Active Projects - FY 2024
New LDRD Projects
Lead Scientist: Zeeshan Ahmed
This project seeks to make advances in analysis, simulation, and experimental techniques to enable possible substantial improvement in constraints or a discovery of cosmic inflation. Efforts here will be aimed at improvements in galactic foreground and atmospheric modeling and mitigation, as well as modeling and suppression of instrumental systematic effects. Additionally, the project will seek improvements in redshift tomography of growth of structure to better constrain neutrino mass and assess the state of growing discrepancies between early universe and late universe measurements.
Lead Scientist: Sharon Bone
This work seeks to enhance the performance of ion exchange membranes by improving molecular understanding of foulant deposition mechanisms using synchrotron-based X-ray methods. The overall goal is to develop an efficient fouling mitigation scheme to enable long-term use of ion exchange. The fouling mitigation schemes developed would help researchers adopt a strategy that well defines their targeted wastewater streams and treatment chains. Focus is on electrochemical nutrient recovery at the Stanford Codiga Resource Recovery Center, a pilot-scale wastewater treatment plant and testbed affiliated with the Stanford Doerr School of Sustainability.
Lead Scientist: Mark Convery
This project advances the development status of the Grid-Activated Multi-scale Pixel (GAMPix) application-specific integrated circuit (ASIC) in a two-pronged approach: a measurement campaign of the parameters of a large set of foundry-produced transistors at cryogenic temperatures and integration of these models into the design tools. Then, a first iteration of the GAMPix chip will be designed, followed by bench testing to validate the cryogenic design process.
Lead Scientist: Ruaridh Forbes
This project addresses the technical challenges of extending two-dimensional spectroscopy to the ultraviolet, where the scientific community envisions a broad range of applications to address ultrafast dynamics of aromatic amino acids in proteins and DNA bases and to investigate the energy and charge transfer flow in these systems. This effort exploits a novel hollow-core fiber-based laser source to generate tunable femtosecond laser sources across the ultraviolet and manipulate these pulses using adaptive pulse shaping methods. These laser sources can produce few-cycle pulses in the ultraviolet with pulse durations down to a single femtosecond. To showcase the power of experimental methodology, proof-of-principle experiments will be carried out on oligonucleotides.
Lead Scientist: Hongchen Jiang
The goal of the project is to develop world-class computational codes for the simulation of accurate dynamical spectroscopic response in X-ray experiments. This effort seeks to develop a highly efficient computationally spectroscopic program based on dynamical density-matrix renormalization group to comprehend the dynamics of coupled degrees of freedom in quantum materials. Particular emphasis will be placed on experiments to be performed at LCLS-II and SSRL and at ALS, using beamlines focused on angle-resolved photoemission spectroscopy and resonant inelastic X-ray scattering to determine various response functions.
Lead Scientist: Aharon Kapitulnik
The objective of this project is to develop a unique, highly sensitive technique for studying time-resolved Kerr and Faraday effects at the picosecond timescale, thus enabling studies of the dynamics associated with time reversal symmetry breaking effects in quantum materials. This outcome will be achieved by constructing a modified zero area loop Sagnac interferometer and adding a “pump-probe” component that will not affect the performance of the apparatus but will allow for first-ever use of a highly coherent source, which was not possible with the previous continuous wave apparatus. The utility of this instrument as a key component of an all-optical system for topological quantum computing applications using chiral topological super-conductors will be further explored.
Lead Scientist: Kirit Karkare
The objectives of this project are to advance two aspects of on-chip millimeter-wave spectrometers, which will enable the order-of-magnitude sensitivity increases needed for next-generation line intensity mapping experiments to probe cosmology beyond the reach of galaxy surveys. First, fabrication of kinetic inductance detectors using new, low-loss millimeter-wave dielectric materials will be explored. Second, a prototype multi-layer focal plane will be demonstrated, in which detector wafers are arranged in a three-dimensional structure, increasing the sampling efficiency of spectroscopic focal planes by a factor greater than five.
Lead Scientist: Hyunjoon Kim
The primary goal of this project is to develop a new generation of application-specific integrated circuit (ASIC) architecture that implements the memory-centric computing design paradigm for machine learning applications. Compared to the current state-of-the-art data processing paradigm (i.e., Von Neumann architecture), compute in-/near-memory adaptation offers orders of magnitude improvements in energy and hardware area efficiencies. This project aims to develop the intellectual property core catalog of in-/near-memory macro that is reconfigurable and compatible with the SLAC Neural Library framework for machine learning applications, expanding SLAC’s machine learning hardware capability for future data processing needs in next-generation pixel detectors.
Lead Scientist: Patrick Krejcik
The primary goal of this project is to develop the next generation of electron beam streaking technology to measure the longitudinal phase space and temporal structure of the electron bunch in a free electron laser moving into the era of attosecond science. The project will examine the limitations of the present radio frequency systems and demonstrate that higher resolution can only be attained with shorter wavelength operation and a beam synchronous power source at terahertz (THz) frequencies. The new deflector would provide far higher temporal resolution than is capable with existing radio frequency technology.
Lead Scientist: Chao-Lin Kuo
The quantum chromodynamic (QCD) axion simultaneously solves a long-standing problem in particle physics (strong charge parity) and provides a compelling candidate for dark matter. This effort leverages SLAC’s unique technical expertise and facilities to advance a novel axion haloscope design for future high frequency Axion Dark Matter eXperiment (ADMX) searches. The design can improve the scan rate for dark matter axions by more than three orders of magnitude in the centimeter (cm)-wave. The work of this project is based on a new space-filling thin-shell cavity design that avoids the precipitous degradation in sensitivity as a function of frequency.
