1 Introduction

Astronomical research has produced vast datasets that challenge conventional data management and analysis techniques in recent decades, necessitating innovative approaches due to their sheer volume. Astronomical surveys systematically capture and catalog celestial entities and phenomena across the sky, serving as principal sources for new astronomical revelations. Nevertheless, their utilization remains limited, with only a handful of observatories and a fraction of astronomers depending on them, as pointed observations constitute the norm for most telescopes. A pioneering concept known as Virtual Observatories (VO) has emerged to address these challenges, offering astronomers enhanced efficiency in accessing, processing, and collaborating on astronomical data (Cui and Zhao 2007). VOs encompass interconnected networks of telescopes, data centers, and computational systems designed to support astronomers with interfaces for seamless data access and utilization of advanced computing resources.

The International Virtual Observatory Alliance (IVOA) was founded in 2002 to foster the coordinated and structured advancement of VOs (Quinn et al. 2004; Hanisch et al. 2015). IVOA, encompassing VO initiatives from 22 nations and 2 European projects, orchestrates the integration of astronomical resources and datasets by establishing technical standards for creating VOs and the seamless exchange of information between these platforms. Among these efforts, the Armenian Virtual Observatory (ArVO) has participated actively since 2005 (Mickaelian et al. 2016; Mickaelian et al. 2023).

ArVO is a project of the Byurakan Astrophysical Observatory (BAO) and the Institute for Informatics and Automation Problems, created to provide an advanced platform for astronomers to access and analyze astronomical data. BAO is famous for its many extragalactic surveys. The core of ArVO is the Digitized First Byurakan Survey (DFBS) (Mickaelian et al. 2007), which is the largest low-dispersion spectroscopic survey, containing about 40 million spectra for 20 million astronomical objects. The DFBS was carried out by renowned astrophysicist Beniamin Markarian and his colleagues during 1965–1980. It covers 17,000 deg² of the high galactic latitudes of the whole northern sky and part of the southern sky accessible from BAO. Before Gaia spectroscopy, it gave the largest number of spectra among all astronomical databases. These spectra provide spectral energy distribution (SED) and show several properties, like color, broad emission, or absorption lines. The DFBS contains spectra of many objects, such as late-type stars, quasars, galaxies, or white dwarfs. The length, shape, SED, and available spectral lines allow the identification of different types of objects. DFBS contains up to 20 distinguished types based on eye inspection and analysis, but their shapes need a proper spectral classification.

Nonetheless, analyzing these immense datasets presents formidable challenges owing to their voluminous nature and intricate underlying patterns. Automated extraction and classification mechanisms are necessary for studying such many objects. A model for the image-based classification of carbon stars, subdwarfs, Markarian galaxies, and other types of objects in the DFBS has been proposed by the authors (Astsatryan et al. 2021) to classify the DFBS objects based on their shapes and other visual characteristics. Utilizing convolutional neural networks combined with image preprocessing steps, the authors directly extracted and classified patches from the DFBS plates containing the desired objects. The evaluation showed hundreds of thousands of carbon stars, subdwarfs, and UV-excess (Markarian-type) galaxies in the DFBS, thus demonstrating the importance of employing a sub-object classification framework for further research. However, this approach has limitations. The current framework does not delve into sub-object classification, which restrains a more comprehensive understanding of the DFBS dataset. Extending the model to encompass sub-object categorization could usher a deeper insight into the dataset’s diverse range of celestial bodies. Sub-classification enables researchers to identify commonalities and differences within a particular category to find correlations and potential relationships between various subgroups of objects. Furthermore, envisioning this model as a service would magnify its impact by granting researchers streamlined access to its capabilities, fostering collaboration, and expediting scientific advancements.

The paper aims to leverage the potential of cloud-based machine learning (ML) techniques to classify sub-objects to overcome the abovementioned limitations, enabling more efficient and accurate analysis. It presents a comprehensive end-to-end system designed to detect and extract celestial entities from astronomical plates and to categorize them into distinct subgroups, as illustrated in Figure 1.

Within this framework, the service undertakes the classification of various object subtypes. The service classifies carbon stars as characterized by their carbon-rich atmospheres and exhibiting a distinct reddish hue due to lower temperatures. Simultaneously, the service addresses the classification of subdwarfs-metal-poor stars with a fainter and bluer profile than their counterparts, indicative of their early formation. In addition, the system tackles the classification of Markarian galaxies (Huchra 1977), notable for their distinctive attributes as active galaxies featuring intense star formation or active galactic nuclei (AGN), discernible through their strong UV excess.

