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Register a new userA high-level analysis framework for HAWC
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The High Altitude Water Cherenkov (HAWC) Observatory continuously observes gamma-rays between 100 GeV to 100 TeV in an instantaneous field of view of about 2 steradians above the array. The large amount of raw data, the importance of small number statistics, the large dynamic range of gamma-ray signals in time (1 - $10^8$ sec) and angular extent (0.1 - 100 degrees), and the growing need to directly compare results from different observatories pose some special challenges for the analysis of HAWC data. To address these needs, we have designed and implemented a modular analysis framework based on the method of maximum likelihood. The framework facilitates the calculation of a binned Poisson Log-likelihood value for a given physics model (i.e., source model), data set, and detector response. The parameters of the physics model (sky position, spectrum, angular extent, etc.) can be optimized through a likelihood maximization routine to obtain a best match to the data. In a similar way, the parameters of the detector response (absolute pointing, angular resolution, etc.) can be optimized using a well-known source such as the Crab Nebula. The framework was designed concurrently with the Multi-Mission Maximum Likelihood (3ML) architecture, and allows for the definition of a general collection of sources with individually varying spectral and spatial morphologies. Compatibility with the 3ML architecture allows to easily perform powerful joint fits with other observatories. In this contribution, we overview the design and capabilities of the HAWC analysis framework, stressing the overarching design points that have applicability to other astronomical and cosmic-ray observatories.
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Analyzing Ethereum bytecode, rather than the source code from which it was generated, is a necessity when: (1) the source code is not available (e.g., the blockchain only stores the bytecode), (2) the information to be gathered in the analysis is only visible at the level of bytecode (e.g., gas consumption is specified at the level of EVM instructions), (3) the analysis results may be affected by optimizations performed by the compiler (thus the analysis should be done ideally after compilation). This paper presents EthIR, a framework for analyzing Ethereum bytecode, which relies on (an extension of) OYENTE, a tool that generates CFGs; EthIR produces from the CFGs, a rule-based representation (RBR) of the bytecode that enables the application of (existing) high-level analyses to infer properties of EVM code.
The High-Altitude Water Cherenkov (HAWC) Observatory was completed and began full opera- tion on March 20, 2015. The detector consists of an array of 300 water tanks, each containing 200 ktons of purified water and instrumented with 4 PMTs. Located at an elevation of 4100m a.s.l. near the Sierra Negra volcano in central Mexico, HAWC has a threshold for gamma-ray detection well below 1 TeV and a sensitivity to TeV-scale gamma-ray sources an order of magnitude better than previous air-shower arrays. The detector operates 24 hours/day and observes the overhead sky (2 sr), making it an ideal survey instrument. We describe the configuration of HAWC with an emphasis on how the design was optimized, describe the data acquired, reconstructed and an- alyzed. Finally, we will demonstrate the sensitivity of the detector using the observation of the Crab. This paper serves as a detailed technical description of the foundations of the numerous analyses presented at this meeting by members of the HAWC collaboration.
A wide range of data formats and proprietary software have traditionally been used in gamma-ray astronomy, usually developed for a single specific mission or experiment. However, in recent years there has been an increasing effort towards making astronomical data open and easily accessible. Within the gamma-ray community this has translated to the creation of a common data format across different gamma-ray observatories: the gamma-astro-data-format (GADF). Based on a similar premise, open-source analysis packages, such as Gammapy, are being developed and aim to provide a single, robust tool which suits the needs of many experiments at once. In this contribution we show that data from the High-Altitude Water Cherenkov (HAWC) observatory can be made compatible with the GADF and present the first GADF-based production of event lists and instrument response functions for a ground-based wide-field instrument. We use these data products to reproduce with excellent agreement the published HAWC Crab spectrum using Gammapy. Having a common data format and analysis tools facilitates joint analysis between different experiments and effective data sharing. This will be especially important for next-generation instruments, such as the proposed Southern Wide-field Gamma-ray Observatory (SWGO) and the planned Cherenkov Telescope Array (CTA).
The Italian AGILE space mission, with its Gamma-Ray Imaging Detector (GRID) instrument sensitive in the 30 MeV-50 GeV gamma-ray energy band, has been operating since 2007. Agilepy is an open-source Python package to analyse AGILE/GRID data. The package is built on top of the command-line version of the AGILE Science Tools, developed by the AGILE Team, publicly available and released by ASI/SSDC. The primary purpose of the package is to provide an easy to use high-level interface to analyse AGILE/GRID data by simplifying the configuration of the tasks and ensuring straightforward access to the data. The current features are the generation and display of sky maps and light curves, the access to gamma-ray sources catalogues, the analysis to perform spectral model and position fitting, the wavelet analysis. Agilepy also includes an interface tool providing the time evolution of the AGILE off-axis viewing angle for a chosen sky region. The Flare Advocate team also uses the tool to analyse the data during the daily monitoring of the gamma-ray sky. Agilepy (and its dependencies) can be easily installed using Anaconda.
We present Morpheus, a new model for generating pixel-level morphological classifications of astronomical sources. Morpheus leverages advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false-positive identifications of sources. We evaluate Morpheus by performing source detection, source segmentation, morphological classification on the Hubble Space Telescope data in the five CANDELS fields with a focus on the GOODS South field, and demonstrate a high completeness in recovering known GOODS South 3D-HST sources with H < 26 AB. We release the code publicly, provide online demonstrations, and present an interactive visualization of the Morpheus results in GOODS South.
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