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تسجيل مستخدم جديدA maximum-likelihood-based technique for detecting extended gamma-ray sources with VERITAS
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Alisha Chromey
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Gamma-ray observations ranging from hundreds of MeV to tens of TeV are a valuable tool for studying particle acceleration and diffusion within our galaxy. Supernova remnants, pulsar wind nebulae, and star-forming regions are the main particle accelerators in our local Galaxy. Constructing a coherent physical picture of these astrophysical objects requires the ability to distinguish extended regions of gamma-ray emission, the ability to analyze small-scale spatial variation within these regions, and methods to synthesize data from multiple observatories across multiple wavebands. Imaging Atmospheric Cherenkov Telescopes (IACTs) provide fine angular resolution (<0.1 degree) for gamma-rays above 100 GeV. Typical data reduction methods rely on source-free regions in the field of view to estimate cosmic-ray background. This presents difficulties for sources with unknown extent or those which encompass a large portion of the IACT field of view (3.5 degrees for VERITAS). Maximum-likelihood-based techniques are well-suited for analysis of fields with multiple overlapping sources, diffuse background components, and combining data from multiple observatories. Such methods also offer an alternative approach to estimating the IACT cosmic-ray background and consequently an enhanced sensitivity to largely extended sources. In this proceeding, we report on the current status and performance of a maximum likelihood technique for the IACT VERITAS. In particular, we focus on how our method framework employs a dimension for gamma-hadron separation parameters in order to improve sensitivity on extended sources.
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Gamma ray observations from a few hundred MeV up to tens of TeV are a valuable tool for studying particle acceleration and diffusion within our galaxy. Constructing a coherent physical picture of particle accelerators such as supernova remnants, puls
ar wind nebulae, and star-forming regions requires the ability to detect extended regions of gamma ray emission, to analyze small-scale spatial variation within these regions, and to synthesize data from multiple observatories across multiple wavebands. Imaging atmospheric Cherenkov telescopes (IACTs) provide fine angular resolution (<0.1$^circ$) for gamma rays above 100 GeV. However, their limited fields of view typically make detection of extended sources challenging. Maximum likelihood methods are well-suited to simultaneous analysis of multiple fields with overlapping sources and to combining data from multiple gamma ray observatories. Such methods also offer an alternative approach to estimating the IACT cosmic ray background and consequently an enhanced sensitivity to sources that may be as large as the telescope field of view. We report here on the current status and performance of a maximum likelihood technique for the IACT VERITAS.
The AGILE space mission (whose instrument is sensitive in the energy ranges 18-60 keV, and 30 MeV - 50 GeV) has been operating since 2007. Assessing the statistical significance of time variability of gamma-ray sources above 100 MeV is a primary task
of the AGILE data analysis. In particular, it is important to check the instrument sensitivity in terms of Poisson modeling of the data background, and to determine the post-trial confidence of detections. The goals of this work are: (i) evaluating the distributions of the likelihood ratio test for empty fields, and for regions of the Galactic plane; (ii) calculating the probability of false detection over multiple time intervals. In this paper we describe in detail the techniques used to search for short-term variability in the AGILE gamma-ray source database. We describe the binned maximum likelihood method used for the analysis of AGILE data, and the numerical simulations that support the characterization of the statistical analysis. We apply our method to both Galactic and extra-galactic transients, and provide a few examples. After having checked the reliability of the statistical description tested with the real AGILE data, we obtain the distribution of p-values for blind and specific source searches. We apply our results to the determination of the post-trial statistical significance of detections of transient gamma-ray sources in terms of pre-trial values. The results of our analysis allow a precise determination of the post-trial significance of {gamma}-ray sources detected by AGILE.
We report the detection of very-high-energy (VHE) gamma-ray emission from supernova remnant (SNR) G106.3+2.7. Observations performed in 2008 with the VERITAS atmospheric Cherenkov gamma-ray telescope resolve extended emission overlapping the elongate
d radio SNR. The 7.3 sigma (pre-trials) detection has a full angular extent of roughly 0.6deg by 0.4deg. Most notably, the centroid of the VHE emission is centered near the peak of the coincident 12CO (J = 1-0) emission, 0.4deg away from the pulsar PSR J2229+6114, situated at the northern end of the SNR. Evidently the current-epoch particles from the pulsar wind nebula are not participating in the gamma-ray production. The VHE energy spectrum measured with VERITAS is well characterized by a power law dN/dE = N_0(E/3 TeV)^{-G} with a differential index of G = 2.29 +/- 0.33stat +/- 0.30sys and a flux of N_0 = (1.15 +/- 0.27stat +/- 0.35sys)x 10^{-13} cm^{-2} s^{-1} TeV^{-1}. The integral flux above 1 TeV corresponds to ~5 percent of the steady Crab Nebula emission above the same energy. We describe the observations and analysis of the object and briefly discuss the implications of the detection in a multiwavelength context.
We report on VERITAS observations at energies above 200 GeV of known or potential galactic gamma-ray sources. The observed objects comprise pulsars, pulsar wind nebulae, high-mass X-ray binaries and gamma-ray sources with unknown counterparts in othe
r wavelengths. Among the highlights are the observation of variable gamma-ray emission from the X-ray binary LS I +61 303 and the detection of MGRO J1906+06/HESS J1906+063, an extended gamma-ray source which could not be associated with any obvious counterpart at lower energies.
Astrophysical sources are now observed by many different instruments at different wavelengths, from radio to high-energy gamma-rays, with an unprecedented quality. Putting all these data together to form a coherent view, however, is a very difficult
task. Each instrument has its own data format, software and analysis procedure, which are difficult to combine. It is for example very challenging to perform a broadband fit of the energy spectrum of the source. The Multi-Mission Maximum Likelihood framework (3ML) aims to solve this issue, providing a common framework which allows for a coherent modeling of sources using all the available data, independent of their origin. At the same time, thanks to its architecture based on plug-ins, 3ML uses the existing official software of each instrument for the corresponding data in a way which is transparent to the user. 3ML is based on the likelihood formalism, in which a model summarizing our knowledge about a particular region of the sky is convolved with the instrument response and compared to the corresponding data. The user can choose between a frequentist analysis, and a Bayesian analysis. In the former, parameters of the model are optimized in order to obtain the best match to the data (i.e., the maximum of the likelihood). In the latter, the priors specified by the user are used to build the posterior distribution, which is then sampled with Markov Chain Monte Carlo or Multinest. Our implementation of this idea is very flexible, allowing the study of point sources as well as extended sources with arbitrary spectra. We will review the problem we aim to solve, the 3ML concepts and its innovative potential.
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