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We present the results of the optical identification and spectroscopic redshift measurements of 216 galaxy clusters detected in the SRG/eROSITA all-sky X-ray survey. The spectroscopic observations were performed in 2020-2023 with the 6-m BTA telescope at the Special Astrophysical Observatory of the Russian Academy of Sciences, the 2.5-m telescope at the Caucasus Mountain Observatory of the Sternberg Astronomical Institute of the Moscow State University, the 1.6-m AZT-33IK telescope at the Sayan Solar Observatory of the Institute of Solar-Terrestrial Physics of the Siberian Branch of the Russian Academy of Sciences, and the 1.5-m Russian-Turkish telescope (RTT-150) at the T\"{U}B\.{I}TAK Observatory. For all of the galaxy clusters presented here the spectroscopic redshift measurements have been obtained for the first time. Of these, 139 galaxy clusters have been detected for the first time in the SRG/eROSITA survey and 22 galaxy clusters are at redshifts $z_{spec} \gtrsim 0.7$, including three at $z_{spec} \gtrsim 1$. Deep direct images with the rizJK filters have also been obtained for four distant galaxy clusters at $z_{spec} > 0.7$. For these observations the most massive clusters are selected. Therefore, most of the galaxy clusters presented here most likely will be included in the cosmological samples of galaxy clusters from the SRG/eROSITA survey.

We discuss an algorithm whereby the massive galaxy clusters detected in the SRG/eROSITA all-sky survey are identified and their photometric redshifts are estimated. For this purpose, we use photometric redshift estimates for galaxies and WISE forced photometry. To estimate the algorithm operation quality, we used a sample of 634 massive galaxy clusters from the Planck survey with known spectroscopic redshifts in the range $0.1 < z_{spec} < 0.6$. The accuracy of the photometric redshift estimates for this sample is $\delta z_{phot}/(1+z_{phot}) \approx 0.5$%, the fraction of large deviations is 1.3%. We show that these large deviations arise mainly from the projections of galaxy clusters or other large-scale structures at different redshifts in the X-ray source field. Measuring the infrared (IR) luminosities of galaxy clusters allows one to estimate the reliability of the optical identification of the clusters detected in the SRG/eROSITA survey and to obtain an additional independent measurement of their total gravitational masses, $M_{500}$. We show that the masses $M_{500}$ of the galaxy clusters estimated from their IR luminosity measurements have an accuracy $\sigma_{\lg\,M_{500}} = 0.124$, comparable to the accuracy of the mass estimation for the galaxy clusters from their X-ray luminosities.

Volumetric modification of glass materials by ultrashort laser pulses is a powerful technique enabling direct writing of three-dimensional structures for fabrication of optical, photonic, and microfluidic devices. The level of modification is determined by the locally absorbed energy density, which depends on numerous factors. In this work, the effect of the spatial pulse shape on the ultrashort laser excitation of fused silica was investigated experimentally and theoretically for the volumetric modification regimes. We focused on two shapes of laser pulses, Gaussian and doughnut-shaped (DS) ones. It was found that, at relatively low pulse energies, in the range of 1-5 microjoules, the DS pulses are more efficient in volumetric structural changes than Gaussian pulses. It is explained by the intensity clamping effect for the Gaussian pulses, which leads to the delocalization of the laser energy absorption. In the DS case, this effect is overcome due to the geometry of the focused beam propagation, accompanied by the electron plasma formation, which scatters light toward the beam axis. The thermoelastoplastic modeling performed for the DS pulses revealed intriguing dynamics of the shock waves generated because of tubular-like energy absorption. It is anticipated that such a double shock wave structure can induce the formation of high-pressure polymorphs of transparent materials that can be used for investigations of nonequilibrium thermodynamics of warm dense matter. The DS laser pulses of low energies of the order of 100 nanojoules which generate a gentle tubular-like modification can be perspective for a miniature waveguide writing in glass., Comment: 7 pages, 4 figures

Galaxy clusters are the most massive gravitationally bound systems consisting of dark matter, hot baryonic gas and stars. They play an important role in observational cosmology and galaxy evolution studies. We develop a deep learning model for segmentation of Sunyaev-Zeldovich (SZ) signal on ACT+Planck intensity maps and construct a pipeline for microwave cluster detection in the ACT footprint. The proposed model allows us to identify previously unknown galaxy clusters, i.e. it is capable of detecting SZ sources below the detection threshold adopted in the published galaxy clusters catalogues (such as ACT DR5 and PSZ2). In this paper, we use the derived SZ signal map to considerably improve a cluster purity in the extended catalogue of Sunyaev-Zeldovich objects from Planck data (SZcat) in the ACT footprint. From SZcat, we create a new microwave galaxy cluster catalogue (ComPACT), which includes 2,962 SZ objects with cluster purity conservatively estimated as $\gtrsim74-84$\%. We categorise objects in the catalogue into 3 categories, based on their cluster reliability. Within the ComPACT catalogue, there are $\gtrsim{}977$ new clusters with respect to the ACT DR5 and PSZ2 catalogues., Comment: 13 pages, 11 figures. MNRAS accepted

