Search

The optical properties of dielectric materials with subwavelength cylindrical pores are commonly described by effective medium models. We compare the Maxwell Garnett and the Bruggeman effective medium models for porous silicon with simulations and experiments for the case of polarization orthogonal to the pore axis. The Maxwell Garnett model matches the results of the simulations even up to very high porosities. An experimental study of the effective permittivity of macroporous and mesoporous silicon is conducted by analyzing the Fabry-Pérot oscillations in the long-wavelength limit. These experimental results are also consistent with the Maxwell Garnett model. We advocate using this model for media with cylindrical pores in the future.

Discotic ionic liquid crystals (DILCs) consist of self-assembled superdiscs of cations and anions that spontaneously stack in linear columns with high one-dimensional ionic and electronic charge mobility, making them prominent model systems for functional soft matter. Compared to classical nonionic discotic liquid crystals, many liquid crystalline structures with a combination of electronic and ionic conductivity have been reported, which are of interest for separation membranes, artificial ion/proton conducting membranes, and optoelectronics. Unfortunately, a homogeneous alignment of the DILCs on the macroscale is often not achievable, which significantly limits the applicability of DILCs. Infiltration into nanoporous solid scaffolds can, in principle, overcome this drawback. However, due to the experimental challenges to scrutinize liquid crystalline order in extreme spatial confinement, little is known about the structures of DILCs in nanopores. Here, we present temperature-dependent high-resolution optical birefringence measurement and 3D reciprocal space mapping based on synchrotron X-ray scattering to investigate the thermotropic phase behavior of dopamine-based ionic liquid crystals confined in cylindrical channels of 180 nm diameter in macroscopic anodic aluminum oxide membranes. As a function of the membranes’ hydrophilicity and thus the molecular anchoring to the pore walls (edge-on or face-on) and the variation of the hydrophilic-hydrophobic balance between the aromatic cores and the alkyl side chain motifs of the superdiscs by tailored chemical synthesis, we find a particularly rich phase behavior, which is not present in the bulk state. It is governed by a complex interplay of liquid crystalline elastic energies (bending and splay deformations), polar interactions, and pure geometric confinement and includes textural transitions between radial and axial alignment of the columns with respect to the long nanochannel axis. Furthermore, confinement-induced

The fabrication of hybrid materials consisting of nanoporous hosts with conductive polymers is a challenging task, since the extreme spatial confinement often conflicts with the stringent physico-chemical requirements for polymerization of organic constituents. Here, several low-threshold and scalable synthesis routes for such hybrids are presented. First, the electrochemical synthesis of composites based on mesoporous silicon (pSi) and the polymers PANI, PPy and PEDOT is discussed and validated by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Polymer filling degrees of ≥74 % are achieved. Second, the production of PEDOT/pSi hybrids, based on the solid-state polymerization (SSP) of DBEDOT to PEDOT is shown. The resulting amorphous structure of the nanopore-embedded PEDOT is investigated via in-situ synchrotron-based X-ray scattering. In addition, a twofold increase in the electrical conductivity of the hybrid compared to the porous silicon host is shown, making this system particularly promising for thermoelectric applications.

This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS., Comment: 2-page spread version. The online version is available at https://www.usparticlephysics.org/2023-p5-report/ and the graphics at https://usparticlephysics.org/media-assets-library

Excursion sets of Gaussian random fields are used to model the three-dimensional (3D) morphology of differently manufactured porous glasses (PGs), which vary with respect to their mean pore widths measured by mercury intrusion porosimetry. The stochastic 3D model is calibrated by means of volume fractions and two-point coverage probability functions estimated from tomographic image data. Model validation is performed by comparing model realizations and image data in terms of morphological descriptors which are not used for model fitting. For this purpose, we consider mean geodesic tortuosity and constrictivity of the pore space, quantifying the length of the shortest transportation paths and the strength of bottleneck effects, respectively. Additionally, a stereological approach for parameter estimation is presented, i.e., the 3D model is calibrated using merely two-dimensional (2D) cross-sections of the 3D image data. Doing so, on average, a comparable goodness of fit is achieved as well. The variance of the calibrated model parameters is discussed, which is estimated on the basis of randomly chosen, individual 2D cross-sections. Moreover, interpolating between the model parameters calibrated to differently manufactured glasses enables the predictive simulation of virtual but realistic PGs with mean pore widths that have not yet been manufactured. The predictive power is demonstrated by means of cross-validation. Using the presented approach, relationships between parameters of the manufacturing process and descriptors of the resulting morphology of PGs are quantified, which opens possibilities for an efficient optimization of the underlying manufacturing process.

