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We describe design studies for a pulsed quasi-monoenergetic 2-keV neutron source for calibration of sub-keV nuclear recoils. Such a calibration is required for detectors sensitive to sub-GeV dark matter and also the coherent elastic scattering of reactor neutrinos. In our design, neutrons from a commercial deuterium-tritium generator are moderated to the keV scale and then filtered to the monoenergetic spectrum using a feature in the neutron cross section of scandium. In this approach, unmoderated high-energy neutrons form a challenging background, along with gammas from neutron capture in the moderator materials. We describe the optimization of the moderator+filter and shielding geometry, and find a geometry that in simulation achieves both the target neutron flux at 2 keV and subdominant rates of background interactions. Lastly, we describe a future path to lower-energy (few eV scale) calibrations using time-of-flight and sub-keV neutrons., Comment: 10 pages, 7 figures

The application of large-language models (LLMs) to digital hardware code generation is an emerging field. Most LLMs are primarily trained on natural language and software code. Hardware code, such as Verilog, represents only a small portion of the training data and few hardware benchmarks exist. To address this gap, the open-source VerilogEval benchmark was released in 2023, providing a consistent evaluation framework for LLMs on code completion tasks. It was tested on state-of-the-art models at the time including GPT-4. However, VerilogEval and other Verilog generation benchmarks lack failure analysis and, in present form, are not conducive to exploring prompting techniques. Also, since VerilogEval's release, both commercial and open-source models have seen continued development. In this work, we evaluate new commercial and open-source models of varying sizes against an improved VerilogEval benchmark suite. We enhance VerilogEval's infrastructure and dataset by automatically classifying failures, introduce new prompts for supporting in-context learning (ICL) examples, and extend the supported tasks to specification-to-RTL translation. We find a measurable improvement in commercial state-of-the-art models, with GPT-4 Turbo achieving a 59% pass rate on spec-to-RTL tasks. We also study the performance of open-source and domain-specific models that have emerged, and demonstrate that models can benefit substantially from ICL. We find that recently-released Llama 3.1 405B achieves a pass rate of 58%, effectively matching that of GPT-4 Turbo, and that the much smaller domain-specific RTL-Coder 6.7B models achieve an impressive 37% pass rate. However, prompt engineering is key to achieving good pass rates, and varies widely with model and task. A benchmark infrastructure that allows for prompt engineering and failure analysis is key to continued model development and deployment., Comment: This paper revisits and improves the benchmark first presented in arXiv:2309.07544. Seven pages, three figures

Quantum information processing at scale will require sufficiently stable and long-lived qubits, likely enabled by error-correction codes. Several recent superconducting-qubit experiments, however, reported observing intermittent spatiotemporally correlated errors that would be problematic for conventional codes, with ionizing radiation being a likely cause. Here, we directly measured the cosmic-ray contribution to spatiotemporally correlated qubit errors. We accomplished this by synchronously monitoring cosmic-ray detectors and qubit energy-relaxation dynamics of 10 transmon qubits distributed across a 5x5x0.35 mm$^3$ silicon chip. Cosmic rays caused correlated errors at a rate of 1/(10 min), accounting for 17$\pm$1% of all such events. Our qubits responded to essentially all of the cosmic rays and their secondary particles incident on the chip, consistent with the independently measured arrival flux. Moreover, we observed that the landscape of the superconducting gap in proximity to the Josephson junctions dramatically impacts the qubit response to cosmic rays. Given the practical difficulties associated with shielding cosmic rays, our results indicate the importance of radiation hardening -- for example, superconducting gap engineering -- to the realization of robust quantum error correction., Comment: 34 pages, 16 figures

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) offers a valuable approach in searching for physics beyond the Standard Model. The Ricochet experiment aims to perform a precision measurement of the CE$\nu$NS spectrum at the Institut Laue-Langevin nuclear reactor with cryogenic solid-state detectors. The experiment plans to employ an array of cryogenic thermal detectors, each with a mass around 30 g and an energy threshold of sub-100 eV. The array includes nine detectors read out by Transition-Edge Sensors (TES). These TES based detectors will also serve as demonstrators for future neutrino experiments with thousands of detectors. In this article we present an update in the characterization and modeling of a prototype TES detector., Comment: Submitted to LTD20 proceeding

