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Register a new userUsing TMine for the Fermi-LAT Event Analysis
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The Large Area Telescope (LAT) event analysis is the final stage in the event reconstruction responsible for the creation of high-level variables (e.g., event energy, incident direction, particle type, etc.). We discuss the development of TMine, a powerful new tool for designing and implementing event classification analyses (e.g., distinguishing photons from charged particles). TMine is structured on ROOT, a data analysis framework that is the de-facto standard for current high energy physics experiments; thus, TMine fits naturally into the ROOT-based data processing pipeline of the LAT. TMine provides a visual development environment for the LAT event analysis and utilizes advanced multivariate classification algorithms implemented in ROOT. We discuss the application of TMine to the next iteration of the event analysis (Pass 8), the LAT charged particle analyses, and the classification of unassociated LAT gamma-ray sources.
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With the advent of the Large Array Telescope (LAT) on board the Fermi satellite, a new window on the Universe has been opened. Publicly available, the Fermi-LAT data come together with an analysis software named ScienceTools (ST, http://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) which can be run through a Python interface. Nevertheless, for the user, the ST can be hard to run and imply several steps. Users already contributed with scripts for a specific task but no tool allowing a complete analysis is currently available. We present a Python package called {tt Enrico}, designed to facilitate the data analysis. Using only configuration files and front end tools from the command line, the user can easily perform/reproduce an entire Fermi analysis and make plots for publications. It also include new features like debug plots, pipeline execution on one or several CPUs, downloading of the Fermi data or the generation of a sky model from the Fermi catalogue. {tt Enrico} is an open-source project currently available for download at url{https://github.com/gammapy/enrico}
The current version of the Fermi Large Area Telescope data (P8R2) has been publicly available since June 2015, with the caveat that the residual background of all event classes, except ULTRACLEANVETO, was not fully isotropic: it was enhanced by a factor ~2 at 1-3 GeV within ~20 deg of the Ecliptic compared to the poles. By investigating the residual background using data only, we were able to find two sources of residual background: one due to non-interacting heavy ions and one due to cosmic-ray electrons leaking through the ribbons of the Anti-Coincidence Detector, the latter source being responsible for the background anisotropy. A set of simple cuts allows us to reject these events while losing less than 1% of the SOURCE class acceptance. This new selection has been used to produce a new version of the LAT data (P8R3).
Previous analyses of point sources in the gamma-ray range were done either below 30 MeV or above 100 MeV. Below 30 MeV, the imaging Compton telescope (COMPTEL) onboard NASAs Compton Gamma-Ray Observatory detected 26 steady sources in the energy range from 0.75 to 30 MeV. At high energy, the Fermi Large Area Telescope (LAT) has detected more than three thousand sources between 100 MeV and 300 GeV. Since the Fermi LAT detects gamma rays also below 100 MeV, we apply a point source detection algorithm in the energy range between 30 MeV and 100 MeV. In the analysis we use PGWave, which is a background independent tool based on a wavelet transform.
The event selection developed for the Fermi Large Area Telescope before launch has been periodically updated to reflect the constantly improving knowledge of the detector and the environment in which it operates. Pass 7, released to the public in August 2011, represents the most recent major iteration of this incremental process. In parallel, the LAT team has undertaken a coherent long-term effort aimed at a radical revision of the entire event-level analysis, based on the experience gained in the prime phase of the mission. This includes virtually every aspect of the data reduction process, from the simulation of the detector to the event reconstruction and the background rejection. The potential improvements include (but are not limited to) a significant reduction in background contamination coupled with an increased effective area, a better point-spread function, a better understanding of the systematic uncertainties and an extension of the energy reach for the photon analysis below 100 MeV and above a few hundred GeV. We present an overview of the work that has been done or is ongoing and the prospects for the near future.
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