The second "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'24) meeting was held January 8-11, 2024 in Arlington, VA, USA, hosted by Virginia Tech's Center for Neutrino Physics. This document collects contributions from this workshop, providing an overview of activities in the field. MDvDM'24 was the second topical workshop dedicated to the emerging field of mineral detection of neutrinos and dark matter, following a meeting hosted by IFPU in Trieste, Italy in October 2022. Mineral detectors have been proposed for a wide variety of applications, including searching for dark matter, measuring various fluxes of astrophysical neutrinos over gigayear timescales, monitoring nuclear reactors, and nuclear disarmament protocols; both as paleo-detectors using natural minerals that could have recorded the traces of nuclear recoils for timescales as long as a billion years and as detectors recording nuclear recoil events on laboratory timescales using natural or artificial minerals. Contributions to this proceedings discuss the vast physics potential, the progress in experimental studies, and the numerous challenges lying ahead on the path towards mineral detection. These include a better understanding of the formation and annealing of recoil defects in crystals; identifying the best classes of minerals and, for paleo-detectors, understanding their geology; modeling and control of the relevant backgrounds; developing, combining, and scaling up imaging and data analysis techniques; and many others. During the last years, MDvDM has grown rapidly and gained attention. Small-scale experimental efforts focused on establishing various microscopic readout techniques are underway at institutions in North America, Europe and Asia. We are looking ahead to an exciting future full of challenges to overcome, surprises to be encountered, and discoveries lying ahead of us., Comment: Summary and proceedings of the MDvDM'24 conference, Jan 8-11 2024

The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative writing. Consequently, distillation through imitation of teacher responses has emerged as a popular technique to transfer knowledge from LLMs to more accessible, Small Language Models (SLMs). While this works well for simpler tasks, there is a substantial performance gap on tasks requiring intricate language comprehension and creativity, such as humor generation. We hypothesize that this gap may stem from the fact that creative tasks might be hard to learn by imitation alone and explore whether an approach, involving supplementary guidance from the teacher, could yield higher performance. To address this, we study the effect of assigning a dual role to the LLM - as a "teacher" generating data, as well as a "critic" evaluating the student's performance. Our experiments on humor generation reveal that the incorporation of feedback significantly narrows the performance gap between SLMs and their larger counterparts compared to merely relying on imitation. As a result, our research highlights the potential of using feedback as an additional dimension to data when transferring complex language abilities via distillation.

Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, their effectiveness in task-oriented dialogues (TOD), which requires not only response generation but also effective dialogue state tracking (DST) within specific tasks and domains, remains less satisfying. In this work, we propose a novel approach FnCTOD for solving DST with LLMs through function calling. This method improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. Our experimental results demonstrate that our approach achieves exceptional performance with both modestly sized open-source and also proprietary LLMs: with in-context prompting it enables various 7B or 13B parameter models to surpass the previous state-of-the-art (SOTA) achieved by ChatGPT, and improves ChatGPT's performance beating the SOTA by 5.6% average joint goal accuracy (JGA). Individual model results for GPT-3.5 and GPT-4 are boosted by 4.8% and 14%, respectively. We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities. We have made the code publicly available at https://github.com/facebookresearch/FnCTOD, Comment: ACL 2024 Main. Code available at: https://github.com/facebookresearch/FnCTOD

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond., Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. Changes to Table1 and small changes to eliminate minor inconsistencies

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond., Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. Changes to Table1 and small changes to eliminate minor inconsistencies

