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Register a new userA new method to perform data/model comparison in Fermi-LAT analysis
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P. Bruel
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The analysis of Fermi Large Area Telescope (LAT) gamma-ray data in a given Region Of Interest (RoI) usually consists of performing a binned log-likelihood fit in order to determine the sky model that, after convolution with the instrument response, best accounts for the distribution of observed counts. While tools are available to perform such a fit, it is not easy to check the goodness-of-fit. The difficulty of the assessment of the data/model agreement is twofold. First of all, the observed and predicted counts are binned in three dimensions (two spatial dimensions and one energy dimension) and comparing two 3D maps is not straightforward. Secondly, gamma-ray source spectra generally decrease with energy as the inverse of the energy square. As a consequence the number of counts above several GeV generally falls into the Poisson regime, which precludes performing a simple $chi^2$ test. We propose a method that overcomes these two obstacles by producing and comparing spatially integrated count spectra for data and model at each pixel of the analysed RoI. The comparison is performed following a log-likelihood approach that extends the $chi^2$ test to histograms with low statistics. This method can take into account likelihood weights that are used to account for systematic uncertainties. We optimize the new method so that it provides a fast and reliable tool to assess the goodness-of-fit of Fermi-LAT data and we use it to check the latest gamma-ray source catalog on 10~years of data.
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Four years into the mission, the understanding of the performance of the Fermi Large Area Telescope (LAT) and data analysis have increased enormously since launch. Thanks to a careful analysis of flight data, we were able to trace back some of the most significant sources of systematic uncertainties to using non-optimal calibration constants for some of the detectors. In this paper we report on a major effort within the LAT Collaboration to update these constants, to use them to reprocess the first four years of raw data, and to investigate the improvements observed for low- and high-level analysis. The Pass 7 reprocessed data, also known as P7REP data, are still being validated against the original Pass~7 (P7) data by the LAT Collaboration and should be made public, along with the corresponding instrument response functions, in the spring of 2013.
We report the discovery of nine previously unknown gamma-ray pulsars in a blind search of data from the Fermi Large Area Telescope (LAT). The pulsars were found with a novel hierarchical search method originally developed for detecting continuous gravitational waves from rapidly rotating neutron stars. Designed to find isolated pulsars spinning at up to kHz frequencies, the new method is computationally efficient, and incorporates several advances, including a metric-based gridding of the search parameter space (frequency, frequency derivative and sky location) and the use of photon probability weights. The nine pulsars have spin frequencies between 3 and 12 Hz, and characteristic ages ranging from 17 kyr to 3 Myr. Two of them, PSRs J1803-2149 and J2111+4606, are young and energetic Galactic-plane pulsars (spin-down power above 6e35 erg/s and ages below 100 kyr). The seven remaining pulsars, PSRs J0106+4855, J0622+3749, J1620-4927, J1746-3239, J2028+3332, J2030+4415, J2139+4716, are older and less energetic; two of them are located at higher Galactic latitudes (|b| > 10 deg). PSR J0106+4855 has the largest characteristic age (3 Myr) and the smallest surface magnetic field (2e11 G) of all LAT blind-search pulsars. PSR J2139+4716 has the lowest spin-down power (3e33 erg/s) among all non-recycled gamma-ray pulsars ever found. Despite extensive multi-frequency observations, only PSR J0106+4855 has detectable pulsations in the radio band. The other eight pulsars belong to the increasing population of radio-quiet gamma-ray pulsars.
The past decade has seen a dramatic improvement in the quality of data available at both high (HE: 100 MeV to 100 GeV) and very high (VHE: 100 GeV to 100 TeV) gamma-ray energies. With three years of data from the Fermi Large Area Telescope (LAT) and deep pointed observations with arrays of Cherenkov telescope, continuous spectral coverage from 100 MeV to $sim10$ TeV exists for the first time for the brightest gamma-ray sources. The Fermi-LAT is likely to continue for several years, resulting in significant improvements in high energy sensitivity. On the same timescale, the Cherenkov Telescope Array (CTA) will be constructed providing unprecedented VHE capabilities. The optimisation of CTA must take into account competition and complementarity with Fermi, in particularly in the overlapping energy range 10$-$100 GeV. Here we compare the performance of Fermi-LAT and the current baseline CTA design for steady and transient, point-like and extended sources.
We perform a comprehensive stacking analysis of data collected by the Fermi Large Area Telescope (LAT) of gamma-ray bursts (GRB) localized by the Swift spacecraft, which were not detected by the LAT but which fell within the instruments field of view at the time of trigger. We examine a total of 79 GRBs by comparing the observed counts over a range of time intervals to that expected from designated background orbits, as well as by using a joint likelihood technique to model the expected distribution of stacked counts. We find strong evidence for subthreshold emission at MeV to GeV energies using both techniques. This observed excess is detected during intervals that include and exceed the durations typically characterizing the prompt emission observed at keV energies and lasts at least 2700 s after the co-aligned burst trigger. By utilizing a novel cumulative likelihood analysis, we find that although a bursts prompt gamma-ray and afterglow X-ray flux both correlate with the strength of the subthreshold emission, the X-ray afterglow flux measured by Swifts X-ray Telescope (XRT) at 11 hr post trigger correlates far more significantly. Overall, the extended nature of the subthreshold emission and its connection to the bursts afterglow brightness lend further support to the external forward shock origin of the late-time emission detected by the LAT. These results suggest that the extended high-energy emission observed by the LAT may be a relatively common feature but remains undetected in a majority of bursts owing to instrumental threshold effects.
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a short-exposure ASM generated from one-week observation by applying three ML based image processing algorithms: dictionary learning, U-net, and Noise2Noise. Although the inference based on ML is less clear compared to standard likelihood analysis, the quality of the ASM was generally improved. In particular, the complicated diffuse emission associated with the galactic plane was successfully reproduced only from one-week observation data to mimic a ground truth (GT) generated from a one-year observation. Such ML algorithms can be implemented relatively easily to provide sharper images without various assumptions of emission models. In contrast, large deviations between simulated ML maps and GT map were found, which are attributed to the significant temporal variability of blazar-type active galactic nuclei (AGNs) over a year. Thus, the proposed ML methods are viable not only to improve the image quality of an ASM, but also to detect variable sources, such as AGNs, algorithmically, i.e., without human bias. Moreover, we argue that this approach is widely applicable to ASMs obtained by various other missions; thus, it has the potential to examine giant structures and transient events, both of which are rarely found in pointing observations.
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