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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Anomalous thermal transport in Eshelby twisted van der Waals nanowires

Dislocations in van der Waals (vdW) layered nanomaterials induce strain and structural changes that substantially impact thermal transport. Understanding these effects could enable the manipulation of dislocations for improved thermoelectric and optoelectronic applications, but experimental insights remain limited. Here, in this study, we use synthetic Eshelby twisted vdW GeS nanowires (NWs) with single screw dislocations as a model system to explore the interplay between dislocation-induced structural modifications and lattice thermal conductivity. Our measurements reveal a monoclinic structure stabilized by the dislocation, leading to a substantial drop in thermal conductivity for larger-diameter NWs (70% at room temperature), supported by first-principles calculations. Interestingly, we also find an anomalous enhancement of thermal conductivity with decreasing diameter in twisted NWs, contrary to typical trends in non-twisted GeS NWs. This is attributed to increased conductivity near the NW cores due to compressive strain around the central dislocations, and aligns with a density-functional-theory-informed core–shell model. Our results highlight the critical role of dislocations in thermal conduction, providing fundamental insights for defect and strain engineering in advanced thermal applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Metal Removal by a Free Surface Constructed Wetland and Prediction of Metal Bioavailability and Toxicity with Diffusive Gradients in Thin Films (DGT) and Biotic Ligand Model (BLM)

The H-02 constructed wetland is a free water surface wetland to remove copper (Cu) and zinc (Zn) from the industrial wastewater. In this study, we evaluated the performance of the wetland from 2018 to 2019 and coupled the diffusive gradients in thin films (DGTs) and biotic ligand model (BLM) to explore metal speciation and bioavailability in wetland waters. Surface water samples were collected and piston DGTs were deployed in different sites of the wetland. The H-02 wetland functioned well during the sampling period with high removal efficiencies (Cu: 73.8 ± 1.2% and Zn: 75.2 ± 16.0%). In our study, with the assumption that the combination of BLM predicted inorganic metals species, BLM Cu(II) and BLM Zn(II), were the bioavailable and toxic species, DGT-Cu did not correlate to BLM Cu(II) (P = 0.47), but DGT-Zn positively correlated to BLM Zn(II) (R 2 = 0.35, P < 0.001). Compared to the modeling results of BLM, DGT-indicated labile and/or bioavailable Cu included not only free Cu ions and inorganic Cu complexes but also a high percentage of Cu-labile organic matter complexes. DGT-indicated Zn included free Zn ion, inorganic Zn, and only a low percentage of Zn-labile organic matter complexes. Our findings illustrated the appropriate use of passive sampling techniques and geological modeling when biomonitoring could be substituted. Furthermore, the close monitoring of metal concentrations, speciation, and bioavailability helps us understand metal biogeochemistry and metal removal processes and ensure the long-term sustainability of the constructed wetland.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Exploring New Ways to Classify Industries for Energy Analysis and Modeling

As the US moves closer to embracing a net zero greenhouse gas emissions position, combustion processes outside the power sector are becoming urgent concerns. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning that even a single product group can vary broadly in its production routes and associated energy usage. In the US, the North American Industry Classification System (NAICS) serves as the basis for data collection and reporting. In turn, data based on NAICS is the foundation of most US energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for plans to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data to build more detail into heterogeneous sectors is scarce. This work explores alternative classification schemes for industry based on energy use characteristics, and provides a validation of an approach to make facility-level energy use estimates based on publicly available data from the greenhouse gas reporting program. First, several approaches to industrial taxonomies and their usefulness for industrial energy modeling are summarized. Data from Industrial Assessment Centers is analyzed using unsupervised machine learning techniques to detect clusters. Cladistics, an approach from biology, is adapted to energy and process characteristics of industries. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation. Finally, validation is performed for facility-level energy estimates from the US EPA Greenhouse Gas Reporting Program. This validation assists in making this data source available for use in energy modeling. Together, this work explores alternative approaches for categorizing industries in a way that aids understanding energy use, and presenting pathways for the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Biological mechanisms may contribute to soil carbon saturation patterns