Minerals are solid state nuclear track detectors - nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred degrees C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the $O(50)$ MeV fission fragments left by spontaneous fission of $^{238}$U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as $O(1)$ keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for $E_R \gtrsim O(1)$ keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over $O(10)$ Myr$-O(1)$ Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the $O(1) - O(100)$ nm damage features in crystals left by $O(0.1) - O(100)$ keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence., Comment: 115 pages, many pictures of tracks. Please see the source file for higher resolution versions of some plots. v2: matches the published version

Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems’ abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the “wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems, support widespread contributions to and acceptance of solutions to these challenges, and provide concrete benchmarks to measure progress and understand tradeoffs among strategies along multiple dimensions? This article introduces and defines food systems informatics (FSI) as a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems. Specific benefits we foresee in further development of FSI platforms include the creation of capacity-enabling verifiable claims of sustainability, food safety, and human health benefits relevant to particular locations and products; the creation of better incentives for the adoption of more sustainable land use practices and for the creation of more diverse agro-ecosystems; the wide-spread use of improved and verifiable metrics of sustainability, resilience, and health benefits; and improved human health through better diets.

Linking structure to mechanical and elastic properties is a major concern for the development of novel electroactive materials. This work reports on the potential-induced changes in thickness and Young modulus of a substrate supported, perchlorate doped polypyrrole thin film (<100 nm) investigated with electrochemical atomic force microscopy (AFM) under in situ conditions. This was accomplished by nanomechanical mapping of potentiodynamically electropolymerized polypyrrole film in electrolyte solution with AFM during redox cycling. The polypyrrole film thickness and Young modulus follow the electrical potential nearly linearly, increasing due to solvent and ion influx as the film is oxidized, and decreasing during reduction. Our measurements also confirm the presence of a potential-independent, passive swelling which is accompanied by softening of the film, likely caused by osmotic effects. Additionally, the heterogeneous distribution of the Young modulus can be directly traced to the typical nodular surface topography of polypyrrole, with the top of the nodular area possessing lower modulus, thus highlighting the complex relationship between topography and elastic properties.

Macroscopic strain experiments have revealed that silicon crystals traversed by parallel, channel-like nanopores functionalized with the artificial muscle polymer polypyrrole (PPy) exhibit large and reversible electrochemomechanical actuation in aqueous electrolytes. On a macroscopic scale these actuation properties are well understood. However, on the microscopical level this system still bears open questions, as to how the electrochemical expansion and contraction of PPy acts on to np-Si pore walls and how the collective motorics of the pore array emerges from the single-nanopore behavior. Here we present synchrotron-based, in operando x-ray diffraction on the evolving electrostrains in epilayers of this material grown on bulk silicon. An analysis of these experiments with micromechanical finite-element simulations, that are based on a full three-dimensional reconstruction of the nanoporous medium by transmission electron microscopy (TEM) tomography, shows that the in-plane mechanical response is dominantly isotropic despite the anisotropic elasticity of the single-crystalline host matrix. However, the structural anisotropy originating from the parallel alignment of the nanopores led to significant differences between the in- and out-of-plane electromechanical response. This response is not describable by a simple two-dimensional arrangement of parallel cylindrical channels. Rather, the simulations highlight that the dendritic shape of the silicon pore walls, including pore connections between the main channels, causes complex, highly inhomogeneous stress-strain fields in the crystalline host. Time-dependent x-ray scattering experiments on the dynamics of the actuator properties hint towards the importance of diffusion limitations, plastic deformation, and creep in the nanoconfined polymer upon (counter)ion adsorption and desorption, the very pore-scale processes causing the macroscopic electroactuation. From a more general perspective, our study demonstrates that

