part -- once a maintainer begins to sign, they tend to continue doing so; and (4) although many supply chain attacks are mitigable via signing, signing adoption is primarily affected by registry policy rather than by public knowledge of attacks, new engineering standards, etc. These findings highlight the importance of software package registry managers and signing infrastructure., Comment: Accepted at IEEE Security & Privacy 2024 (S&P'24)

The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies., Comment: 5 pages, published in the proceedings of the 2023 ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering in the Ideas-Visions-Reflections track (ESEC/FSE-IVR'23)

The detection and study of magnetic fields surrounding galaxies is important to understand galaxy evolution since magnetic fields are tracers for dynamical processes in the circumgalactic medium (CGM) and can have a significant impact on the evolution of the CGM. The Faraday rotation measure (RM) of the polarized light of background radio sources passing through the magnetized CGM of intervening galaxies can be used as a tracer for the strength and extent of magnetic fields around galaxies. We use rotation measures observed by the MIGHTEE-POL (MeerKAT International GHz Tiered Extragalactic Exploration POLarisation) survey by MeerKAT in the XMM-LSS and COSMOS fields to investigate the RM around foreground star-forming galaxies. We use spectroscopic catalogs of star-forming and blue cloud galaxies to measure the RM of MIGHTEE-POL sources as a function of the impact parameter from the intervening galaxy. We then repeat this procedure using a deeper galaxy catalog with photometric redshifts. For the spectroscopic star-forming sample we find a redshift-corrected |RM| excess of 5.6 +/- 2.3 rad m-2 which corresponds to a 2.5 sigma significance around galaxies with a median redshift of z = 0.46 for impact parameters below 130 kpc only selecting the intervenor with the smallest impact parameter. Making use of a photometric galaxy catalog and taking into account all intervenors with Mg < -13.6 mag, the signal disappears. We find no indication for a correlation between redshift and RM, nor do we find a connection between the total number of intervenors to the total |RM| . We have presented tentative evidence that the CGM of star-forming galaxies is permeated by coherent magnetic fields within the virial radius. We conclude that mostly bright, star-forming galaxies with impact parameters less than 130 kpc significantly contribute to the RM of the background radio source., Comment: 11 pages, 8 figures, accepted for publication in A&A

As we increasingly depend on software systems, the consequences of breaches in the software supply chain become more severe. High-profile cyber attacks like those on SolarWinds and ShadowHammer have resulted in significant financial and data losses, underlining the need for stronger cybersecurity. One way to prevent future breaches is by studying past failures. However, traditional methods of analyzing these failures require manually reading and summarizing reports about them. Automated support could reduce costs and allow analysis of more failures. Natural Language Processing (NLP) techniques such as Large Language Models (LLMs) could be leveraged to assist the analysis of failures. In this study, we assessed the ability of Large Language Models (LLMs) to analyze historical software supply chain breaches. We used LLMs to replicate the manual analysis of 69 software supply chain security failures performed by members of the Cloud Native Computing Foundation (CNCF). We developed prompts for LLMs to categorize these by four dimensions: type of compromise, intent, nature, and impact. GPT 3.5s categorizations had an average accuracy of 68% and Bard had an accuracy of 58% over these dimensions. We report that LLMs effectively characterize software supply chain failures when the source articles are detailed enough for consensus among manual analysts, but cannot yet replace human analysts. Future work can improve LLM performance in this context, and study a broader range of articles and failures., Comment: 22 pages, 9 figures

The current framework for dark matter searches at beam dump and fixed target experiments relies on four benchmark models, the complex scalar, inelastic scalar, pseudo-Dirac and finally, Majorana DM models. While this approach has so far been successful in the interpretation of the available data, it a priori excludes the possibility that DM is made of spin-1 particles -- a restriction which is neither theoretically nor experimentally justified. In this work we extend the current landscape of sub-GeV DM models to a set of models for spin-1 DM, including a family of simplified models (involving one DM candidate and one mediator -- the dark photon) and an ultraviolet complete model based on a non-abelian gauge group where DM is a spin-1 Strongly Interacting Massive Particle. For each of these models, we calculate the DM relic density, the expected number of signal events at beam dump experiments, the rate of energy injection in the early universe thermal bath and in the Intergalactic Medium, as well as the helicity amplitudes for forward processes subject to the unitary bound. We then compare these predictions with experimental results from Planck, CMB surveys, IGM temperature observations, LSND, MiniBooNE, NA64, and BaBar and with available projections from LDMX and Belle II. Through this comparison, we identify the regions in the parameter space of the models considered in this work where DM is simultaneously thermally produced, compatible with present observations, and within reach at Belle II and LDMX. We find that the simplified models are strongly constrained by current beam dump experiments and the unitarity bound, and will thus be conclusively probed in the first stage of LDMX data taking. We also find that the SIMP model explored in this work predicts the observed DM abundance, is compatible with current observations and within reach at LDMX in a wide region of the parameter space.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as "model hubs" support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult - there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset. The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F~%2F. Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357., Comment: 5 pages, 2 figures, Accepted to MSR'23

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries., Comment: Proceedings of the ACM/IEEE 45th International Conference on Software Engineering (ICSE) 2023

The Nab experiment at Oak Ridge National Laboratory, USA, aims to measure the beta-antineutrino angular correlation following neutron $\beta$ decay to an anticipated precision of approximately 0.1\%. The proton momentum is reconstructed through proton time-of-flight measurements, and potential systematic biases in the timing reconstruction due to detector effects must be controlled at the nanosecond level. We present a thorough and detailed semiconductor and quasiparticle transport simulation effort to provide precise pulse shapes, and report on relevant systematic effects and potential measurement schemes.