Detection Of Point Sources Research Articles

SiamVIT: A patchwise network for gamma-ray point source detection

Conventional point source detection methods generally work in a pixelwise manner and can hardly exploit the overall semantic information of sources; consequently, these methods usually suffer from low precision. In this work we achieve point source detection in fully patchwise mode by proposing a siamese network called SiamVIT that includes a visual transformer (VIT). SiamVIT can effectively and accurately locate point sources from gamma -ray maps with high purity not only in higher flux regions, but also in lower flux regions, which is extremely challenging to achieve with state-of-the-art methods. SiamVIT consists of two VIT branches and a matching block. In the feature extraction stage, gamma -ray maps are fed into one VIT branch to obtain patch representations with adequate semantic and contextual information, whereas detection templates with location information are fed into the other branch to produce template representations. In the location stage, a patch representation and all template representations are fed into the matching block to determine whether the associated gamma -ray map patch contains a point source and where that point source is located, if applicable. We compare our proposed SiamVIT with the current advanced methods and find that SiamVIT has significantly better purity and completeness and a superior Dice coefficient on the test set. In addition, when point sources overlap, SiamVIT can better distinguish different point sources.

Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission

Abstract. Reducing methane (CH4) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20×20 m2 pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100–150 kg h−1 at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (∼ 20×20 m2) mapped over large swaths (∼ 100×100 km2) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130×400 m2 pixel size over a target area measuring ∼ 200×200 km2), thereby delivering quantitative data on basin-wide methane emissions.

Study of a method for theoretical calculation of the efficiency of a LaBr3(Ce) for detecting gamma-rays from point sources

A theoretical model for calculating the full-energy peak efficiency of scintillator detector is proposed in this paper. The effective penetration distance of gamma-rays in Ø3.81cm × 3.81 cm LaBr3(Ce) crystal, aluminum shell and MgO reflector is analyzed. The calculation model for point source detection efficiency is derived, and the attenuation of gamma-rays in the detector shell is computed by theoretical calculation method. This method is used to calculate the efficiency of LaBr3(Ce) with a size of Ø3.81 × 3.81 cm for detecting gamma-rays from point sources of 137Cs and 60Co located in different positions. Compared with experimental data and Monte Carlo (MC) simulation results, the maximum relative deviation is less than 5%. The attenuation of gamma-rays with an energy of 60 keV is more than 50%. The fitted efficiency calibration curve in the energy range of 60 keV–1500 keV is in good agreement with the simulation results. The results show that this method is suitable for calculating the efficiency of the LaBr3(Ce) detector using point sources, especially for analyzing low-energy gamma ray sources. By taking into account the attenuation of the gamma-rays by the detector shell, the measurement accuracy can be effectively improved. The proposed method can be extended for the efficiency calibration of scintillator detectors with various shapes and provides a new approach for 'source-less' efficiency calibration of detectors.

Controlled Discovery and Localization of Signals via Bayesian Linear Programming

Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locations of causal variants. So the statistical task is to output as many disjoint regions containing a signal as possible, each as small as possible, while controlling false positives. Similar problems arise, for example, when locating stars in astronomical surveys and in changepoint detection. Common Bayesian approaches to these problems involve computing a posterior distribution over signal locations. However, existing procedures to translate these posteriors into credible regions for the signals fail to capture all the information in the posterior, leading to lower power and (sometimes) inflated false discoveries. We introduce Bayesian Linear Programming (BLiP), which can efficiently convert any posterior distribution over signals into credible regions for signals. BLiP overcomes an extremely high-dimensional and nonconvex problem to verifiably nearly maximize expected power while controlling false positives. Applying BLiP to existing state-of-the-art analyses of UK Biobank data (for genetic fine-mapping) and the Sloan Digital Sky Survey (for astronomical point source detection) increased power by 30%–120% in just a few minutes of additional computation. BLiP is implemented in pyblip (Python) and blipr (R). Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

In-fiber illumination-detection-synchronized confocal microscopy for high-precision fast axial scanning