Poröses Silizium ist ein äußerst vielseitiges Funktionsmaterial. In dieser Arbeit werden zwei verschiedene, von der Elektrochemie inspirierte Ansätze entwickelt und untersucht, um eine Aktuator-funktion in poröses Silizium zu integrieren. Der erste Ansatz zielt auf die Nutzung der elektrochemisch-mechanischen Kopplung an der Grenzfläche einer porösen Siliziumelektrode, die in eine wässrige Elektrolytlösung eingetaucht ist. Beim zweiten Ansatz wird das elektroaktive, elektrisch leitfähige Polymer Polypyrrol in die poröse Siliziumporenstruktur integriert, um ein Hybridmaterial zu schaffen. Beide Ansätze erzielen eine robuste und hoch reproduzierbare Aktuation, die dem angelegten Potential linear folgt und deren Arbeitsdichte mit klassischen Piezoaktoren vergleichbar ist., Porous silicon is highly versatile functional material. In this thesis two different electrochemistry-inspired approaches are developed and investigated to integrate an actuator functionality into porous silicon. The first approach aims to utilise electrochemo-mechanical coupling at the interface of a porous silicon electrode immersed in an aqueous electrolyte solution. The second approach comprises the integration of the electroactive, electrically conductive polymer polypyrrole into the porous silicon pore structure to create a hybrid material. Both approaches achieve a robust and highly reproducible actuation that follows the applied potential linear and whose work density compares well to classical piezo actuators.

The forest-based sector plays a significant role in supporting Europe on its pathway towards a more integrated and bio-based circular economy. Beyond the supply of timber, forest ecosystems offer a wide range of products and services beneficial to human wellbeing. Non-wood forest products (NWFPs) play an integral role in provisioning forest ecosystem services and constitute a huge portfolio of species from various taxonomic kingdoms. As diverse as the resources themselves is the list of end-products that may be derived from raw non-wood materials. Multiple value-chains of NWFPs provide benefits to actors across all stages of the supply chain. Forest management has not yet directed full attention towards NWFPs, since timber production remains the main management objective, although multi-purpose management is recognised as a key principle of the sector’s sustainability paradigm. Lack of knowledge of the socio-economic relevance of NWFPs for European societies and diverse property rights frameworks increase the complexity in forest-based decision making additionally. In this study, the future potential of 38 NWFPs for diversifying the forest bioeconomy is investigated by means of multi-criteria analysis, including stakeholder interaction and expert involvement. The results for six case studies in different biogeographical zones in Europe indicate the latent opportunities NWFPs provide to forest owners who are willing to focus their management on the joint production of wood and non-wood resources as well as their value networks. This study intends to unravel perspectives for forest owners in particular, as they often represent principal decision makers in forest ecosystem management, act as main suppliers of NWFP raw materials, and thus can be understood as key stakeholders in a forest bioeconomy. Even though regional perspectives differ, due to varying socio-economic and ecological environments, there is huge potential to strengthen the economic viability of rural ar

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for unsupervised and semi-supervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.

Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond., Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. This version fixes an important omission from Table 2, adds two references that were not available at the time of the original version, fixes a minor few typos, and adds a small amount of material to section 1.1.3

Minerals are solid state nuclear track detectors - nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred degrees C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the $O(50)$ MeV fission fragments left by spontaneous fission of $^{238}$U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as $O(1)$ keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for $E_R \gtrsim O(1)$ keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over $O(10)$ Myr$-O(1)$ Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the $O(1) - O(100)$ nm damage features in crystals left by $O(0.1) - O(100)$ keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence., Comment: 115 pages, many pictures of tracks. Please see the source file for higher resolution versions of some plots. v2: matches the published version

Many biological materials exhibit a multiscale porosity with small, mostly nanoscale pores as well as large, macroscopic capillaries to simultaneously achieve optimized mass transport capabilities and lightweight structures with large inner surfaces. Realizing such a hierarchical porosity in artificial materials necessitates often sophisticated and expensive top-down processing that limits scalability. Here, an approach that combines self-organized porosity based on metal-assisted chemical etching (MACE) with photolithographically induced macroporosity for the synthesis of single-crystalline silicon with a bimodal pore-size distribution is presented, i.e., hexagonally arranged cylindrical macropores with 1 µm diameter separated by walls that are traversed by pores 60 nm across. The MACE process is mainly guided by a metal-catalyzed reduction–oxidation reaction, where silver nanoparticles (AgNPs) serve as the catalyst. In this process, the AgNPs act as self-propelled particles that are constantly removing silicon along their trajectories. High-resolution X-ray imaging and electron tomography reveal a resulting large open porosity and inner surface for potential applications in high-performance energy storage, harvesting and conversion or for on-chip sensorics and actuorics. Finally, the hierarchically porous silicon membranes can be transformed structure-conserving by thermal oxidation into hierarchically porous amorphous silica, a material that could be of particular interest for opto-fluidic and (bio-)photonic applications due to its multiscale artificial vascularization.