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: domain-adaptive tokenization, domain-adaptive continued pretraining, model alignment with domain-specific instructions, and domain-adapted retrieval models. We evaluate these methods on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our evaluations demonstrate that domain-adaptive pretraining of language models, can lead to superior performance in domain related downstream tasks compared to their base LLaMA2 counterparts, without degradations in generic capabilities. In particular, our largest model, ChipNeMo-70B, outperforms the highly capable GPT-4 on two of our use cases, namely engineering assistant chatbot and EDA scripts generation, while exhibiting competitive performance on bug summarization and analysis. These results underscore the potential of domain-specific customization for enhancing the effectiveness of large language models in specialized applications., Comment: Updated results for ChipNeMo-70B model

The increasing popularity of large language models (LLMs) has paved the way for their application in diverse domains. This paper proposes a benchmarking framework tailored specifically for evaluating LLM performance in the context of Verilog code generation for hardware design and verification. We present a comprehensive evaluation dataset consisting of 156 problems from the Verilog instructional website HDLBits. The evaluation set consists of a diverse set of Verilog code generation tasks, ranging from simple combinational circuits to complex finite state machines. The Verilog code completions can be automatically tested for functional correctness by comparing the transient simulation outputs of the generated design with a golden solution. We also demonstrate that the Verilog code generation capability of pretrained language models could be improved with supervised fine-tuning by bootstrapping with LLM generated synthetic problem-code pairs., Comment: ICCAD 2023 Invited Paper. Prior version contained errors in the numbers reported for gpt-4 in Table II

Software developers typically rely upon a large network of dependencies to build their applications. For instance, the NPM package repository contains over 3 million packages and serves tens of billions of downloads weekly. Understanding the structure and nature of packages, dependencies, and published code requires datasets that provide researchers with easy access to metadata and code of packages. However, prior work on NPM dataset construction typically has two limitations: 1) only metadata is scraped, and 2) packages or versions that are deleted from NPM can not be scraped. Over 330,000 versions of packages were deleted from NPM between July 2022 and May 2023. This data is critical for researchers as it often pertains to important questions of security and malware. We present npm-follower, a dataset and crawling architecture which archives metadata and code of all packages and versions as they are published, and is thus able to retain data which is later deleted. The dataset currently includes over 35 million versions of packages, and grows at a rate of about 1 million versions per month. The dataset is designed to be easily used by researchers answering questions involving either metadata or program analysis. Both the code and dataset are available at https://dependencies.science.

The SPICE/HeRALD collaboration is performing R&D to enable studies of sub-GeV dark matter models using a variety of target materials. Here we report our recent progress on instrumenting a superfluid $^4$He target mass with a transition-edge sensor based calorimeter to detect both atomic signals (scintillation) and $^4$He quasiparticle (phonon and roton) excitations. The sensitivity of HeRALD to the critical "quantum evaporation" signal from $^4$He quasiparticles requires us to block the superfluid film flow to the calorimeter. We have developed a heat-free film-blocking method employing an unoxidized Cs film, which we implemented in a prototype "HeRALD v0.1" detector of ~10 g target mass. This article reports initial studies of the atomic and quasiparticle signal channels. A key result of this work is the measurement of the quantum evaporation channel's gain of 0.15 $\pm$ 0.01, which will enable $^4$He-based dark matter experiments in the near term. With this gain the HeRALD detector reported here has an energy threshold of 145 eV at 5 sigma, which would be sensitive to dark matter masses down to 220 MeV/c$^2$., Comment: 14 pages, 9 figures

