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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

AmeriFlux FLUXNET-1F US-xSP NEON Soaproot Saddle (SOAP)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xSP NEON Soaproot Saddle (SOAP). This is the FLUXNET version of the carbon flux data for the site US-xSP NEON Soaproot Saddle (SOAP) produced by applying the standard ONEFlux (1F) software. Site Description - The Soaproot Saddle is a complex terrain of coarse hills, steep slopes and narrow drainages. With an elevation of 3274 - 4537’ this site encompasses 1438 acres of mixed conifer forests that are experiencing high levels of mortality due to native Pine beetles. At the core of this site stands a 171’ tall flux tower that collects physical and chemical properties of atmosphere and related process. Soaproot Saddle also hosts an array of sensor measurements along with field observations collected by highly trained NEON staff. The automated instrument measurements and some of the terrestrial observational safor this field site are colocted with NEON's aquatic site, Upper Big Creek, which is located just north of Soaproot Saddle's site boundaries.

Network), NEON (National Ecological Observatory [N↗
DOE OSTI2023

Reinforcement Learning Approach to Cybersecurity in Space (RELACSS)

Securing satellite groundstations against cyber-attacks is vital to national security missions. However, these cyber threats are constantly evolving. As vulnerabilities are discovered and patched, new vulnerabilities are discovered and exploited. In order to automate the process of discovering existing vulnerabilities and the means to exploit them, a reinforcement learning framework is presented in this report. We demonstrate that this framework can learn to successfully navigate an unknown network and detect nodes of interest despite the presence of a moving target defense. The agent then exfiltrates a file of interest from the node as quickly as possible. This framework also incorporates a defensive software agent that learns to impede the attacking agents progress. This setup allows for the agents to work against each other and improve their abilities. We anticipate that this capability will help uncover unforeseen vulnerabilities and the means to mitigate them. The modular nature of the framework enables users to swap out learning algorithms and modify the reward functions in order to adapt the learning tasks to various use cases and environments. Several algorithms, viz., tabular Q learning, deep Q networks, proximal policy optimization, advantage actor-critic, generative adversarial imitation learning, are explored for the agents and the results highlighted. The agent learns to solve the tasks in a light-weight abstract environment. Once the agent learns to perform sufficiently well, it can be deployed in a minimega virtual machine environment (or a real network) with wrappers that map abstract actions to software commands. The agent also uses a local representation of the actions called a ‘slot-mechanism’. This allows the agent to learn in a certain network and generalize it to different networks. The defensive agent learns to predict the actions taken by an offensive agent and uses that information to anticipate the threat. This information can then either be used to raise an alarm or to take actions to thwart the attack. We believe that with the appropriate reward design, a representative environment, and action set, this framework can be generalized to tackle other cybersecurity tasks. By sufficiently training these agents, we can anticipate vulnerabilities leading to robust future designs. We can also deploy automated defensive agents that can help secure satellite groundstation and their vital national security missions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Enhancement of operational Flexibility of Power Plants Using IN740

