arXiv subjects

Ilya N. Pashchenko

Publications and source records attributed to Ilya N. Pashchenko.

4 recordsLinked to original sources

arXiv2026

RadioAstron space-VLBI imaging of the jet in M87: II. The parsec-scale structure at 4.8 GHz

We present RadioAstron observations of the M87 jet obtained on 2014 February 4-5 at 4.8 GHz ($λ=6.2$ cm). The correlated signals between the 10-m space antenna and ground-based radio telescopes were detected up to projected baselines of $\thicksim3$\,Earth diameters (0.65~G$λ$). The orientation of the jet downstream of the core shifts northward, consistent with the long-term oscillating trend reported in other studies. An elliptical body mode of the Kelvin-Helmholtz instability can create the observed helical shape of the jet. The estimated wavelength of this instability mode suggests that its origin may be related to some processes in the accretion disc. We present 1.7 - 4.8 GHz and 4.8 - 15.4 GHz spectral index maps showing flattening of the spectrum along the outer streamlines and along the jet spine. Simulations and analysis show that this spectral flattening is partially artificial and is a common systematic effect of the CLEAN procedure of VLBI imaging. The spectral index of the M87 jet is consistent with the optically thin emission, while its steepening can be produced by the propagation of the instability modes causing higher pressure regions near the jet boundary and in its interior.

astro-ph.HE↗

arXiv2021

A ring accelerator? Unusual jet dynamics in the IceCube candidate PKS 1502+106

On 2019/07/30.86853 UT, IceCube detected a high-energy astrophysical neutrino candidate. The Flat Spectrum Radio Quasar PKS 1502+106 is located within the 50 percent uncertainty region of the event. Our analysis of 15 GHz Very Long Baseline Array (VLBA) and astrometric 8 GHz VLBA data, in a time span prior and after the IceCube event, reveals evidence for a radio ring structure which develops with time. Several arc-structures evolve perpendicular to the jet ridge line. We find evidence for precession of a curved jet based on kinematic modelling and a periodicity analysis. An outflowing broad line region (BLR) based on the C IV line emission (Sloan Digital Sky Survey, SDSS) is found. We attribute the atypical ring to an interaction of the precessing jet with the outflowing material. We discuss our findings in the context of a spine-sheath scenario where the ring reveals the sheath and its interaction with the surroundings (narrow line region, NLR, clouds). We find that the radio emission is correlated with the $γ$-ray emission, with radio lagging the $γ$-rays. Based on the $γ$-ray variability timescale, we constrain the $γ$-ray emission zone to the BLR (30-200 $r_{\rm g}$) and within the jet launching region. We discuss that the outflowing BLR provides the external radiation field for $γ$-ray production via external Compton scattering. The neutrino is most likely produced by proton-proton interaction in the blazar zone (beyond the BLR), enabled by episodic encounters of the jet with dense clouds, i.e. some molecular cloud in the NLR.

astro-ph.HE↗

arXiv2019

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

Physical parameters of AGN jets observed with Very Long Baseline Interferometry (VLBI) are usually inferred from the core shift measurements or flux and size measured at a peak frequency of the synchrotron spectrum. Both are preceded by modelling of the observed VLBI jet structure with a simple Gaussian templates. We propose to infer the jets parameters using the inhomogeneous jet model directly - bypassing the modelling of the source structure with a Gaussian templates or image deconvolution. We applied Bayesian analysis to multi-frequency VLBA observations of radio galaxy NGC 315 and found that its parsec-scale jet is well described by the inhomogeneous conical model. Our results favour electron-positron jet. We also detected a component in a counter jet. Its position implies the presence of an external absorber with a steep density gradient at close ($r=0.1$ pc) distance from the central engine.

astro-ph.GA↗

arXiv2018

Machine learning search for variable stars

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 robust 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.

astro-ph.IM↗