Asteroid Detection Research Articles

ICC-BiFormer: A Deep-Learning Model for Near-Earth Asteroid Detection via Image Compression and Local Feature Extraction

Detecting near-Earth asteroids (NEAs) is crucial for research in solar system and planetary science. In recent year, deep-learning methods have almost dominated the task. Since NEAs represent only one-thousandth of the pixels in images, we proposed an ICC-BiFormer model that includes an image compression and contrast enhancement block and a BiFormer model to capture local features in input images, which is different from previous models based on Convolutional Neural Network (CNN). Furthermore, we utilize a larger input size of the model, which corresponds to the side length of the input image matrix, and design a cropping algorithm to prevent NEAs from being truncated and better divide NEAs and satellites. We apply our ICC-BiFormer model into a dataset of approximately 20,000 streak and 40,000 non-streak images to train a binary classification model. The ICC-BiFormer achieves 99.88% accuracy, which is superior to existing models. Focusing on local features has been proven effective in detecting NEAs.

The Sensitivity of NEO Surveyor to Low-perihelion Asteroids

Asteroids with low orbital perihelion distances experience extreme heating from the Sun that can modify their surfaces and trigger nontypical activity mechanisms. These objects are generally difficult to observe from ground-based telescopes due to their frequent proximity to the Sun. The Near-Earth Object (NEO) Surveyor mission, however, will regularly survey down to solar elongations of 45° and is well suited for the detection and characterization of low-perihelion asteroids. Here, we use the survey simulation software tools developed for mission verification to explore the expected sensitivity of NEO Surveyor to these objects. We find that NEO Surveyor is expected to be >90% complete for near-Sun objects larger than D ∼ 300 m. Additionally, if the asteroid (3200) Phaethon underwent a disruption event in the past to form the Geminid meteor stream, Surveyor will be >90% complete to any fragments larger than D ∼ 200 m. For probable disruption models, NEO Surveyor would be expected to detect dozens of objects on Phaethon-like orbits, compared to a predicted background population of only a handful of asteroids, setting strong constraints on the likelihood of this scenario.

Recurrent Convolution Neural Network for Space Debris and Asteroids Detection and Classification on the Monitored Satellite Information Using Deep Space Network

Satellites are used for many monitoring, prediction and forecasting application operation in space , hence it has to be continuously monitored and controlled against the catastrophic damage due to collision of space debris and asteroids debris in the space. Timely and efficient Controlling of satellite will eliminate the heavy irreparable damages as number of space debris and asteroids debris is continuously increasing in the space. Hence monitoring of the satellites increases the sustainability and its safety navigation in the space operations. In order to achieve this objective, deep space network (Satellite ground station ) has to be designed as space situational awareness mechanism on basis of the detection and classification of the space objects towards providing optimal path for satellite trajectory with high processing speed and reduction of memory in data processing. In this work, a new deep learning architecture entitled as optimized recurrent convolution neural network is designed to satellite ground station towards detection and classification of space debris and asteroids debris. Initially architecture is interfaced with feature extraction technique named as principle component analysis to extract the space features and those extracted feature were detected and classified using deep learning model. In this work, convolution neural network composed of convolution layer is used to filter the discrete features, max pooling layer for downsizing the features and represent the feature map and finally fully connected layer to detect and classify the space debris and predict the optimal path for satellite trajectory on basis of the velocity of the debris in the particular orbit. Experimental results of the proposed model has been evaluated using accuracy and precision metric. Further design is implemented and verified using Xilinx Software. The performance analysis of the proposed model yields better accuracy on the classifying the monitored information on comparing with conventional approaches such as support vector machine and K- Nearest Neighbour Technique. Keywords: Satellite Collision Analysis, Deep learning, Space Debris , Asteroid Debris, Convolution Neural Network, Principle Component analysis , Deep Space Network

The International Astronomical Search Collaboration (IASC)—Citizen Scientist System for Asteroid Discovery

We describe a citizen science asteroid detection system developed by the International Astronomical Search Collaboration (IASC) and the Institute for Astronomy at the University of Hawaii, utilizing data from the Pan-STARRS telescopes. The goals of this project are to (i) educate and engage citizen scientists (mostly high school students) in science and astronomy, (ii) search for new asteroids to extend the limiting magnitudes of existing asteroid surveys, and (iii) find missed Near-Earth Objects (NEOs—objects with perihelia q < 1.3 au) to support planetary defense efforts. Over the past 15 yr, 50,000 citizen scientists from 96 countries around the world have detected ∼12,000 main-belt asteroids and ∼5 NEOs. Citizen scientists use the software Astrometrica during scheduled campaigns to search for and measure asteroid astrometry and photometry, and submit the data to IASC for vetting. Candidate detections not already submitted by Pan-STARRS are then submitted to the Minor Planet Center, and are typically ∼0.30 ± 0.07 mag fainter.