Headers are crucial components within diverse industries, especially in the energy sector, as they enable the efficient transfer of fluids. The selection of materials for headers is determined by their specific applications; for example, Grade 91 and Grade 92 steels are commonly utilized. In our research, we recommend the adoption of INCONEL alloy 740 due to its exceptional robustness and heat-resistant characteristics. One critical parameter in header systems is the heat transfer coefficient, which directly influences the efficiency of heat exchange processes and can thereby impact the structural integrity of the headers. This coefficient is closely linked with factors such as the Nusselt number, which is influenced by fluid flow characteristics and thermal properties. The flow direction within headers, whether unidirectional or multidirectional—significantly affects the overall dynamics of heat transfer. This research focuses on exploring the behavior of headers, a specific pipeline system component, using ANSYS simulation software. The study aims to predict heat transfer and mechanical behavior within headers under various conditions through steady-state and transient simulations (parts 1 and 2 of the report). Key parameters such as heat transfer coefficient, velocity, and temperature are examined with the goal of optimizing header design. Part 3 of the report addresses the critical yet underexplored relationship between pressure drop and heat transfer coefficients in the transient flow regime within headers, specifically focusing on smooth horizontal circular tubes. Limited experimental work has been conducted in this area, prompting the need for comprehensive analysis. By leveraging machine learning techniques, this research aims to establish a correlation between pressure drop and heat transfer across various flow conditions, including laminar, transient, quasi-turbulent, and turbulent regimes. The data utilized for this analysis were meticulously gathered from existing literature, capturing simultaneous measurements of pressure drops and heat transfer. Part 4: In the context of power plants, header pipes are essential components that significantly influence system performance by facilitating the collection and distribution of steam. This report highlights the critical role of header pipes in enhancing reliability, efficiency, and overall power plant performance. A key aspect of this investigation is shape optimization, which aims to maximize performance while minimizing material usage. By focusing on shape optimization, this research contributes to improved efficiency and a reduced environmental footprint for power plant installations. The methodology developed in this study emphasizes optimizing header shapes to decrease reliance on expensive alloy materials and lower maintenance costs. Furthermore, a case study was conducted using a header from an operational power plant to validate the proposed optimization techniques. Finally, part 5 addresses the challenges associated with flexible operations in boiler systems, the study outlines several strategies for enhancing the durability and reliability of steam headers. Key approaches include material selection, which involves utilizing advanced materials with superior high-temperature properties and enhanced fatigue resistance to extend the lifespan of steam headers. Design modifications are also recommended, focusing on implementing changes that mitigate thermal stress and cyclic loading to reduce the likelihood of failures under varying operating conditions. Additionally, the study emphasizes the importance of regular inspection and monitoring by establishing rigorous protocols to detect early signs of damage, allowing for timely maintenance and minimizing the risks of catastrophic failures. Furthermore, operational guidelines are developed to minimize the frequency and severity of thermal transients, ensuring stable and efficient operations. Collectively, these strategies provide a robust framework for overcoming the unique challenges posed by flexible operation in boiler systems, ultimately contributing to improved reliability and performance in power plant operations. Through this comprehensive approach, the study not only enhances the understanding of pressure drop and heat transfer relationships but also promotes advancements in design and operation that will benefit the entire power generation industry.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities. The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Dragonstone Strategy Kickoff Report [Slides]

The oil & natural gas (ONG) system like any other heavy industrial process, relies on a complex system of information technology (IT) and operational technology (OT) devices. Cyber-preparedness varies across the ONG industry from organization to organization. Common challenges include remote locations, long-lived field assets, and insufficient capabilities to find and track malware. ONG companies tend to be concerned with lack of cyber-awareness from employees, risk stemming from remote access for operations & maintenance, and software vulnerabilities within third-party equipment. For a variety of reasons, the general consensus from stakeholders interviewed by LLNL is that the state of cybersecurity within the electric grid is currently outpacing its ONG cousin. Existing strategies for the resilience and cybersecurity of the electric grid can and should be leveraged to provide immediate benefits to the ONG system. In LLNL’s view, there are two key factors currently limiting the development of necessary cyber-practices within the ONG industry: regulatory overlap and inefficiencies, and lack of industry awareness of potentially useful technologies for solving known issues. In the months following this report, LLNL will develop a cyber-resilience roadmap for the oil & gas pipeline sector, coupling threat intelligence, industry knowledge base, and ongoing outreach

02 PETROLEUM↗
DOE OSTI2024

Application of a Prize Mechanism to Address Data Utilization Challenges at Utilities

The electric industry sector is facing an “explosion” of data from a variety of sources. Electric sector stakeholders need to define how to capitalize on large datasets, both those they create and those from other sources (like data on weather, buildings, electric vehicles, etc.), to improve reliability and resilience and meet the changing system dynamics from renewable integration. For the electricity sector to fully utilize these vast new datasets, it must undergo a transformation in how it manages data quality, storage, and processing. The U.S. Department of Energy (DOE) Office of Electricity (OE) is committed to accelerating research, development, and demonstration of new technologies and tools within the electricity sector to advance reliability, resilience, and affordable operation of the power system. Through the prize mechanism, OE identified two widespread data-related challenges for utilities—load modeling and data analysis automation—and offered an opportunity for utilities and teams of software engineers to identify additional challenges faced by utilities. After completing one round of the American-Made Digitizing Utilities Prize, OE, the National Renewable Energy Laboratory (NREL) as the prize administrator, and Pacific Northwest National Laboratory (PNNL) as the domain experts have compiled the results and lessons learned to feed into the second round of the prize.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Modeling supercritical CO2 injection induced rupture of a minor fault embedded in a poroelastic layered reservoir-caprock system

