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3C 84: Observational Evidence for Precession and a Possible Relation to TeV Emission

Silke Britzen, Christian Fendt, Michal Zajaček, Frédéric Jaron, Ilya Pashchenko, Margo F. Aller, Hugh D. Aller
2019 Galaxies  
3C 84 (NGC 1275, Perseus A) is a bright radio source at the center of an ongoing merger, where HST observations show two colliding spiral galaxies. 3C 84 holds promise to improve our understanding about how of the activity of active galactic nuclei, the formation of supermassive binary black holes, feedback processes, and galaxy collisions are interrelated. 3C,84 is one of only six radio galaxies, which reveal TeV emission. The origin of this TeV emission is still a matter of debate. Our
more » ... study is based on high resolution radio interferometric observations (15 GHz) of the pc-scale jet in this complex radio galaxy. We have re-modeled and re-analyzed 42 VLBA observations of 3C 84, performed between 1999.99 and 2017.65. In order to enable a proper alignment of the VLBA observations, we developed a method of a "differential" alignment whereby we select one reference point and minimize the deviations from this reference point in the remaining epochs. As a result, we find strong indication for a precession of the 3C 84 jet—not only for its central regions, but also for the outer lobe at 10 mas distance. These findings are further supported by our kinematic precession modeling of the radio flux-density monitoring data provided by the University of Michigan Radio Observatory and the Owens Valley Radio Observatory, which yields a precession time scale of about 40 yr. This time scale is further supported by literature maps obtained about 40 yr ago (1973 and 1974.1) which reveal a similar central radio structure. We suggest that the TeV flare detected by MAGIC may correlate with the precession of 3C 84, as we disentangle a projected reversal point of the precessing motion that correlates with the flaring time. This may physically be explained by a precessing jet sweeping over a new region of so far undisturbed X-ray gas which would then lead to shock-produced TeV-emission. In addition, we perform a correlation analysis between the radio data and GeV data obtained by the Fermi Gamma-ray Space Telescope and find that the γ -ray data are lagging the radio data by 300–400 days. A possible explanation could be that the radio and the GeV data stem from different emission regions. We discuss our findings and propose that the detected jet precession can also account for the observed cavities in the X-ray emission on kpc-scales.
doi:10.3390/galaxies7030072 fatcat:kszomgq42jdtzljqikvepdtptu

Inferring AGN jet parameters using Bayesian analysis of VLBI data with non-uniform jet model [article]

Ilya N. Pashchenko, Alexander V. Plavin
2019 arXiv   pre-print
However core shift estimates obtained using the fitting of the VLBI core with a Gaussian are biased with bias that depends on resolution (Plavin et al. 2018 ) and observed jet parameters (Pashchenko  ... 
arXiv:1904.07057v1 fatcat:qqpfsaifzjealfv6p5cb7brxbq

Machine learning search for variable stars

Ilya N Pashchenko, Kirill V Sokolovsky, Panagiotis Gavras
2017 Monthly notices of the Royal Astronomical Society  
Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected systematic errors limit the practical applicability of this approach to high-amplitude variability and well-behaving data sets. Searching for a new variability detection technique that would be applicable to a wide range of variability types while being
more » ... t to outliers and underestimated measurement uncertainties, we propose to consider variability detection as a classification problem that can be approached with machine learning. We compare several classification algorithms: Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects identified) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, 13 low-amplitude variables are new discoveries. We find that the considered machine learning classifiers are more efficient (they find more variables and less false candidates) compared to traditional techniques that consider individual variability indices or their linear combination. The NN, SGB, SVM and RF show a higher efficiency compared to LR and kNN.
doi:10.1093/mnras/stx3222 fatcat:pznzg7m6m5gi5iizh5rfp4pvcy