Modern high-throughput sequencing assays efficiently capture not only gene expression and different levels of gene regulation but also a multitude of genome variants. Focused analysis of alternative alleles of variable sites at homologous chromosomes of the human genome reveals allele-specific gene expression and allele-specific gene regulation by assessing allelic imbalance of read counts at individual sites. Here we formally describe an advanced statistical framework for detecting the allelic imbalance in allelic read counts at single-nucleotide variants detected in diverse omics studies (ChIP-Seq, ATAC-Seq, DNase-Seq, CAGE-Seq, and others). MIXALIME accounts for copy-number variants and aneuploidy, reference read mapping bias, and provides several scoring models to balance between sensitivity and specificity when scoring data with varying levels of experimental noise-caused overdispersion.

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarise the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data product representing a 2000~h SKA MID spectral line observation from redshifts 0.25 to 0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, `reproducibility awards' were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy -- which combined predictions from two independent machine learning techniques to yield a 20 percent improvement in overall performance -- underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical datasets., Comment: Under review by MNRAS; 28 pages, 16 figures

We present a probabilistic model built for the optical cross-match between the SRG/eROSITA X-ray sources and photometric data from the DESI Legacy Imaging Surveys. The model relies both on positional and photometric information on optical objects nearby X-ray sources and allows performing selection with precision and recall $\approx94$% (for $F_{\rm X,0.5-2}>10^{-14}$ erg/s/cm$^2$). With this model, we calibrated positional error of the SRG/eROSITA sources detected in the Lockman Hole: $\sigma_{\rm corr} = 0.87\sqrt{ \sigma_{\rm det}^{2.53} + 1.12^2}$. The model will become a part of the SRGz system for data analysis of the X-ray data obtained from the all-sky SRG/eROSITA survey., Comment: 19 pages, 18 figures, published in Astronomy Letters

The results of multiwavelength observations of the very massive galaxy cluster SRGe CL2305.2-2248 detected in X-rays during the first SRG/eROSITA all-sky survey are discussed. This galaxy cluster was also detected earlier in microwave band through the observations of Sunyaev-Zeldovich effect in South Pole Telescope (SPT-CL J2305-2248), and in Atacama Cosmological Telescope (ACT-CL J2305.1-2248) surveys. Spectroscopic redshift measurement, $z=0.7573$, was measured at the Russian 6-m BTA telescope of SAO RAS, in good agreement with its photometric estimates, including a very accurate one obtained using machine learning methods. In addition, deep photometric measurements were made at the Russian-Turkish 1.5-m telescope (RTT150), which allows to study cluster galaxies red sequence and projected galaxies distribution. Joint analysis of the data from X-ray and microwave observations show that this cluster can be identified as a very massive and distant one using the measurements of its X-ray flux and integral comptonization parameter only. The mass of the cluster estimated according to the eROSITA data is $M_{500}=(9.0\pm2.6)\cdot10^{14}\, M_\odot$. We show that this cluster is found among of only several dozen of the most massive clusters in the observable Universe and among of only a few the most massive clusters of galaxies at $z>0.6$., Comment: 10 pages, 6 figures, accepted for publication in Astronomy Letters

We present the first sample of tidal disruption events (TDEs) discovered during the SRG all-sky survey. These 13 events were selected among X-ray transients detected in the 0

Accurate photo-z measurements are important to construct a large-scale structure map of X-ray Universe in the ongoing SRG/eROSITA All-Sky Survey. We present machine learning Random Forest-based models for probabilistic photo-z predictions based on information from 4 large photometric surveys (SDSS, Pan-STARRS, DESI Legacy Imaging Survey, and WISE). Our models are trained on the large sample of $\approx$580000 quasars and galaxies selected from the SDSS DR14 spectral catalog and take into account Galactic extinction and uncertainties in photometric measurements for target objects. On the Stripe82X test sample we obtained photo-z accuracy for X-ray sources: $NMAD=0.034$ (normalized median absolute deviation) and $n_{>0.15}=0.088$ (catastrophic outliers fraction), which is almost $\sim2$ times better than best photo-z results available in the literature., Comment: 4 pages, to appear in the Proceedings of ADASS XXX (Granada, Spain November 8-12, 2020), Astronomical Society of the Pacific (ASP) Conference Series

