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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The 'ABCD method' provides a reliable framework for background estimation by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two smoothly varying, statistically independent variables. In practice, even slight correlations between the two variables can significantly undermine the method's performance. Thus, choosing appropriate variables by hand can present a formidable challenge, especially when background and signal differ only subtly. To address this issue, the ABCD with distance correlation (ABCDisCo) method was developed to construct two learned variables via a neural network trained to provide strong signal-background discrimination with small values of the distance correlation (DisCo) measure between the two learned variables. However, relying solely on minimizing the DisCo can result in learned variables that may not have distributions of background events that are smoothly varying and localized at extreme values, as necessary for the validity of the background estimation. The ABCDisCo training enhanced with closure (ABCDisCoTEC) method is introduced to solve this issue by directly minimizing the nonclosure, expressed as a dedicated differentiable loss term. This extended method is applied to a data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the CERN Large Hadron Collider. Additionally, given the complexity of the minimization problem with constraints on multiple loss terms, the modified differential method of multipliers is applied and shown to greatly improve the stability and robustness of the ABCDisCoTEC method, compared to grid search hyperparameter optimization procedures.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2026

pyFLANK, a graph neural network based null distribution inference model for F ST outlier detection

Detecting genomic regions under selection is essential for understanding how populations adapt to different environments, yet it remains challenging due to the confounding effects of demographic history and linkage disequilibrium (LD). Fixation index (F ST ) is a widely used statistic to identify genomic regions under adaptation. However, identifying genes under selection by defining F ST outliers often remains challenging, owing to confounding effects of underlying demographic history. Traditional methods assume independence among loci and rely on simple demographic models, while newer models perform much better but are computationally expensive and not easily scalable. Here, we present pyFLANK, an open-source and automated Python implementation which detects F ST outliers using a null distribution inferred from quasi-independent loci. Our tool integrates three approaches to identify loci obeying a null distribution: graph neural network (GNN) inference, linkage disequilibrium (LD)-based inference, and user-defined input. Because pyFLANK uses GNN-based inference of quasi-independent loci, it yields a more accurate null model with less need for user parameter input. In simulation experiments, pyFLANK achieved lower false positive rates than current methods while maintaining comparable detection power, indicating that its refined null model better distinguishes true adaptive loci from background variation. The GNN-based model, in particular, detected additional loci associated with phenotypic variance that were not identified by existing methods. Assessments of simulation and real data from different species demonstrate that pyFLANK achieves lower false positive rates compared with other commonly used F ST outlier detectors, while maintaining comparable detection power and excellent computational performance, providing a robust and user-friendly tool for identifying loci under divergent selection. It extends existing F ST outlier frameworks by incorporating explicit LD-aware strategies for null model calibration. The method is intended as a practical and scalable complement to existing genome scan approaches.

FST↗
NASA NTRS1981

Future prospects for gamma-ray astronomy

As gamma-ray astronomy moves from the discovery to the exploratory phase, the promise of gamma-ray astrophysics noted by theorists in the late 1940s and 1950s is beginning to be realized. In the future, satellites should carry instruments that will have over an order of magnitude greater sensitivity than those flown thus far, and, for at least some portions of the gamma-ray energy range, these detectors will also have substantially improved energy and angular resolution. The information to be obtained from these experiments should greatly enhance our knowledge of several astrophysical phenomena including the very energetic and nuclear processes associated with compact objects, astrophysical nucleosynthesis, solar particle acceleration, the chemical composition of the planets and other bodies of the solar system, the structure of our galaxy, the origin and dynamic pressure effects of the cosmic rays, high energy particles and energetic processes in other galaxies especially active ones, and the degree of matter-antimatter symmetry of the universe. The gamma-ray results of the forthcoming programs such as Gamma-I, the Gamma Ray Observatory, the gamma-ray burst network, Solar Polar, and very high energy gamma-ray telescopes on the ground will almost certainly provide justification for more sophisticated telescopes. These advanced instruments might be placed on the Space Platform currently under study by N.A.S.A.