Methods of computational modeling of coronary heart vessels for its digital twin

Ilya Naplekov, Ivan Zheleznikov, Dmitry Pashchenko, Polina Kobysheva, Anna Moskvitina, Ravil Mustafin, Maria Gnutikova, Alina Mullagalieva, Pavel Uzlov, V. Jayakumar, S. Ranganathan, D. Devika (+1 others)
2018 MATEC Web of Conferences  
In this work, methods of numerical modelling of the coronary vessels system of the human heart have been studied. This investigation includes transient flow of the liquidblood and dynamics of zones of shear stress at vessels. The main goal of the research is obtaining of hemodynamic and shear stress for creating the digital twin of coronary heart vessels. The results were obtained for low Reynolds numbers about 20 of three-dimensional laminar flow. With this Reynolds number the turbulent flow
more » ... the blood is modelled by Realizable k-ε model, and SST models to the narrowing, expansions, and blocks inside the vessels. Loads caused by the additional energy consumption because of the turbulent flow of the blood (increase in arterial blood pressure) have been analyzed. A twodimensional model of a separated vessel with fixed blood back-flow prevention is developed. Presence of a turbulent flow core is discovered. By the means of stress-strain properties of the model, visual representation of the wearing process of the blood back-flow preventer, and heart diseases progression is obtained.
doi:10.1051/matecconf/201817201009 fatcat:3n76vq5owvfsdoikvybiyhk5km

emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC

Daniel Foreman-Mackey, Will Farr, Manodeep Sinha, Anne Archibald, David Hogg, Jeremy Sanders, Joe Zuntz, Peter Williams, Andrew Nelson, Miguel de Val-Borro, Tobias Erhardt, Ilya Pashchenko (+1 others)
2019 Journal of Open Source Software  
emcee is a Python library implementing a class of affine-invariant ensemble samplers for Markov chain Monte Carlo (MCMC). This package has been widely applied to probabilistic modeling problems in astrophysics where it was originally published, with some applications in other fields. When it was first released in 2012, the interface implemented in emcee was fundamentally different from the MCMC libraries that were popular at the time, such as PyMC, because it was specifically designed to work
more » ... th "black box" models instead of structured graphical models. This has been a popular interface for applications in astrophysics because it is often non-trivial to implement realistic physics within the modeling frameworks required by other libraries. Since emcee's release, other libraries have been developed with similar interfaces, such as dynesty (Speagle 2019). The version 3.0 release of emcee is the first major release of the library in about 6 years and it includes a full re-write of the computational backend, several commonly requested features, and a set of new "move" implementations.
doi:10.21105/joss.01864 fatcat:g5s6gw6knjcz5flowygfkahfkm


2020 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA)  
and Alexander Ponomarev 521 Analysis and Synthesis of the Modified MRAC-MIT System and the MRAC-Lyapunov System Alexander Ponomarev, Yuri Kudinov, Fedor Pashchenko and Eugene Duvanov 527 Analysis and  ...  Gusev 513 Development of a Modular Control System for an Industrial Dismantling Robot Daria Vladimirova 517 Optimal Control of the Silica Capillaries Drawing Process Eugene Duvanov, Yuri Kudinov, Fedor Pashchenko  ... 
doi:10.1109/summa50634.2020.9280691 fatcat:7kmyfu5varfsvoic3bdz4nyxae

Page 5354 of Mathematical Reviews Vol. , Issue 84m [page]

1984 Mathematical Reviews  
See «92001 Pashaev, O.K. 81045, 81205 Pashchenko, N.T. .... See +35107 ee er 78004 OS, ee 20065 Paszkowski, Stefan .... See +65001 Passtor, Ana Patel, T. N. Pathak, R. S. Pati, R.  ...  B. 49058, 90042ab Piatetski-Shapiro, Ilja losifovitch 10019, «20046, 22029 Piatetski-Shapiro, Ilya . See Piatetski- Shapiro, Ilja losifovitch Piazza, G. Picard, Colette Piccioni, M. Picco, P.  ... 


