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Reduction of supernova light curves by vector Gaussian processes
Monthly Notices of the Royal Astronomical Society. 2023. Vol. 526. No. 2. P. 1822–1831.
Bolometric light curves play an important role in understanding the underlying physics of various astrophysical phenomena, as they allow for a comprehensive modelling of the event and enable comparison between different objects. However, constructing these curves often requires the approximation and extrapolation from multicolour photometric observations. In this study, we introduce vector Gaussian processes as a new method for reduction of supernova light curves. This method enables us to approximate vector functions, even with inhomogeneous time-series data, while considering the correlation between light curves in different passbands. We applied this methodology to a sample of 29 superluminous supernovae (SLSNe) assembled using the Open Supernova Catalog. Their multicolour light curves were approximated using vector Gaussian processes. Subsequently, under the blackbody assumption for the SLSN spectra at each moment of time, we reconstructed the bolometric light curves. The vector Gaussian processes developed in this work are accessible via the python library gp-multistate-kernel on GitHub. Our approach provides an efficient tool for analysing light curve data, opening new possibilities for astrophysical research.
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 3 P. 855–862
Tasks related to the automation of medical data processing are becoming more urgent. Particular attention is paid to systems for monitoring and analyzing human physiological parameters. Such systems often use specialized sensors to capture biomedical images, such as infrared cameras. This article describes our study of the problem of segmenting the eye pupil and iris ...
Added: September 21, 2026
Aleksei Samarin, Nazarenko A., Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 3 P. 844–854
Improving image quality is becoming an increasingly popular task, especially when working with mobile devices. One common approach to image enhancement is the use of convolutional neural networks. However, to achieve good results, such networks must be large enough, otherwise there is a risk of unwanted artifacts. In addition, large convolutional neural networks require significant ...
Added: September 21, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Optical Memory and Neural Networks (Information Optics) 2024 Vol. 33 P. 424–434
This study explores the development of classifiers for microbial images, specifically focusing on streptococci captured via microscopy of live samples. Our approach uses AutoML-based techniques and automates the creation and analysis of feature spaces to produce optimal descriptors for classifying these microscopic images. This technique leverages interpretable taxonomic features based on the external geometric attributes ...
Added: September 21, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 4 P. 1053–1060
This paper presents a novel approach to classification in biomedical imaging, specifically targeting polyp recognition in video endoscopy snapshots. Our method leverages specialized image descriptors to enhance the accuracy and robustness of polyp recognition. By employing these specialized descriptors, we address the challenges inherent in analyzing biomedical images from open datasets. Our approach not only ...
Added: September 21, 2026
Самарин А. В., Торопов А. Г., Савельев А. Г. et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 4 P. 1044–1052
This paper presents a study aimed at improving the detection quality of small-sized microorganisms under challenging microscopic conditions through the application of a lightweight combined image preprocessing model. We focused on the task of detecting diplococci in images obtained through dynamic sample microscopy. The proposed approach employs predefined filters for image preprocessing, combined with the ...
Added: September 21, 2026
Singapore: Springer Singapore, 2025.
Added: September 21, 2026
Tomat A., Sergei O. Kuznetsov, International Journal of Approximate Reasoning 2026 Vol. 197 Article 109754
Interval Pattern Structures (IPS) provide a natural way to represent local, human-readable explanations for predictions on numerical data through vectors of intervals interpreted as axis-parallel hyper-rectangles. In this paper, we develop and evaluate an IPS-based k-nearest neighbors classifier, IPS-KNN, that explains each prediction through a single local interval description rather than through the aggregation of ...
Added: September 21, 2026
Aleksei Samarin, Nazarenko A., Kotenko E. et al., / Series arXiv "math". 2025. No. 2511.20141.
This paper presents a novel approach to neural network compression that addresses redundancy at both the filter and architectural levels through a unified framework grounded in information flow analysis. Building on the concept of tensor flow divergence, which quantifies how information is transformed across network layers, we develop a two-stage optimization process. The first stage ...
Added: September 21, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2025 Vol. 35 No. 2 P. 169–178
This study explores the incorporation of specialized self-attention mechanisms into deep learning architectures, with a particular emphasis on segmenting human iris and pupil regions in infrared images. In this work, we present some modified versions of nonlocal blocks designed to enhance self-attentive properties while addressing the distinct characteristics of infrared imaging data. By applying these customized ...
Added: September 21, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Journal of Imaging 2025 Vol. 11 No. 10 Article 359
Timely identification and accurate delineation of ultra-early ischemic stroke lesions in non-contrast computed tomography (CT) scans of the human brain are of paramount importance for prompt medical intervention and improved patient outcomes. In this study, we propose a deep learning-driven methodology specifically designed for segmenting ultra-early ischemic regions, with a particular emphasis on both the ...
Added: September 21, 2026
Springer, Cham, 2025.
Added: September 21, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Journal of Imaging 2025 Vol. 11 No. 6 P. 1–20
This study presents a unified low-parameter approach to multi-class classification of microorganisms (micrococci, diplococci, streptococci, and bacilli) based on automated machine learning. The method is designed to produce interpretable taxonomic descriptors through analysis of the external geometric characteristics of microorganisms, including cell shape, colony organization, and dynamic behavior in unfixed microscopic scenes. A key advantage ...