Fichtel, C.↗
NASA NTRS2008

NASA Tech Briefs, December 2008

Topics covered include: Crew Activity Analyzer; Distributing Data to Hand-Held Devices in a Wireless Network; Reducing Surface Clutter in Cloud Profiling Radar Data; MODIS Atmospheric Data Handler; Multibeam Altimeter Navigation Update Using Faceted Shape Model; Spaceborne Hybrid-FPGA System for Processing FTIR Data; FPGA Coprocessor for Accelerated Classification of Images; SiC JFET Transistor Circuit Model for Extreme Temperature Range; TDR Using Autocorrelation and Varying-Duration Pulses; Update on Development of SiC Multi-Chip Power Modules; Radio Ranging System for Guidance of Approaching Spacecraft; Electromagnetically Clean Solar Arrays; Improved Short-Circuit Protection for Power Cells in Series; Electromagnetically Clean Solar Arrays; Logic Gates Made of N-Channel JFETs and Epitaxial Resistors; Improved Short-Circuit Protection for Power Cells in Series; Communication Limits Due to Photon-Detector Jitter; System for Removing Pollutants from Incinerator Exhaust; Sealing and External Sterilization of a Sample Container; Converting EOS Data from HDF-EOS to netCDF; HDF-EOS 2 and HDF-EOS 5 Compatibility Library; HDF-EOS Web Server; HDF-EOS 5 Validator; XML DTD and Schemas for HDF-EOS; Converting from XML to HDF-EOS; Simulating Attitudes and Trajectories of Multiple Spacecraft; Specialized Color Function for Display of Signed Data; Delivering Alert Messages to Members of a Work Force; Delivering Images for Mars Rover Science Planning; Oxide Fiber Cathode Materials for Rechargeable Lithium Cells; Electrocatalytic Reduction of Carbon Dioxide to Methane; Heterogeneous Superconducting Low-Noise Sensing Coils; Progress toward Making Epoxy/Carbon-Nanotube Composites; Predicting Properties of Unidirectional-Nanofiber Composites; Deployable Crew Quarters; Nonventing, Regenerable, Lightweight Heat Absorber; Miniature High-Force, Long-Stroke SMA Linear Actuators; "Bootstrap" Configuration for Multistage Pulse-Tube Coolers; Reducing Liquid Loss during Ullage Venting in Microgravity; Ka-Band Transponder for Deep-Space Radio Science; Replication of Space-Shuttle Computers in FPGAs and ASICs; Demisable Reaction-Wheel Assembly; Spatial and Temporal Low-Dimensional Models for Fluid Flow; Advanced Land Imager Assessment System; Range Imaging without Moving Parts.

Source record↗
NASA NTRS1988

Digital carrier demodulation for the DSN Advanced Receiver

The digital in-phase and quadrature (I and Q) carrier demodulation for the Deep Space Network's (DSN) Advanced Receiver is described and analyzed. The system provides coherent demodulation for a variety of modulation formats including Binary Phase Shift Keying (BPSK), BPSK with a carrier residual, Quadrature Phase Shift Keying (QPSK), Offset-QPSK (OQPSK), and Minimum Shift Keying (MSK). The focus is on the theory and hardware design of the halfband filters which are the integral part of the demodulator. The underlying theory of the filters is summarized, a breadboard hardware design is described, and a VLSI implementation is proposed which significantly decreases the hardware. A second important problem analyzed is DC-offset in the demodulator. This is a serious problem which translates into bias error in the residual carrier phase detector. The dynamic range of the complex mixer is analyzed using a probabilistic approach. It is deduced that the resulting static phase error is less than 0.2 deg when the ratio of carrier power to noise power in the demodulator input bandwidth is -72 dB or higher. Thus, the static phase error is negligible at a carrier power to noise spectral density of 0 dB-Hz for a 15 MHz bandwidth demodulator.

Sadr, R.↗
DOE OSTI2024

A First Search for Argon-Bound Neutron-Antineutron Oscillation using the MicroBooNE LArTPC

The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n ₋ n̄) oscillation. The n ₋ n̄ oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n ₋ n̄ oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n ₋ n̄ oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC

The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n – n̄) oscillation. The n – n̄ oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n – n̄ oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n – n̄ oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2008

Implementing a Digital Phasemeter in an FPGA

Firmware for implementing a digital phasemeter within a field-programmable gate array (FPGA) has been devised. In the original application of this firmware, the phase that one seeks to measure is the difference between the phases of two nominally-equal-frequency heterodyne signals generated by two interferometers. In that application, zero-crossing detectors convert the heterodyne signals to trains of rectangular pulses, the two pulse trains are fed to a fringe counter (the major part of the phasemeter) controlled by a clock signal having a frequency greater than the heterodyne frequency, and the fringe counter computes a time-averaged estimate of the difference between the phases of the two pulse trains. The firmware also does the following: Causes the FPGA to compute the frequencies of the input signals; Causes the FPGA to implement an Ethernet (or equivalent) transmitter for readout of phase and frequency values; and Provides data for use in diagnosis of communication failures. The readout rate can be set, by programming, to a value between 250 Hz and 1 kHz. Network addresses can be programmed by the user.