CO2 injection for geologic carbon sequestration involves hydromechanical processes that lead to changes in fluid pressure and stresses that can activate existing faults. This paper presents a new method and workflow of modeling fault activation considering more complex three-dimensional geometry of natural faults using the TOUGH-FLAC multiphase fluid flow and geomechanical simulator. In this method and workflow, FLAC3D mechanical interfaces and TOUGH3 finite volume elements are discretized using computer aided design and gridding software along with a tailored mesh translation routine. The method and workflow are demonstrated with a model of a curved minor fault embedded in a poro-elastic layered reservoir-caprock system. The model is used for a comprehensive sensitivity analysis of fault responses to fault length, injection mass rate, injection schedule, well-fault distance, and well locations versus fault location. Four metrics (CO2 plume, shear state of fault, pressure and stress path at fault monitoring points) are selected to assess CO2 migration, pressure change, and the reactivation of faults. The results reveal that CO2 can bypass around the tip of the minor impermeable fault, building up pressure and poro-elastic stress on both sides that tends to impede fault rupture. Our study shows the benefit of carefully designing the injection to achieve the targeted final storage volume, starting at a relatively low rate for considerable time, and then ramping up the injection rate to the full rate of injection. The initial low injection has two distinct benefits: (1) it allows for the formation of an extensive CO2 plume with a much higher mobility through a low viscosity that will result in a lower pressure for a given injection rate, and (2) it allows for gradual build-up of horizontal poro-elastic stress within the reservoir that will tend to impede activation of steeply dipping faults. The injection scenario starting at a low injection rate, denoted here as conservative injection, can significantly reduce the risk of fault activation as high fluid mobility and reservoir strengthening poro-elastic stress has been established long before reaching the peak injection rates. Moreover, simultaneous injection in two injection wells on both sides of fault can provide further reservoir strengthening through poro-elastic stress buildup acting on a fault under normal faulting stress regime. The findings presented in the paper can provide practical and effective guidance on long-term, safe, and reliable geological CO2 storage.

Cao, Meng↗
DOE OSTI2023

Pynta-An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics

Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. Here, we present Pynta, a workflow software for automating the calculation of surface and gas–surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

BuildingSync® v.2.7.0 (released 9.11.2025) [SWR-18-28]

BuildingSync® is a building data exchange schema to better enable integration between software tools and building data workflows. The schema's original use case was focused on commercial building energy audits; however, several additional use cases have been realized including building energy modeling and more high-level generic building data exchange. Version 2.7.0 adds new elements for file attachment feature and FederalBuilding, and generalizes usage of Optional Elements (e.g. EquipmentCondition, EquipmentID) to all assets/systems. BuildingSync helps streamline the data exchange process, improving the value of the data, minimizing duplication of effort for subsequent building data collection efforts (including audits), and facilitating the achievement of greater energy efficiency. This in done in part by standardizing on (a) reporting audits in an electronic format, (b) tracking proposed, implemented, and discarded energy conservation measures, and (c) storing building characteristics (at multiple levels) for audits, benchmarking, and building energy analysis. BuildingSync has several documents and tools available to help users understand how to best leverage BuildingSync. The list below are only a subset of the resources available. If new resources are discovered, then feel free to create a new pull request with the additions. Generic BuildingSync information is available on the DOE website and the project website. BuildingSync Examples - These examples are kept up to date and show a wide range of implementations. Any new update to BuildingSync is required to pass validation on these example files. BuildingSync Use Case Validator allows for users to determine if their instance complies with a specific use case for BuildingSync by checking if the required elements are implemented in an uploaded instance. An API is also provided for automated integration into other tools. Also, the website contains an easy way to view the entirety of the schema and how elements relate to the Building Exchange Data Exchange Specification. The Validator is open sourced here Use Case TestSuite provides a Python package for easier generation of BuildingSync use cases. BuildingSync use cases depend on the generation of schematron documents, which is time-consuming and difficult to implement well. The TestSuite allows users to define a use case using a more palatable CSV template, which it then turns into a Schematron document. The source code is available here. BuildingSync to OpenStudio/EnergyPlus. The translator is open sourced here. This project will translate a Level 1 (and partial Level 2) ASHRAE Energy Audit to a fully defined OpenStudio and EnergyPlus model. This project is in early Beta testing and any feedback is welcome!