Increasing soil organic carbon (SOC) storage is a key strategy to mitigate rising atmospheric CO 2 , yet SOC pools often appear to saturate, or increase at a declining rate, as carbon (C) inputs increase. Soil C saturation is commonly hypothesized to result from the finite amount of reactive mineral surface area available for retaining SOC, and is accordingly represented in SOC models as a physicochemically determined SOC upper limit. However, mineral-associated SOC is largely microbially generated. In this perspective, we present the hypothesis that apparent SOC saturation patterns could emerge as a result of ecological constraints on microbial biomass—for example, via competition or predation—leading to reduced C flow through microbes and a reduced rate of mineral-associated SOC formation as soil C inputs increase. Microbially explicit SOC models offer an opportunity to explore this hypothesis, yet most of these models predict linear microbial biomass increases with C inputs and insensitivity of SOC to input rates. Synthesis of 54 C addition studies revealed constraints on microbial biomass as C inputs increase. Different hypotheses limiting microbial density were embedded in a three-pool SOC model without explicit limits on mineral surface area. As inputs increased, the model demonstrated either no change, linear, or apparently saturating increases in mineral-associated and particulate SOC pools. Taken together, our results suggest that microbial constraints are common and could lead to reduced mineral-associated SOC formation as input rates increase. We conclude that SOC responses to altered C inputs—or any environmental change—are influenced by the ecological factors that limit microbial populations, allowing for a wider range of potential SOC responses to stimuli. Understanding how biotic versus abiotic factors contribute to these patterns will better enable us to predict and manage soil C dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MetaText: Compositional Generalization in Deep Language Models

Compositional generalization, the ability to infer and reason using novel combinations of previously encountered entities and structures, is a trait of great utility across a variety of deep learning tasks; it implies a certain ability to reason using consistent and therefore interpretable rules, to understand the constituent parts of its input at multiple levels of granularity, to be robust to spurious differences between semantically equivalent inputs, and more. We seek to understand the degree to which existing natural language models achieve or fall short of compositional generalization across a variety of tasks, whether there are distinct types of compositional generalization in practice, and to identify avenues of intervention through which we can improve models’ compositional generalization ability. After exploring these questions using an array of models, tasks, and potential interventions we see that large, pretrained language models have encountered sufficient training data to account for a variety of fine-grained compositional behavior but still struggle to reason at the level of phrases or larger language structures. Non-intrusive, data-based interventions in the form of augmenting individual sequences with compressed versions of themselves or deriving new examples from induced grammars prove insufficient to encourage greater levels of compositional reasoning, indicating that future work might benefit most from focusing on changes to a model’s inductive bias at the architectural or loss level, or by integrating compositionality-boosting data interventions into the large-scale pretraining process itself.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ionization Yield in Silicon for eV-Scale Electron-Recoil Processes

The development of single charge resolving, macroscopic silicon detectors has opened a window into rare processes at the O(eV) scale. In order to reconstruct the energy of a given event, or model the charge signal obtained for a given amount of energy absorbed by the electrons in a detector, an accurate charge yield model is needed. In this paper we review existing measurements of charge yield in Silicon, focusing in particular on the region below 1 keV. We highlight a calibration gap between 12-50 eV (referred to as the "UV-gap") and employ a phenomenological model of impact ionization to explore the likely charge yield in this energy regime. Finally, we explore the impact of variations in this model on a test case, that of dark matter scattering off electrons, to illustrate the scientific impact of uncertainties in charge yield.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Fundamental problems in astroparticle physics and cosmology (Final Technical Report)

In the standard cosmological model, structure forms when gas cools and condenses to the center of dark matter halos. The onset of structure is governed by rapid star formation and subsequent evolution of the first massive stars. One of the endpoints of stellar evolution is the formation of a black hole. We showed that all black hole merger events must occur after the universe is about 85 million years old. This prediction is important because it means that if a future experiment makes a detection of a black hole merger event at an earlier time then our understanding of cosmology and the onset of structure formation is not fully developed. In addition, we explored dark matter models that have either self-interactions or experience decays. In the first case, self-interactions may lead to the formation of a thick disk that can be co-planar to the Galactic disc due to correlations in angular momentum between dark matter and baryons that are predicted by tidal torque theory. We showed that for heavy dark matter particles, either the self interaction may not be strong enough to solve the small scale structure motivation or a dark disc cannot be present in the Milky Way. In the second-case, we showed that if dark matter experiences decays into two particles, one of which behaves like radiation, then it is possible to relieve the tension that exists between measurements of the local expansion rate. Finally, we developed methods on how to distinguish gamma-ray excesses from promising dark matter targets as compared to conventional astrophysical sources. This is an important toolset that is applicable to any suspected dark matter source.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks

This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.