2010 Proceedings of the International Astronomical Union  
Michael Grachev, Stanislav Papushev, Pavel Vashkovyak, Sofja Grebenev, Sergej Parfinenko, Leonid Vassiliev, Nikolaj Grebenikov, Evgenij Parijskij, Yurij Vereshchagin, Sergej Grechnev, Victor Pashchenko  ...  Terasranta, Harri Harju, Jorma Nevalainen, Jukka Tiuri, Martti Heinamaki, Pekka Niemi, Aimo Tornikoski, Merja Huovelin, Juhani Nilsson, Kari Tuominen, Ilkka Jetsu, Lauri Nurmi, Pasi Usoskin, Ilya  ... 
doi:10.1017/s1743921310005429 fatcat:bn5xysp2fbdzda72vwkilj6dtq

Page 1518 of Mathematical Reviews Vol. , Issue Author Index [page]

Mathematical Reviews  
Pashchenko, F. F. (with Durgaryan, I. S.) Identification of objects by the criterion of the maximum amount of information. (Russian.  ...  (Ilya Shapiro) 2002j:81162 81T20 (81T30, 81T70) — (with Rosales, J. J.; Tkach, Vladimir I.; Tsulaia, M. M.) » = 4 supersymmetry for the FRW model. (English summary) Phys. Rev.  ... 

Page 1338 of Mathematical Reviews Vol. 27, Issue Index [page]

Mathematical Reviews  
(with Pashchenko, N. V.) Application of Morse inequalities in the theory of continuous phase transitions. (Russian. Russian summary) /zv. Vyssh. Uchebn. Zaved. Severo-Kavkaz. Reg. Estestv.  ...  (Shri Nivas Bhatt) 95g:40009 40D20 (40C05) Taliaferro, Steven D. see Bakelman, Ilya J., 95k:35063 Talifujiang (with Yong, Xue Rong) The highest order k-step accurate computation formula for solving equation  ... 

Page 1916 of Mathematical Reviews Vol. , Issue Index [page]

Mathematical Reviews  
(English summary) Dedicated to our teacher, mentor and friend, Nobel laureate, Ilya Prigogine. Chaos Solitons Fractals 19 (2004), no. 1, 109-128. (Kotik K.  ...  (Summary) 2004j:91205 91B82 (62F10, 62P20) Pashchenko, S. V. (with Kryazhimskii, Arkadii V.) On the solution of the linear time-optimal control problem with mixed constraints. Optimal control, 4. J.  ... 

New Imperial History

Marina Mogilner
2014 Revue d?études comparatives Est-Ouest - RECEO  
The phenomenon of nascent imperial social engineering of the turn of the twentieth century is analyzed by Ilya Gerasimov (Gerasimov, 2009a) .  ...  As examples of Eurasianist historiography see: Pashchenko (2002 ), Dzhanguzhin (2002 ), Ochirova (2004) . 22. Russian translation, Mark von Hagen (2004) .  ... 
doi:10.3917/receo.452.0025 fatcat:7lpzgqjaazguxibtr4tww3xpnu

A geometrical interpretation for the properties of multiband optical variability of the blazar S5 0716+714 [article]

Marina S. Butuzova
2020 arXiv   pre-print
Kovalev, Ilya Pashchenko for the useful discussion of the results of this work. The theoretical part of this research was supported by the Russian Science Foundation grant 19-72-00105. .  ... 
arXiv:2005.08161v1 fatcat:7xblmaf7yzdjbpviglb4nttmiu

Parsec-scale properties of the peculiar gigahertz-peaked spectrum quasar 0858-279 [article]

N. A. Kosogorov
2021 arXiv   pre-print
., 1994b, We thank the anonymous referee and Alan Marscher, Ilya A&A, 292, 33 Pashchenko, Alexander Pushkarev, Alexander Plavin and Eduardo Greisen E.  ... 
arXiv:2104.08544v2 fatcat:gsbrpvtmx5fjjfve7s2jgsj77a

Comparative performance of selected variability detection techniques in photometric time series data

K. V. Sokolovsky, P. Gavras, A. Karampelas, S. V. Antipin, I. Bellas-Velidis, P. Benni, A. Z. Bonanos, A. Y. Burdanov, S. Derlopa, D. Hatzidimitriou, A. D. Khokhryakova, D. M. Kolesnikova (+11 others)
2016 Monthly notices of the Royal Astronomical Society  
Ilya Pashchenko for critically reading the manuscript, Nikolas Laskaris for the discussion of algorithms performance, Dr. Valerio Nascimbeni and Dr.  ... 
doi:10.1093/mnras/stw2262 fatcat:wqjjhu5k55c2rimbx7z4bw3j4y
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