Matteo Palermo “Estimation of the probability of observing a gamma-ray flare based on the analysis of the Fermi data” Student: Matteo Palermo Supervisors: Elisa Bernardini Jose Luis Bazo Alba

Matteo Palermo 2 Cosmic rays  Cosmic Rays: Energetic particles (mainly protons) which energy spectrum extends several orders of magnitude. Gamma Rays Neutrinos Charged particles Usually are divided in: Protons, electrons and ionized nuclei. Because of their charge their path is changed by the magnetic field  NO info about the direction of the source They can travel for long distances without any deflection  Direction’s info, but attenuation problems ( 0.5 MeV TeV) No charge and interact only via the weak force  Small attenuation and direction’s info ( E > 10 GeV )  Multi-messenger Astronomy: correlation studies between these 3 kinds of particles in CR

Matteo Palermo 3 A time dependent OFFLINE analysis using photon and neutrino data enhances the discovery probability by profiting from the photon-neutrino correlation. PROBLEM: many gamma-ray telescopes have a small field of view, thus they canNOT look at a wide region of sources at the same time (neutrino detectors can look at the entire sky). Moreover they are NOT taking data CONTINUOUSLY!  for many sources there might be missing photon data, so NO offline analysis SOLUTION: ONLINE ANALYSIS! Multi-messenger Astronomy

Matteo Palermo Once combined observations have been performed   it’s necessary to estimate the overall probability of RANDOM positive detection  this can be done by calculating the following quantity: Probability of observing at least Nobs alerts above the THR & detecting at least Ncoinc gamma ray flares NToO (Neutrino Triggered Target of Opportunity) ALERTALERT

Matteo Palermo The Fermi experiment it’s not possible to use IACTs (Imaging Atmospheric Cherenkov Telescopes) data to well estimate Pgam Why Fermi? Because Fermi is a satellite telescope which energy range is close to MAGIC’s, has a larger field of view than IACTs and is taking data CONTINOUSLY performances: energy range 30 MeV – 300 GeV angular resolution 0.15, 0.9 and 10, 1 and 0.1 GeV respectively 30 minutes of lifetime for each point in the sky (every 3 hours)

Matteo Palermo 6 Analysis The basic idea to estimate this probability: Set a threshold in order to define what is a flare and what is not the estimation of the probability will be roughly  THR 3C 273 Use Fermi data to calculate the Light Curve for a long period (e.g. 2 year)  LC: graph which shows the light intensity over a period of time  selection in energy and direction, bin size = 1 day

Matteo Palermo Threshold I took the Flux distribution from the Light Curve, which is simply the projection on the flux axis (marginal probability density function for the flux) 1. lognormal fit: the variations in the flux are found to have a lognormal distribution 2. Gaussian fit of the log(flux) distribution: using this fit mode to compare with the previous one since the results should be the same 3. Gaussian fit of the peak of the flux distribution (excluding the tail): this fit should be worse than the other two

Matteo Palermo The cumulative approach Once we defined the threshold we computed the Pgam by evaluating the cumulative of the flux distribution, actually In practice we integrated the resulting function from the lognormal fit. Gauss Lognormal Gauss log(flux)

Matteo Palermo Sources Criteria used to select these sources: they have been classified as variable in the Fermi/LAT bright source catalog they should have been observed in TeV scale they are monitored by MAGIC

Matteo Palermo Energy ranges We did the same analysis for two different energy ranges, namely: from 100 MeV to 300 GeV from 1 GeV to 300 GeV in order to answer to the following question: is the Pgam still the same in the TeV scale (MAGIC range)? The idea is that if the Pgam does NOT change  it is likely that it will NOT change even in the TeV scale

Matteo Palermo ResultsSource 100MeV-300 GeV 1GeV-300GeV 3C 273 M 87 W Comae Mrk 421 3C 66 A/B Mrk 501 1ES NGC 1275 PG LSI S

Matteo Palermo Improvements evaluate the errors for the mean and the variance (thus for the Pgam) for the lognormal and log(flux) fit mode re-do the same analysis but with the energy ranges completely separated define properly the confidence level for each results evaluate the actual spectral index for each source and re-do the analysis (so far to evaluate the exposure we used the same spectral index for all the sources)

Matteo Palermo Thanks for the attention

Matteo Palermo

Matteo Palermo NToO (Neutrino Triggered Target of Opportunity) 1. from the South Pole to Madison (USA), using IRIDIUM SATELLITE (24/7) 2.Chek for the visibility of that source from MAGIC, if so 3.Regular to MAGIC to the “shifter” in La Palma 4.If possible, focus on it FUTURE: automatic procedure is under development

Matteo Palermo

Matteo Palermo

Matteo Palermo