Future Data Sets Research Articles

SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation

ABSTRACT Type Ia supernovae (SNe Ia), standardizable candles that allow tracing the expansion history of the Universe, are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses scale poorly to future large data sets, are limited to simplified probabilistic descriptions, and must explicitly sample a high-dimensional latent posterior to infer the few parameters of interest, which makes them inefficient. Marginal likelihood-free inference, on the other hand, is based on forward simulations of data, and thus can fully account for complicated redshift uncertainties, contamination from non-SN Ia sources, selection effects, and a realistic instrumental model. All latent parameters, including instrumental and survey-related ones, per object and population-level properties, are implicitly marginalized, while the cosmological parameters of interest are inferred directly. As a proof of concept, we apply truncated marginal neural ratio estimation (TMNRE), a form of marginal likelihood-free inference, to bahamas, a Bayesian hierarchical model for salt parameters. We verify that TMNRE produces unbiased and precise posteriors for cosmological parameters from up to 100 000 SNe Ia. With minimal additional effort, we train a network to infer simultaneously the ${\sim}100\, 000$ latent parameters of the supernovae (e.g. absolute brightnesses). In addition, we describe and apply a procedure that utilizes local amortization of the inference to convert the approximate Bayesian posteriors into frequentist confidence regions with exact coverage. Finally, we discuss the planned improvements to the model that are enabled by using a likelihood-free inference framework, like selection effects and non-Ia contamination.

Local shape descriptors for neuron segmentation

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient—a critical requirement for the processing of future petabyte-sized datasets.

A sampling of environmental data, and its presentation, from a multi-role U.S. coast guard aircraft

Abstract This work presents a summary of environmental data from 3600 h of data collected during operations of a U.S. Coast Guard HC-130H aircraft performing its routine missions over a five-year period. The aircraft operated from multiple U.S. and deployed locations during the data collection period in the tropical and mid-latitudes at altitudes up to 8.5 km (28 kft). The aircraft collected pressure, temperature and humidity data during the period. No aerosol components were sampled (chlorides, sulfur dioxide, etc.). While there was no Time of Wetness sensor, the ISO 9223 definition is used for the present work as an estimator. These data are presented in the context of research into corrosion and environmental severity, or the potential conditions for it. About 40% of the data were collected while the aircraft was parked on the ground at various based and deployed locations. The purpose of this work is to present and summarize the flight data as an aid in providing some context for future research into environmental effects as they relate to both corrosion and environmentally assisted cracking (EAC) of aircraft structure, and perhaps for comparisons with existing or future datasets from aircraft having other missions. Prediction models need realistic, real-world inputs, such as those presented here. An example for the use of these data in corrosion prediction based on the ISO 9223 international standard is presented. Perhaps not surprisingly, these data show that, for this aircraft, conditions most favorable to corrosion occurred while the aircraft was on the ground.

Effects of climate change and drought attributes in Nigeria based on RCP 8.5 climate scenario

Historical observatory and future simulated datasets based on representative concentration pathway (RCP) 8.5 scenario covering from 1951 to 2100 were analysed. Four well accepted drought indices: the standardized precipitation index (SPI), standardized runoff index (SRI), reconnaissance drought index (RDI) and standardized precipitation and evapotranspiration index (SPEI) were used in drought characterization. The ensemble of the seven GCMs using RCA-4 captured some pragmatic features of the climatology of Nigeria in the historical period. The correlations between SRI and SPEI/SPI are significant. The major peaks can be divided into three drought cycles with 1–2, 2–4 and 8–16 periods (years). On a general note, there is a projection of an increase in both the frequency and intensity of drought in the five classified agroecological zones in Nigeria. Considering the output of drought characteristics as measured by SPI, future climates were predicted to be more wet when compared to the historical period as indicated in the study. In contrast, SPEI and RDI forecasted dryer climates. The prediction of SPEI and RDI must have been predicated on the influence of increasing temperature due to global warming as captured by RCP 8.5, which would in turn affect the rate of evapotranspiration. In view of drought analysis using the RCP 8.5 scenario, the frequency of severe/extreme drought will likely become more intense especially towards the end of the 21st century.