In the sub-object classification, it is essential to differentiate the following spectral types:

• Markarian galaxy
  • Mrk AGN (Markarian Active Galactic Nuclei, 13^m–16^m brightness)—active galactic nuclei within the sample. AGNs are characterized by a supermassive black hole at the center of a galaxy, which emits large amounts of radiation along the range of electromagnetic wavelengths due to the accretion of matter.
  • Mrk SB (Markarian Starburst Galaxies, 13^m–16^m brightness)—starburst galaxies that undergo intense bursts of star formation, leading to high levels of stellar activity, emission lines, and young stellar populations.
  • Mrk Abs (Markarian Absorption-Line Galaxies, 13^m–16^m brightness)—galaxies exhibiting prominent spectra absorption lines. Absorption-line galaxies are characterized by the vital contribution of stars that mostly have absorption lines, thus making up a broadened absorption-line spectrum of galaxies.
• Carbon Stars
  • C-R (Carbon Stars with Enhanced Red Emission, 13^m–16^m)—exhibit enhanced red emission features in their spectra, attributed to the presence of dust grains and organic compounds in the circumstellar environment of these stars.
  • C-N (Carbon Stars with Enhanced Nitrogen, 13^m–16^m)—exhibit enhanced nitrogen absorption features in their spectra. The enhanced nitrogen abundance results from nuclear processing and dredge-up of material within these stars.
  • C-H (Carbon Stars with Hydrogen, 13^m–16^m)—exhibit the presence of hydrogen in their spectra. They are characterized by excessive carbon in their atmospheres, leading to distinctive molecular absorption features.
• Subdwarf
  • sdO (Subdwarf O Stars, 13^m–16^m)—hot, luminous stars with helium-dominated atmospheres. They are more massive and hotter than subdwarf B stars. These are among the hottest known stars.
  • sdB (Subdwarf B Stars, 13^m–16^m)—hot, compact stars with atmospheres primarily composed of hydrogen and helium. They are typically more massive than white dwarfs but less massive than main-sequence stars.
  • sdA (Subdwarf A Stars, 13^m–16^m)—cooler and less massive than subdwarf B stars. They exhibit spectral features indicative of their hydrogen-dominated atmospheres.

By distinguishing these spectral types, the sub-object classification provides a finer level of categorization and enables more precise characterization and analysis of celestial objects within the mentioned groups. Besides, the photographic emulsion response is not linear (the characteristic curve characterizes it), causing additional difficulties both for the photometry and the classification. Thus, objects with different brightness have different shapes, and one should develop the classification for each magnitude range individually. For instance, very faint C stars are like dots, making distinguishing them in the DFBS fields difficult, as many defects and artifacts are also present. They resemble triangles when having 15^m–16^m, as only the red part is observable. For bright objects, one can follow some indefinite continuation of the spectra to the blue with any powerful red parts. To create a relevant classification scheme, we have grouped the spectra by magnitudes as follows: 13^m objects (12.6^m–13.5^m), 14^m (13.6^m–14.5^m), 15^m (14.6^m–15.5^m), 16^m (15.6^m–16.5^m), and 17^m (16.6^m–17.5^m). The last group is conditional, as the faintest spectra (~17^m near the survey limit) cannot be appropriately classified. On the other hand, bright objects (<12.5^m) are overexposed, and it is impossible to carry out any proper classification. So, relevant classification is mainly expected for the objects of 13^m, 14^m, 15^m, and 16^m groups.

The ML classification model contains a robust network to fit the data without underfitting, achieve high accuracy scores on relatively small data, and be scalable for inferring enormous amounts of data. Additionally, the paper aims to provide a comprehensive cloud-based service for the image-based classification of astronomical objects. The paper introduces the following key contributions:

• A robust convolutional neural network for sub-object classification of objects in DFBS is superior to the previous methods based on quantitative evaluation metrics on astronomical objects and sub-object classification datasets.
• A four-step classic image processing module to effectively identify and extract objects from DFBS (Cloud-ML, 2021), augmenting the previous image extraction pipeline and eliminating the necessity for resource-intensive object detection networks.
• Support for local and cloud-based experiments, accommodating diverse frameworks and adapting seamlessly to varying environments and data scales.
• A comprehensive cloud-based ML service encompassing training, visualization, testing, and fine-tuning components, all within a user-friendly interface tailored to the image-based classification of astronomical objects.