Confocal microscopy remains a major workhorse in the 3D surface topography measurement owing to its reliability and compatibility for workpieces with various reflectance, which, however, suffers from slow scanning speed and limited axial resolution. Commercial confocal microscopy primarily relies on the axial movement of a sample or objective lens to create a series of 2D image stacks, and then reconstruct 3D map. Nevertheless, most samples and objective lens are too bulky to scan rapidly, let alone that multiple scans are required to increase the signal-to-noise ratio (SNR). To address this issue, an original approach named illumination-detection-synchronized confocal microscopy was developed, which enabled high-precision fast axial scanning via a compact fiber-multiplexed port with the high-speed synchronous motion of point source and point detection. The approach can effectively improve the peak localization precision while not requiring a high-precision motion stage. Theoretically, via controlling the magnification of the optical system M > 1, the total peak localization precision of axial light intensity response curve can be improved by M3 times. Experiments show that the single-point repeat measurement precision by this approach is improved by 4 times, and the profile measurement precision is enhanced by 2.3 times. Moreover, this approach can effectively avoid collisions between the sample and objective lens, and has the potential to design a low-cost single-point confocal sensor or confocal profilometer.

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

Cosmological prediction of the CSST Ultra Deep Field Type Ia supernova photometric survey

ABSTRACT Type Ia supernova (SN Ia) as a standard candle is an ideal tool to measure cosmic distance and expansion history of the Universe. Here, we investigate the SN Ia photometric measurement in the China Space Station Telescope Ultra Deep Field (CSST-UDF) survey, and study the constraint power on the cosmological parameters, such as the equation of state of dark energy. The CSST-UDF survey is expected to cover a 9 deg2 sky area in 2 yr with 250 s × 60 exposures for each band. The magnitude limit can reach i ≃ 26 AB mag for 5σ point source detection with a single exposure. We generate light-curve mock data for SNe Ia and different types of core-collapse SNe (CCSNe). sncosmo is chosen as the framework by utilizing the salt3 model to simulate SN Ia data. After selecting high-quality data and fitting the light curves, we derive the light-curve parameters and identify CCSNe as contamination, resulting in ∼2200 SNe with an $\sim\!\! 7{{\ \rm per\, cent}}$ CCSN contamination rate. We adopt a calibration method similar to Chauvenet’s criterion, and apply it to the distance modulus data to further reduce the contamination. We find that this method is effective and can suppress the contamination fraction to $\sim\!\! 3.5{{\ \rm per\, cent}}$ with 2012 SNe Ia and 73 CCSNe. In the cosmological fitting stage, we did not distinguish between SNe Ia and CCSNe. We find that the constraint accuracies on ΩM, ΩΛ, and w are about two times better than the current SN surveys, and they could be further improved by a factor of ∼1.4 if including the baryon acoustic oscillation data from the CSST spectroscopic wide-field galaxy survey.

SMA Detection of an Extreme Millimeter Flare from the Young Class III Star HD 283572

We present evidence of variable 1.3 mm emission from the 1 to 3 Myr, spectral-type G2–G5 class III young stellar object (YSO), HD 283572. HD 283572 was observed on eight dates with the Submillimeter Array between 2021 December and 2023 May, with a total on-source time of 10.2 hr, probing a range of timescales down to 5.2 s. Averaging all data obtained on 2022 January 17 shows a 4.4 mJy (8.8σ) point source detection with a negative spectral index (α = −2.7 ± 1.2), with peak emission rising to 13.8 mJy in one 3 minute span, and 25 mJy in one 29.7 s integration (L ν = 4.7 × 1017 erg s−1 Hz−1). Combining our data for the other seven dates shows no detection, with an rms noise of 0.24 mJy beam−1. The stochastic millimeter enhancements on time frames of seconds–minutes–hours with negative spectral indices are most plausibly explained by synchrotron or gyrosynchrotron radiation from stellar activity. HD 283572's 1.3 mm lightcurve has similarities with variable binaries, suggesting HD 283572's activity may have been triggered by interactions with an as-yet undetected companion. We additionally identify variability of HD 283572 at 10 cm, from VLASS data. This study highlights the challenges of interpreting faint millimeter emission from evolved YSOs that may host tenuous disks, and suggests that a more detailed temporal analysis of spatially unresolved data is generally warranted. The variability of class III stars may open up new ground for understanding the physics of flares in the context of terrestrial planet formation.