Mukherjee, Sayak↗
DOE OSTI2022

The influence of thermochemistry on the reactivity of propane, the pentane isomers and $n$-heptane in the low temperature regime

In this study, the influence of thermochemistry on the reactivity of fuels at low temperatures (600–1000 K) is studied here. Specifically, the effect of different sets of thermochemistry on chemical model predictions is explored, where various sets are calculated at different levels of theory in addition to recently updated group additivity values. Experimentally measured ignition delay times for propane, the pentane isomers and n-heptane are simulated using NUIGMech1.2 and replacing the thermochemistry of the low-temperature species with the calculated values. For propane, three different thermochemistry sets were calculated, namely CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//B2PLYP-D3/TZ (QM1), CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//ωB97X-D/TZ (QM2) and B2PLYP-D3/TZ/ωB97X-D/6-31G*//ωB97X-D/6-31G* (QM3). The QM2 results provide parameters to optimize new group additivity (NGA) values which are used to calculate the fourth set of thermochemistry. The model predictions using these four sets are compared to those using NUIGMech1.2 for propane. As the QM1 and QM2 calculations are expensive, the thermochemistry calculated from the QM3 and NGA calculations are used in the pentane isomer and n-heptane models. For all of the models, it is found that the thermochemistry of the species involved in the low-temperature reaction sequence ($RH$, $\dot{R}$, $RO_2H$, $R\dot{O}_2$, $\dot{Q}OOH$ and $\dot{O}_2QOOH$ species) significantly affect fuel reactivity. The NGA values were developed based on all of these species except $\dot{O}_2QOOH$ radicals. The thermochemistry of $\dot{O}_2QOOH$ species cannot be accurately calculated with the NGA representations due to the importance of non-next-nearest neighbor interactions of –OOH substitution. Further development of the NGA method to capture such interactions is in progress. Overall, the model developed using the NGA thermochemistry shows better agreement with experimental data than the model using thermochemistry from affordable and prominent QM methods, such as QM3. Based on the results presented for propane, the pentane isomers and n-heptane, the thermochemistry calculated using the NGA method can be used to model the oxidation of higher order hydrocarbons at low temperatures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
DOE OSTI2024

Machine learning surrogates for surface complexation model of uranium sorption to oxides

Abstract The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Exploration of groundwater and geothermal characteristics of Tohatchi Hot Springs aquifer at local and regional scales

Objective: Aquifer sustainability for geothermal resources exploration of Tohatchi Hotsprings, New Mexico. Tasks: (1) Generate 3D numerical models using PFLOTRAN to explore the geothermal resources in the Tohatchi spring aquifer and its contiguous areas and (2) estimate the sustainability of the aquifer for future geothermal resource development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterizing the diverse hydrogeology underlying rivers and estuaries using new floating transient electromagnetic methodology

We report the hydrogeology below large surface water features such as rivers and estuaries is universally under-informed at the long reach to basin scales (tens of km+). This challenge inhibits the accurate modeling of fresh/saline groundwater interfaces and groundwater/surface water exchange patterns at management-relevant spatial extents. Here we introduce a towed, floating transient electromagnetic (TEM) system (i.e. FloaTEM) for rapid (up to 15 km/h) high resolution electrical mapping of the subsurface below large water bodies to depths often a factor of 10 greater than other towed instruments. The novel FloaTEM system is demonstrated at a range of diverse 4th through 6th-order riverine settings across the United States including 1) the Farmington River, near Hartford, Connecticut; 2) the Upper Delaware River near Barryville, New York; 3) the Tallahatchie River near Shellmound, Mississippi; and, 4) the Eel River estuary, on Cape Cod, near Falmouth, Massachusetts. Airborne frequency-domain electromagnetic and land-based towed TEM data are also compared at the Tallahatchie River site, and streambed geologic scenarios are explored with forward modeling. A range of geologic structures and pore water salinity interfaces were identified. Process-based interpretation of the case study data indicated FloaTEM can resolve varied sediment-water interface materials, such as the accumulation of fines at the bottom of a reservoir and permeable sand/gravel riverbed sediments that focus groundwater discharge. Bedrock layers were mapped at several sites, and aquifer confining units were defined at comparable resolution to airborne methods. Terrestrial fresh groundwater discharge with flowpaths extending hundreds of meters from shore was also imaged below the Eel River estuary, improving on previous hydrogeological characterizations of that nutrient-rich coastal exchange zone. In summary, the novel FloaTEM system fills a critical gap in our ability to characterize the hydrogeology below surface water features and will support more accurate prediction of groundwater/surface water exchange dynamics and fresh-saline groundwater interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A combined theoretical and experimental study of the phase coexistence and morphotropic boundaries in ferroelectric-antiferroelectric-antiferrodistortive multiferroics