Despite the lower resolutions of the extracted objects, the proposed lightweight convolutional network and image extraction algorithm not only outperform the previous methods on group classification in DFBS but also attain satisfactory outcomes across both the training and validation sub-object classification datasets.

The rest of the paper is structured as follows. Section II reviews related work. The architecture and new concepts are presented in Section III. Section IV provides experimental results based on evaluations of different configurations of the testing dataset. Besides, the section describes the overall pipeline of the cloud-based infrastructure. Finally, conclusions and future research directions are discussed in Section V.

2 Related Work

The field of astronomy is witnessing an unprecedented surge in both the volume and intricacy of data. Many projects explore and acquire spectral sky images (Heymans et al. 2012; York et al. 2000). The scale of these endeavors has reached a point where manual classification is no longer feasible. ML algorithms have garnered significant attention in astrophysics, offering solutions to various challenges. However, the efficacy of these methods hinges on the availability of substantial datasets. Numerous ML and deep learning (DL) techniques have been proposed for classifying astronomical objects, encompassing classical machine learning approaches, Convolutional Neural Networks (CNNs), and object detection networks. While many of these networks excel at classifying high-resolution objects and synthetic datasets, their performance needs to improve when confronted with low-resolution images and limited data volumes, often succumbing to overfitting.

CNNs have found widespread application in the realm of astronomical image classification. These networks operate by routing an image through convolutional layers, activating multiple neurons, with the number of active neurons aligning with the predetermined number of classes. For instance, Kim and Brunner (2016) employed CNNs for star-galaxy classification. Their network encompassed convolutional and pooling layers, culminating in two dense layers. The authors used a similar method with data preprocessing to classify Markarian galaxies, quasars, compact galaxies, and other objects in the DFBS.

As the data volume expands, the network architecture depth must be correspondingly enhanced. Residual connections avoid vanishing gradient problems and other training difficulties in deep networks (He et al. 2016). This concept has been leveraged by (Alawi and Al-Roainy 2021) to deploy and assess four distinct ResNet architectures to classify stars and galaxies. Their findings underscored the positive correlation between increased residual blocks and enhanced accuracy, mainly when ample data is available. Ethiraj and Bolla (2021) took a different approach by employing transfer learning networks, including Resnet50, DenseNet121, and Xception, supplemented with ImageNet weights, for their classification endeavors. Fine-tuning different layers within these networks demonstrated that DenseNet121 and Xception achieved the highest accuracy among the seven transfer learning networks assessed on the SDSS-IV dataset (Abolfathi et al. 2018). Nonetheless, it’s essential to exercise caution when deploying large CNNs with comparatively smaller training datasets than those employed in the models above. Such networks are prone to overfitting due to the limited data, and their performance diminishes as regularizations increase, leading to slower learning rates.

Harnessing their aptitude for detecting and categorizing numerous objects, object detection neural networks are a unifying solution for addressing extraction and classification challenges within a singular framework. For instance, the versatility of modified Faster R-CNN has been harnessed to tackle detection and classification tasks (Jia et al. 2020). In a related context, Burke et al. (Burke et al. 2019) successfully employed Mask R-CNN to deblend and detect sources within multiband astronomical images and classify them. Notably, Mask R-CNN represents an advancement over Faster R-CNN, exhibiting favorable performance metrics at moderate intersection over union thresholds (He et al. 2017). Designed initially for RGB images, adapting Mask R-CNN to detect single-channel images necessitates code-level and training optimization-level adjustments. R-CNNs, two-stage detection models, exhibit significant drawbacks regarding efficiency and suitability when handling large quantities of images, rendering them an unsuitable option for rapid detection in extensive-scale scenarios.

Better solutions for detection in terms of time effectiveness are real-time networks. Thus, González et al. (2018) have used YOLO (You Only Look Once), a single-phase detector that works faster than the models described above. Currently, there are newer versions of YOLO that outperform the latter both in accuracy and speed. YOLO is speedy and learns generalizable representations of objects so that when trained on natural images and tested on the artwork, the algorithm outperforms other top detection methods. Although one-stage and two-stage object detection networks are end-to-end, they bring up some difficulties. First, data annotation must be performed, including classifying objects and marking their bounding boxes. Besides, detection networks are inferior to classification networks in terms of time complexity.