Long, Nicholas [National Renewable Energy Lab. (NR↗
DOE OSTI2023

Cyber-Informed Engineering Implementation Guide: Version 1.0 [Slides]

This Implementation Guide describes the principles of Cyber-Informed Engineering (CIE) and outlines questions that engineering teams should consider during each phase of a system's lifecycle to effectively employ these principles. It describes what it means to engineer systems in a cyber-informed way, rather than offering a comprehensive, step-by-step process or procedure for CIE implementation. This guide complements - but does not replace - the application of cybersecurity standards or practices currently in place within an organization. Engineers and technicians that design critical energy infrastructure installations can use this Implementation Guide to integrate the 12 principles of CIE into each phase of the engineering lifecycle, from concept to retirement. The guide is aimed at system or design engineers, rather than software engineers or operational cybersecurity practitioners. The engineers who design, build, operate, and maintain the physical infrastructure are best positioned to leverage a system's engineering design to diminish the severity of cyber attacks or digital technology failures. CIE expands cybersecurity decisions into the engineering space, not by asking engineers to become cyber experts, but by calling on engineers to apply engineering tools and make engineering decisions that improve cybersecurity outcomes. CIE examines the engineering consequences that a sophisticated cyber attacker could achieve and drives engineering changes that may provide deterministic mitigations to limit or eliminate those consequences.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Janus: A Python Package for Agent-Based Modeling of Land Use and Land Cover Change

Janus is an open source Python package for agent-based modeling (ABM) of land use and land cover change (LULCC). Many ABMs of LULCC have been created across platforms, some of which are not ideal for large scale, high resolution scenarios. This model provides a simple object-oriented framework for creating ABMs specific to LULCC. The organizational philosophy of the modeling framework is to create software objects (agents) that are associated with specific and contextual attributes which are isolated from where those agents exist in the spatial setting of the model, yet provide clear linkages between the agent, their environment, and other agents in the simulation. In this way, the framework allows for assembly of LULCC ABMs with low (programmatic) overhead, making the models extensible and providing clear mechanisms for integrating them with process-oriented biophysical models. Provided with Janus is a suite of geospatial data preprocessing tools that can use arbitrary land cover products as an input. Crop choice decisions are based on potential crop prices, these can be created synthetically, or drawn from integrated human-Earth systems models such as the GCAM. Janus is publicly accessible through GitHub and provides an example dataset for testing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Cyber-Informed Engineering Implementation Guide

This Implementation Guide describes the principles of Cyber-Informed Engineering (CIE) and outlines questions that engineering teams should consider during each phase of a system’s lifecycle to effectively employ these principles. It describes what it means to engineer systems in a cyber-informed way, rather than offering a comprehensive, step-by-step process or procedure for CIE implementation. This guide complements—but does not replace—the application of cybersecurity standards or practices currently in place within an organization. Engineers and technicians that design critical energy infrastructure installations can use this Implementation Guide to integrate the 12 principles of CIE into each phase of the engineering lifecycle, from concept to retirement. The guide is aimed at system or design engineers, rather than software engineers or operational cybersecurity practitioners. The engineers who design, build, operate, and maintain the physical infrastructure are best positioned to leverage a system’s engineering design to diminish the severity of cyber attacks or digital technology failures. CIE expands cybersecurity decisions into the engineering space, not by asking engineers to become cyber experts, but by calling on engineers to apply engineering tools and make engineering decisions that improve cybersecurity outcomes. CIE examines the engineering consequences that a sophisticated cyber attacker could achieve and drives engineering changes that may provide deterministic mitigations to limit or eliminate those consequences.