The physical nature of the ferroelectric (FE), ferrielectric (FEI) and antiferroelectric (AFE) phases, their coexistence and spatial distributions underpins the functionality of antiferrodistortive (AFD) multiferroics in the vicinity of morphotropic phase transitions. In this work, using Landau-Ginzburg-Devonshire (LGD) phenomenology and a semi-microscopic four sublattice model (FSM), we explore the behavior of different AFE, FEI, and FE long-range orderings and their coexistence at the morphotropic phase boundaries in FE-AFE-AFD multiferroics. These theoretical predictions are compared with the experimental observations for dense Bi 1-y R y FeO 3 ceramics, where R is Sm or La atoms with the fraction 0 ≤ y ≤ 0.25, as confirmed by the X-ray diffraction (XRD) and Piezoresponse Force Microscopy (PFM). These complementary measurements were used to study the macroscopic and nanoscopic transformation of the crystal structure with doping. The comparison of the measured and calculated AFE/FE phase fractions demonstrate that the LGD-FSM approach well describes the experimental results obtained by XRD and PFM for Bi 1-y R y FeO 3 . Hence, this combined theoretical and experimental approach provides further insight into the origin of the morphotropic boundaries and coexisting FE and AFE states in model rare-earth doped multiferroics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM

Abstract Mesoscale convective systems (MCSs) are one of the most climatically significant forms of convection because of their large role in water and energy cycles. The mesoscale features associated with MCS are difficult to represent in climate models because the relevant dynamics and physics are absent or poorly represented with coarse model resolution (∼100 km). Using a regionally refined model (RRM) with 0.25° grid spacing embedded in the Energy Exascale Earth System Model (E3SM), we explore the impact of cloud microphysics parameterizations on the simulation of precipitation, particularly MCS precipitation over the contiguous United States. The Predicted Particle Properties (P3) cloud microphysics scheme has been modified and implemented into E3SM to overcome the limitations of the default Morrison and Gettelman (MG2) scheme in which rimed precipitating ice particles (graupel/hail) are absent and frozen particles are artificially partitioned into cloud ice and snow. We show that P3 improves the simulation of precipitation statistics including frequency distribution compared with MG2 with a limited effect on the diurnal cycle. P3 predicts higher hourly rain rates, resulting in 20% more MCSs and a higher total MCS precipitation (4.4%) compared to MG2, agreeing better with observations. The improvements with P3 mainly result from improved representations of ice microphysics, which not only produces higher rain rates through melting but also leads to a stronger large‐scale ascending motion by releasing more latent heating. This study suggests that improving microphysics parameterization is important for simulating MCS precipitation as future climate model resolutions continue to increase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2025

Optimization of the BDX experiment for Light Dark Matter searches at JLab

The Light Dark Matter (LDM) hypothesis postulates the existence of a new class of sub GeV particles, neutral under Standard Model (SM) interactions. In its simplest form, LDM consists of particles ¿ with masses below 1 GeV/c2, interacting with SM particles via a new force mediated by a light, spin-1 boson A0, commonly referred to as “Dark Photon”. This framework envisions a distinct “Dark Sector” with its own particles and interactions, offering a theoretically well motivated explanation for Dark Matter, consistent with astrophysical observations and a thermal production mechanism. The Beam Dump eXperiment (BDX) is an approved experiment at Jefferson Lab designed to search for Light Dark Matter. BDX will utilize an 11 GeV electron beam impinging on a thick target to produce a forward-boosted secondary beam of Light Dark Matter particles, which will then be detected by a dedicated downstream detector. Approved in 2018, BDX is expected to be commissioned in 2026 and run in 2027-2029. My thesis focuses on the preparatory work to deploy the BDX experiment. The detector design has been optimized to balance practicality with enhanced Light Dark Matter detection capabilities. Extensive characterization of detector components has been performed to ensure a precise understanding of detector response. A custom Monte Carlo framework has been developed to simulate Light Dark Matter signal and explore various theoretical models of interest. Additionally, a comprehensive data analysis framework has been developed to maximize the experiment sensitivity to Light Dark Matter, with optimizations based on expected detector performance. The ultimate goal of this thesis is to optimize BDX as a flagship experiment in Light Dark Matter searches, enabling it to probe different Dark Matter models.

Spreafico, M. [Univ. of Genova (Italy)]↗