arXiv subjects

Michael Mommert

Publications and source records attributed to Michael Mommert.

At least 19 recordsLinked to original sources

arXiv2026

An improved periodic activation for PINNs reconstructing convective flows

Architectures with periodic activation functions have already been shown to be beneficial in comparison to monotonic counterparts for a wide range of applications of physics-informed neural networks. Here, we investigate a network architecture which uses the complex exponential function, generating pairs of sine and cosine outputs as activation functions. Testing it against comparable, sine-activated multi-layer perceptrons for the task of temperature reconstruction from sparse velocity data for cubic Rayleigh-Bénard convection reveals significant improvements in the reconstruction quality without a substantial increase in computational cost per training step. Vice versa, the improved architecture enables reaching similar reconstruction qualities for a fraction of the expense. Analyzing the mathematical structure of these networks points to the improvements being rooted in the property of passing both a sine and cosine function forward. This way, the subsequent layer is able to adapt the phase of the provided latent periodic functions, and doing it individually for each of its neurons.

physics.flu-dyn↗

arXiv2026

Probing the Taxonomic Diversity of the Small Near-Earth Object Population

Small near-Earth objects (NEOs) represent the immediate source population of meteors and meteorites and provide a direct window into the compositional and dynamical evolution of the inner Solar System. However, their small sizes, rapid rotation rates, and short observability windows have historically limited systematic compositional characterization. We present results from a multi-year rapid-response near-infrared spectrophotometric survey of recently discovered NEOs conducted with the UKIRT-WFCAM instrument between 2016 and 2024. Our campaign obtained 371 observation sequences, yielding reliable color measurements and probabilistic taxonomic classifications for 122 NEOs, with diameters ranging from 5 m to 150 m and a median size of 60 m. We employed lightcurve-corrected Z, J, H, and K photometry and a machine-learning-based classification framework to derive both observed and debiased taxonomic distributions as a function of object size. The observed taxonomic fractions show a population of small NEOs dominated by S-complex and CX-complex objects in nearly equal proportions. After accounting for observational selection biases, however, the inferred intrinsic population is instead dominated by dark, carbonaceous (CX-complex) bodies, with important implications for the intrinsic composition of the small NEO population. We further explore the relationship between small NEOs, meteorite fall statistics, and atmospheric filtering effects, finding that a substantial fraction of carbonaceous material likely does not survive atmospheric entry. These results highlight the importance of debiased surveys of small NEOs for interpreting the meteorite record, refining delivery models from the main belt, and informing the selection of future mission targets and planetary defense strategies.

astro-ph.EP↗

arXiv2026

NEO Colors from The Mission Accessible Near-Earth Object Survey (MANOS)

We present spectro-photometric griz colors for 189 near-Earth objects (NEOs) collected by the Mission Accessible Near-Earth Object Survey (MANOS). Data acquisition involved non-simultaneous multi-band exposures, thus particular attention was given to the influence of rotational lightcurves on the derived colors. We show that colors measured without accounting for lightcurve variations can significantly influence results for individual objects and potentially have systematic offsets for ensemble studies. Color-based taxonomic classifications were used to investigate the distribution of spectral types. Our results were combined with other visible wavelength surveys to highlight a previously reported change in the observed taxonomic distribution of NEOs as a function of size, namely a decrease in S complex and an increase in X complex objects with increasing absolute magnitude. Plausibility arguments are given to suggest that Main Belt source region, thermal modification, discovery bias, tidal resurfacing, regolith grain size, and impact shock darkening are unlikely explanations for this size-dependent trend. Consistent with recent NEO population models and work on the connection between meteorites and young asteroid families in the Main Belt, this trend is best explained by a compositional gradient in the NEO population. In particular, the observed abundance of S complex or ordinary chondrite-like NEOs decreases by a factor of two from ~65% of the population at km-scales down to a third at sizes <50m. This result has implications for understanding the initial pre-impact population of meteorite parent bodies prior to atmospheric filtering. Furthermore, this will have implications for probabilistic impact risk assessment models.