Rao, Shanti R.↗
NASA NTRS1990

Simple technique for optical flow estimation

The theoretical principles, design, and implementation of an image-motion detection system with possible robotics and autonomous-navigation applications are discussed. The operation of a simple one-dimensional second-derivative edge-motion detector is explained; its extension to a two-dimensional motion sensor based on the Laplacian of a Gaussian (Marr and Hildreth, 1980) is outlined; a center-surround array of 13 of these sensors (12 in a circle around a central sensor) is proposed; and processing techniques to overcome the aperture problem are examined. Simple video images involving unidirectional motion are then analyzed using a prototype version of this design; the sensors employed are based on a 15 x 15-pixel mask made up of 13 7 x 7-pixel kernels. Accurate measurements of image velocity are obtained, as required for navigation through complex environments. It is pointed out that the procedure is noniterative and well suited to implementation as a parallel network.

Perrone, John A.↗
NASA NTRS2004

Neural Network for Image-to-Image Control of Optical Tweezers

A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

Decker, Arthur J.↗
NASA NTRSDate not supplied

The International Space Station Lightning Imaging Sensor: Performance, Applications, and Cross-Platform Science

The Lightning Imaging Sensor (LIS), an optically based lightning detector developed at NASA Marshall Space Flight Center, was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm-scale horizontal resolution (4-km) during both day and night. ISS LIS data are available 1 March 2017 to present. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground-based lightning sensors. Initial comparisons with those other sensors suggest flash detection efficiency around 60% (diurnal variability of 51-75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform flash detection capability of ISS LIS from low-Earth orbit allows assessment of spatially varying flash detection efficiency for other sensors and networks, particularly the Geostationary Lightning Mappers (GLMs). ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as realtime lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long-term global climatology of lightning from space, and is the only recent platform that extends the global record to higher latitudes (± 55°). The global spatial distribution of lightning from ISS LIS is broadly similar to previous datasets, with globally averaged seasonal/annual flash rates (3-year average: ~44 s) about 5-10% lower. This difference is likely due to reduced flash detection efficiency that will be mitigated in future ISS LIS data processing, as well as the shorter ISS LIS period of record. The expected land/ocean contrast in the diurnal variability of global lightning is also observed. The near-realtime ISS LIS data make the instrument important to operational weather forecasting and related applications, including public safety. Finally, ISS LIS is demonstrating utility as part (or potential part) of cross-platform studies examining a diverse array of topics, including lightning physics, thunderstorm processes, convective precipitation, and atmospheric composition.

Timothy Lang↗
DOE OSTI2022

Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN

In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. Furthermore, this event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Maximizing quantum enhancement in axion dark matter experiments

We provide a comprehensive comparison of linear amplifiers and microwave photon counters in axion dark matter experiments. The study is done assuming a range of realistic operating conditions and detector parameters, over the frequency range between 1 and 30 GHz. As expected, photon counters are found to be advantageous under low background, at high frequencies (𝜈 >5 GHz), if they can be implemented with robust wide-frequency tuning or a very low dark count rate. Additional noteworthy observations emerging from this study include: (1) an expanded applicability of off-resonance photon background reduction, including the single-quadrature state squeezing, for scan rate enhancements; (2) a much broader appeal for operating the haloscope resonators in the overcoupling regime, up to 𝛽 ∼10; (3) the need for a detailed investigation into the cryogenic and electromagnetic conditions inside haloscope cavities to lower the photon temperature for future experiments; (4) the necessity to develop a distributed network of coupling ports in high-volume axion haloscopes to utilize these potential gains in the scan rate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Anomaly Detection and Mitigation in FACTS-based Wide-Area Voltage Control Systems using Machine Learning

With the increasing deployment of Flexible AC Transmission System (FACTS) devices in wide-area voltage control systems (WAVCS) for achieving improved voltage stability of bulk power systems, the possibility for cyber attacks on these systems is also increasing. Successful stealthy cyber attacks that are difficult to detect by traditional informational technology (IT)-based cybersecurity solutions or threshold-based bad data detectors can lead to a voltage collapse in power grid. This paper presents the testbed-based attacks implementation and real-time evaluation of machine learning (ML) algorithm for detecting and mitigating stealthy cyber attacks on FACTS-based WAVCS on a hardware-in-the-loop (HIL) testbed. Initially, we discuss the implementation of a fuzzy logic controller (FLC) that controls a Static VAR Compensator (SVC) device deployed in a two-area four-machine Kundur power system for improving transient voltage stability. Later, the ML-based Anomaly Detection and Mitigation (ADM) system is implemented on the cyber-physical HIL testbed to detect and mitigate various stealthy cyber attacks, which are injected in real-time over the wide-area network (WAN). The experimental results show accurate and effective performance of ADM system in detecting and mitigating anomalies while keeping the grid stable and within the system operating limits, as defined by the North America Electric Reliability Corporation (NERC).