Detection and Identification of Asteroids with the 4-m ILMT

Detection and Identification of Asteroids with the 4-m ILMT

Debiased population of very young asteroid families

Context. Asteroid families that are less than one million years old offer a unique possibility to investigate recent asteroid disruption events and test ideas about their dynamical evolution. Observations provided by powerful all-sky surveys have led to an enormous increase in the number of detected asteroids over the past decade. When the known populations are well characterized, they can be used to determine asteroid detection probabilities, including those in young families, as a function of their absolute magnitude. Aims. We use observations from the Catalina Sky Survey (CSS) to determine the bias-corrected population of small members in four young families down to sizes equivalent to several hundred meters. Methods. Using the most recent catalog of known asteroids, we identified members from four young families for which the population has grown appreciably over recent times. A large fraction of these bodies have also been detected by CSS. We used synthetic populations of asteroids, with their magnitude distribution controlled by a small number of parameters, as a template for the bias-corrected model of these families. Applying the known detection probability of the CSS observations, we could adjust these model parameters to match the observed (biased) populations in the young families. Results. In the case of three families, Datura, Adelaide, and Rampo, we find evidence that the magnitude distribution transitions from steep to shallow slopes near 300 to 400 meters. Conversely, the Hobson family population may be represented by a single power-law model. The Lucascavin family has a limited population; no new members have been discovered over the past two decades. We consider a model of parent body rotational fission with the escaping secondary tidally split into two components (thereby providing three members within this family). In support of this idea, we find that no other asteroid with absolute magnitude H ≤ 18.3 accompanies the known three members in the Lucascavin family. A similar result is found for the archetypal asteroid pair Rheinland–Kurpfalz.

Experimental investigation on drilling anchorage for asteroid exploration based on digital image correlation

Anchorage is an important means in geotechnical engineering and has a wide range of application scenarios. This study aims to investigate the anchorage failure mechanism of a drilling anchor device for asteroid exploration using the digital image correlation (DIC) method. A high-speed camera records the anchorage failure experiment process, and the recorded images are adopted to calculate the displacement and strain of geomaterials by DIC. The anchorage failure processes in different drilling depths, anchoring angles, and geomaterials are analyzed based on DIC calculation results and failure surface theory. The results indicate that the formation process and area of the failure surface significantly impact the anchoring force. Moreover, cracks on the geomaterial’s surface indicate that the anchorage is on the verge of failure. This research would contribute to a deeper understanding of the factors influencing anchorage performance and provide a reference for anchoring engineering and asteroid detection missions. This research contributes to a deeper understanding of the factors influencing anchorage performance and enhances the knowledge in anchoring engineering and asteroid detection missions.

Deep space debris—Detection of potentially hazardous asteroids and objects from the southern hemisphere

Space debris are composed of both natural and human made objects, some in near Earth orbits while others are passing through deep space. Asteroids may represent one form of near Earth and deep space debris. In this article we report on a set of asteroid observations from the southern hemisphere. We indicate that Apollo and Aten class asteroids represent another form of deep space debris of a potentially hazardous nature to orbiting spacecraft and/or Earth based locations. We also show some of the operational challenges, types of facilities and the importance of geographic diversity, that is, necessary for detecting, observing and characterising asteroids, especially PHA’s. For many years, space agencies and institutions have observed and monitored near Earth asteroids and objects (NEO’s) using high gain radio frequency antennas and optical telescopes in the northern hemisphere (GSSR, Arecibo, Catalina, Pan-STARRS, Atlas and Linear) 1) However a regular operational system to monitor the southern skies does not have the same level of maturity and is where a percentage of asteroids and various human made objects are not detected until they pass into northern skies. To fill that gap the Southern Hemisphere Asteroid Radar Program (SHARP) 2) located in Australia uses available antenna time on either a 70 or 34 m beam waveguide antenna located at the Canberra Deep Space Communication Complex (CDSCC) to transmit a Doppler compensated continuous radio wave at 2.114 GHz (14.2 cm) and 7.15945 GHz (4.2 cm) toward the NEO and receive its echoes at the 64 m Parkes or 6 m × 22 m Australia Telescope Compact Array (ATCA) antennas at Narrabri in Australia. This mode of NEO observation is termed a deep space bistatic radar. The southern hemisphere program has also recently been joined by the 12 m University of Tasmania antennas at Hobart (Tasmania) and Katherine (Northern Territory). Combining SHARPS bistatic radar with small optical apertures located at the University of New South Wales (UNSW) and University of Western Australia (UWA) allows combined optical/RF NEO detections. Whilst sub-metre class optical instruments have contributed independently to asteroid detection over decades, the use of coordinated small 0.3–0.5 m instruments synchronized to large asteroid radars offers an observational flexibility and adaptability when larger optical systems 3) are dedicated to other forms of professional optical astronomy. Since 2015, SHARP has illuminated and tracked over 30 NEO’s ranging in diameter from 7 to 5000 m at ranges of 0.1–18 lunar distances (LD) from Australia.