Simulating the current and future northern limit of permafrost on the Qinghai–Tibet Plateau

Abstract. Permafrost has been warming and thawing globally, with subsequent effects on the climate, hydrology, and the ecosystem. However, the permafrost thermal state variation in the northern lower limit of the permafrost zone (Xidatan) on the Qinghai–Tibet Plateau (QTP) is unclear. This study attempts to explore the changes and variability in this permafrost using historical (1970–2019) and future projection datasets from remote-sensing-based land surface temperature product (LST) and climate projections from Earth system model (ESM) outputs of the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5, CMIP6). Our model considers phase-change processes of soil pore water, thermal-property differences between frozen and unfrozen soil, geothermal flux flow, and the ground ice effect. Our model can consistently reproduce the vertical ground temperature profiles and active layer thickness (ALT), recognizing permafrost boundaries, and capture the evolution of the permafrost thermal regime. The spatial distribution of permafrost and its thermal conditions over the study area were controlled by elevation with a strong influence of slope orientation. From 1970 to 2019, the mean annual ground temperature (MAGT) in the region warmed by 0.49 ∘C in the continuous permafrost zone and 0.40 ∘C in the discontinuous permafrost zone. The lowest elevation of the permafrost boundary (on the north-facing slopes) rose approximately 47 m, and the northern boundary of discontinuous permafrost retreated southwards by approximately 1–2 km, while the lowest elevation of the permafrost boundary remained unchanged for the continuous permafrost zone. The warming rate in MAGT is projected to be more pronounced under shared socioeconomic pathways (SSPs) than under representative concentration pathways (RCPs), but there are no distinct discrepancies in the areal extent of the continuous and discontinuous permafrost and seasonally frozen ground among SSP and RCP scenarios. This study highlights the slow delaying process of the response of permafrost in the QTP to a warming climate, especially in terms of the areal extent of permafrost distribution.

Improving the accuracy of single-trial fMRI response estimates using GLMsingle.

Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.

Streamflow of the Betwa River under the Combined Effect of LU-LC and Climate Change

We estimate the combined effect of climate and landuse-landcover (LU-LC) change on the streamflow of the Betwa River; a semi-arid catchment in Central India. We have used the observed and future bias-corrected climatic datasets from 1980–2100. To assess the LU-LC change in the catchment, we have processed and classified the Landsat satellite images from 1990–2020. We have used Artificial Neural Network (ANN) based Cellular Automata (CA) model to simulate the future LU-LC. Further, we coupled the observed and projected LU-LC and climatic variables in the SWAT (Soil and water assessment tool) model to simulate the streamflow of the Betwa River. In doing so, we have setup this model for the observed (1980–2000 and 2001–2020) and projected (2023–2060 and 2061–2100) time periods by using the LU-LC of the years 1990, 2018, and 2040, 2070, respectively. We observed that the combined effect of climate and LU-LC change resulted in the reduction in the mean monsoon stream flow of the Betwa River by 16% during 2001–2020 as compared to 1982–2000. In all four CMIP6 climatic scenarios (SSP126, SSP245, SSP370, and SSP585), the mean monsoon stream flow is expected to decrease by 39–47% and 31–47% during 2023–2060 and 2061–2100, respectively as compared to the observed time period 1982–2020. Furthermore, average monsoon rainfall in the catchment will decrease by 30–35% during 2023–2060 and 23–30% during 2061–2100 with respect to 1982–2020.

