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Register a new userA 3-Dimensional Likelihood analysis method for detecting extended sources in VERITAS
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Alisha Chromey
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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, pulsar 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.
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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.
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.
The Global Network of Optical Magnetometers for Exotic physics searches (GNOME) is a network of time-synchronized, geographically separated, optically pumped atomic magnetometers that is being used to search for correlated transient signals heralding exotic physics. GNOME is sensitive to exotic couplings of atomic spins to certain classes of dark matter candidates, such as axions. This work presents a data analysis procedure to search for axion dark matter in the form of topological defects: specifically, walls separating domains of discrete degenerate vacua in the axion field. An axion domain wall crossing the Earth creates a distinctive signal pattern in the network that can be distinguished from random noise. The reliability of the analysis procedure and the sensitivity of the GNOME to domain-wall crossings is studied using simulated data.
In this paper we discuss a simple method of testing for the presence of energy-dependent dispersion in high energy data-sets. It uses the minimisation of the Kolmogorov distance between the cumulative distribution of two probability functions as the statistical metric to estimate the magnitude of any spectral dispersion within transient features in a light-curve and we also show that it performs well in the presence of modest energy resolutions (~20%) typical of gamma-ray observations. After presenting the method in detail we apply it to a parameterised simulated lightcurve based on the extreme VHE gamma-ray flare of PKS 2155-304 observed with H.E.S.S. in 2006, in order to illustrate its potential through the concrete example of setting constraints on quantum-gravity induced Lorentz invariance violation (LIV) effects. We obtain comparable limits to those of the most advanced techniques used in LIV searches applied to similar datasets, but the present method has the advantage of being particularly straightforward to use. Whilst the development of the method was motivated by LIV searches, it is also applicable to other astrophysical situations where energy-dependent dispersion is expected, such as spectral lags from the acceleration and cooling of particles in relativistic outflows.
We present a sophisticated likelihood reconstruction algorithm for shower-image analysis of imaging Cherenkov telescopes. The reconstruction algorithm is based on the comparison of the camera pixel amplitudes with the predictions from a Monte Carlo based model. Shower parameters are determined by a maximisation of a likelihood function. Maximisation of the likelihood as a function of shower fit parameters is performed using a numerical non-linear optimisation technique. A related reconstruction technique has already been developed by the CAT and the H.E.S.S. experiments, and provides a more precise direction and energy reconstruction of the photon induced shower compared to the second moment of the camera image analysis. Examples are shown of the performance of the analysis on simulated gamma-ray data from the VERITAS array.
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