Authors have dedicated considerable efforts to harnessing ML techniques within the context of DFBS, yielding supervised and unsupervised approaches for object classification. Notably, these endeavors encompass detecting bounding boxes of objects on astronomical plates and classifying prominent astronomical entities, such as Markarian galaxies, Planetary Nebulae, and Carbon stars, employing supervised learning methodologies. These studies yielded an average accuracy of 87%, substantiating their efficacy. Moreover, the trained networks were extrapolated to predict the classification of other objects within the survey. This article stands poised to transcend the limitations observed in prior investigations and make substantial contributions to the advancement of astronomical data analysis and the pursuit of discoveries within this field.

3 Methodology

The astronomical object extraction and image-based classification methodology involves several main actions illustrated in Figure 2. The initial stage encompasses a four-step algorithm (depicted in Figure 2(a)) diligently orchestrated to extract objects from the DFBS plates. This multifaceted process is followed by data cleaning, normalizing, and transformation to establish harmonious compatibility with the ensuing ML algorithms. Afterward, the extracted objects are augmented and fed into a classification network for training and validation (Figure 2(b)). Finally, new and unlabeled objects are predicted using the previously trained network.

4 Experimental Results

The section provides a comprehensive performance evaluation, including details about the sub-group and group classification datasets, optimal hyperparameters, and training configurations, like the number of epochs, batch size, or learning rate schedule. Then, the quantitative results of the proposed framework are presented on both datasets, focusing on classification performance evaluation metrics and other quantitative scores. These results provide insights into the accuracy of classifying astronomical objects. They are also compared with the previous work to demonstrate the superiority of the proposed method on both tasks. The trained model was inferenced on two massive datasets from the DFBS by employing the second object extraction approach for unlabeled objects discussed in Section 3.1. The results are revealed later in this section.

The astronomical objects have been extracted from astronomical plates based on the pipeline introduced in the previous work. The one key difference is the four-step preprocessing mechanism described in Section 3.2 instead of the preceding three-step approach. The enhanced method better outlines astronomical objects and automates the invalid objects filtering process. As a result, it extracts up to 30% more objects than the previous method, given the same dataset containing objects’ RA and Dec parameters.

The sub-object classification dataset consists of 2,107 training and 405 validation samples. These numbers could be higher because some classes only have 40–50 samples. The similarity of image characteristics of sub-objects within the same group (e.g., sdB and sdO subdwarfs) combined with one class having relatively fewer samples than the other class results in confusing the model always to predict the dominant class. Experiments have been conducted on three sub-groups taken from the extracted objects to avoid biased framework results caused by low numbers of training samples in some of the classes.

The comparison of the frameworks on groups’ classification has been conducted by utilizing the publicly available dataset of the previous method in the GitHub repository (Astsatryan et al. 2021). The dataset has been divided into training and validation splits with 1,478 and 258 observations, respectively. The training pipelines have been reproduced for both methods on both datasets with minor hyperparameter adjustments to maximize accuracy scores without altering the theoretical foundations of the two methods.

5 Summary and Conclusion

The paper presents a framework for automated detection and sub-object classification of astronomical objects in the DFBS. The framework combines a four-step image-processing pipeline with deep neural network architectures to achieve promising astronomical sub-object classification task results. The proposed framework is the pioneering work on the image-based sub-object classification of astronomical objects. The framework’s effectiveness is demonstrated by evaluating astronomical subtypes with sufficient examples. The proposed framework for sub-object classification demonstrated its validity with more than 96% accuracy. Moreover, it illustrated its superiority on the astronomical group classification task in the DFBS by outperforming the preceding work by 5% accuracy refinement. Additional experiments on 1 million and 4 million inference datasets revealed the object numbers of selected subgroups in the DFBS. According to the findings, 1/20 of the 20 million astronomical objects belong to the subjects of the inner group classification.

Additionally, this work contributes to the astronomical scientific community by providing a general cloud-based service for image-based astronomical object classification. The service offers users a cloud-based infrastructure for different ML experiments on provided and proprietary astronomical data. Training, fine-tuning, evaluation, and inference pipelines are implemented within the service, allowing for integration and execution of the entire ML workflow. Additionally, it enables users to monitor their pipelines using TensorBoard-based visualizations and other performance metrics.

Future work aims to increase the number of sub-object classes by including poorly represented classes into the model and leveraging distributed computing approaches for large-scale inference on the overall First Byurakan Survey database. The authors intend to annotate additional sub-object datasets to address these issues and provide researchers with a more comprehensive toolset for applying to a broader range of astronomical surveys.