Comparing the climate change mitigation potentials of alternative land uses: Crops for biofuels or biochar vs. natural regrowth

Using biomass from dedicated crops for energy production and natural vegetation regrowth are key elements in future climate change mitigation scenarios. However, there are still uncertainties about the mitigation potentials that can be achieved by the different land-based systems and how they perform relative to each other. In this study, we use harmonized future land use datasets to identify global land areas dedicated to second generation bioenergy crop production in 2050 under different climate scenarios. We then assess the global climate change mitigation potentials of using biomass for producing bioethanol with (BECCS) or without carbon capture and storage, biochar, or a synthetic fuel (e-methanol). For the latter, the electricity required to produce hydrogen for e-methanol synthesis is sourced from either wind power or the projected average electricity mix in 2050. Mitigation potential from natural regrowth on the identified land is also quantified. For all the cases, we modelled emissions of greenhouse gases from the life-cycle stages and use parameterized models to estimate local biomass growth rates. The identified land areas range from 1.95 to 13.8 million hectares and can provide from 30 to 178 mega ton (Mt) dry biomass annually from dedicated crops. Climate change mitigation potentials range from 11 to 257 MtCO2-eq. yr−1, depending on technological option and land availability. The largest mitigation is delivered by BECCS, but e-methanol can achieve similar findings when hydrogen is sourced from wind power. If hydrogen is produced from grid electricity, e-methanol can result in net positive emissions. E-methanol can also deliver more final energy than bioethanol (4.04 vs. 1.27 EJ yr−1). Natural vegetation regrowth can generally achieve higher mitigation than bioethanol, but less than biochar. An optimal combination of BECCS and natural vegetation regrowth can achieve a larger mitigation, up to 281 MtCO2-eq. yr−1, indicating that integrated solutions can help to achieve successful land management strategies for climate change mitigation.

Performance Evaluation of Bias Correction Methods and Projection of Future Precipitation Changes Using Regional Climate Model over Thanjavur, Tamil Nadu, India

The use of regional climate models in climate change risk assessments is uncertain because of the threat of biases. Prior to using simulations, some corrections must be made. The aim of this study is to evaluate the effectiveness of simple and complex correction approaches on CORDEX (Coordinated Regional Climate Downscaling Experiment) precipitation datasets over Thanjavur in Tamil Nadu, India. The various bias correction approaches were assessed using the two-sample cross-validation technique, and the best method was chosen using statistical metrics such as the correlation coefficient, root mean square error, and percent bias for correcting the future scenario datasets. According to the findings, all methods significantly improved raw simulated estimations after correction. The Delta Change method was used to adjust the RCP2.6 and RCP8.5 scenarios datasets for this study region because it produced good agreement (0.98) and low wet bias (1%) against observed precipitation. In addition, this study found that the annual mean precipitation projections showed an increase in precipitation intensity of between 4.2 and 6.8% under the RCP2.6 scenario and between 14 and 20.1% under the RCP8.5 scenario, compared to observed data (1976–2005). Furthermore, the analysis showed that most years in the twenty-first century will experience heavy precipitation, resulting in floods. This research has offered reliable knowledge about future precipitation to environmentalists, urban administrators, and policy-makers, enable them to make plausible strategies to mitigate the detrimental effects of climate change.

ESA-ASO: An enhanced search ability based atom search optimization algorithm for epileptic seizure detection

Epilepsy is a complex process and the prediction of seizure is uncertain. Currently, many researchers are exploring different optimization methods to detect the epileptic seizure. Atomic search optimization (ASO) shows a better search capability by using interaction force and the constraint force but it still some deficiency from a local optimum and a low search efficiency. To overcome these demerits, a new enhanced search ability based ASO named as ESAASO is proposed. In this study, inertia weight, levy flight and ranking strategies are integrated into ASO to improve the search performance. The proposed method has been studied on CHB-MIT scalp EEG database. There are 13 number of features are extracted by using TQWT and features are selected using genetic algorithm (GA). The proposed detection method has been computed using twelve well known optimization algorithms with LSSVM classifier. We have obtained average accuracy, sensitivity, selectivity, specificity, average detection rate, G-Mean and area under curve (AUC) values as 98.37%, 91.11%, 91.67%, 91.46%, 91.28%, 91.28% and 0.992 respectively using 10-fold cross-validation method. The proposed algorithm is found outperform as compared to conventional ASO for seizure and non-seizure detection. The proposed method can be experimented using short term EEG datasets in future.

Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: deficient, low, moderate, high, and very high. Based on the evaluation of accuracy in the training stage, the following models showed a high level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the very high class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures.

Is nontargeted data acquisition for target analysis (nDATA) in mass spectrometry a forward-thinking analytical approach?

Targeted mass spectrometry is extensively used for the quantitative measurement of various molecules present in complex matrices. It is certainly one of the most important analytical duties in a mass spectrometry laboratory. Systematic development of selected-reaction monitoring (SRM), multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) methods for targeted mass spectrometry-based analysis was performed without considering future opportunities. The advancement of hardware and software technologies has resulted in greater resolution, accuracy, speed and depth. For sure, SRM, MRM or PRM acquisitions can quantify molecules very accurately at trace levels. However, they do not provide datasets allowing future data mining. Obviously, we cannot truly quantify something that we do not know is there. However, using non-targeted data acquisition for target analysis, we can generate a MS1 and MS2 digital libraries of each sample, providing future proof datasets. This is instrumental for data mining following new questions potentially arising in time permitting new and deeper processing and interpretation. This perspective article provides thoughts on why we believe it is time to question the status quo in targeted mass spectrometry.

A national hydrological projections service for Australia

Australia’s water management and infrastructure decision making needs detailed high-resolution climate and water forecasts and projections to inform the process of water-sensitive decision making, raise awareness and understand future risks, and to mitigate, adapt, and capitalise on the impacts of a changing climate in industries ranging from agriculture and water management to fire risk. Until now no nationally consistent information on hydrological change has existed preventing standardised and comparable impact assessments across multiple spatial and temporal scales, particularly for the management of hazards and risks spanning jurisdictional boundaries. Information available for Australian hydrological impact assessments to date has been produced through state and territory based regional downscaling efforts, across limited timescales and using different interpretation methods, particularly regarding downscaling and assessing hydrological impacts.To address this need, the Australian Bureau of Meteorology has released an innovative, seamless national landscape water service called the Australian Water Outlook. This service provides historical data on water availability with forecast products, as well as hydrological impact projections, using the Bureau’s operational Australian Water Landscape Water Balance model (AWRA-L). The service combines state-of-the-art hydrological research with a user-centred design approach to develop the service and user interface to meet the needs of Australia water resource managers. The innovative science underpinning the hydrological projections service includes nationally consistent downscaling and bias-correction approaches using three statistical methods in an operational framework to produce a future projections dataset including climate (rainfall, solar radiation, temperature, and wind) and water (soil moisture, potential evapotranspiration, and runoff) information in a consistent format.The hydrological projections component of the service showcases novel ways of communicating the plausible impacts of climate change on Australia’s water resources through a ‘storylines’ approach. As part of the service development the Bureau of Meteorology is also working with early adopters and priority customers to demonstrate the application of the service to water management. This paper describes the inception and development of the service informed by a process of user-centred design, and provides examples of storyline approaches for water resources management.

Dynamical cluster masses from photometric surveys

ABSTRACT The masses of galaxy clusters can be measured using data obtained exclusively from wide photometric surveys in one of two ways: directly from the amplitude of the weak lensing signal or, indirectly, through the use of scaling relations calibrated using binned lensing measurements. In this paper, we build on a recently proposed idea and implement an alternative method based on the radial profile of the satellite distribution. This technique relies on splashback, a feature associated with the apocentre of recently accreted galaxies that offers a clear window into the phase-space structure of clusters without the use of velocity information. We carry out this dynamical measurement using the stacked satellite distribution around a sample of luminous red galaxies in the fourth data release of the Kilo-Degree Survey and validate our results using abundance-matching and lensing masses. To illustrate the power of this measurement, we combine dynamical and lensing mass estimates to robustly constrain scalar–tensor theories of gravity at cluster scales. Our results exclude departures from General Relativity of the order of unity. We conclude the paper by discussing the implications for future data sets. Because splashback mass measurements scale only with the survey volume, stage-IV photometric surveys are well-positioned to use splashback to provide high-redshift cluster masses.