Added: September 21, 2026
Shilov A. L., Elesin L., Kravtsov M. et al., Nature Communications 2026 Vol. 17 No. 1 Article 9734
Identifying the microscopic processes limiting conductivity is essential for understanding correlated quantum materials. In twisted bilayer graphene, metallic resistivity follows ρ ~ Tα with widely varying α, fueling competing interpretations from phonon-limited transport and umklapp scattering to strange metallicity and heavy-fermion renormalization. Here, we use terahertz excitation to selectively heat electrons while keeping the lattice cold, separating ...
Added: September 20, 2026
Springer, 2026.
Two volumes of the SPECOM 2026 proceedings contain a collection of submitted papers presented at SPECOM 2026, which were thoroughly reviewed by members of the Program Committee and additional reviewers consisting of almost 80 experts in the conference topic areas. In total, 65 regular full papers out of 99 submissions made via the EasyChair electronic ...
Added: September 20, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2026 Vol. 36 No. 2 P. 323–334
In this paper, an improved approach for automatic wildlife detection in natural environments based on the integration of a neural network architecture with a two-stream attention mechanism and a novel preclassification step based on infrared data has been presented. The proposed method addresses one of the key challenges in environmental monitoring: the need for scalable ...
Added: September 19, 2026
Aleksei Samarin, Nazarenko A., Kotenko E. et al., Proceedings of the ACM on Management of Data, USA 2026 Vol. 4 No. 1 P. 1–28
Modern knowledge and large volumes of data are increasingly encoded within neural networks, making the task of simplifying their structures and reducing the number of parameters especially relevant, both to improve efficiency and to facilitate deployment in resource-constrained environments. This paper presents a novel approach to neural network compression that addresses redundancy at both the ...
Added: September 19, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2025 Vol. 35 No. 2 P. 148–158
This paper describes our research on creating classifiers for microbial images (micrococci microscopy images) obtained from pictures of unfixed microscopic scenes. In our work, we propose an AutoML approach based on the automatic generation and analysis of the feature space for constructing the most optimal descriptors of microorganism images for subsequent classification. This makes it ...
Added: September 19, 2026
Aleksei Samarin, Alexander Savelev, Aleksei Toropov et al., Pattern Recognition and Image Analysis 2026 Vol. 36 No. 2 P. 302–312
The lack of annotated microscopic datasets remains a major obstacle to training robust deep learning models for microbial classification. In this paper, a novel data augmentation pipeline that uses visual–linguistic large-scale models to generate synthetic microscopic images of six different bacterial and nonbacterial classes has been proposed. Synthetic samples have gradually been added to the ...
Added: September 19, 2026
Kondratiev I. A., Moiseenko S. G., Bisnovatyi-Kogan G. S., Lobachevskii Journal of Mathematics 2024 Vol. 45 No. 1 P. 50–59
Magnetorotational processes can significantly affect the dynamics of core-collapse supernovae, resulting to magnetically driven jet-like explosions. The magnetic field topology of progenitor stellar cores is believed to be quite complex and is not known precisely. Such complex structures may differ substantially from usual dipole or quadrupole field, which may cause to additional anisotropy of magnetorotational ...
Added: January 28, 2025
Belkin S., Pozanenko Alexei, , in: Data Analytics and Management in Data Intensive Domains. 23rd International Conference, DAMDID/RCDL 2021, Moscow, Russia, October 26–29, 2021, Revised Selected Papers.: Springer, 2022. P. 74–91.
More than 10000 gamma-ray bursts (GRBs) have been detected since discovery. Long-term observations of about 850 GRB afterglow in optic since 1998 have shown that a core-collapse supernova (SN) accompanies about 50 nearby GRB sources. We have collected about two dozen SNe’ multicolor light curves associated with GRBs. The sample is based on published data, ...
Added: January 31, 2023
Pruzhinskaya M., Volnova A., Matwey Kornilov et al., Research Notes of the AAS 2022 Vol. 6 No. 6 Article 122
The SNAD team reports the discovery of SNAD160 (AT2018lzi) within the Zwicky Transient Facility third data release. The transient has been found using the active anomaly detection algorithm, an adaptive learning strategy aimed at incorporating expert knowledge into machine learning models. Our preliminary analysis shows that SNAD160 could be a superluminous supernova powered by a ...
Added: January 26, 2023
Belkin S., Pozanenko A. S., Astronomy Letters 2020 Vol. 46 No. 12 P. 783–811
Multicolor photometric observations in the optical band and a comprehensive study in the X-ray, gamma-ray, and radio bands are presented for GRB 181201A and its afterglow. The optical observations began ∼0.5 days after the burst and lasted almost continuously for ~24 days. They were resumed after eight months, which allowed us to determine the contribution of the ...
Added: February 6, 2022
Теоремы сравнения для вероятностей малых уклонений весовых L_2-норм гриновских гауссовских процессов
Pusev R., Назаров А. И., Алгебра и анализ 2013 Т. 25 № 3 С. 131–146
Comparison theorems are proved for small ball probabilities of the Green Gaussian processes in weighted L_2-norms. The sharp small ball asymptotics are found for many classical processes under quite general assumptions on the weight. ...
Added: January 28, 2019
Nikitin Y. Y., Pusev R., Теория вероятностей и ее применения 2012 Т. 57 № 1 С. 98–123
We find exact small deviation asymptotics with respect to a weighted Hilbert norm for some well-known Gaussian processes. Our approach does not require knowledge of the eigenfunctions of the covariance operator of a weighted process. Such a peculiarity of the method makes it possible to generalize many previous results in this area. We also obtain ...
Added: January 28, 2019