The future Ricochet experiment aims to search for new physics in the electroweak sector by measuring the Coherent Elastic Neutrino-Nucleus Scattering process from reactor antineutrinos with high precision down to the sub-100 eV nuclear recoil energy range. While the Ricochet collaboration is currently building the experimental setup at the reactor site, it is also finalizing the cryogenic detector arrays that will be integrated into the cryostat at the Institut Laue Langevin in early 2024. In this paper, we report on recent progress from the Ge cryogenic detector technology, called the CryoCube. More specifically, we present the first demonstration of a 30~eVee (electron equivalent) baseline ionization resolution (RMS) achieved with an early design of the detector assembly and its dedicated High Electron Mobility Transistor (HEMT) based front-end electronics. This represents an order of magnitude improvement over the best ionization resolutions obtained on similar heat-and-ionization germanium cryogenic detectors from the EDELWEISS and SuperCDMS dark matter experiments, and a factor of three improvement compared to the first fully-cryogenic HEMT-based preamplifier coupled to a CDMS-II germanium detector. Additionally, we discuss the implications of these results in the context of the future Ricochet experiment and its expected background mitigation performance., Comment: 10 pages, 5 figures, 1 table

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) offers valuable sensitivity to physics beyond the Standard Model. The Ricochet experiment will use cryogenic solid-state detectors to perform a precision measurement of the CE$\nu$NS spectrum induced by the high neutrino flux from the Institut Laue-Langevin nuclear reactor. The experiment will employ an array of detectors, each with a mass of $\sim$30 g and a targeted energy threshold of 50 eV. Nine of these detectors (the "Q-Array") will be based on a novel Transition-Edge Sensor (TES) readout style, in which the TES devices are thermally coupled to the absorber using a gold wire bond. We present initial characterization of a Q-Array-style detector using a 1 gram silicon absorber, obtaining a baseline root-mean-square resolution of less than 40 eV., Comment: 18 pages, 8 figures

The NPM package repository contains over two million packages and serves tens of billions of downloads per-week. Nearly every single JavaScript application uses the NPM package manager to install packages from the NPM repository. NPM relies on a "semantic versioning" ('semver') scheme to maintain a healthy ecosystem, where bug-fixes are reliably delivered to downstream packages as quickly as possible, while breaking changes require manual intervention by downstream package maintainers. In order to understand how developers use semver, we build a dataset containing every version of every package on NPM and analyze the flow of updates throughout the ecosystem. We build a time-travelling dependency resolver for NPM, which allows us to determine precisely which versions of each dependency would have been resolved at different times. We segment our analysis to allow for a direct analysis of security-relevant updates (those that introduce or patch vulnerabilities) in comparison to the rest of the ecosystem. We find that when developers use semver correctly, critical updates such as security patches can flow quite rapidly to downstream dependencies in the majority of cases (90.09%), but this does not always occur, due to developers' imperfect use of both semver version constraints and semver version number increments. Our findings have implications for developers and researchers alike. We make our infrastructure and dataset publicly available under an open source license.

The COVID-19 pandemic forced Virginia school divisions to limit the amount of time students spent learning in-person in school buildings during the 2020-21 school year. Divisions, therefore, needed to make many decisions on how to provide instruction in each student's home. We collected and coded over a thousand documents pertaining to the SY 2020-21 operations of Virginia's 132 school divisions. We coded documents for information on divisions' plans to provide students with access to technology (e.g., whether and to which students the divisions provided a personal computing device), access to internet, and access to technological support. Our descriptive analyses explore how divisions responded to the COVID-19 pandemic through their planned use of remote technology overall and by division-level characteristics. We find that a majority of divisions guaranteed access to technology for all students, divisions with higher concentrations of minoritized students and economically disadvantaged students were more likely to guarantee students' access to technology, divisions were more likely to rely on community hotspots to provide access to the internet, divisions in which more residences had access to broadband internet were more likely than divisions with less access to assist students in connecting to the internet at home, and that most divisions provided technological support to students, but fewer also offered these supports to staff. During such an unprecedented school year, it is important that we identify the heterogenous responses of school divisions across the state.