Lead Scientist: Adi Natan
This research will disentangle structural dynamics at the interface from the bulk crystal and liquid, enabling a deeper mechanistic understanding of the surface-induced solvent dynamics using a joint theory-experiment effort aimed at electron solvation dynamics at two-dimensional metal/liquid interfaces. The project seeks to: demonstrate the ability to use ultrafast X-ray scattering and advanced analysis schemes to interrogate structural dynamics at interfaces with unprecedented resolution; demonstrate the ability of joint experimental and theoretical ultrafast X-ray scattering studies for understanding surface-liquid interfacial dynamics; create the framework for future research capitalizes on the core strengths of the LCLS and the Chemical Science Division at SLAC.
Lead Scientist: Quynh Nguyen
This project seeks to expand the horizon of complex photoelectron spectrometers with multi-lens configurable electron optics coupled with state-of-the-art free electron laser modes to explore exotic quantum materials, nanomaterials, and complex gas phase systems by developing an end-to-end machine learning-directed model to automate the alignment of electron optics within the time of flight. Efforts here focus on two specific use cases at LCLS: a Time-of-Flight Momentum Microscope and the Multi-Resolution Cookiebox. The overall aim is to build digital twins of spectrometers to construct a digital electron imaging lens stack with a high-fidelity multi-physics model capable of describing the micro and macro features and mirroring the state of and the behavior of the physical system.
Lead Scientist: Aldo Pena Perez
The goal of this project is to co-design, fabricate, and characterize Application Specific Integrated Circuits (ASICs) for 4 Kelvin (K) cryo-complementary metal-oxide-semiconductor operation, addressing current technology gaps for successful implementation. Electrical components identified will reduce complexity and enhance performance in instrumentation for extreme environments. SLAC expertise and leadership in next-generation experiments with superconducting sensors, e.g., Cosmic Microwave Background Stage 4, will be leveraged to identify the key performance specifications of the circuits to be developed.
Lead Scientist: Monika Schleier-Smith
Arrays of neutral atoms in optical tweezers are among the most promising platforms for scalable quantum information processing. Realizing fault-tolerant quantum computation remains a major challenge requiring significant advances in quantum error correction: necessary capabilities include mid-circuit measurements and feedback; the ability to implement gates with nonlocal connectivity is another promising enhancement. This project will develop both conceptual schemes and technical capabilities for realizing error correction protocols incorporating mid-circuit measurements and non-local gates. The key enabling technology will be an innovative experimental apparatus featuring an array of Rydberg atoms trapped within a superconducting millimeter-wave resonator.
Lead Scientist: Tom Shutt
This project addresses two major areas of technical risk in liquid xenon detector technology—high voltage and liquid purification—which are critical to execution of a low-background 100-tonne-class time projection chamber to search for dark matter and other rare processes aligned with the DOE mission. Despite the overall maturity of liquid xenon time projection chamber technology, better understanding of fundamental processes and methods in both areas will address technical and performance risks. Project success would advance the necessary capabilities for critical detector requirements that comprise some of the leading technical risks.
Lead Scientist: Kazuhiro Terao
This project pursues research within the “AI center for Neutrino Physics (AINU)”. AINU is designed to be complementary to existing Artificial Intelligence and Machine Learning (AI/ML) institutes and organizations in the High Energy Physics (HEP) community, and to develop common tools and technical standards to be shared within the community. Research and development efforts will focus on: developing a set of curated, well-documented datasets across neutrino experiments with user-friendly web interfaces and integrations; developing high-impact AI/ML applications that address open research challenges in neutrino physics from these datasets.
Lead Scientist: Joshua Turner
Focusing on tensor network algorithms for quantum systems, the goal of this project is to build a general quantum simulation pathway to study highly interesting and applicable physical states for quantum magnetism, specifically with advanced designs for a quantum circuit structure. Sophisticated theoretical tools will be used to produce different quantum spin liquid states capable of running on newly available quantum processors, addressing how to prepare a particular spin liquid state with the noisy intermediate-scale quantum device. The simulation to run on this primitive quantum computer will be prepared, with a secondary result being the prediction of the natural quantum spin liquid state.
Lead Scientist: Yan-Kai Tzeng
The goal of this project is to create a state-of-the-art quantum sensing and ion-imaging platform that will be used to monitor the movement of single ions in batteries during the charge and discharge processes. This platform will be based on the unique properties of the "defect color center" in hexagonal boron nitride, enabling a better understanding of electrochemical processes hindering the performance of alkali metals ion batteries to develop the next generation of energy storage devices. This platform would also allow for a deeper understanding of solid-state-electrolyte formation, which plays a crucial role in the performance of alkali metals ion batteries.
Lead Scientist: Samuel Webb
Conventional fixed-aperture raster-based X-ray imaging methods are highly inefficient at the often-critical task of finding sparsely distributed, small sized materials distributed in a matrix because scanning a large unknown area at fixed high spatial resolution inherently allocates a very large time fraction to scanning empty space. Machine learning (ML) in this field is most often utilized at the “back end” of data collection, i.e., after a signature rich data set has been collected. However, deploying ML at the “front end” of the analysis workflow could produce major efficiency gains. This project will develop reinforcement learning-based learning algorithms on a platform with multiple optics objectives in order to achieve higher efficiencies to locate and analyze sparse distributions of actinide particles.
