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Register a new userPredicting the redshift of gamma-ray loud AGNs using supervised machine learning
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AGNs are very powerful galaxies characterized by extremely bright emissions coming out from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems such as the evolution of the early stars, their formation along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multi-wavelength observations, often involving various astronomical facilities. Here, we employ machine learning algorithms to estimate redshifts from the observed gamma-ray properties and photometric data of gamma-ray loud AGN from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm, using LASSO selected set of predictors. We obtain a tight correlation, with a Pearson Correlation Coefficient of 71.3% between the inferred and the observed redshifts, an average {Delta}z_norm = 11.6 x 10^-4. We stress that notwithstanding the small sample of gamma-ray loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine learning models.
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The very existence of more than a dozen of high-redshift (z>4) blazars indicates that a much larger population of misaligned powerful jetted AGN was already in place when the Universe was <1.5 Gyr old. Such parent population proved to be very elusive, and escaped direct detection in radio surveys so far. High redshift blazars themselves seem to be failing in producing extended radio-lobes, raising questions about the connection between such class and the vaster population of radio-galaxies. We show that the interaction of the jet electrons with the intense cosmic microwave background (CMB) radiation explains the lack of extended radio emission in high redshift blazars and in their parent population, helping to explain the apparently missing misaligned counterparts of high redshift blazars. On the other hand, the emission from the more compact and more magnetised hot spots are less affected by the enhanced CMB energy density. By modelling the spectral energy distribution of blazar lobes and hot spots we find that most of them should be detectable by low frequency deep radio observations, e.g., by LOw-Frequency ARray for radio astronomy (LOFAR) and by relatively deep X-ray observations with good angular resolution, e.g., by the Chandra satellite. At high redshifts, the emission of a misaligned relativistic jet, being de-beamed, is missed by current large sky area surveys. The isotropic flux produced in the hot spots can be below ~1 mJy and the isotropic lobe radio emission is quenched by the CMB cooling. Consequently, even sources with very powerful jets can go undetected in current radio surveys, and misclassified as radio-quiet AGNs.
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Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here we introduce machine-z, a redshift prediction algorithm and a high-z classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time our high-z classifier can achieve 80% recall of true high-redshift bursts, while incurring a false positive rate of 20%. With 40% false positive rate the classifier can achieve ~100% recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.
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