astro-ph.EP↗

arXiv2025

An Efficient Shift-and-Stack Algorithm Applied to Detection Catalogs

The boundary of solar system object discovery lies in detecting its faintest members. However, their discovery in detection catalogs from imaging surveys is fundamentally limited by the practice of thresholding detections at signal-to-noise (SNR) $\geq 5$ to maintain catalog purity. Faint moving objects can be recovered from survey images using the shift-and-stack algorithm, which coadds pixels from multi-epoch images along a candidate trajectory. Trajectories matching real objects accumulate signal coherently, enabling high-confidence detections of very faint moving objects. Applying shift-and-stack comes with high computational cost, which scales with target object velocity, typically limiting its use to searches for slow-moving objects in the outer solar system. This work introduces a modified shift-and-stack algorithm that trades sensitivity for speedup. Our algorithm stacks low SNR detection catalogs instead of pixels, the sparsity of which enables approximations that reduce the number of stacks required. Our algorithm achieves real-world speedups of $10$--$10^3 \times$ over image-based shift-and-stack while retaining the ability to find faint objects. We validate its performance by recovering synthetic inner and outer solar system objects injected into images from the DECam Ecliptic Exploration Project (DEEP). Exploring the sensitivity--compute time trade-off of this algorithm, we find that our method achieves a speedup of $\sim30\times$ with $88\%$ of the memory usage while sacrificing $0.25$ mag in depth compared to image-based shift-and-stack. These speedups enable the broad application of shift-and-stack to large-scale imaging surveys and searches for faint inner solar system objects. We provide a reference implementation via the find-asteroids Python package and this URL: https://github.com/stevenstetzler/find-asteroids.

astro-ph.IM↗

arXiv2025

Curvature-based energy spectra revealing flow regime changes in Rayleigh-Bénard convection

We use the local curvature derived from velocity vector fields or particle tracks as a surrogate for structure size to compute curvature-based energy spectra. An application to homogeneous isotropic turbulence shows that these spectra replicate certain features of classical energy spectra such as the slope of the inertial range extending towards the equivalent curvature of the Taylor microscale. As this curvature-based analysis framework is sampling based, it also allows further statistical analyses of the time evolution of the kinetic energies and curvatures considered. The main findings of these analyses are that the slope for the inertial range also appears as a salient point in the probability density distribution of the angle of the vector comprising the two time evolution components. This density distribution further exhibits changing features of its shape depending on the Rayleigh number. This Rayleigh number evolution allows to observe a change in the flow regime between the Rayleigh numbers $10^6$ and $10^7$. Insight into this regime change is gathered by conditionally sampling the salient time evolution behaviours and projecting them back into physical space. Concretely, the regime change is manifested by a change in the spatial distribution for the different time evolution behaviours. Finally, we show that this analysis can be applied to measured Lagrangian particle tracks.

physics.flu-dyn↗

arXiv2025

A PINN Methodology for Temperature Field Reconstruction in the PIV Measurement Plane: Case of Rayleigh-Bénard Convection

We present a method to infer temperature fields from stereo particle-image velocimetry (PIV) data in turbulent Rayleigh-Bénard convection (RBC) using Physics-informed neural networks (PINNs). The physical setup is a cubic RBC cell with Rayleigh number $\text{Ra}=10^7$ and Prandtl number $\text{Pr}=0.7$. With data only available in a vertical plane $A:x=x_0$, the residuals of the governing partial differential equations are minimised in an enclosing 3D domain around $A$ with thickness $δ_x$. Dynamic collocation point sampling strategies are used to overcome the lack of 3D labelled information and to optimize the overall convergence of the PINN. In particular, in the out-of-plane direction $x$, the collocation points are distributed according to a normal distribution, in order to emphasize the region where data is provided. Along the vertical direction, we leverage meshing information and sample points from a distribution designed based on the grid of a direct numerical simulation (DNS). This approach points greater attention to critical regions, particularly the areas with high temperature gradients within the thermal boundary layers. Using planar three-component velocity data from a DNS, we successfully validate the reconstruction of the temperature fields in the PIV plane. We evaluate the robustness of our method with respect to characteristics of the labelled data used for training: the data time span, the sampling frequency, some noisy data and boundary data omission, aiming to better accommodate the challenges associated with experimental data. Developing PINNs on controlled simulation data is a crucial step toward their effective deployment on experimental data. The key is to systematically introduce noise, gaps, and uncertainties in simulated data to mimic real-world conditions and ensure robust generalization.

physics.flu-dyn↗

arXiv2024

The DECam Ecliptic Exploration Project (DEEP). VII. The Strengths of Three Superfast Rotating Main-belt Asteroids from a Preliminary Search of DEEP Data