The significance and related technologies of asteroid detection

Asteroid detection mission plays an important role in the field of deep space exploration, and it has multiple theoretical and practical guidance advantages for asteroid detection missions, and also promotes all critical technologies of deep space exploration. At present, many asteroid exploration missions have been carried out around the world, and China also plans to launch the "Tianwen-2" probe around 2025 to carry out an integrated exploration mission of fly-by, orbit detection, attachment control and sample-return mission for asteroid 2016HO3. In this paper, we discuss the significance of asteroid detection and several related critical technologies. The theoretical value of asteroid detection covers such as promoting theoretical research on the formation and evolution of the solar system, solar system material, geohazard early warning and early warning of spacecraft orbit. As for the key technologies involved in asteroid detection, this paper discusses the design of transfer trajectory and near-orbit operation, autonomous landing, optical navigation, laser communication and electric propulsion, and summarizes the research and application results of these technologies in the international deep space exploration field.

Dynamics Research and Analysis of landing on the surface of Asteroid

Asteroid detection has been the main research field of deep space exploration, and even in the future years. Countries have carried out missions to flyby and landing on the surface of asteroids, the sampling can help human explore the origins of small bodies, as well as origin of the solar system. While the particular environmental factors such as small size and low gravity of asteroids make the landing more difficult, this paper designs a four-legged inverted triangle soft lander, supplemented by thrust reversers and rope anchors for stable landing. A mathematical model of the lander landing on the surface of target asteroid is established, and the motion and dynamic response of lander in different landing modes is studied, conditions and safe landing range is well defined. Results of the analysis have demonstrated the effectiveness of dynamic landing model for the proposed lander.

Development of a rotary-percussive ultrasonic drill using a bolt-clamped type piezoelectric actuator

Obtaining rock samples by drilling is an important method of asteroid detection. Due to the strict restrictions on the weight, volume, and weight on bit, conventional drilling devices are difficult to meet the requirements of drilling on an asteroid that has a weak gravitational field. This article proposed a rotary-percussive ultrasonic drill using a bonded-type piezoelectric actuator that can output harmonic vibration and elliptical trajectories simultaneously. The configuration and operation principle of the RPUD is presented and analyzed. Mode shape of the actuator and motions of the driving tips are verified by modal analysis and transient analysis, separately. Ultimately, a prototype of the RPUD is fabricated and experimentally investigated for its output characteristic and drilling performance. The test results indicate that the developed actuator can achieve the maximum no-load rotary speed of 119.2 r/min when the excitation frequency is 17.79 kHz. Furthermore, the maximum torque is 35 mNm, and the corresponding driving speed is about 20 r/min. The maximum drilling rate of the prototype to the sandstone is 7 mm per minute under a weight on bit of 5 N. The experimental results confirm that the proposed RPUD is feasible to drill rocks under a low weight on bit, which has the potential to obtain rock samples on an asteroid.

Towards asteroid detection in microlensing surveys with deep learning

Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. Over the years, high cadence microlensing surveys have amassed several terabytes of data while scanning primarily the Galactic Bulge and Magellanic Clouds for microlensing events and thus provide a treasure trove of opportunities for scientific data mining. In particular, numerous asteroids have been observed by visual inspection of selected images. This paper presents novel deep learning-based solutions for the recovery and discovery of asteroids in the microlensing data gathered by the MOA project. Asteroid tracklets can be clearly seen by combining all the observations on a given night and these tracklets inform the structure of the dataset. Known asteroids were identified within these composite images and used for creating the labelled datasets required for supervised learning. Several custom CNN models were developed to identify images with asteroid tracklets. Model ensembling was then employed to reduce the variance in the predictions as well as to improve the generalization error, achieving a recall of 97.67%. Furthermore, the YOLOv4 object detector was trained to localize asteroid tracklets, achieving a mean Average Precision (mAP) of 90.97%. These trained networks will be applied to 16 years of MOA archival data to find both known and unknown asteroids that have been observed by the survey over the years. The methodologies developed can be adapted for use by other surveys for asteroid recovery and discovery.

