Microquasar Cyg X-3 -- a unique jet-wind neutrino factory?

The origin of astrophysical neutrinos is one of the most debated topics today. Perhaps the most robust evidence of neutrino counterpart comes from supermassive black holes in active galactic nuclei associated with strongly collimated outflows, or jets, that can accelerate particles to relativistic energies and produce neutrinos through hadronic interactions. Similar outflows can also be found from X-ray binaries, or `microquasars', that consist of a neutron star or a stellar-mass black hole accreting matter from a non-degenerate companion star. In some cases, these systems can accelerate particles up to GeV energies implying an efficient acceleration mechanism in their jets. Neutrino production in microquasar jets can be expected with suitable conditions and a hadronic particle population. Microquasar Cyg X-3 is a unique, short orbital period X-ray binary hosting a Wolf-Rayet companion star with a strong stellar wind. The interaction of the dense stellar wind with a relativistic jet leads to particle collisions followed by high-energy gamma-ray and potentially neutrino emission. Here, using the 10-year neutrino candidate sample of the IceCube neutrino observatory, we find that the events with the highest spatial association with Cyg X-3 occur during short-lived high-energy gamma-ray flaring periods indicating the possible astrophysical nature of these events.Comment: 5 pages, 2 figures, 1 table. This article has been accepted for publication in MNRAS published by Oxford University Press on behalf of the Royal Astronomical Societ

Testing High-energy Emission Models for Blazars with X-Ray Polarimetry

Both leptonic and hadronic emission processes may contribute to blazar jet emission; which dominates in blazars' high-energy emission component remains an open question. Some intermediate synchrotron peaked blazars transition from their low- to high-energy emission components in the X-ray band making them excellent laboratories to probe both components simultaneously, and good targets for the newly launched Imaging X-ray Polarimetry Explorer (IXPE). We characterize the spectral energy distributions for three such blazars, CGRaBS J0211+1051, TXS 0506+056, and S5 0716+714, predicting their X-ray polarization behavior by fitting a multizone polarized leptonic jet model. We find that a significant detection of electron synchrotron dominated polarization is possible with a 300 ks observation for S5 0716+714 and CGRaBS J0211+1051 in their flaring states, while even 500 ks observations are unlikely to measure synchrotron self-Compton (SSC) polarization. Importantly, nonleptonic emission processes like proton synchrotron are marginally detectable for our brightest intermediate synchrotron peaked blazar (ISP), S5 0716+714, during a flaring state. Improved IXPE data reduction methods or next-generation telescopes like eXTP are needed to confidently measure SSC polarization.</p

Demonstration of magnetic field tomography with starlight polarization towards a diffuse sightline of the ISM

The availability of large datasets with stellar distance and polarization information will enable a tomographic reconstruction of the (plane-of-the-sky-projected) interstellar magnetic field in the near future. We demonstrate the feasibility of such a decomposition within a small region of the diffuse ISM. We combine measurements of starlight (R-band) linear polarization obtained using the RoboPol polarimeter with stellar distances from the second Gaia data release. The stellar sample is brighter than 17 mag in the R band and reaches out to several kpc from the Sun. HI emission spectra reveal the existence of two distinct clouds along the line of sight. We decompose the line-of-sight-integrated stellar polarizations to obtain the mean polarization properties of the two clouds. The two clouds exhibit significant differences in terms of column density and polarization properties. Their mean plane-of-the-sky magnetic field orientation differs by 60 degrees. We show how our tomographic decomposition can be used to constrain our estimates of the polarizing efficiency of the clouds as well as the frequency dependence of the polarization angle of polarized dust emission. We also demonstrate a new method to constrain cloud distances based on this decomposition. Our results represent a preview of the wealth of information that can be obtained from a tomographic map of the ISM magnetic field.Comment: 25 pages, 14 figures, published in ApJ, data appear in journa

Fermi LAT AGN classification using supervised machine learning

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data Release 3. Missing data hinders the use of machine learning to classify AGN. A previous paper found that Multiple Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGN have missing redshift and the highest energy, we use data imputation with MICE and K-nearest neighbor (kNN) algorithm to fill in these missing variables. Then, we classify AGN into the BLLs or the Flat Spectrum Radio Quasars (FSRQs) using the SuperLearner, an ensemble method that includes several classification algorithms like logistic regression, support vector classifiers, Random Forests, Ranger Random Forests, multivariate adaptive regression spline (MARS), Bayesian regression, Extreme Gradient Boosting. We find that a SuperLearner model using MARS regression and Random Forests algorithms is 91.1% accurate for kNN imputed data and 91.2% for MICE imputed data. Furthermore, the kNN-imputed SuperLearner model predicts that 892 of the 1519 unclassified blazars are BLLs and 627 are Flat Spectrum Radio Quasars (FSRQs), while the MICE-imputed SuperLearner model predicts 890 BLLs and 629 FSRQs in the unclassified set. Thus, we can conclude that both imputation methods work efficiently and with high accuracy and that our methodology ushers the way for using SuperLearner as a novel classification method in the AGN community and, in general, in the astrophysics community.Comment: 15 pages, 8 figures, to be published in Monthly Notices of the Royal Astronomical Societ

Constraining the Limiting Brightness Temperature and Doppler Factors for the Largest Sample of Radio-bright Blazars

Relativistic effects dominate the emission of blazar jets complicating our understanding of their intrinsic properties. Although many methods have been proposed to account for them, the variability Doppler factor method has been shown to describe the blazar populations best. We use a Bayesian hierarchical code called {\it Magnetron} to model the light curves of 1029 sources observed by the Owens Valley Radio Observatory's 40-m telescope as a series of flares with an exponential rise and decay, and estimate their variability brightness temperature. Our analysis allows us to place the most stringent constraints on the equipartition brightness temperature i.e., the maximum achieved intrinsic brightness temperature in beamed sources which we found to be ⟨T_(eq)⟩=2.78 × 10^(11) K ± 26%. Using our findings we estimated the variability Doppler factor for the largest sample of blazars increasing the number of available estimates in the literature by almost an order of magnitude. Our results clearly show that γ-ray loud sources have faster and higher amplitude flares than γ-ray quiet sources. As a consequence they show higher variability brightness temperatures and thus are more relativistically beamed, with all of the above suggesting a strong connection between the radio flaring properties of the jet and γ-ray emission

Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi Gamma-ray Space Telescope's 4LAC Data Release 2 catalog to train a machine-learning (ML) model capable of predicting the redshift reliably. In addition, this project aims at improving and extending with the new 4LAC Catalog the predictive capabilities of the ML methodology published in Dainotti et al. Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional ML techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al. Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation. Using these methods, we provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with more redshift measurements