Eliciting and Learning with Soft Labels from Every Annotator

The labels used to train machine learning (ML) models are of paramount importance. Typically for ML classification tasks, datasets contain hard labels, yet learning using soft labels has been shown to yield benefits for model generalization, robustness, and calibration. Earlier work found success in forming soft labels from multiple annotators' hard labels; however, this approach may not converge to the best labels and necessitates many annotators, which can be expensive and inefficient. We focus on efficiently eliciting soft labels from individual annotators. We collect and release a dataset of soft labels (which we call CIFAR-10S) over the CIFAR-10 test set via a crowdsourcing study (N=248). We demonstrate that learning with our labels achieves comparable model performance to prior approaches while requiring far fewer annotators -- albeit with significant temporal costs per elicitation. Our elicitation methodology therefore shows nuanced promise in enabling practitioners to enjoy the benefits of improved model performance and reliability with fewer annotators, and serves as a guide for future dataset curators on the benefits of leveraging richer information, such as categorical uncertainty, from individual annotators.

The Re-Identification of Previously Unidentifiable Clinical Non-Tuberculous Mycobacterial Isolates Shows Great Species Diversity and the Presence of Other Acid-Fast Genera.

Non-tuberculous mycobacteria that cannot be identified at the species level represent a challenge for clinical laboratories, as proper species assignment is key to implementing successful treatments or epidemiological studies. We re-identified forty-eight isolates of Ziehl–Neelsen (ZN)-staining-positive “acid-fast bacilli” (AFB), which were isolated in a clinical laboratory and previously identified as Mycobacterium species but were unidentifiable at the species level with the hsp65 PCR restriction fragment length polymorphism analysis (PRA). As most isolates also could not be identified confidently via 16S, hsp65, or rpoB DNA sequencing and a nBLAST search analysis, we employed a phylogenetic method for their identification using the sequences of the 16S rDNA, which resulted in the identification of most AFB and a Mycobacterium species diversity not found before in our laboratory. Most were rare species with only a few clinical reports. Moreover, although selected with the ZN staining as AFB, not all isolates belonged to the genus Mycobacterium, and we report for the first time in Latin America the isolation of Nocardia puris, Tsukamurella pulmosis, and Gordonia sputi from sputum samples of symptomatic patients. We conclude that ZN staining does not differentiate between the genus Mycobacterium and other genera of AFB. Moreover, there is a need for a simple and more accurate tree-based identification method for mycobacterial species. For this purpose, and in development in our lab, is a web-based identification system using a phylogenetic analysis (including all AFB genera) based on 16S rDNA sequences (and in the future multigene datasets) and the closest relatives.

Disentangling Multiple Stochastic Gravitational Wave Background Sources in PTA Data Sets

With strong evidence of a common-spectrum stochastic process in the most recent data sets from the NANOGrav Collaboration, the European Pulsar Timing Array (PTA), Parkes PTA, and the International PTA, it is crucial to assess the effects of the several astrophysical and cosmological sources that could contribute to the stochastic gravitational wave background (GWB). Using the same data set creation and injection techniques as in Pol et al., we assess the separability of multiple GWBs by creating single and multiple GWB source data sets. We search for these injected sources using Bayesian PTA analysis techniques to assess recovery and separability of multiple astrophysical and cosmological backgrounds. For a GWB due to supermassive black hole binaries and an underlying weaker background due to primordial gravitational waves with a GW energy-density ratio of ΩPGW/ΩSMBHB = 0.5, the Bayes’ factor for a second process exceeds unity at 17 yr, and increases with additional data. At 20 yr of data, we are able to constrain the spectral index and amplitude of the weaker GWB at this density ratio to a fractional uncertainty of 64% and 110%, respectively, using current PTA methods and techniques. Using these methods and findings, we outline a basic protocol to search for multiple backgrounds in future PTA data sets.