Integration of Light Curve Brightness Information and Layered Discriminative Constrained Energy Minimization for Automatic Binary Asteroid Detection

Binary asteroid exploration is an important research focus in the areas of deep space exploration and space informatics because of its unique scientific value in analyzing the structure and gravitational dynamic properties of asteroids, as well as the origin and evolution of celestial bodies in the solar system. Remote photometric observations can reveal the key characteristics of binary asteroids that differ from other kinds of asteroids, especially unary asteroids, and provide an efficient and convenient way to discover binary asteroids. However, the automatic binary asteroid detection problem by advanced machine learning methodology remains unresolved when handing complex asteroid photometric data. For this problem, this article proposes to simulate unary and binary asteroid systems using cellinoid and oblate sphere shape models and generate the corresponding light curve brightness information with different asteroid physical parameters. Then, a benchmark unary and binary asteroid light curve dataset is constructed. Afterward, a layered discriminative constrained energy minimization (LDCEM) method is developed to train a binary asteroid detector in a layered learning manner by enlarging the response differences between the light curve data of unary and binary asteroids such that the two common kinds of asteroids can be well distinguished. The experimental results on the simulated asteroid light curve data, as well as the real observed asteroid light curve data, show that the proposed LDCEM method can yield promising binary asteroid detection performance in comparison with some representative detection methods.

Astrometric detection of binary asteroids

ABSTRACT Binary asteroids probe thermal-radiation effects on the main-belt asteroids’ evolution. We discuss the possibility of detecting binary minor planet systems by the astrometric wobble of the centre of light around the centre of mass. This method enables the exploration of the phase-space of binary asteroids, which is difficult to explore using common detection techniques. We describe a forward model that projects the centre-of-light position with respect to the centre of mass, as it is seen by the observer. We study the performance of this method using simulated Gaia-like data. We apply the astrometric method to a subset of the Gaia DR2 Solar system catalogue and find no significant evidence of binary asteroids. This is likely because the Gaia DR2 removed astrometric outliers, which in our case may be due to astrophysical signals. Applying this method to binary asteroid (4337) Arecibo, for which Gaia DR3 reported a possible astrometric signal with a period of P = 32.85 ± 0.38 h, reveals a possible 2.2σ solution with a period of 16.26 h (about half the reported period). We find a small, marginally significant, excess of astrometric noise in the known binary asteroid population from Pravec et al. relative to the entire asteroid population in the Gaia DR2 Solar system catalogue. We also discuss some caveats like precession and asteroid rotation.

Çarpışma Riski Bulunan Asteroitlerin Makine Öğrenmesi ile Tespiti

Asteroids have attracted people's attention from the past to the present. It has a wide place in the beliefs and cultures of ancient civilizations. The sense of discovery and curiosity of human beings causes an increase in their interest in these objects. With the technology coming to a certain level, the detection, diagnosis and materials of asteroids can be found clearly. The route and collision effects of these objects require constant observation. In our study, asteroids that are likely to hit the Earth have been classified using an asteroid data set in Kaggle and the source of which is NASA-JPL. The dataset contains 4687 asteroid data. Pre-processing steps such as filling in missing data, anomaly detection and normalization were applied on the data. Then, with the help of correlation, 19 features were determined from the dataset for dangerous situations. Asteroid classification was made by using Decision Tree with features, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machines, K-Nearest Neighbor, Xgboost and Adaboost machine learning algorithms. With the artificial neural network with different number of neurons and layers, the data were trained and compared with classification algorithms. As a result of the comparison, the highest performance was achieved with the AdaBoost algorithm with 99.80%. Hyperparameter optimization was performed using the grid-search method in all the classification algorithms that were run. Thus, a method that requires continuous observation and enables the processing of large amounts of data in a more efficient way has been proposed.