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond., Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. This version fixes an important omission from Table 2, adds two references that were not available at the time of the original version, fixes a minor few typos, and adds a small amount of material to section 1.1.3

Minerals are solid state nuclear track detectors - nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred degrees C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the $O(50)$ MeV fission fragments left by spontaneous fission of $^{238}$U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as $O(1)$ keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for $E_R \gtrsim O(1)$ keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over $O(10)$ Myr$-O(1)$ Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the $O(1) - O(100)$ nm damage features in crystals left by $O(0.1) - O(100)$ keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence., Comment: 115 pages, many pictures of tracks. Please see the source file for higher resolution versions of some plots. v2: matches the published version

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics.

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks. FLEE-GNN addresses the limitations of current methodologies, such as entropy-based methods, in terms of generalizability, scalability, and data privacy. It combines the robustness and adaptability of graph neural networks with the privacy-conscious and decentralized aspects of federated learning on food supply network resilience analysis across geographical regions. This paper also discusses FLEE-GNN's innovative data generation techniques, experimental designs, and future directions for improvement. The results show the advancements of this approach to quantifying the resilience of multicommodity food flow networks, contributing to efforts towards ensuring global food security using AI methods. The developed FLEE-GNN has the potential to be applied in other spatial networks with spatially heterogeneous sub-network distributions., Comment: 10 pages, 5 figures

Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population and integration with food system ontologies. We have created NLP models that can automatically classify organizations with respect to categories associated with environmental issues as well as Standard Industrial Classification (SIC) codes, which are used by the U.S. government to characterize business activities. As input, the NLP models are provided with text snippets retrieved by the Google search engine for each organization, which serves as a textual description of the organization that is used for learning. Our experimental results show that NLP models can achieve reasonably good performance for these two classification tasks, and they rely on a general framework that could be applied to many other classification problems as well. We believe that NLP models represent a promising approach for automatically harvesting information to populate knowledge graphs and aligning the information with existing ontologies through shared categories and concepts., Comment: Presented at IFOW 2023 Integrated Food Ontology Workshop at the Formal Ontology in Information Systems Conference (FOIS) 2023 in Sherbrooke, Quebec, Canada July 17-20th, 2023

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics., Comment: Editors: Nikita Blinov, Mauricio Bustamante, Kevin J. Kelly, Yue Zhang. 29 pages, 16 figures, plus references. Contribution to Snowmass 2021

This report documents a sustainability assessment for banana production in Ecuador. The overarching goal of this assessment was to aid the Ecuadorian banana industry in improving its sustainability footprint for the entire industry—at a national level.  The starting point for improving system‐wide sustainability was to conduct a baseline assessment of the current sustainability situation to identify and measure a suite of indicators for use as a benchmark in both guiding strategies and measuring progress. This assessment was designed to review the state of the banana sector and correlated activities within Ecuador and not function as a comparative assessment against other countries or actors – which would require a similar assessment for each actor.

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics., Comment: Editors: Nikita Blinov, Mauricio Bustamante, Kevin J. Kelly, Yue Zhang. 29 pages, 16 figures, plus references. Contribution to Snowmass 2021

Neutrino factories, neutrino beams produced in the decay of a muon or antimuon beam inside a storage ring, yield cleaner, richer, and more flexible neutrino beams relative to super-beams. We explore the physics case for this type of beam both for standard oscillation as well as new physics searches and present some machine options. We argue that there is a rich program beyond what the current neutrino program can cover and a string synergy with the muon collider program., Comment: 32 pages, 4 figures. White paper contribution to Snowmass 2021

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics., Comment: Editors: Nikita Blinov, Mauricio Bustamante, Kevin J. Kelly, Yue Zhang. 29 pages, 16 figures, plus references. Contribution to Snowmass 2021

