Synchrotron and Compton Components and their Variability in BL Lac Objects

BL Lacertae objects are extreme extragalactic sources characterized by the emission of strong and rapidly variable nonthermal radiation over the entire electromagnetic spectrum. Synchrotron emission followed by inverse Compton scattering in a relativistic beaming scenario is generally thought to be the mechanism powering these objects. ...Comment: 4 pages, TeX plus 3 figures. Proceedings of the conference "X-ray Astronomy 1999", September 6-10,1999, Bologn

The red blazar PMN J2345-1555 becomes blue

The Flat Spectrum Radio Quasar PMN J2345-1555 is a bright gamma-ray source, that recently underwent a flaring episode in the IR, UV and gamma-ray bands. The flux changed quasi simultaneously at different frequencies, suggesting that it was produced by a single population of emitting particles, hence by a single and well localized region of the jet. While the overall Spectral Energy Distribution (SED) before the flare was typical of powerful blazars (namely two broad humps peaking in the far IR and below 100 MeV bands, respectively), during the flare the peaks moved to the optical-UV and to energies larger than 1 GeV, to resemble low power BL Lac objects, even if the observed bolometric luminosity increased by more than one order of magnitude. We interpret this behavior as due to a change of the location of the emission region in the jet, from within the broad line region, to just outside. The corresponding decrease of the radiation energy density as seen in the comoving frame of the jet allowed the relativistic electrons to be accelerated to higher energies, and thus produce a "bluer" SED.Comment: 5 pages, 4 figures, MNRAS Letters, in pres

Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images

One of the most relevant problems in the extraction of scientifically useful information from wide field astronomical images (both photographic plates and CCD frames) is the recognition of the objects against a noisy background and their classification in unresolved (star-like) and resolved (galaxies) sources. In this paper we present a neural network based method capable to perform both tasks and discuss in detail the performance of object detection in a representative celestial field. The performance of our method is compared to that of other methodologies often used within the astronomical community.Comment: 6 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199

The NuSTAR view on Hard-TeV BL Lacs

Hard-TeV BL Lacs are a new type of blazars characterized by a hard intrinsic TeV spectrum, locating the peak of their gamma-ray emission in the spectral energy distribution (SED) above 2-10 TeV. Such high energies are problematic for the Compton emission, using a standard one-zone leptonic model. We study six examples of this new type of BL Lacs in the hard X-ray band with the NuSTAR satellite. Together with simultaneous observations with the SWIFT satellite, we fully constrain the peak of the synchrotron emission in their SED, and test the leptonic synchrotron self-Compton (SSC) model. We confirm the extreme nature of 5 objects also in the synchrotron emission. We do not find evidence of additional emission components in the hard X-ray band. We find that a one-zone SSC model can in principle reproduce the extreme properties of both peaks in the SED, from X-ray up to TeV energies, but at the cost of i) extreme electron energies with very low radiative efficiency, ii) conditions heavily out of equipartition (by 3 to 5 orders of magnitude), and iii) not accounting for the simultaneous UV data, which then should belong to a different emission component, possibly the same as the far-IR (WISE) data. We find evidence of this separation of the UV and X-ray emission in at least two objects. In any case, the TeV electrons must not "see" the UV or lower-energy photons, even if coming from different zones/populations, or the increased radiative cooling would steepen the VHE spectrum.Comment: 13 pages, 2 figures. Version accepted for publication in MNRAS. Fig. 2 corrected for a small plotting erro

On the detection of very high redshift Gamma Ray Bursts with Swift

We compute the probability to detect long Gamma Ray Bursts (GRBs) at z>5 with Swift, assuming that GRBs form preferentially in low-metallicity environments. The model fits well both the observed BATSE and Swift GRB differential peak flux distribution and is consistent with the number of z>2.5 detections in the 2-year Swift data. We find that the probability to observe a burst at z>5 becomes larger than 10% for photon fluxes P<1 ph s^{-1} cm^{-2}, consistent with the number of confirmed detections. The corresponding fraction of z>5 bursts in the Swift catalog is ~10%-30% depending on the adopted metallicity threshold for GRB formation. We propose to use the computed probability as a tool to identify high redshift GRBs. By jointly considering promptly-available information provided by Swift and model results, we can select reliable z>5 candidates in a few hours from the BAT detection. We test the procedure against last year Swift data: only three bursts match all our requirements, two being confirmed at z>5. Other three possible candidates are picked up by slightly relaxing the adopted criteria. No low-z interloper is found among the six candidates.Comment: 5 pages, 2 figures, MNRAS in pres

Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural Extractor): a new Neural Network (NN) based package capable to detect objects and to perform both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first discriminated from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold and then they are classified as stars or as galaxies through diagnostic diagrams having variables choosen accordingly to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of "what an object is" (id est, it keeps all structures composed by more than one pixels) and performs the detection via an unsupervised NN approaching detection as a clustering problem which has been thoroughly studied in the artificial intelligence literature. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features, we use a NN to select the most significant features among the large number of measured ones, and then we use their selected features to perform the classification task. In order to optimise the performances of the system we implemented and tested several different models of NN. The comparison of the NExt performances with those of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at http://www.na.astro.it/~andreon/listapub.htm