A portable monoenergetic 24 keV neutron source based on the $^{124}$Sb-$^9$Be photoneutron reaction and an iron filter has been constructed and characterized. The coincidence of the neutron energy from SbBe and the low interaction cross-section with iron (mean free path up to 29 cm) makes pure iron specially suited to shield against gamma rays from $^{124}$Sb decays while letting through the neutrons. To increase the $^{124}$Sb activity and thus the neutron flux, a $>$1 GBq $^{124}$Sb source was produced by irradiating a natural Sb metal pellet with a high flux of thermal neutrons in a nuclear reactor. The design of the source shielding structure makes for easy transportation and deployment. A hydrogen gas proportional counter is used to characterize the neutrons emitted by the source and a NaI detector is used for gamma background characterization. At the exit opening of the neutron beam, the characterization determined the neutron flux in the energy range 20-25 keV to be 5.36$\pm$0.20 neutrons per cm$^2$ per second and the total gamma flux to be 213$\pm$6 gammas per cm$^2$ per second (numbers scaled to 1 GBq activity of the $^{124}$Sb source). A liquid scintillator detector is demonstrated to be sensitive to neutrons with incident kinetic energies from 8 to 17 keV, so it can be paired with the source as a backing detector for neutron scattering calibration experiments. This photoneutron source provides a good tool for in-situ low energy nuclear recoil calibration for dark matter experiments and coherent elastic neutrino-nucleus scattering experiments., Comment: 11 pages, 20 figures

Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by using MultiPL-E to translate two popular Python code generation benchmarks to 18 additional programming languages. We use MultiPL-E to extend the HumanEval benchmark and MBPP benchmark to 18 languages that encompass a range of programming paradigms and popularity. Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex, CodeGen, and InCoder. We find that Codex matches or even exceeds its performance on Python for several other languages. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible, making it straightforward to evaluate new models, benchmarks, and languages.

The performance of superconducting qubits is degraded by a poorly characterized set of energy sources breaking the Cooper pairs responsible for superconductivity, creating a condition often called ``quasiparticle poisoning". Both superconducting qubits and low threshold dark matter calorimeters have observed excess bursts of quasiparticles or phonons that decrease in rate with time. Here, we show that a silicon crystal glued to its holder exhibits a rate of low-energy phonon events that is more than two orders of magnitude larger than in a functionally identical crystal suspended from its holder in a low-stress state. The excess phonon event rate in the glued crystal decreases with time since cooldown, consistent with a source of phonon bursts which contributes to quasiparticle poisoning in quantum circuits and the low-energy events observed in cryogenic calorimeters. We argue that relaxation of thermally induced stress between the glue and crystal is the source of these events., Comment: 19 pages (main + supplementary), 6 figures. W. A. Page and R. K. Romani contributed equally to this work. Correspondence should be addressed to R. K. Romani

The future Ricochet experiment aims at searching for new physics in the electroweak sector by providing a high precision measurement of the Coherent Elastic Neutrino-Nucleus Scattering (CENNS) process down to the sub-100 eV nuclear recoil energy range. The experiment will deploy a kg-scale low-energy-threshold detector array combining Ge and Zn target crystals 8.8 meters away from the 58 MW research nuclear reactor core of the Institut Laue Langevin (ILL) in Grenoble, France. Currently, the Ricochet collaboration is characterizing the backgrounds at its future experimental site in order to optimize the experiment's shielding design. The most threatening background component, which cannot be actively rejected by particle identification, consists of keV-scale neutron-induced nuclear recoils. These initial fast neutrons are generated by the reactor core and surrounding experiments (reactogenics), and by the cosmic rays producing primary neutrons and muon-induced neutrons in the surrounding materials. In this paper, we present the Ricochet neutron background characterization using $^3$He proportional counters which exhibit a high sensitivity to thermal, epithermal and fast neutrons. We compare these measurements to the Ricochet Geant4 simulations to validate our reactogenic and cosmogenic neutron background estimations. Eventually, we present our estimated neutron background for the future Ricochet experiment and the resulting CENNS detection significance., Comment: 14 pages, 14 figures, 1 table