Superfast rotators (SFRs) are small solar system objects that rotate faster than generally possible for a cohesionless rubble pile. Their rotational characteristics allow us to make inferences about their interior structure and composition. Here, we present the methods and results from a preliminary search for SFRs in the DECam Ecliptic Exploration Project (DEEP) data set. We find three SFRs from a sample of 686 main-belt asteroids, implying an occurrence rate of 0.4 -0.3/+0.1 percent - a higher incidence rate than has been measured by previous studies. We suggest that this high occurrence rate is due to the small sub-kilometer size regime to which DEEP has access: the objects searched here were as small as 500 m. We compute the minimum required cohesive strength for each of these SFRs and discuss the implications of these strengths in the context of likely evolution mechanisms. We find that all three of these SFRs require strengths that are more than that of weak regolith but consistent with many cohesive asteroid strengths reported in the literature. Across the full DEEP data set, we have identified ~70,000 Main-Belt Asteroids and expect ~300 SFRs - a result that will be assessed in a future paper.

astro-ph.EP↗

arXiv2024

Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh-Bénard convection

We apply physics-informed neural networks to three-dimensional Rayleigh-Bénard convection in a cubic cell with a Rayleigh number of Ra = 10^6 and a Prandtl number of Pr = 0.7 to assimilate the velocity vector field from given temperature fields and vice versa. With the respective ground truth data provided by a direct numerical simulation, we are able to evaluate the performance of the different activation functions applied (sine, hyperbolic tangent and exponential linear unit) and different numbers of neurons (32, 64, 128, 256) for each of the five hidden layers of the multi-layer perceptron. The main result is that the use of a periodic activation function (sine) typically benefits the assimilation performance in terms of the analyzed metrics, correlation with the ground truth and mean average error. The higher quality of results from sine-activated physics-informed neural networks is also manifested in the probability density function and power spectra of the inferred velocity or temperature fields. Regarding the two assimilation directions, the assimilation of temperature fields based on velocities appears to be more challenging in the sense that it exhibits a sharper limit on the number of neurons below which viable assimilation results can not be achieved.

physics.flu-dyn↗

arXiv2024

Design and Performance of the Upgraded Mid-InfraRed Spectrometer and Imager (MIRSI) on the NASA Infrared Telescope Facility

We describe the new design and current performance of the Mid-InfraRed Spectrometer and Imager (MIRSI) on the NASA Infrared Telescope Facility (IRTF). The system has been converted from a liquid nitrogen/liquid helium cryogen system to one that uses a closed-cycle cooler, which allows it to be kept on the telescope at operating temperature and available for observing on short notice, requiring less effort by the telescope operators and day crew to maintain operating temperature. Several other enhancements have been completed, including new detector readout electronics, an IRTF-style standard instrument user interface, new stepper motor driver electronics, and an optical camera that views the same field as the mid-IR instrument using a cold dichroic mirror, allowing for guiding and/or simultaneous optical imaging. The instrument performance is presented, both with an engineering-grade array used from 2021-2023, and a science-grade array installed in the fall of 2023. Some sample astronomical results are also shown. The upgraded MIRSI is a facility instrument at the IRTF available to all users.

astro-ph.IM↗

arXiv2024

SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction

Soil Organic Carbon (SOC) constitutes a fundamental component of terrestrial ecosystem functionality, playing a pivotal role in nutrient cycling, hydrological balance, and erosion mitigation. Precise mapping of SOC distribution is imperative for the quantification of ecosystem services, notably carbon sequestration and soil fertility enhancement. Digital soil mapping (DSM) leverages statistical models and advanced technologies, including machine learning (ML), to accurately map soil properties, such as SOC, utilizing diverse data sources like satellite imagery, topography, remote sensing indices, and climate series. Within the domain of ML, self-supervised learning (SSL), which exploits unlabeled data, has gained prominence in recent years. This study introduces a novel approach that aims to learn the geographical link between multimodal features via self-supervised contrastive learning, employing pretrained Vision Transformers (ViT) for image inputs and Transformers for climate data, before fine-tuning the model with ground reference samples. The proposed approach has undergone rigorous testing on two distinct large-scale datasets, with results indicating its superiority over traditional supervised learning models, which depends solely on labeled data. Furthermore, through the utilization of various evaluation metrics (e.g., RMSE, MAE, CCC, etc.), the proposed model exhibits higher accuracy when compared to other conventional ML algorithms like random forest and gradient boosting. This model is a robust tool for predicting SOC and contributes to the advancement of DSM techniques, thereby facilitating land management and decision-making processes based on accurate information.