Geostationary operational environmental satellite-R advanced baseline imager reflective solar band absolute validation using Sonoran desert scenes

The advanced baseline imagers (ABIs) on board the geostationary operational environmental satellite (GOES)-R series, GOES-16 and GOES-17 satellites, have accumulated long data series that have been updated throughout their missions to enhance their utility for imaging the Earth’s weather, oceans, and environment. In preparing for a reprocessing effort aimed at improving the consistency and image quality and reducing radiometric uncertainties necessary for some Earth science applications, we developed a vicarious validation technique for five of the six reflective solar bands. This technique uses Sonoran desert surface reflectance data derived from the moderate resolution imaging spectrometer bidirectional reflectance distribution function parameters product along with other atmospheric products to generate a predicted at-sensor radiance for both GOES-16 and GOES-17 for comparison with ABI observations and to enable ABI intercomparisons. This technique identified known instrument biases and demonstrated absolute validation over the course of a year, showing its capability to detect changes, monitor stability, and assess radiometric uncertainties for operational data applications and future reprocessed data sets.

Asteroid Measurements at Millimeter Wavelengths with the South Pole Telescope

We present the first measurements of asteroids in millimeter wavelength data from the South Pole Telescope (SPT), which is used primarily to study the cosmic microwave background (CMB). We analyze maps of two ∼270 deg2 sky regions near the ecliptic plane, each observed with the SPTpol camera ∼100 times over 1 month. We subtract the mean of all maps of a given field, removing static sky signal, and then average the mean-subtracted maps at known asteroid locations. We detect three asteroids—(324) Bamberga, (13) Egeria, and (22) Kalliope—with signal-to-noise ratios (S/N) of 11.2, 10.4, and 6.1, respectively, at 2.0 mm (150 GHz); we also detect (324) Bamberga with an S/N of 4.1 at 3.2 mm (95 GHz). We place constraints on these asteroids’ effective emissivities, brightness temperatures, and light-curve modulation amplitude. Our flux density measurements of (324) Bamberga and (13) Egeria roughly agree with predictions, while our measurements of (22) Kalliope suggest lower flux, corresponding to effective emissivities of 0.64 ± 0.11 at 2.0 and < 0.47 at 3.2 mm. We predict the asteroids detectable in other SPT data sets and find good agreement with detections of (772) Tanete and (1093) Freda in recent data from the SPT-3G camera, which has ∼10× the mapping speed of SPTpol. This work is the first focused analysis of asteroids in data from CMB surveys, and it demonstrates we can repurpose historic and future data sets for asteroid studies. Future SPT measurements can help constrain the distribution of surface properties over a larger asteroid population.

Asteroid taxonomy from cluster analysis of spectrometry and albedo

Context. The classification of the minor bodies of the Solar System based on observables has been continuously developed and iterated over the past 40 yr. While prior iterations followed either the availability of large observational campaigns or new instrumental capabilities opening new observational dimensions, we see the opportunity to improve primarily upon the established methodology. Aims. We developed an iteration of the asteroid taxonomy which allows the classification of partial and complete observations (i.e. visible, near-infrared, and visible-near-infrared spectrometry) and which reintroduces the visual albedo into the classification observables. The resulting class assignments are given probabilistically, enabling the uncertainty of a classification to be quantified. Methods. We built the taxonomy based on 2983 observations of 2125 individual asteroids, representing an almost tenfold increase of sample size compared with the previous taxonomy. The asteroid classes are identified in a lower-dimensional representation of the observations using a mixture of common factor analysers model. Results. We identify 17 classes split into the three complexes C, M, and S, including the new Z-class for extremely-red objects in the main belt. The visual albedo information resolves the spectral degeneracy of the X-complex and establishes the P-class as part of the C-complex. We present a classification tool which computes probabilistic class assignments within this taxonomic scheme from asteroid observations, intrinsically accounting for degeneracies between classes based on the observed wavelength region. The taxonomic classifications of 6038 observations of 4526 individual asteroids are published. Conclusions. The ability to classify partial observations and the reintroduction of the visual albedo into the classification provide a taxonomy which is well suited for the current and future datasets of asteroid observations, in particular provided by the Gaia, MITHNEOS, NEO Surveyor, and SPHEREx surveys.