Structural Equation Modeling (SEM) is an umbrella term that includes numerous multivariate statistical techniques that are employed throughout a plethora of research areas, ranging from social to natural sciences. Until recently, SEM software was either commercial or restricted to niche languages, and the lack of SEM packages compatible with more mainstream programming languages was dire. To combat that, we introduced a Python package semopy 1 that surpassed other state-of-the-art software in terms of performance and estimation accuracy. Yet, it was lacking in functionality and its usage was burdened with unnecessary boilerplate code. Here, we introduce a complete overhaul of semopy that improves upon the previous results and comes with lots of new capabilities. Furthermore, we propose a novel SEM model that combines in itself a notion of random effects from linear mixed models (LMMs) to model numerous phenomena, such as spatial data, time series or population stratification in genetics.

Based on the observation of sizeable target-transverse-spin asymmetries in single-hadron and hadron-pair production in Semi-Inclusive measurements of Deep Inelastic Scattering (SIDIS), the chiral-odd transversity quark distribution functions $h_1^q$ are nowadays well established. Several possible channels to access these functions were originally proposed. One candidate is the measurement of the polarisation of $\Lambda$ hyperons produced in SIDIS off transversely polarised nucleons, where the transverse polarisation of the struck quark might be transferred to the final-state hyperon. In this article, we present the COMPASS results on the transversity-induced polarisation of $\Lambda$ and $\bar{\Lambda}$ hyperons produced in SIDIS off transversely polarised protons. Within the experimental uncertainties, no significant deviation from zero was observed. The results are discussed in the context of different models taking into account previous experimental results on $h_1^u$ and $h_1^d$., Comment: 18 pages, 6 figures

The task of morphological classification is complex for simple parameterization, but important for research in the galaxy evolution field. Future galaxy surveys (e.g. EUCLID) will collect data about more than a $10^9$ galaxies. To obtain morphological information one needs to involve people to mark up galaxy images, which requires either a considerable amount of money or a huge number of volunteers. We propose an effective semi-supervised approach for galaxy morphology classification task, based on active learning of adversarial autoencoder (AAE) model. For a binary classification problem (top level question of Galaxy Zoo 2 decision tree) we achieved accuracy 93.1% on the test part with only 0.86 millions markup actions, this model can easily scale up on any number of images. Our best model with additional markup achieves accuracy of 95.5%. To the best of our knowledge it is a first time AAE semi-supervised learning model used in astronomy.

SRGE J170245.3+130104 was discovered by the eROSITA telescope aboard the SRG space observatory on March 13-15, 2020 during the first half-year scan of its all-sky X-ray survey. The optical counterpart of the X-ray source was photometrically identified as a distant quasar candidate at $z\approx5.5$. Follow-up spectroscopic observations, done in August/September 2020 with the SCORPIO-II instrument at the BTA 6-m telescope, confirmed that SRGE J170245.3+130104 is a distant quasar at redshift z=5.466. The X-ray luminosity of the quasar during the first half-year scan of the eROSITA all-sky survey was $3.6^{+2.1}_{-1.5}\times 10^{46}$ erg/s (in the 2-10 keV energy range), whereas its X-ray spectrum could be described by a power law with a slope of $\Gamma=1.8^{+0.9}_{-0.8}$. Six months later (September 13-14, 2020), during the second half-year scan of the eROSITA all-sky survey, the quasar was detected again and its X-ray luminosity had decreased by a factor of 2 (at the $\approx 1.9\sigma$ confidence level). The SRGE J170245.3+130104 proves to be the most X-ray luminous among all known X-ray quasars at $z>5$. It is also one of the radio-loudest distant quasars (with radio-loudness $R\sim10^3$), which may imply that it is a blazar. In the Appendix, we present the list of all $z>5$ quasars detected in X-rays to date., Comment: Preprint: Original russian text: Pis'ma v Astronomichesky Zhurnal 2021, volume 47,Number 3, pages 155-173

Cyber-physical systems and the Internet of things (IoT) is becoming an integral part of the digital society. The use of IoT services improves human life in many ways. Protection against cyber threats is an important aspect of the functioning of IoT devices. Malicious activities lead to confidential data leakages and incorrect performance of devices are becoming critical. Therefore, development of effective solutions that can protect both IoT devices data and data exchange networks turns in to a real challenge. This study provides a critical analysis of the feasibility of using blockchain technology to protect constrained IoT devices data, justifies the choice of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for implementation on such devices, and simulates the main distributed ledger scenarios using PBFT. The simulation results demonstrate the efficiency of the blockchain technology for constrained devices and make it possible to evaluate the applicability limits of the chosen consensus algorithm., Comment: 12 pages, 5 figures