Single-blind test of nine methane-sensing satellite systems from three continents

Abstract. Satellite-based remote sensing enables detection and mitigation of large point sources of climate-warming methane. These satellites will have the greatest impact if stakeholders have a clear-eyed assessment of their capabilities. We performed a single-blind test of nine methane-sensing satellites from three continents and five countries, including both commercial and government satellites. Over 2 months, we conducted 82 controlled methane releases during satellite overpasses. Six teams analyzed the resulting data, producing 134 estimates of methane emissions. Of these, 80 (58 %) were correctly identified, with 46 true positive detections (34 %) and 34 true negative non-detections (25 %). There were 41 false negatives, in which teams missed a true emission, and 0 false positives, in which teams incorrectly claimed methane was present. All eight satellites that were given a nonzero emission detected methane at least once, including the first single-blind evaluation of the EnMAP, Gaofen 5, and Ziyuan 1 systems. In percent terms, quantification error across all satellites and teams is similar to aircraft-based methane remote sensing systems, with 55 % of mean estimates falling within ±50 % of the metered value. Although teams correctly detected emissions as low as 0.03 metric tons of methane per hour, it is unclear whether detection performance in this test is representative of real-world field performance. Full retrieval fields submitted by all teams suggest that in some cases it may be difficult to distinguish true emissions from background artifacts without a known source location. Cloud interference is significant and appears to vary across teams and satellites. This work confirms the basic efficacy of the tested satellite systems in detecting and quantifying methane, providing additional insight into detection limits and informing experimental design for future satellite-focused controlled methane release testing campaigns.

Space Imaging Point Source Detection and Characterization

Space Imaging Point Source Detection and Characterization

Are There Terrestrial Planets Lurking in the Outer Solar System?

Motivated by recent measurements of the free-floating-planet mass function at terrestrial masses, we consider the possibility that the solar system may have captured a terrestrial planet early in its history. We show that ∼1.2 captured free-floating planets with mass strictly greater than that of Mars may exist in the outer solar system, with a median predicted distance of ∼1400 au. If we consider a logarithmic bin centered on the mass of Mars, rather than a cutoff, we find that ∼2.7 captured free-floating planets with mass comparable to Mars may exist in the outer solar system. We derive an expectation value of ∼0.9 for the number of captured free-floating planets with mass comparable to that of Mars (∼1.4 for mass comparable to that of Mercury) that are currently brighter than the 10 yr coadded point-source detection limits of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. Blind shift-and-stack searches could potentially enable the detection of such a planet if it is currently in the Southern sky. The theoretical argument presented here does not rely on the existence of posited patterns in the orbital elements of small bodies in and beyond the Kuiper Belt, in contrast with other hypothetical outer-solar-system planets motivated in recent years.

Planetary system architectures with low-mass inner planets

Context. The discovery of planets orbiting at less than 1 au from their host star and less massive than Saturn in various exoplanetary systems revolutionized our theories of planetary formation. The fundamental question is whether these close-in low-mass planets could have formed in the inner disk interior to 1 au, or whether they formed further out in the planet-forming disk and migrated inward. Exploring the role of additional giant planet(s) in these systems may help us to pinpoint their global formation and evolution. Aims. We searched for additional substellar companions by using direct imaging in systems known to host close-in small planets. The use of direct imaging complemented by radial velocity and astrometric detection limits enabled us to explore the giant planet and brown dwarf demographics around these hosts to investigate the potential connection between both populations. Methods. We carried out a direct imaging survey with SPHERE at VLT to look for outer giant planets and brown dwarf companions in 27 systems hosting close-in low-mass planets discovered by radial velocity. Our sample is composed of very nearby (<20 pc) planetary systems, orbiting G-, K-, and M-type mature (0.5–10 Gyr) stellar hosts. We performed homogeneous direct imaging data reduction and analysis to search for and characterize point sources, and derived robust statistical detection limits. The final direct imaging detection performances were globally considered together with radial velocity and astrometric sensitivity. Results. Of 337 point-source detections, we do not find any new bound companions. We recovered the emblematic very cool T-type brown dwarf GJ 229 B. Our typical sensitivities in direct imaging range from 5 to 30 MJup beyond 2 au. The non-detection of massive companions is consistent with predictions based on models of planet formation by core accretion. Our pilot study opens the way to a multi-technique approach for the exploration of very nearby exoplanetary systems with future ground-based and space observatories.