cs.CV↗

arXiv2023

The DECam Ecliptic Exploration Project (DEEP) II. Observational Strategy and Design

We present the DECam Ecliptic Exploration Project (DEEP) survey strategy including observing cadence for orbit determination, exposure times, field pointings and filter choices. The overall goal of the survey is to discover and characterize the orbits of a few thousand Trans-Neptunian Objects (TNOs) using the Dark Energy Camera (DECam) on the Cerro Tololo Inter-American Observatory (CTIO) Blanco 4 meter telescope. The experiment is designed to collect a very deep series of exposures totaling a few hours on sky for each of several 2.7 square degree DECam fields-of-view to achieve a magnitude of about 26.2 using a wide VR filter which encompasses both the V and R bandpasses. In the first year, several nights were combined to achieve a sky area of about 34 square degrees. In subsequent years, the fields have been re-visited to allow TNOs to be tracked for orbit determination. When complete, DEEP will be the largest survey of the outer solar system ever undertaken in terms of newly discovered object numbers, and the most prolific at producing multi-year orbital information for the population of minor planets beyond Neptune at 30 au.

astro-ph.EP↗

arXiv2023

The DECam Ecliptic Exploration Project (DEEP) III: Survey characterization and simulation methods

We present a detailed study of the observational biases of the DECam Ecliptic Exploration Project's (DEEP) B1 data release and survey simulation software that enables direct statistical comparisons between models and our data. We inject a synthetic population of objects into the images, and then subsequently recover them in the same processing as our real detections. This enables us to characterize the survey's completeness as a function of apparent magnitudes and on-sky rates of motion. We study the statistically optimal functional form for the magnitude, and develop a methodology that can estimate the magnitude and rate efficiencies for all survey's pointing groups simultaneously. We have determined that our peak completeness is on average 80\% in each pointing group, and our magnitude drops to $25\%$ of this value at $m_{25} = 26.22$. We describe the freely available survey simulation software and its methodology. We conclude by using it to infer that our effective search area for objects at 40 au is $14.8°^2$, and that our lack of dynamically cold distant objects means that there at most $8\times 10^3$ objects with $60 < a < 80$ au and absolute magnitudes $H \leq 8$.

astro-ph.EP↗

arXiv2023

The DECam Ecliptic Exploration Project (DEEP) VI: first multi-year observations of trans-Neptunian objects

We present the first set of trans-Neptunian objects (TNOs) observed on multiple nights in data taken from the DECam Ecliptic Exploration Project (DEEP). Of these 110 TNOs, 105 do not coincide with previously known TNOs and appear to be new discoveries. Each individual detection for our objects resulted from a digital tracking search at TNO rates of motion, using two to four hour exposure sets, and the detections were subsequently linked across multiple observing seasons. This procedure allows us to find objects with magnitudes $m_{VR} \approx 26$. The object discovery processing also included a comprehensive population of objects injected into the images, with a recovery and linking rate of at least $94\%$. The final orbits were obtained using a specialized orbit fitting procedure that accounts for the positional errors derived from the digital tracking procedure. Our results include robust orbits and magnitudes for classical TNOs with absolute magnitudes $H \sim 10$, as well as a dynamically detached object found at 76 au (semi-major axis $a\approx 77 \, \mathrm{au}$). We find a disagreement between our population of classical TNOs and the CFEPS-L7 three component model for the Kuiper belt.

astro-ph.EP↗

arXiv2023

The DECam Ecliptic Exploration Project (DEEP): I. Survey description, science questions, and technical demonstration

We present here the DECam Ecliptic Exploration Project (DEEP), a three year NOAO/NOIRLab Survey that was allocated 46.5 nights to discover and measure the properties of thousands of trans-Neptunian objects (TNOs) to magnitudes as faint as VR~27, corresponding to sizes as small as 20 km diameter. In this paper we present the science goals of this project, the experimental design of our survey, and a technical demonstration of our approach. The core of our project is "digital tracking," in which all collected images are combined at a range of motion vectors to detect unknown TNOs that are fainter than the single exposure depth of VR~23 mag. Through this approach we reach a depth that is approximately 2.5 magnitudes fainter than the standard LSST "wide fast deep" nominal survey depth of 24.5 mag. DEEP will more than double the number of known TNOs with observational arcs of 24 hours or more, and increase by a factor of 10 or more the number of known small (<50 km) TNOs. We also describe our ancillary science goals, including measuring the mean shape distribution of very small main belt asteroids, and briefly outline a set of forthcoming papers that present further aspects of and preliminary results from the DEEP program.