We study the optimization of a green-field, two-baseline reactor experiment with respect to the sensitivity for electron antineutrino disappearance in search of a light sterile neutrino. We consider both commercial and research reactors and identify as key factors the distance of closest approach and detector energy resolution. We find that a total of 5 tons of detectors deployed at a commercial reactor with a closest approach of 25 m can probe the mixing angle sin(2) 2 theta down to similar to 5 x 10(-3) around Delta m(2) similar to 1 eV(2). The same detector mass deployed at a research reactor can be sensitive up to Delta m(2) similar to 20-30 eV(2) assuming a closest approach of 3 m and excellent energy resolution, such as that projected for the Taishan Antineutrino Observatory. We also find that lithium doping of the reactor could be effective in increasing the sensitivity for higher Delta m(2) values.

Increasing amounts of spent nuclear fuel (SNF) are stored in dry storage casks for prolonged periods of time. To date no effective technology exists to reverify cask contents should this become necessary. We explore the applicability of coherent-elastic neutrino-nucleus scattering (CEvNS) to monitor the content of SNF from dry storage casks. SNF produces neutrinos chiefly from Sr-90 decays. We compare these results with what can be achieved via inverse beta decay (IBD). We demonstrate that at low nuclear recoil energies CEvNS events rates exceed the IBD event rates by 2-3 orders of magnitude for a given detector mass. We find that a 10 kg argon or germanium detector 3 meters from a fuel cask can detect over 100 events per year if a nuclear recoil threshold under 100 eV can be achieved.

We evaluate the statistical significance of the 3+1 sterile-neutrino hypothesis using nu(e) and (nu) over bar (e) disappearance data from reactor, solar and gallium radioactive source experiments. Concerning the latter, we investigate the implications of the recent BEST results. For reactor data we focus on relative measurements independent of flux predictions. For the problem at hand, the usual chi(2)-approximation to hypothesis testing based on Wilks' theorem has been shown in the literature to be inaccurate. We therefore present results based on Monte Carlo simulations, and find that this typically reduces the significance by roughly 1 sigma with respect to the naive expectation. We find no significant indication in favor of sterile-neutrino oscillations from reactor data. On the other hand, gallium data (dominated by the BEST result) show more than 5 sigma of evidence supporting the sterile-neutrino hypothesis, favoring oscillation parameters in agreement with constraints from reactor data. This explanation is, however, in significant tension (similar to 3 sigma) with solar neutrino experiments. In order to assess the robustness of the signal for gallium experiments we present a discussion of the impact of cross-section uncertainties on the results.

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics., Comment: Editors: Nikita Blinov, Mauricio Bustamante, Kevin J. Kelly, Yue Zhang. 29 pages, 16 figures, plus references. Contribution to Snowmass 2021

We argue that neutrino oscillations at JUNO offer a unique opportunity to study Sorkin's triple path interference, which is predicted to be zero in canonical quantum mechanics by virtue of the Born rule. In particular, we compute the expected bounds on triple path interference at JUNO and demonstrate that they are comparable to those already available from electromagnetic probes. Furthermore, the neutrino probe of the Born rule is much more direct due to an intrinsic independence from any boundary conditions, whereas such dependence on boundary conditions is always present in the case of electromagnetic probes. Thus, neutrino oscillations present an ideal probe of this aspect of the foundations of quantum mechanics.

The recent agreement to transfer nuclear submarine reactors and technology from two nuclear-weapon states to a non-nuclear-weapon state (AUKUS deal) highlights an unsolved problem in international safeguards: how to safeguard naval reactor fuel while it is on board an operational nuclear submarine. Proposals to extend existing safeguards technologies and practices are complicated by the need for civilian international inspectors to gain access to the interior of the submarine and the reactor compartment, which raises national security concerns. In this Letter we show that implementing safeguards on submarine propulsion reactors using a low-energy antineutrino reactor-off method, between submarine patrols, can by-pass the need for onboard access all together. We find that, using inverse beta decay, detectors can achieve a timely and high level of assurance that a submarine???s nuclear core has not been diverted (detector mass of around 100 kg) nor its enrichment level changed (detector mass of around 10 tons).