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2005

NASA's Next Generation Sunphotometer for Ground-based Remote Sensing Applications

Atmospheric aerosol concentrations and their optical properties, in terms of which cause differential warming/cooling effects in the atmosphere and on the surface, constitute one of the largest sources of uncertainty in current assessments and predictions of global climatic change. This is especially true over the regions of bright-reflecting surface, such as desert and urban areas. Under the name of AERONET since the 90's, Cimel's sunphotometers have been deployed worldwide as the standard instrument for aerosol monitoring network, developed to support NASA, CNES, and NASDA's Earth satellite systems. PREDE's skyradiometers, as deployed in SKYNET, serve the similar role. One of the key ingredients for achieving accurate aerosol retrievals from satellite observations is comprehensive understanding of surface spectral BRF's (Bidirectional Reflectance Factors), defined as a ratio of radiance measurements reflected from a targeted surface and from a spectral-angular featureless referencing plate. Although the weather-resistant, automatic, sun/sky-scanning spectroradiometers enable Frequent measurements of atmospheric aerosol optical properties and precipitable water at remote sites, they are too slow for surface BRF measurements (20-25 seconds per 360 degree scan, in addition to filter wheel rotation time). We have designed a next generation sun photometer whose sensor head has no moving parts. A dedicated detector for each channel enables 12 simultaneous measurements ranging from the UV (380 nm) to shortwave-IR (2.13 micron) regions. The scan platform will be capable of traveling 360 degrees in about 6 seconds. This is sufficient to finish a BRDF scan every 30 degrees in azimuth and 15 degrees in elevation in less than 4 minutes. More details about this instrument will be presented, together with its applications to the aerosol and trace gas studies. The current plan for this instrument is to deploy during the EAST-AIRE (East Asian Study of Tropospheric Aerosols: an International Regional Experiment) and BASE-ASIA (Biomass-burning Aerosols in South East-Asia: Smoke Impact Assessment).

Shu, Peter K.↗
NASA NTRS2003

Review of the High Performance Antiproton Trap (HiPAT) Experiment at the Marshall Space Flight Center

The significant energy density of matter-antimatter annihilation is attractive to the designers of future space propulsion systems, with the potential to offer a highly compact source of power. Many propulsion concepts exist that could take advantage of matter-antimatter reactions, and current antiproton production rates are sufficient to support basic proof-of-principle evaluation of technology associated with antimatter- derived propulsion. One enabling technology for such experiments is portable storage of low energy antiprotons, allowing antiprotons to be trapped, stored, and transported for use at an experimental facility. To address this need, the Marshall Space Flight Center's Propulsion Research Center is developing a storage system referred to as the High Performance Antiproton Trap (HiPAT) with a design goal of containing 10(exp 12) particles for up to 18 days. The HiPAT makes use of an electromagnetic system (Penning- Malmberg design) consisting of a 4 Telsa superconductor, high voltage electrode structure, radio frequency (RF) network, and ultra high vacuum system. To evaluate the system normal matter sources (both electron guns and ion sources) are used to generate charged particles. The electron beams ionize gas within the trapping region producing ions in situ, whereas the ion sources produce the particles external to the trapping region and required dynamic capture. A wide range of experiments has been performed examining factors such as ion storage lifetimes, effect of RF energy on storage lifetime, and ability to routinely perform dynamic ion capture. Current efforts have been focused on improving the FW rotating wall system to permit longer storage times and non-destructive diagnostics of stored ions. Typical particle detection is performed by extracting trapped ions from HiPAT and destructively colliding them with a micro-channel plate detector (providing number and energy information). This improved RF system has been used to detect various plasma modes for both electron and ion plasmas in the two traps at MSFC, including axial, cyclotron, and diocotron modes. New diagnostics are also being added to HiPAT to measure the axial density distribution of the trapped cloud to match measured RF plasma modes to plasma conditions.

Pearson, J. B.↗
NASA NTRS2004

Using the Moon and Mars as Giant Detectors for Strange Quark Nuggets

On the Earth, the detectability of small seismic signals is limited by pervasive seismic background noise, caused primarily by interactions of the atmosphere and oceans with the solid surface. Mars, with a very thin atmosphere and no ocean is expected to have a noise level at least an order of magnitude lower than the Earth, and the airless Moon is even quieter still. These pristine low-vibration environments are ideal for searching for nuggets of "strange quark matter." Strange quark matter was postulated by Edward Witten [Phys. Rev. D30, 272, 1984] as the lowest possible energy state of matter. It would be made of up, down, and strange quarks, instead of protons and neutrons made only of up and down quarks. It would have nuclear densities, and hence be difficult to detect. Micron-sized nuggets would weigh in the ton range. As suggested by de Rujula and Glashow [Nature 312 (5996): 734, 1984], a massive strange quark nugget can generate a trail of seismic waves, as it traverses a celestial body. We discuss the mission concept for deploying a network of sensitive seismometers on Mars and on the Moon for such a search.

Chui, Talso↗