Package managers such as NPM have become essential for software development. The NPM repository hosts over 2 million packages and serves over 43 billion downloads every week. Unfortunately, the NPM dependency solver has several shortcomings. 1) NPM is greedy and often fails to install the newest versions of dependencies; 2) NPM's algorithm leads to duplicated dependencies and bloated code, which is particularly bad for web applications that need to minimize code size; 3) NPM's vulnerability fixing algorithm is also greedy, and can even introduce new vulnerabilities; and 4) NPM's ability to duplicate dependencies can break stateful frameworks and requires a lot of care to workaround. Although existing tools try to address these problems they are either brittle, rely on post hoc changes to the dependency tree, do not guarantee optimality, or are not composable. We present PacSolve, a unifying framework and implementation for dependency solving which allows for customizable constraints and optimization goals. We use PacSolve to build MaxNPM, a complete, drop-in replacement for NPM, which empowers developers to combine multiple objectives when installing dependencies. We evaluate MaxNPM with a large sample of packages from the NPM ecosystem and show that it can: 1) reduce more vulnerabilities in dependencies than NPM's auditing tool in 33% of cases; 2) chooses newer dependencies than NPM in 14% of cases; and 3) chooses fewer dependencies than NPM in 21% of cases. All our code and data is open and available.

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) is a process in which neutrinos scatter on a nucleus which acts as a single particle. Though the total cross section is large by neutrino standards, CE$\nu$NS has long proven difficult to detect, since the deposited energy into the nucleus is $\sim$ keV. In 2017, the COHERENT collaboration announced the detection of CE$\nu$NS using a stopped-pion source with CsI detectors, followed up the detection of CE$\nu$NS using an Ar target. The detection of CE$\nu$NS has spawned a flurry of activities in high-energy physics, inspiring new constraints on beyond the Standard Model (BSM) physics, and new experimental methods. The CE$\nu$NS process has important implications for not only high-energy physics, but also astrophysics, nuclear physics, and beyond. This whitepaper discusses the scientific importance of CE$\nu$NS, highlighting how present experiments such as COHERENT are informing theory, and also how future experiments will provide a wealth of information across the aforementioned fields of physics., Comment: contribution to Snowmasss 2021. Contact authors: P. S. Barbeau, R. Strauss, L. E. Strigari

We have designed and tested a large-area (0.15~m$^2$) neutron detector based on neutron capture on \ce{^{6}Li}. The neutron detector design has been optimized for the purpose of tagging the scattering angle of keV-scale neutrons. These neutron detectors would be employed to calibrate the low-energy ($<$100 eV) nuclear recoil in detectors for dark matter and coherent elastic neutrino nucleus scattering (CE$\nu$NS). We describe the design, construction, and characterization of a prototype. The prototype is designed to have a tagging efficiency of $\sim$25\% at the relevant $\mathcal{O}$(keV) neutron energies, and with a mean capture time of $\sim$17$~\mu$s. The prototype was characterized using a \ce{^{252}Cf} neutron source and agreement with the simulation was observed within a few percent level., Comment: 25 pages, 14 figures, and 2 tables

Many low-threshold experiments observe sharply rising event rates of yet unknown origins below a few hundred eV, and larger than expected from known backgrounds. Due to the significant impact of this excess on the dark matter or neutrino sensitivity of these experiments, a collective effort has been started to share the knowledge about the individual observations. For this, the EXCESS Workshop was initiated. In its first iteration in June 2021, ten rare event search collaborations contributed to this initiative via talks and discussions. The contributing collaborations were CONNIE, CRESST, DAMIC, EDELWEISS, MINER, NEWS-G, NUCLEUS, RICOCHET, SENSEI and SuperCDMS. They presented data about their observed energy spectra and known backgrounds together with details about the respective measurements. In this paper, we summarize the presented information and give a comprehensive overview of the similarities and differences between the distinct measurements. The provided data is furthermore publicly available on the workshop's data repository together with a plotting tool for visualization., Comment: 44 pages, 20 figures; Editors: A. Fuss, M. Kaznacheeva, F. Reindl, F. Wagner; updated copyright statements and funding information

