Interpolation of Instrument Response Functions for the Cherenkov Telescope Array in the Context of pyirf

The Cherenkov Telescope Array (CTA) will be the next generation ground-based very-high-energy gamma-ray observatory, constituted by tens of Imaging Atmospheric Cherenkov Telescopes at two sites once its construction and commissioning are finished. Like its predecessors, CTA relies on Instrument Response Functions (IRFs) to relate the observed and reconstructed properties to the true ones of the primary gamma-ray photons. IRFs are needed for the proper reconstruction of spectral and spatial information of the observed sources and are thus among the data products issued to the observatory users. They are derived from Monte Carlo simulations, depend on observation conditions like the telescope pointing direction or the atmospheric transparency and can evolve with time as hardware ages or is replaced. Producing a complete set of IRFs from simulations for every observation taken is a time-consuming task and not feasible when releasing data products on short timescales. Consequently, interpolation techniques on simulated IRFs are investigated to quickly estimate IRFs for specific observation conditions. However, as some of the IRFs constituents are given as probability distributions, specialized methods are needed. This contribution summarizes and compares the feasibility of multiple approaches to interpolate IRF components in the context of the pyirf python software package and IRFs simulated for the Large-Sized Telescope prototype (LST-1). We will also give an overview of the current functionalities implemented in pyirf.Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219

First Study of Combined Blazar Light Curves with FACT and HAWC

For studying variable sources like blazars, it is crucial to achieve unbiased monitoring, either with dedicated telescopes in pointing mode or survey instruments. At TeV energies, the High Altitude Water Cherenkov (HAWC) observatory monitors approximately two thirds of the sky every day. It uses the water Cherenkov technique, which provides an excellent duty cycle independent of weather and season. The First G-APD Cherenkov Telescope (FACT) monitors a small sample of sources with better sensitivity, using the imaging air Cherenkov technique. Thanks to its camera with silicon-based photosensors, FACT features an excellent detector performance and stability and extends its observations to times with strong moonlight, increasing the duty cycle compared to other imaging air Cherenkov telescopes. As FACT and HAWC have overlapping energy ranges, a joint study can exploit the longer daily coverage given that the observatories' locations are offset by 5.3 hours. Furthermore, the better sensitivity of FACT adds a finer resolution of features on hour-long time scales, while the continuous duty cycle of HAWC ensures evenly sampled long-term coverage. Thus, the two instruments complement each other to provide a more complete picture of blazar variability. In this presentation, the first joint study of light curves from the two instruments will be shown, correlating long-term measurements with daily sampling between air and water Cherenkov telescopes. The presented results focus on the study of the variability of the bright blazars Mrk 421 and Mrk 501 during the last two years featuring various flaring activities.Comment: 6 pages, 2 figures. Contribution to the 6th International Symposium on High Energy Gamma-Ray Astronomy (Gamma2016), Heidelberg, Germany. To be published in the AIP Conference Proceeding

Gammapy: A Python package for gamma-ray astronomy

In this article, we present Gammapy, an open-source Python package for the analysis of astronomical $\gamma$-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different $\gamma$-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting $\gamma$-ray instruments. After the data are binned, the flux and morphology of one or more $\gamma$-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. After describing the structure of the package, we show, using publicly available $\gamma$-ray data, the capabilities of Gammapy in multiple traditional and novel $\gamma$-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexibility and power are displayed in a final multi-instrument example, where datasets from different instruments, at different stages of data reduction, are simultaneously fitted with an astrophysical flux model.Comment: 26 pages, 16 figure

Fractional variability—a tool to study blazar variability

Active Galactic Nuclei emit radiation over the whole electromagnetic spectrum up to TeV energies. Blazars are one subtype with their jets pointing towards the observer. One of their typical features is extreme variability on timescales, from minutes to years. The fractional variability is an often used parameter for investigating the degree of variability of a light curve. Different detection methods and sensitivities of the instruments result in differently binned data and light curves with gaps. As they can influence the physics interpretation of the broadband variability, the effects of these differences on the fractional variability need to be studied. In this paper, we study the systematic effects of completeness in time coverage and the sampling rate. Using public data from instruments monitoring blazars in various energy ranges, we study the variability of the bright TeV blazars Mrk 421 and Mrk 501 over the electromagnetic spectrum, taking into account the systematic effects, and compare our findings with previous results. Especially in the TeV range, the fractional variability is higher than in previous studies, which can be explained by the much longer (seven years compared to few weeks) and more complete data sample

ctapipe – Prototype Open Event Reconstruction Pipeline for the Cherenkov Telescope Array

The Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory currently under construction. It will improve over the current genera-tion of imaging atmospheric Cherenkov telescopes (IACTs) by a factor of five to ten in sensitivity and it will be able to observe the whole sky from a combination of two sites: a northern site in La Palma, Spain, and a southern one in Paranal, Chile. CTAO will also be the first open ground-based gamma-ray observatory. Accordingly, the CTAO data processing pipeline is developed as open-source software and ctapipe will be a core package therein. The event reconstruction pipeline accepts raw data of the telescopes and processes it to produce suitable input for the higher-level science tools. Its primary tasks include reconstructing the physical properties of each recorded air shower and providing the corresponding instrument response functions. ctapipe is a python framework providing algorithms and command-line tools to facilitate raw data calibration, image extraction, image parametrization and event reconstruction. Its current main focus is the analysis of simulated data but it has also been successfully applied for the analysis of data obtained with the CTA prototype telescopes, and first science results have now been obtained by the LST-1 collaboration using ctapipe. A plugin system also allows the processing of non-CTA data. Recent updates, including event reconstruction using machine learning and a new plugin system as well as the roadmap towards a 1.0 release will be presented

cta-observatory/lstosa: v0.10.3

On-site data processing pipeline for the LST-

CTAO Monte Carlo Simulations - Eventlists on DL2 data level - prod5

Author: Cherenkov Telescope Array Observatory; Cherenkov Telescope Array Consortium Contact: [email protected] The Cherenkov Telescope Array Observatory (CTAO) will be the next-generation gamma-ray observatory and is currently under construction on the island of La Palma (Spain) and near Paranal (Chile). This repository provides access to reconstructed event information (DL2- and DL1-level, simulation parameters) from Monte Carlo simulations of the CTAO Northern Array (production 5). The Monte Carlo simulations for prod5 are described in arXiv:2108.04512 , the simulation telescopes models used in the telescope simulation program sim_telarray and the configuration used in the air-shower code CORSIKA are available from the Zenodo archive for CTA Prod5 Telescope Models . MC events are calibrated and reconstructed using the ctapipe package and stored for the following data levels: - R1-MC: simulated raw data (output from sim_telarray) - DL1 (this repository) : telescope level data including images and image parameters - DL2 (this repository) : reconstructed event parameters such as energy, direction, gamma/hadron discrimination parameters - DL3: selected events with associated instrument response functions (IRFs). Preliminary DL3-IRFs are available from [here](https://doi.org/10.5281/zenodo.5499839). For a description of the file format and data model, see CTAPipe Data Model . Data set description: - using ctapipe version 0.17 ( GitHub release page , Zenodo page ) - CTAO Northern Array on La Palma for the Alpha configuration (4 large-sized telescopes, 9 mid-sized telescopes) - Zenith angles of 20 deg, telescope pointing direction north and south - Primary particles: photons, protons - For convenience, the same data is provided in a single, large file per particle type for the whole dataset which do not contain the low-level DL1 image information and a larger number of files including this information. We explicitly note that the products provided are preliminary and do not reflect the final performance of the CTA Observatory, neither are data structure or formats finalized. We also note that these data products are different to those used for the CTAO Instrument Response Functions . In cases in which the data provided in this repository are used in a research project, we ask that the following acknowledgment is added in any resulting publication: 'This research has made use of the CTA DL1 and DL2 Event lists provided by the CTA Observatory and Consortium (version prod5-DL2-release1-DL2)' and cite this repository in the reference section of your publication. We would like to thank the computing centers that provided resources for the generation of the Prod5 simulation set, click here for a list of service providers

cta-observatory/pyirf: v0.10.1 – 2023-09-15

*pyirf* is a python3-based library for the generation of Instrument Response Functions (IRFs) and sensitivities for the Cherenkov Telescope Array (CTA

Flux States of Active Galactic Nuclei

Blazars are known to show variability on time scales from minutes to years covering a wide range of flux states. Studying the flux distribution of a source allows for various insights. The shape of the flux distribution can provide information on the nature of the underlying variability processes. The level of a possible quiescent state can be derived from the main part of the distribution that can be described by a Gaussian distribution. Dividing the flux states into quiescent and active, the duty cycle of a source can be calculated. Finally, this allows alerting the multi-wavelength and multi-messenger community in case a source is in an active state. To get consistent and conclusive results from flux distributions, unbiased long-term observations are crucial. Only like this is a complete picture of the variability and flux states, e.g., an all-time quiescent state, possible. In seven years of monitoring of bright TeV blazars, the first G-APD Cherenkov telescope (FACT) has collected a total of more than 11,700 hours of physics data with 1500 hours to 3000 hours per source for Mrk 421, Mrk 501, 1ES 1959+650, and 1ES 2344+51