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We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 < MJD < 58483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert., 22 pages with appendix, 12 figures, 2 tables, accepted for publication in Astronomy and Astrophysics

In this work we address the problem of anomaly detection in large astronomical databases by machine learning methods. The importance of such study is justified by the existence of a large amount of astronomical data that can not be processed only by human resource. We evaluated five anomaly detection algorithms to find anomalies in the light curve data of the Open Supernova Catalog. Comparison of the algorithms revealed that expert supervised active anomaly detection method shows the best performance, while among purely unsupervised techniques Gaussian mixture model and one-class support vector machine methods outperform isolation forest and local outlier factor methods.

International audience; The search for objects with unusual astronomical properties, or anomalies, is one of the most anticipated results to be delivered by the next generation of large scale astronomical surveys. Moreover, given the volume and complexity of current data sets, machine learning algorithms will undoubtedly play an important role in this endeavor. The SNAD team is specialized in the development, adaptation and improvement of such techniques with the goal of constructing optimal anomaly detection strategies for astronomy. We present here the preliminary results from the third annual SNAD workshop (https://snad.space/2020/) that was held on-line in 2020 July.

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stages: feature extraction, search of outliers with machine learning algorithms and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of 4 automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinised by an expert. From these, 188 (68%) were found to be bogus light curves -- including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24%) were previously reported sources whereas 23 (8%) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e. g. 1 spectroscopically confirmed RS Canum Venaticorum star, 4 supernovae candidates, 1 red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation which can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available at https://github.com/snad-space/zwad, Comment: 31 pages, 28 figures, 4 tables, accepted for publication in MNRAS

Approaches to estimating for the number of periodic and almost periodic solutions of ordinary differential equations are considered. Conditions that allow determinating both the upper and lower bounds for these solutions are found. The existence of periodic and almost periodic solutions in problems are considered.

The next generation of astronomical surveys will revolutionize our understanding of the Universe, raising unprecedented data challenges in the process. One of them is the impossibility to rely on human scanning for the identification of unusual/unpredicted astrophysical objects. Moreover, given that most of the available data will be in the form of photometric observations, such characterization cannot rely on the existence of high resolution spectroscopic observations. We introduce an analysis of anomaly detection in the Open Supernova Catalog (http://sne.space/) with use of machine learning. We developed a strategy and pipeline — where anomalous objects are identified and then submitted to careful individual analysis. This project represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire.

We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the PLAsTiCC challenge and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. We show that, in the real data scenario, AAD was able to identify ~80\% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys., Comment: 10 pages, 5 figures, updated to include PLAsTiCC results

In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered transients will belong to the known classes of astronomical objects. However, it is expected that some transients will be rare or completely new events of unknown physical nature. The task of finding them can be framed as an anomaly detection problem. In this work, we perform for the first time an automated anomaly detection analysis in the photometric data of the Open Supernova Catalog (OSC), which serves as a proof of concept for the applicability of these methods to future large scale surveys. The analysis consists of the following steps: 1) data selection from the OSC and approximation of the pre-processed data with Gaussian processes, 2) dimensionality reduction, 3) searching for outliers with the use of the isolation forest algorithm, 4) expert analysis of the identified outliers. The pipeline returned 81 candidate anomalies, 27 (33%) of which were confirmed to be from astrophysically peculiar objects. Found anomalies correspond to a selected sample of 1.4% of the initial automatically identified data sample of ~2000 objects. Among the identified outliers we recognised superluminous supernovae, non-classical Type Ia supernovae, unusual Type II supernovae, one active galactic nucleus and one binary microlensing event. We also found that 16 anomalies classified as supernovae in the literature are likely to be quasars or stars. Our proposed pipeline represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. All code and products of this investigation are made publicly available., Comment: 18 pages, 19 figures, accepted in MNRAS

NICA is a new accelerator collider complex under construction at JINR. The facility is aimed at providing collider experiments with heavy ions up to Uranium in a center of mass energy range from 4 to 11 GeV/u and an average luminosity up to 1027 cm -2 s-1. The collisions of polarized deuterons are also foreseen. The facility includes two injector chains, a new superconducting booster synchrotron, the existing 6 AGeV superconducting synchrotron Nuclotron, and a new superconducting collider consisting of two rings, each of about 500 m in circumference. The booster synchrotron and the NICA collider are based on an iron-dominated “window frame”-type magnet with a hollow superconductor winding analogous to the Nuclotron magnet. The status of the development of the full size model magnets for the booster synchrotron as well as for the NICA collider is presented. The test results of model magnets are discussed. The status of the creation of a facility for serial tests of superconducting magnets for the NICA project is described.

Technical characteristics of the plants, such as petroleum gas gathering and transportation, petroleum gas-lift, transportation of natural gas at both linear and booster gas compressing stations, etc., are given. Trends of further development of compressor plants having an aircraft-type gas turbine drive with a power of as much as 6.3 MW are formulated.

An investigation was made of the formation of beams of fast electrons in different gases at pressures of from 0.01 to 100 kPa. Plots were made of the dependences of the electron beam currents Ie on the gas pressure p for different electric field strengths E. The dependences Ie=f(p) for air were found to intersect the similar dependences for other gases (helium, nitrogen, neon, and argon) at a pressure of p=10.6 kPa and for E=2.3·105 V·cm−1. This fact is explained by the influence of the oxygen ions and atoms on the electron beam formation process. Another experimental result, the appearance of a minimum in the dependences Ie=f(p) for all gases, is explained by defocusing of the electron beam, the appearance of a reverse current, and magnetic neutralization of the beam. Electron beams were obtained having a pulse duration of 15–20 nsec and a current of 105-106 A/m2 per unit cathode area.

The feasibility of producing high-current, high-energy electron beams was studied for the application of nanosecond voltage pulses with amplitudes to 300 kV in accelerating gaps. The pressures of the gap gases (helium and nitrogen) measured from 10−1 torr to atmospheric. Experiments showed that the placement of two barriers in the accelerating gap significantly increased the electron beam current due to an increase in the burn time of the volume discharge under conditions involving significant over-voltage in the electrical field. Electron beams with energies of up to 250 keV and currents up to 260 A were obtained.

To elucidate the mechanisms responsible for reorganization in the immune system in patients in the acute period of myocardial parameters for immune status were examined in 40 patients with acute myocardial infarction. The findings were processed by statistic methods of factor analysis. The value of factor exercise with the same parameters varied with the period of the disease. On the first day of myocardial infarction, immune complex concentrations were found to be mostly affected by this process. On day 7, chemiluminescence underwent changes, so did circulating antibody titers on day 30. Evidently, it is advisable to bear in mind the aforementioned relationships while studying the effect of immune responses on the course and prognosis of myocardial infarction.