astro-ph.EP↗

arXiv2023

Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

Deep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in most cases, only single or few modalities are typically considered. In this work, we present the ben-ge dataset, which supplements the BigEarthNet-MM dataset by compiling freely and globally available geographical and environmental data. Based on this dataset, we showcase the value of combining different data modalities for the downstream tasks of patch-based land-use/land-cover classification and land-use/land-cover segmentation. ben-ge is freely available and expected to serve as a test bed for fully supervised and self-supervised Earth observation applications.

cs.CV↗

arXiv2023

(523599) 2003 RM: The asteroid that wanted to be a comet

We report a statistically significant detection of nongravitational acceleration on the sub-kilometer near-Earth asteroid (523599) 2003 RM. Due to its orbit, 2003 RM experiences favorable observing apparitions every 5 years. Thus, since its discovery, 2003 RM has been extensively tracked with ground-based optical facilities in 2003, 2008, 2013, and 2018. We find that the observed plane-of-sky positions cannot be explained with a purely gravity-driven trajectory. Including a transverse nongravitational acceleration allows us to match all observational data, but its magnitude is inconsistent with perturbations typical of asteroids such as the Yarkovsky effect or solar radiation pressure. After ruling out that the orbital deviations are due to a close approach or collision with another asteroid, we hypothesize that this anomalous acceleration is caused by unseen cometary outgassing. A detailed search for evidence of cometary activity with archival and deep observations from Pan-STARRS and the VLT does not reveal any detectable dust production. However, the best-fitting H$_2$O sublimation model allows for brightening due to activity consistent with the scatter of the data. We estimate the production rate required for H$_2$O outgassing to power the acceleration, and find that, assuming a diameter of 300 m, 2003 RM would require Q(H$_2$O)$\sim10^{23}$ molec s$^{-1}$ at perihelion. We investigate the recent dynamical history of 2003 RM and find that the object most likely originated in the mid-to-outer main belt ($\sim86\%$) as opposed to from the Jupiter-family comet region ($\sim11\%$). Further observations, especially in the infrared, could shed light on the nature of this anomalous acceleration.

astro-ph.EP↗

arXiv2022

Sublimation Origin of Active Asteroid P/2018 P3

Active asteroids show (typically transient) cometary activity, driven by a range of processes. A sub-set, sometimes called main-belt comets, may be driven by sublimation and so could be useful for tracing the present-day distribution of asteroid ice. Object P/2018 P3 has a Tisserand parameter 3.096 but a high eccentricity 0.415, placing it within the dynamical boundary between asteroids and comets. We aim to determine the cause of activity (sublimation or something else) and assess the dynamical stability of P3, in order to better constrain the intrinsic ice content in the main belt. We obtained Hubble Space Telescope images of P3 at the highest angular resolution. We compared the observations with a Monte Carlo model of dust dynamics. We identified and analyzed archival CFHT (2013) and NEOWISE (2018) data. In addition, we numerically integrated the orbits of P3 clones for 100 Myr. P3 has been recurrently active near two successive perihelia (at 1.76 AU), indicative of a sublimation origin. The absence of 4.6 um band excess indicates zero or negligible CO or CO2 gas production from P3. The properties of the ejected dust are remarkably consistent with those found in other main-belt comets (continuous emission of ~0.05-5 mm particles at 0.3-3 m/s speeds), with mass-loss rates of >~2 kg/s. The orbit of P3 is unstable on timescales ~10 Myr. We speculate that P3 has recently arrived from a more stable source (either the Kuiper Belt or elsewhere in the main belt) and has been physically aged at its current location, finally becoming indistinguishable from a weakly sublimating asteroid in terms of its dust properties. Whatever the source of P3, given the dynamical instability of its current orbit, P3 should not be used to trace the native distribution of asteroid ice.

astro-ph.EP↗

arXiv2021

Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mitigate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Existing models for surface-level air pollution rely on extensive land-use datasets which are often locally restricted and temporally static. This work proposes a deep learning approach for the prediction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satellite imagery with satellite-based atmospheric column density air pollution measurements enables the scaling of air pollution estimates (in this case NO$_2$) to high spatial resolution (up to $\sim$10m) at arbitrary locations and adds a temporal component to these estimates. The proposed model performs with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error $<$6$~μg/m^3$). Our results enable the identification and temporal monitoring of major sources of air pollution and GHGs.

cs.LG↗