This report documents a sustainability assessment for banana production in Ecuador. The overarching goal of this assessment was to aid the Ecuadorian banana industry in improving its sustainability footprint for the entire industry—at a national level.  The starting point for improving system‐wide sustainability was to conduct a baseline assessment of the current sustainability situation to identify and measure a suite of indicators for use as a benchmark in both guiding strategies and measuring progress. This assessment was designed to review the state of the banana sector and correlated activities within Ecuador and not function as a comparative assessment against other countries or actors – which would require a similar assessment for each actor.

Neutrinos are the Standard Model (SM) particles which we understand the least, often due to how weakly they interact with the other SM particles. Beyond this, very little is known about interactions among the neutrinos, i.e., their self-interactions. The SM predicts neutrino self-interactions at a level beyond any current experimental capabilities, leaving open the possibility for beyond-the-SM interactions across many energy scales. In this white paper, we review the current knowledge of neutrino self-interactions from a vast array of probes, from cosmology, to astrophysics, to the laboratory. We also discuss theoretical motivations for such self-interactions, including neutrino masses and possible connections to dark matter. Looking forward, we discuss the capabilities of searches in the next generation and beyond, highlighting the possibility of future discovery of this beyond-the-SM physics., Comment: Editors: Nikita Blinov, Mauricio Bustamante, Kevin J. Kelly, Yue Zhang. 29 pages, 16 figures, plus references. Contribution to Snowmass 2021

Neutrino factories, neutrino beams produced in the decay of a muon or antimuon beam inside a storage ring, yield cleaner, richer, and more flexible neutrino beams relative to super-beams. We explore the physics case for this type of beam both for standard oscillation as well as new physics searches and present some machine options. We argue that there is a rich program beyond what the current neutrino program can cover and a string synergy with the muon collider program., Comment: 32 pages, 4 figures. White paper contribution to Snowmass 2021

The PALEOCCENE concept offers the potential for room-temperature, passive and robust detectors in the gram to kilogram range for the detection of low-energy nuclear recoil events. Nuclear recoil events can be caused by neutron scattering, coherent elastic neutrino nucleus scattering (CEvNS) or dark matter scattering and therefore, PALEOCCENE could find applications in all three areas. In this white paper we present current and planned R&D efforts to study the feasibility of this technique., Comment: 22 pages, 6 figures, comments welcome. White paper contribution to Snowmass 2021

This report summarizes the current status of neutrino physics and the broad and exciting future prospects identified for the Neutrino Frontier as part of the 2021 Snowmass Process., Comment: 49 pages, contribution to: 2021 Snowmass Summer Study. Minor updates

We study the online detection by gallium capture of mono-energetic neutrinos produced by a $^{51}$Cr radioactive source in a scintillation experiment. We find that cerium-doped gadolinium aluminum gallium garnet (GAGG) is a suitable scintillator which contains about 21% of gallium per weight and has a high mass density and light yield. Combined with a highly efficient light detection system this allows tagging of the subsequent germanium decay and thus a clean distinction of gallium capture and elastic neutrino electron scattering events. With 1.5 tons of scintillator and 10 source runs of 3.4MCi, each, we obtain about 760 gallium capture events with a purity of 85% and 680,000 neutrino electron scattering events, where the latter provide a precise normalization independent of any nuclear physics. This configuration would allow to test the gallium anomaly at more than $5\sigma$ in an independent way., Comment: 8 pages, 3 figures, version accepted in PRD

The PALEOCCENE concept offers the potential for room-temperature, passive and robust detectors in the gram to kilogram range for the detection of low-energy nuclear recoil events. Nuclear recoil events can be caused by neutron scattering, coherent elastic neutrino nucleus scattering (CEvNS) or dark matter scattering and therefore, PALEOCCENE could find applications in all three areas. In this white paper we present current and planned R&D efforts to study the feasibility of this technique., Comment: 22 pages, 6 figures, comments welcome. White paper contribution to Snowmass 2021