First Detection of CO2 Emission in a Centaur: JWST NIRSpec Observations of 39P/Oterma

Centaurs are minor solar system bodies with orbits transitioning between those of trans-Neptunian scattered disk objects and Jupiter-family comets (JFCs). 39P/Oterma (39P) is a frequently active centaur that has recently held both centaur and JFC classifications and was observed with the JWST NIRSpec instrument on 2022 July 27 UTC while it was 5.82 au from the Sun. For the first time, CO2 gas emission was detected in a centaur, with a production rate of = (5.96 ± 0.80) × 1023 molecules s−1. This is the lowest detection of CO2 of any centaur or comet. CO and H2O were not detected down to constraining upper limits. Derived mixing ratios of Q CO/Q ≤ 2.03 and /Q ≥ 0.60 are consistent with CO2 and/or CO outgassing playing large roles in driving the activity, but not water, and show a significant difference between the coma abundances of 29P/Schwassmann–Wachmann 1, another centaur at a similar heliocentric distance, which may be explained by thermal processing of 39P’s surface during its previous JFC orbit. To help contextualize the JWST data we also acquired visible CCD imaging data on two dates in 2022 July (Gemini-North) and September (Lowell Discovery Telescope). Image analysis and photometry based on these data are consistent with a point-source detection and an estimated effective nucleus radius of 39P in the range of R nuc = 2.21–2.49 km.

A synthetic Roman Space Telescope High-Latitude Time-Domain Survey: supernovae in the deep field

ABSTRACT NASA will launch the Nancy Grace Roman Space Telescope (hereafter Roman) in the second half of this decade, which will allow for a generation-defining measurement of dark energy through multiple probes, including Type Ia supernovae (SNe Ia). To improve decisions on survey strategy, we have created the first simulations of realistic Roman images that include artificial SNe Ia injected as point sources in the images. Our analysis combines work done on Roman simulations for weak gravitational lensing studies as well as catalogue-level simulations of SN Ia samples. We have created a time series of images over 2 yr containing ∼1050 SNe Ia, covering a 1 deg2 subarea of a planned 5 deg2 deep survey. We have released these images publicly for community use along with input catalogues of all injected sources. We create secondary products from these images by generating coadded images and demonstrating recovery of transient sources using image subtraction. We perform first-use analyses on these images in order to measure galaxy detection efficiency, point source detection efficiency, and host-galaxy association biases. The simulated images can be found here at https://roman.ipac.caltech.edu/sims/SN_Survey_Image_sim.html.

Laser diode photoacoustic point source detection: machine learning-based denoising and reconstruction.

A new development in photoacoustic (PA) imaging has been the use of compact, portable and low-cost laser diodes (LDs), but LD-based PA imaging suffers from low signal intensity recorded by the conventional transducers. A common method to improve signal strength is temporal averaging, which reduces frame rate and increases laser exposure to patients. To tackle this problem, we propose a deep learning method that will denoise point source PA radio-frequency (RF) data before beamforming with a very few frames, even one. We also present a deep learning method to automatically reconstruct point sources from noisy pre-beamformed data. Finally, we employ a strategy of combined denoising and reconstruction, which can supplement the reconstruction algorithm for very low signal-to-noise ratio inputs.

Single-blind validation of space-based point-source detection and quantification of onshore methane emissions

Satellites are increasingly seen as a tool for identifying large greenhouse gas point sources for mitigation, but independent verification of satellite performance is needed for acceptance and use by policy makers and stakeholders. We conduct to our knowledge the first single-blind controlled methane release testing of satellite-based methane emissions detection and quantification, with five independent teams analyzing data from one to five satellites each for this desert-based test. Teams correctly identified 71% of all emissions, ranging from 0.20 [0.19, 0.21] metric tons per hour (t/h) to 7.2 [6.8, 7.6] t/h. Three-quarters (75%) of quantified estimates fell within ± 50% of the metered value, comparable to airplane-based remote sensing technologies. The relatively wide-area Sentinel-2 and Landsat 8 satellites detected emissions as low as 1.4 [1.3, 1.5, 95% confidence interval] t/h, while GHGSat’s targeted system quantified a 0.20 [0.19, 0.21] t/h emission to within 13%. While the fraction of global methane emissions detectable by satellite remains unknown, we estimate that satellite networks could see 19–89% of total oil and natural gas system emissions detected in a recent survey of a high-emitting region.

