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DOE OSTI2022

Gravitational memory and compact extra dimensions

Here we develop a general formalism for treating radiative degrees of freedom near $\mathscr{I}^+$ in theories with an arbitrary Ricci-flat internal space. These radiative modes are encoded in a generalized news tensor which decomposes into gravitational, electromagnetic, and scalar components. We find a preferred gauge which simplifies the asymptotic analysis of the full nonlinear Einstein equations and makes the asymptotic symmetry group transparent. This asymptotic symmetry group extends the Bondi–Metzner–Sachs (BMS) group to include angle-dependent isometries of the internal space. We apply this formalism to study memory effects, which are expected to be observed in future experiments, that arise from bursts of higher-dimensional gravitational radiation. We outline how measurements made by gravitational wave observatories might probe properties of the compact extra dimensions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Quenching factor measurements of neon nuclei in neon gas

The NEWS-G collaboration uses spherical proportional counters (SPCs) to search for weakly interacting massive particles (WIMPs). Here, in this paper, we report the first measurements of the nuclear quenching factor in neon gas at 2 bar using an SPC deployed in a neutron beam at the TUNL facility. The energy-dependence of the nuclear quenching factor is modeled using a simple power law: $αE^{β}_{nr}$; we determine its parameters by simultaneously fitting the data collected with the detector over a range of energies. We measured the following parameters in Ne : CH 4 at 2 bar: α= 0.2801±0.0050 (fit) ±0.0045 (sys) and β=0.0867±0.020 (fit) ±0.006 (sys). Our measurements do not agree with expected values from SRIM or Lindhard theory. We demonstrated the feasibility of performing quenching factor measurements at sub-keV energies in gases using SPCs and a neutron beam.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

digital oligarchy↗

DOE OSTI2024

MOLLER Spectrometer Magnet Design With Measured Mechanical Properties of Irradiated S2-Glass Reinforced Cyanate Ester Resin at Elevated Temperature

Here, the aim of this study was to retire the risk of maintaining the integrity of S2-glass reinforced CTD-403 (a cyanate ester resin) that is exposed to radiation and elevated temperature over the life of the measurement of a lepton–lepton electroweak reaction (MOLLER) experiment in experimental Hall A at Jefferson Lab, Newport News, VA. In this article, the shear strength and flexural modulus of irradiated S2-glass reinforced CTD-403 specimens were measured at 65 °C (the magnets are to operate at less than 65 °C) under two scenarios: vacuum and gaseous nitrogen. The testing method is the short-beam shear (SBS) test according to ASTM D2344. The specimens were exposed to neutrons and gamma rays up to 124 MGy. The results show that specimens have excellent resistance against radiation, only 23% degradation of apparent shear strength with 124 MGy at 65 °C under vacuum. At the highest dose areas of the coils, tungsten plates are used to reduce the radiation dose to the resin system. The conclusion is that S2-glass reinforced CTD-403 is well-suited for electrical insulation of MOLLER magnets.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Recommended conventions for reporting results from direct dark matter searches

Abstract The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Measurements of the ionization efficiency of protons in methane

The amount of energy released by a nuclear recoil ionizing the atoms of the active volume of detection appears “quenched” compared to an electron of the same kinetic energy. This different behavior in ionization between electrons and nuclei is described by the Ionization Quenching Factor (IQF) and it plays a crucial role in direct dark matter searches. For low kinetic energies (below 50 keV ), IQF measurements deviate significantly from common models used for theoretical predictions and simulations. We report measurements of the IQF for proton, an appropriate target for searches of Dark Matter candidates with a mass of approximately 1 GeV , with kinetic energies in between 2 keV and 13 keV in 100 mbar of methane. We used the Comimac facility in order to produce the motion of nuclei and electrons of controlled kinetic energy in the active volume, and a NEWS-G SPC to measure the deposited energy. The Comimac electrons are used as a reference to calibrate the detector with 7 energy points. A detailed study of systematic effects led to the final results well fitted by IQF (E K )=E$^α_K$ / (β+E$^α_K$) with α=0.70±0.08 and β=1.32±0.17 . In agreement with some previous works in other gas mixtures, we measured less ionization energy than predicted from SRIM simulations, the difference reaching 33% at 2 keV .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

jupyterhub-announcement v0.8

This is an announcement service for JupyterHub that JupyterHub admins can manage through a JupyterHub-styled UI. JupyterHub administrators can use it to communicate with their hub's users about status, upcoming outages, or share news. It allows admins to post a current announcement, and show previous announcements in reverse chronological order. It provides a REST API hook that admins can use to post the latest announcement on JupyterHub itself (with custom templates).

Thomas, Rollin↗

DOE OSTI2023

EyeON

EyeON: Eye on Operational technology Software Supply Chain attacks have risen drastically over the past few years, none more well-known and impactful than the SolarWinds compromise. Criminal organizations inserted an attack vector into a specific version of the source code, giving themselves an air of credibility. Once news broke on SolarWinds, identifying compromised sites was very difficult, even knowing the culprit update. Software Bills of Materials (SBOM) have been touted as the solution to reclaiming control of your software supply chain. Deployment of SBOMs has been slow, however, due to conflicting standards, opaque storage requirements, and vendor adoption. Additionally, the path from obtaining an SBOM and securing your supply chain is unclear; how can an SBOM library be leveraged to provide insight to your attack surface? The EyeON tool, sponsored by Department of Energy Cybersecurity, Energy Security, and Emergency Response (DoE CESER), aims to address these gaps by providing an encapsulated solution to tracking which updates have been installed in an enterprise, and alerting system administrators to vulnerabilities as they become known. Similar to a virus scanner, EyeON is a command line tool to parse either a single file, nested directory structure, or filesystem. It collects data such as signature (hashes), version information, VirusTotal tags, compiler, compilation date, and code signing information. Users will anonymously submit scan data periodically to DoE CESER, who will then compile a database of known software products employed by Critical Infrastructure and broadcast alerts based on discovered flaws as they arise.

Tenzing, Wangmo↗

DOE OSTI2026

Do solar panels contain PFAS?

The presence and potential leaching of PFAS (Per- and Polyfluoroalkyl Substances) from solar panels are increasingly mentioned in news articles, raising public concerns. Such concerns may slow the adoption of photovoltaic (PV) technology, despite its central role in the renewable energy sector. The limited transparency from manufacturers about fluorinated materials used in PV modules, along with the scarcity of publicly available testing data, contributes to uncertainty and speculation. This perspective aims to clarify the current state of PFAS presence in solar PV. Although certain fluoropolymers are used in PV manufacturing, the scientific consensus on their toxicity indicates they should not be classified as PFAS. Portraying fluoropolymers as toxic PFAS unnecessarily amplifies concerns and unfairly undermines the perceived environmental sustainability of PV technology.

environmental impact↗

DOE OSTI2020

Raman-based sorting of microbial cells to link functions to their genes

In our recent work, we developed an optofluidic platform that allows a direct link to be made between the phenotypes (functions) and the genotypes (genes) of microbial cells within natural communities. By combining stable isotope probing, optical tweezers, Raman microspectroscopy, and microfluidics, the platform performs automated Raman-based sorting of taxa from within a complex community in terms of their functional properties. In comparison with manual sorting approaches, our method provides high throughput (up to 500 cells per hour) and very high sorting accuracy (98.3 ± 1.7%), and significantly reduces the human labour required. The system provides an efficient manner to untangle the contributions of individual members within environmental and host-associated microbiomes. In this News and Thoughts, we provide an overview of our platform, describe potential applications, suggest ways in which the system could be improved, and discuss future directions in which Raman-based analysis of microbial populations might be developed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models

Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors --- the context of how and where content is posted --- to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.

machine learning (ML), machine learning explanatio↗

DOE OSTI2021

Evaluating Deception Detection Model Robustness To Linguistic Variation

With the increasing use of automated, machine learning-driven tools and the downstream impact that algorithmic judgements can have, it is critical to develop models that are robust to evolving or manipulated inputs. Evaluating the reliability of multimodal models across linguistic variations to understand model susceptibility to intentional linguistic adversarial attacks as well as natural linguistic variations is essential in this pursuit. We present extensive analysis of model robustness and susceptibility to linguistic variations in the setting of deceptive news detection, a difficult classification task that is an increasingly important problem to solve with the impact of misinformation spread online. We evaluate the effectiveness of incorporating adversarial defense strategies and measure model susceptibility to state-of-the-art adversarial attacks using two types of linguistic attacks — character and word perturbations. We consider two multiclass prediction tasks — a 3-way classification of tweets as trustworthy, propaganda, or disinformation; and a 4-way classification as clickbait, hoax, satire, or conspiracy — and compare the performance of three embeddings that have been state-of-the-art for several NLP tasks — GloVe, ELMo, and BERT — to highlight consistent trends in susceptibility, high confidence misclassifications, and high impact failures. We find that character or mixed ensemble models are the most effective defense mechanisms and that character perturbations are a more effective attack than word perturbations for deception classification.

adversarial evaluation↗

DOE OSTI2022

EXCESS workshop: Descriptions of rising low-energy spectra

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.

Adari, Prakruth↗

DOE OSTI2020

Biodefense Knowledge Management System

After the Amerithrax attacks, the Department of Homeland Security (DHS) recognized the need to link technical scientific information with law enforcement and national security information in the fight against bioterrorism. The Biodefense Knowledge Center (BKC) was formed to provide timely analysis of technical biodefense-related topics to support DHS and its customers in the interagency. The Biodefense Knowledge Management System (BKMS) was created as the backbone for information management within the BKC. It is a scalable, web-based knowledge management system for organizing disparate data sources. By combining information from 35+ data sources, the BKMS provides a searchable repository for technical information including scientific reports, journal articles, news articles, research funding databases, patent databases, and genomic datasets. The current system contains over 58 million documents and 450 million genomic records, and operates at several classification levels.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

Physics Flash Spring 2020

Published by the Physical Sciences Directorate at LANL, the Spring 2020 issue of this newsletter contains an update from the division leader, Tanja Pietraß, Physics Division news roundaup, welcome to new hires, snapshots of Physics managers' work-from-home offices, and service celebrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

NA-22 Quarterly Report: April-June, 2020

At LLNL we contribute to two projects in NNSA-IAEC Science and Technology Working Group Area V: Basic Science of Waste Management & Subsurface Science. The motivation behind Topic Area V is to evaluate the scientific and safety case for an intermediate borehole to house Israel’s nuclear waste. WM1 (SNL lead) is Thermomechanical Damage (initially called Damage Accumulation and Wellbore Stability). This work focuses on evaluating the damage to the host rock induced by excavation and heat released from a possible radioactive waste repository/borehole. WM2 (LLNL lead) is Radionuclide Facilitated Transport in Carbonate Rock (initially called Colloidal Transport of Radionuclides). This work focuses on better understanding the transport of radionuclides and colloids from a nuclear waste repository/borehole in a vadose zone environment through experimentation and modeling. In the second quarter (this report) our work has been delayed in part due to COVID-19. LLNL went into shelter in place (SIP), minimum safe operations mid-March. Operations at LLNL have been slowly ramping up with 50% of the workforce back onsite in limited capacity and telecommuting widely used. The good news is that our labs opened up the last week in June and experiments are expected to start up again for WM2 Radionuclide Facilitated Transport in July. LLNL’s contribution to WM1 Thermochemical Damage modeling efforts also started back up in late June. We expect to be able to catch up on much of our work by the end of next quarter. The three national laboratories communication on a bi-monthly basis to make sure Area V projects remain on track and we have a monthly conference call with our Israeli counterparts to make sure we are communicating priorities and coordinating project details.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

NA-22 Quarterly Report: April-June, 2020

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

SunSpec Cyberguardians Fall Recruitment Webinar 20200901

The webinar was used to promote the Cyberguardians program and to ensure ample news coverage to reach veterans, employers, and others that would benefit from this program. It covers the landscape for Distributed Energy employment, why we need the Cyberguardians program, opportunities for veteran students and job seekers, and employer benefits.

14 SOLAR ENERGY↗