42 ENGINEERING↗
DOE OSTI2023

Numerical modeling of hydrogen mixing in a direct-injection engine fueled with gaseous hydrogen

Hydrogen is considered as one of the most promising options to achieve effective decarbonization of the energy and transportation sectors. As such, it has recently been receiving increasing attention because of its promising potential as an energy carrier for advanced energy and propulsion systems. With a focus on internal combustion engines, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential for high engine efficiency and specific power while reducing the risk of backfiring and pre-ignition issues. Therefore, many experimental and numerical efforts have recently been dedicated to understanding the physical and chemical behaviors of hydrogen in engine during mixing and combustion. This study focuses on computational fluid dynamics (CFD) modeling of the hydrogen DI process in a hydrogen optical research engine. Under the conditions studied, gaseous hydrogen is injected into the combustion chamber via a centrally located single-hole injector at a pressure of 100 bar. Two configurations, namely low-and high-tumble, are investigated to understand the impact of different in-cylinder flow patterns on the fuel-air mixture preparation. Simulations are carried out using the commercial CFD software CONVERGE. Here, the in-cylinder turbulence is modeled with an unsteady Reynolds-averaged Navier-Stokes (URANS) formulation closed by the renormalization group (RNG) k-ε model. Several numerical methods and model constants, including but not limited to turbulent Schmidt number, are evaluated. The numerical results are systematically compared against experimental measurements of velocity and hydrogen concentration fields on the vertical center plane to assess the performance of the CFD model, unveil the physics of hydrogen mixing, and establish best practices for modeling hydrogen DI under relatively high injection pressure conditions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A spatially resolved spectral analysis of giant radio galaxies with MeerKAT

ABSTRACT In this study we report the spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6, MGTC J100016.84+015133.0, and MGTC J100022.85+031520.4. One of these galaxies, MGTC J100022.85+031520.4, is reported here for the first time, with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unlike the other two, it is associated with a brightest cluster galaxy (BCG), making it one of the few GRGs known to inhabit cluster environments. We examine the spectral age distributions of the three GRGs using new MeerKAT UHF-band (544–1088 MHz) observations, and L-band (900–1670 MHz) data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. We test two models of spectral ageing, the Jaffe–Perola and Tribble models, using the Broadband Radio Astronomy Tools (brats) software, and find that they agree well with each other. We estimate the Tribble spectral age for MGTC J095959.63+024608.6 as 68 Myr, for MGTC J100016.84+015133.0 as 47 Myr, and for MGTC J100022.85+031520.4 as 67 Myr. We find significant disagreements between these spectral age estimates and the estimates of the dynamical ages of these GRGs, modelled in cluster and group environments. Our results highlight the need for additional processes that are not accounted for in either the dynamic age or the spectral age estimations.

Charlton, K. K. L. (ORCID:0000000229252047)↗
DOE OSTI2020

Using a Genetic Algorithm to Optimize Configurations in a Data-Driven Application

Users of highly-configurable software systems often want to optimize a particular objective such as improving a functional outcome or increasing system performance. One approach is to use an evolutionary algorithm. However, many applications today are data-driven, meaning they depend on inputs or data which can be complex and varied. Hence, a search needs to be run (and re-run) for all inputs, making optimization a heavy-weight and potentially impractical process. In this paper, we explore this issue on a data-driven highly-configurable scientific application. We build an exhaustive database containing 3,000 configurations and 10,000 inputs, leading to almost 100 million records as our oracle, and then run a genetic algorithm individually on each of the 10,000 inputs. We ask if (1) a genetic algorithm can find configurations to improve functional objectives; (2) whether patterns of best configurations over all input data emerge; and (3) if we can we use sampling to approximate the results. We find that the original (default) configuration is best only 34% of the time, while clear patterns emerge of other best configurations. Out of 3,000 possible configurations, only 112 distinct configurations achieve the optimal result at least once across all 10,000 inputs, suggesting the potential for lighter weight optimization approaches. We show that sampling of the input data finds similar patterns at a lower cost.

Sinha, Urjoshi↗
DOE OSTI2020

Comparison of unfolding methods using RooFitUnfold

In this study we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson–Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, bin-by-bin and inversion methods on several example problems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