AIROPA III: testing simulated and on-sky data

Adaptive optics (AO) images from the W. M. Keck Observatory have delivered numerous influential scientific results, including detection of multi-system asteroids, the supermassive black hole at the center of the Milky Way, and directly imaged exoplanets. Specifically, the precise and accurate astrometry these images yield was used to measure the mass of the supermassive black hole using orbits of the surrounding star cluster. Despite these successes, one of the major obstacles to improved astrometric measurements is the spatial and temporal variability of the point-spread function delivered by the instruments. Anisoplanatic and Instrumental Reconstruction of Off-axis PSFs for AO (AIROPA) is a software package for the astrometric and photometric analysis of AO images using point-spread function fitting together with the technique of point-spread function reconstruction. In AO point-spread function reconstruction, the knowledge of the instrument performance and of the atmospheric turbulence is used to predict the long-exposure point-spread function of an observation. We present the results of our tests using AIROPA on both simulated and on-sky images of the Galactic Center. We find that our method is very reliable in accounting for the static aberrations internal to the instrument, but it does not improve significantly the accuracy on sky, possibly due to uncalibrated telescope aberrations.

Probing the dark Solar system: detecting binary asteroids with a space-based interferometric asteroid explorer

ABSTRACT With the inception of gravitational wave astronomy, astrophysical studies using interferometric techniques have begun to probe previously unknown parts of the Universe. In this work, we investigate the potential of a new interferometric experiment to study a unique group of gravitationally interacting sources within our Solar system: binary asteroids. We present the first study into binary asteroid detection via gravitational signals. We identify the interferometer sensitivity necessary for detecting a population of binary asteroids in the asteroid belt. We find that the space-based gravitational wave detector LISA will have negligible ability to detect these sources as these signals will be well below the LISA noise curve. Consequently, we propose a 4.6 au and a 1 au arm-length interferometer specialized for binary asteroid detection, targeting frequencies between 10−6 and 10−4 Hz. Our results demonstrate that the detection of binary asteroids with space-based gravitational wave interferometers is possible though very difficult, requiring substantially improved interferometric technology over what is presently proposed for space-based missions. If that threshold can be met, an interferometer may be used to map the asteroid belt, allowing for new studies into the evolution of our Solar system.

The utility of the euronear software in the detection of NEA asteroids

This paper presents some of the software techniques used in astronomy to detect celestial bodies near the Earth, called NEO bodies, Near Earth Object, including NEA asteroids, Near Earth Asteroids. The software and utilities created or only used in the research projects coordinated by the EURONEAR consortium were Astrometrica and Nearby, respectively among the utilities necessary for the new asteroid detections were Asteroids Identifications, Astrocheck, Nea Checker, Neo Checker-MPC, Find Orb Project Pluto-MPC, Minor Planet Center. Packages of images were taken through the ING Telescope in the Canary Islands, La Palma, Spain. These software techniques were implemented and tested at the University of Craiova, in research projects and through students of the Faculty of Sciences.

Design and Experimental Research of a New Type of Asteroid Anchoring System

Asteroid detection is of great significance to the study of the formation of the solar system and the origin of life. However, there are many types of asteroids, and they are far away from the earth, and the understanding of their various characteristics is not clear, which brings huge technical challenges to the landing and attachment of star catalogs. At present, the world is mainly based on surround, overflight, and short-term contact detection, and long-term attachment detection has not yet been realized. In order to solve the long-term attachment detection requirements of asteroids, focusing on the geological characteristics of various types of stars, this paper proposes a new type of asteroid attachment mechanism based on the beetle bionic theory, which can realize intelligent and flexible attachment and has strong adaptability. Around this design, this paper analyzes the mechanism of adhesion and realizes the adaptive matching of unascertained terrain landing point adhesion. On this basis, a prototype of the asteroid landing attachment mechanism was developed and verified by experiments. The experiment proved that the mechanism has strong multiterrain matching ability and can obtain an adhesion force of not less than 36 N on ordinary concrete ground.

The Umbrella software suite for automated asteroid detection

We present the Umbrella software suite for asteroid detection, validation, identification and reporting. The current core of Umbrella is an open-source modular library, called Umbrella2, that includes algorithms and interfaces for all steps of the processing pipeline, including a novel detection algorithm for faint trails. Building on the library, we have also implemented a detection pipeline accessible both as a desktop program (ViaNearby) and via a web server (Webrella), which we have successfully used in near real-time data reduction of a few asteroid surveys on the Wide Field Camera of the Isaac Newton Telescope. In this paper we describe the library, focusing on the interfaces and algorithms available, and we present the results obtained with the desktop version on a set of well-curated fields used by the EURONEAR project as an asteroid detection benchmark.