We present an overview of recent progress towards the Ricochet coherent elastic neutrino nucleus scattering CE$\nu$NS experiment. The ILL research reactor in Grenoble, France has been selected as the experiment site, after in situ studies of vibration and particle backgrounds. We present background rate estimates specific to that site, along with descriptions of the planned CryoCube and Q-Array detector payloads., Comment: Proceedings for the 19th International Workshop on Low Temperature Detectors (LTD19)

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) offers a valuable approach in searching for physics beyond the Standard Model. The Ricochet experiment aims to perform a precision measurement of the CE$\nu$NS spectrum at the Institut Laue-Langevin (ILL) nuclear reactor with cryogenic solid-state detectors. The experiment will employ an array of cryogenic thermal detectors, each with a mass of around 30 g and an energy threshold of 50 eV. One section of this array will contain 9 Transition Edge Sensor (TES) based calorimeters. The design will not only fulfill requirements for Ricochet, but also act as a demonstrator for future neutrino experiments that will require thousands of macroscopic detectors. In this article we present an updated TES chip design as well as performance predictions based on a numerical modeling.

In order to provide secure clamping with a low thermal conductance, some low temperature detectors are held with point contact sapphire ball clamps. While this method is increasingly common, the thermal conductance across this interface has not been well studied. We present a direct measurement of the thermal conductance of such clamps between 200 and 600~mK, with a clamping force of approximately 2.7~$\pm0.3$~N/clamp. The thermal conductance of a single sapphire-on-copper clamp was found to be 660$^{+360}_{-210}$~$(T/K)^{3.1}$~[nW/K]. For a sapphire-on-silicon clamp the conductance was found to be 380$^{+190}_{-120}$~$(T/K)^{2.8}$~[nW/K]. The conductance measured is consistent with thermal boundary resistance., Comment: 8 pages, 4 figures, Proceedings for the 19th International Workshop on Low Temperature Detectors (LTD19). J Low Temp Phys (2022)

Superfluid $^4$He is a promising target material for direct detection of light ($<$ 1 GeV) dark matter. Possible signal channels available for readout in this medium include prompt photons, triplet excimers, and roton and phonon quasiparticles. The relative yield of these signals has implications for the sensitivity and discrimination power of a superfluid $^4$He dark matter detector. Using a 16~cm$^3$ volume of 1.75~K superfluid $^4$He read out by six immersed photomultiplier tubes, we measured the scintillation from electronic recoils ranging between 36.3 and 185 keV$_\mathrm{ee}$, yielding a mean signal size of $1.25^{+0.03}_{-0.03}$~phe/keV$_\mathrm{ee}$, and nuclear recoils from 53.2 to 1090 keV$_\mathrm{nr}$. We compare the results of our relative scintillation yield measurements to an existing semiempirical model based on helium-helium and electron-helium interaction cross sections. We also study the behavior of delayed scintillation components as a function of recoil type and energy, a further avenue for signal discrimination in superfluid $^4$He., Comment: 17 pages, 19 figures

Latency-insensitive design mitigates increasing interconnect delay and enables productive component reuse in complex digital systems. This design style has been adopted in high-level design flows because untimed functional blocks connected through latency-insensitive interfaces provide a natural communication abstraction. However, latency-insensitive design with high-level languages also introduces a unique set of verification challenges that jeopardize functional correctness. In particular, bugs due to invalid consumption of inputs and deadlocks can be difficult to detect and debug with dynamic simulation methods. To tackle these two classes of bugs, we propose formal model checking methods to guarantee that a high-level latency-insensitive design is unaffected by invalid input data and is free of deadlock. We develop a well-structured verification wrapper for each property to automatically construct the corresponding formal model for checking. Our experiments demonstrate that the formal checks are effective in realistic bug scenarios from high-level designs.