We evaluate the statistical significance of the 3+1 sterile-neutrino hypothesis using nu(e) and (nu) over bar (e) disappearance data from reactor, solar and gallium radioactive source experiments. Concerning the latter, we investigate the implications of the recent BEST results. For reactor data we focus on relative measurements independent of flux predictions. For the problem at hand, the usual chi(2)-approximation to hypothesis testing based on Wilks' theorem has been shown in the literature to be inaccurate. We therefore present results based on Monte Carlo simulations, and find that this typically reduces the significance by roughly 1 sigma with respect to the naive expectation. We find no significant indication in favor of sterile-neutrino oscillations from reactor data. On the other hand, gallium data (dominated by the BEST result) show more than 5 sigma of evidence supporting the sterile-neutrino hypothesis, favoring oscillation parameters in agreement with constraints from reactor data. This explanation is, however, in significant tension (similar to 3 sigma) with solar neutrino experiments. In order to assess the robustness of the signal for gallium experiments we present a discussion of the impact of cross-section uncertainties on the results.

We study the optimization of a green-field, two-baseline reactor experiment with respect to the sensitivity for electron antineutrino disappearance in search of a light sterile neutrino. We consider both commercial and research reactors and identify as key factors the distance of closest approach and detector energy resolution. We find that a total of 5 tons of detectors deployed at a commercial reactor with a closest approach of 25 m can probe the mixing angle sin(2) 2 theta down to similar to 5 x 10(-3) around Delta m(2) similar to 1 eV(2). The same detector mass deployed at a research reactor can be sensitive up to Delta m(2) similar to 20-30 eV(2) assuming a closest approach of 3 m and excellent energy resolution, such as that projected for the Taishan Antineutrino Observatory. We also find that lithium doping of the reactor could be effective in increasing the sensitivity for higher Delta m(2) values.

Increasing amounts of spent nuclear fuel (SNF) are stored in dry storage casks for prolonged periods of time. To date no effective technology exists to reverify cask contents should this become necessary. We explore the applicability of coherent-elastic neutrino-nucleus scattering (CEvNS) to monitor the content of SNF from dry storage casks. SNF produces neutrinos chiefly from Sr-90 decays. We compare these results with what can be achieved via inverse beta decay (IBD). We demonstrate that at low nuclear recoil energies CEvNS events rates exceed the IBD event rates by 2-3 orders of magnitude for a given detector mass. We find that a 10 kg argon or germanium detector 3 meters from a fuel cask can detect over 100 events per year if a nuclear recoil threshold under 100 eV can be achieved.

We argue that neutrino oscillations at JUNO offer a unique opportunity to study Sorkin's triple path interference, which is predicted to be zero in canonical quantum mechanics by virtue of the Born rule. In particular, we compute the expected bounds on triple path interference at JUNO and demonstrate that they are comparable to those already available from electromagnetic probes. Furthermore, the neutrino probe of the Born rule is much more direct due to an intrinsic independence from any boundary conditions, whereas such dependence on boundary conditions is always present in the case of electromagnetic probes. Thus, neutrino oscillations present an ideal probe of this aspect of the foundations of quantum mechanics.

The recent agreement to transfer nuclear submarine reactors and technology from two nuclear-weapon states to a non-nuclear-weapon state (AUKUS deal) highlights an unsolved problem in international safeguards: how to safeguard naval reactor fuel while it is on board an operational nuclear submarine. Proposals to extend existing safeguards technologies and practices are complicated by the need for civilian international inspectors to gain access to the interior of the submarine and the reactor compartment, which raises national security concerns. In this Letter we show that implementing safeguards on submarine propulsion reactors using a low-energy antineutrino reactor-off method, between submarine patrols, can by-pass the need for onboard access all together. We find that, using inverse beta decay, detectors can achieve a timely and high level of assurance that a submarine???s nuclear core has not been diverted (detector mass of around 100 kg) nor its enrichment level changed (detector mass of around 10 tons).

Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most recent discourse parsers (and consequently downstream tasks) still rely on small-scale human-annotated discourse treebanks, trying to infer general-purpose discourse structures from very limited data in a few narrow domains. To overcome this dire situation and allow discourse parsers to be trained on larger, more diverse and domain-independent datasets, we propose a framework to generate "silver-standard" discourse trees from distant supervision on the auxiliary task of sentiment analysis., Comment: Extended Abstract. Non Archival. 2 pages