2,380 research outputs found
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
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