Image-Free Target Classification With Semiactive Laser Detection System

Semiactive laser (SAL) guidance is a point-source detection technology without target feature extraction ability. In this article, the quadrant detector (QD)-based seeker and a spatially modulated designator are used in cooperation to achieve image-free target classification via a hybrid convolutional neural network (CNN) architecture. The convolutional layer is implemented optically by illuminating the target with training-obtained modulated patterns, and the detected intensity signals are regarded as compressed target features. The convolutional layers and classification net are trained together on an electronic platform. Layer normalization (LN) is adopted to improve network generalization ability and accelerate the training process. Network hyperparameters such as the channel numbers and kernel sizes of convolutional layers are analyzed and selected. The feasibility and robustness of the network are verified by both numerical simulation and experiments, whose classification accuracies are 88% and 66%, respectively. The proposed SAL detection and classification system is calculation-efficient and convenient to be adopted in existing guidance platforms or other real-time applications with limited computing resources.

Recent Advances Toward Transparent Methane Emissions Monitoring: A Review.

Given that anthropogenic greenhouse gas (GHG) emissions must be immediately reduced to avoid drastic increases in global temperature, methane emissions have been placed center stage in the fight against climate change. Methane has a significantly larger warming potential than carbon dioxide. A large percentage of methane emissions are in the form of industry emissions, some of which can now be readily identified and mitigated. This review considers recent advances in methane detection that allow accurate and transparent monitoring, which are needed for reducing uncertainty in source attribution and evaluating progress in emissions reductions. A particular focus is on complementary methods operating at different scales with applications for the oil and gas industry, allowing rapid detection of large point sources and addressing inconsistencies of emissions inventories. Emerging airborne and satellite imaging spectrometers are advancing our understanding and offer new top-down assessment methods to complement bottom-up methods. Successfully merging estimates across scales is vital for increased certainty regarding greenhouse gas emissions and can inform regulatory decisions. The development of comprehensive, transparent, and spatially resolved top-down and bottom-up inventories will be crucial for holding nations accountable for their climate commitments.

A Validated Preharvest Sampling Simulation Shows that Sampling Plans with a Larger Number of Randomly Located Samples Perform Better than Typical Sampling Plans in Detecting Representative Point-Source and Widespread Hazards in Leafy Green Fields.

Commercial leafy greens customers often require a negative preharvest pathogen test, typically by compositing 60 produce sample grabs of 150 to 375 g total mass from lots of various acreages. This study developed a preharvest sampling Monte Carlo simulation, validated it against literature and experimental trials, and used it to suggest improvements to sampling plans. The simulation was validated by outputting six simulated ranges of positive samples that contained the experimental number of positive samples (range, 2 to 139 positives) recovered from six field trials with point source, systematic, and sporadic contamination. We then evaluated the relative performance between simple random, stratified random, or systematic sampling in a 1-acre field to detect point sources of contamination present at 0.3% to 1.7% prevalence. Randomized sampling was optimal because of lower variability in probability of acceptance. Optimized sampling was applied to detect an industry-relevant point source [3 log(CFU/g) over 0.3% of the field] and widespread contamination [-1 to -4 log(CFU/g) over the whole field] by taking 60 to 1,200 sample grabs of 3 g. More samples increased the power of detecting point source contamination, as the median probability of acceptance decreased from 85% with 60 samples to 5% with 1,200 samples. Sampling plans with larger total composite sample mass increased power to detect low-level, widespread contamination, as the median probability of acceptance with -3 log(CFU/g) contamination decreased from 85% with a 150-g total mass to 30% with a 1,200-g total mass. Therefore, preharvest sampling power increases by taking more, smaller samples with randomization, up to the constraints of total grabs and mass feasible or required for a food safety objective. IMPORTANCE This study addresses a need for improved preharvest sampling plans for pathogen detection in leafy green fields by developing and validating a preharvest sampling simulation model, avoiding the expensive task of physical sampling in many fields. Validated preharvest sampling simulations were used to develop guidance for preharvest sampling protocols. Sampling simulations predicted that sampling plans with randomization are less variable in their power to detect low-prevalence point source contamination in a 1-acre field. Collecting larger total sample masses improved the power of sampling plans in detecting widespread contamination in 1-acre fields. Hence, the power of typical sampling plans that collect 150 to 375 g per composite sample can be improved by taking more, randomized smaller samples for larger total sample mass. The improved sampling plans are subject to feasibility constraints or to meet a particular food safety objective.

Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks

Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys.