Skip to main content

Analysis Methods for Gamma-Ray Astronomy

  • Reference work entry
  • First Online:
Handbook of X-ray and Gamma-ray Astrophysics
  • 128 Accesses

Abstract

The launch of the Fermi satellite in 2008, with its Large-Area Telescope (LAT) on board, has opened a new era for the study of gamma-ray sources at GeV (109 eV) energies. Similarly, the commissioning of the third generation of imaging atmospheric Cherenkov telescopes (IACTs) – H.E.S.S., MAGIC, and VERITAS – in the mid-2000s has firmly established the field of TeV (1012 eV) gamma-ray astronomy. Together, these instruments have revolutionized our understanding of the high-energy gamma-ray sky, and they continue to provide access to it over more than six decades in energy. In recent years, the ground-level particle detector arrays HAWC, Tibet, and LHAASO have opened a new window to gamma rays of the highest energies, beyond 100 TeV. Soon, the next-generation facilities such as CTA and SWGO will provide even better sensitivity, thus promising a bright future for the field. In this chapter, we provide a brief overview of methods commonly employed for the analysis of gamma-ray data, focusing on those used for Fermi-LAT and IACT observations. We describe the standard data formats, explain event reconstruction and selection algorithms, and cover in detail high-level analysis approaches for imaging and extraction of spectra, including aperture photometry as well as advanced likelihood techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 4,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • A.A. Abdo, B.T. Allen, R. Atkins et al., Observation and spectral measurements of the Crab Nebula with Milagro. ApJ. 750, 63 (2012). https://doi.org/10.1088/0004-637X/750/1/63, 1110.0409

  • A.A. Abdo, M. Ajello, A. Allafort et al., The second Fermi large area telescope catalog of gamma-ray pulsars. ApJ. 208, 17 (2013). https://doi.org/10.1088/0067-0049/208/2/17, 1305.4385

  • J. Aleksić, E.A. Alvarez, L.A. Antonelli et al., Observations of the Crab Pulsar between 25 and 100 GeV with the MAGIC I Telescope. ApJ. 742, 43 (2011). https://doi.org/10.1088/0004-637X/742/1/43, 1108.5391

  • E. Aliu, T. Arlen, T. Aune et al., Detection of pulsed gamma rays above 100 GeV from the Crab Pulsar. Science 334, 69 (2011). https://doi.org/10.1126/science.1208192, 1108.3797

  • J. Aschersleben, R. Peletier, M. Vecchi, M. Wilkinson, Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov telescope array data, in Proceedings 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 697. https://doi.org/10.22323/1.395.0697, 2108.00834

  • W. Atwood, A. Albert, L. Baldini, M. Tinivella, J. Bregeon, M. Pesce-Rollins, C. Sgrò, P. Bruel, E. Charles, A. Drlica-Wagner, A. Franckowiak, T. Jogler, L. Rochester, T. Usher, M. Wood, J. Cohen-Tanugi, S. Zimmer, Pass 8: toward the full realization of the Fermi-LAT scientific potential (2013). arXiv e-prints https://doi.org/10.48550/arXiv.1303.3514

  • L. Baldini, The Silicon Strip Tracker of the Fermi Large Area Telescope: the first five years in orbit. PoS Vertex2013, 039 (2013). https://doi.org/10.22323/1.198.0039

  • D. Berge, S. Funk, J. Hinton, Background modelling in very-high-energy γ-ray astronomy. A&A 466, 1219–1229 (2007). https://doi.org/10.1051/0004-6361:20066674, astro-ph/0610959

  • K. Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray. Astropart. Phys. 30, 149–158 (2008). https://doi.org/10.1016/j.astropartphys.2008.07.009, 0808.2253

  • R. Brun, F. Rademakers, ROOT — An object oriented data analysis framework. Nucl. Instr. Methods Phys. Res. A 389, 81–86 (1997). https://doi.org/10.1016/S0168-9002(97)00048-X

    Article  ADS  Google Scholar 

  • P. Bruel, T.H. Burnett, S.W. Digel, G. Johannesson, N. Omodei, M. Wood, Fermi-LAT improved Pass 8 event selection (2018). arXiv e-prints https://doi.org/10.48550/arXiv.1810.11394

  • R. Buehler, J.D. Scargle, R.D. Blandford, L. Baldini, M.G. Baring, A. Belfiore, E. Charles, J. Chiang, F. D’Ammando, C.D. Dermer, S. Funk, J.E. Grove, A.K. Harding, E. Hays, M. Kerr, F. Massaro, M.N. Mazziotta, R.W. Romani, P.M. Saz Parkinson, A.F. Tennant, M.C. Weisskopf, Gamma-ray activity in the Crab Nebula: the exceptional flare of 2011 April. ApJ. 749, 26 (2012). https://doi.org/10.1088/0004-637X/749/1/26, 1112.1979

  • J.V. Cardenzana, A 3D maximum likelihood analysis for studying highly extended sources in VERITAS data. Ph.D. thesis, Iowa State University, 2017. https://doi.org/10.31274/etd-180810-4900

  • V.R. Chitnis, P.N. Bhat, Possible discrimination between gamma rays and hadrons using Čerenkov photon timing measurements. Astropart. Phys. 15, 29–47 (2001). https://doi.org/10.1016/S0927-6505(00)00137-7, astro-ph/0006133

  • J. Christiansen, Characterization of a maximum likelihood gamma-ray reconstruction algorithm for VERITAS, in Proceedings of 35th International Cosmic Ray Conference (ICRC2017), vol. 301 (2017), p. 789. https://doi.org/10.22323/1.301.0789, 1708.05684

  • J.L. Contreras, K. Satalecka, K. Bernlöhr, C. Boisson, J. Bregeon, A. Bulgarelli, G. De Cesare, R. de los Reyes, V. Fioretti, K. Kosack, C. Lavalley, E. Lyard, R. Marx, J. Rico, M. Sanguillot, M. Servillat, R. Walter, J.E. Ward, A. Zoli, Data model issues in the Cherenkov Telescope Array project, in Proceedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236 (2015), p. 960. https://doi.org/10.22323/1.236.0960, 1508.07584

  • CTA Consortium, Science with the Cherenkov Telescope Array. (World Scientific Publishing, 2019). https://doi.org/10.1142/10986, 1709.07997

  • P. Da Vela, A. Stamerra, A. Neronov, E. Prandini, Y. Konno, J. Sitarek, Study of the IACT angular acceptance and Point Spread Function. Astropart. Phys. 98, 1–8 (2018). https://doi.org/10.1016/j.astropartphys.2018.01.002

    Article  ADS  Google Scholar 

  • S. De, W. Maitra, V. Rentala, A.M. Thalapillil, Deep learning techniques for imaging air Cherenkov telescopes. PRD 107, 083026 (2023). https://doi.org/10.1103/PhysRevD.107.083026, 2206.05296

  • A. De Angelis, V. Tatischeff, I.A. Grenier et al., Science with e-ASTROGAM. A space mission for MeV-GeV gamma-ray astrophysics. JHEAp 19, 1–106 (2018). https://doi.org/10.1016/j.jheap.2018.07.001, 1711.01265

  • C. Deil et al., Data formats for gamma-ray astronomy – version 0.3 (2022). https://doi.org/10.5281/zenodo.7304668, https://gamma-astro-data-formats.readthedocs.io

  • C. Deil, C. Boisson, K. Kosack, J. Perkins, J. King, P. Eger, M. Mayer, M. Wood, V. Zabalza, J. Knödlseder, T. Hassan, L. Mohrmann, A. Ziegler, B. Khélifi, D. Dorner, G. Maier, G. Pedaletti, J. Rosado, J.L. Contreras, J. Lefaucheur, K. Brügge, M. Servillat, R. Terrier, R. Walter, S. Lombardi, Open high-level data formats and software for gamma-ray astronomy. AIP Conf. Proc. 1792, 070006 (2017a). https://doi.org/10.1063/1.4969003, 1610.01884

  • C. Deil, R. Zanin, J. Lefaucheur, C. Boisson, B. Khelifi, R. Terrier, M. Wood, L. Mohrmann, N. Chakraborty, J. Watson, R. Lopez-Coto, S. Klepser, M. Cerruti, J.P. Lenain, F. Acero, A. Djannati-Ataï, S. Pita, Z. Bosnjak, C. Trichard, T. Vuillaume, A. Donath, J. King, L. Jouvin, E. Owen, B. Sipocz, D. Lennarz, A. Voruganti, M. Spir-Jacob, J.E. Ruiz, M.P. Arribas, Gammapy – a prototype for the CTA science tools, in Proceedings of 35th International Cosmic Ray Conference (ICRC2017), vol. 301 (2017b), p. 766. 1709.01751. https://doi.org/10.22323/1.301.0766

  • M. Di Mauro, The origin of the Fermi-LAT γ-ray background (2016). arXiv e-prints. https://doi.org/10.48550/arXiv.1601.04323

  • M. de Naurois, L’astronomie γ de très haute énergie. Ouverture d’une nouvelle fenêtre astronomique sur l’Univers non thermique. Habilitation thesis, Université de Paris, 2012. https://inspirehep.net/record/1122589/files/these_short.pdf

    Google Scholar 

  • M. de Naurois, D. Mazin, Ground-based detectors in very-high-energy gamma-ray astronomy. C R. Phys. 16, 610–627 (2015). https://doi.org/10.1016/j.crhy.2015.08.011, 1511.00463

  • M. de Naurois, L. Rolland, A high performance likelihood reconstruction of γ-rays for imaging atmospheric Cherenkov telescopes. Astropart. Phys. 32, 231–252 (2009). https://doi.org/10.1016/j.astropartphys.2009.09.001, 0907.2610

  • E. Domingo-Santamaría, J. Flix, V. Scalzotto, W. Wittek, J. Rico, The DISP analysis method for point-like or extended γ source searches/studies with the MAGIC Telescope, in Proceedings of 29th International Cosmic Ray Conference (ICRC2005) (2005). https://doi.org/10.48550/arXiv.astro-ph/0508274

    Google Scholar 

  • Fermi-LAT Collaboration, The Large area telescope on the fermi gamma-ray space telescope mission. ApJ. 697, 1071 (2009). https://doi.org/10.1088/0004-637X/697/2/1071, 0902.1089

  • Fermi-LAT Collaboration, Development of the model of galactic interstellar emission for standard point-source analysis of Fermi Large Area Telescope data. ApJ. 223, 26 (2016). https://doi.org/10.3847/0067-0049/223/2/26, 1602.07246

  • Fermi-LAT Collaboration, Fermi large area telescope fourth source catalog. ApJ. 247, 33 (2020). https://doi.org/10.3847/1538-4365/ab6bcb, 1902.10045

  • Fermi-LAT Collaboration, Incremental fermi large area telescope fourth source catalog. ApJ. 260, 53 (2022). https://doi.org/10.3847/1538-4365/ac6751, 2201.11184

  • V.P. Fomin, A.A. Stepanian, R.C. Lamb, D.A. Lewis, M. Punch, T.C. Weekes, New methods of atmospheric Cherenkov imaging for gamma-ray astronomy. I. The false source method. Astropart. Phys. 2, 137–150 (1994). https://doi.org/10.1016/0927-6505(94)90036-1

    Article  ADS  Google Scholar 

  • D. Foreman-Mackey, D.W. Hogg, D. Lang, J. Goodman, emcee: The MCMC hammer. PASP 125, 306 (2013). https://doi.org/10.1086/670067, 1202.3665

  • F. Gargano, The high energy cosmic-radiation detection (HERD) facility on board the Chinese space station: hunting for high-energy cosmic rays, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395, (2021) p. 026. https://doi.org/10.22323/1.395.0026

  • M. Gaug, S. Fegan, A.M.W. Mitchell, M.C. Maccarone, T. Mineo, A. Okumura, Using Muon rings for the calibration of the Cherenkov telescope array: a systematic review of the method and its potential accuracy. ApJ. 243, 11 (2019). https://doi.org/10.3847/1538-4365/ab2123, 1907.04375

  • J. Glombitza, V. Joshi, B. Bruno, S. Funk, Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes (2023). arXiv e-prints. https://doi.org/10.48550/arXiv.2305.08674

  • J.E. Grove, W.N. Johnson, The calorimeter of the fermi large area telescope, in Space Telescopes and Instrumentation 2010: Ultraviolet to Gamma Ray, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7732, ed. by M. Arnaud, S.S. Murray, T. Takahashi, (2010), p. 77320J. https://doi.org/10.1117/12.857839

  • J. Hahn, R. de los Reyes, K. Bernlöhr, P. Krüger, Y.T.E. Lo, P.M. Chadwick, M.K. Daniel, C. Deil, H. Gast, K. Kosack, V. Marandon, Impact of aerosols and adverse atmospheric conditions on the data quality for spectral analysis of the HESS telescopes. Astropart. Phys. 54, 25–32 (2014). https://doi.org/10.1016/j.astropartphys.2013.10.003, 1310.1639

  • HAWC Collaboration, Observation of the Crab Nebula with the HAWC gamma-ray observatory. ApJ. 843, 39 (2017). https://doi.org/10.3847/1538-4357/aa7555, 1701.01778

  • HAWC Collaboration, Measurement of the Crab Nebula spectrum past 100 TeV with HAWC. ApJ. 881, 134 (2019). https://doi.org/10.3847/1538-4357/ab2f7d, 1905.12518

  • HAWC Collaboration, A. Donath, S. Funk, Validation of standardized data formats and tools for ground-level particle-based gamma-ray observatories. A&A 667, A36 (2022). https://doi.org/10.1051/0004-6361/202243527, 2203.05937

  • HEGRA Collaboration, First results on the performance of the HEGRA IACT array. Astropart. Phys. 8, 1–11 (1997). https://doi.org/10.1016/S0927-6505(97)00031-5, astro-ph/9704098

  • HEGRA Collaboration, The temporal characteristics of the TeV γ-emission from Mkn 501 in 1997: II. Results from HEGRA CT1 and CT2. A&A 349, 29–44 (1999). astro-ph/9901284

    Google Scholar 

  • W.N. Hess, The Radiation Belt and Magnetosphere. (Blaisdell, Waltham, MA, 1968)

    Google Scholar 

  • H.E.S.S. Collaboration, Calibration of cameras of the HESS detector. Astropart. Phys. 22, 109–125 (2004). https://doi.org/10.1016/j.astropartphys.2004.06.006, astro-ph/0406658

  • H.E.S.S. Collaboration, Observations of the Crab Nebula with HESS. A&A 457, 899–915 (2006). https://doi.org/10.1051/0004-6361:20065351, astro-ph/0607333

  • H.E.S.S. Collaboration, HESS first public test data release (2018). https://doi.org/10.5281/zenodo.1421098, https://www.mpi-hd.mpg.de/hfm/HESS/pages/dl3-dr1/, 1810.04516

  • H.E.S.S. Collaboration, Evidence of 100 TeV γ-ray emission from HESS J1702−420: a new PeVatron candidate. A&A 653, A152 (2021). https://doi.org/10.1051/0004-6361/202140962, 2106.06405

  • H.E.S.S. Collaboration, R. Blackwell, C. Braiding, M. Burton, K. Cubuk, M. Filipović, N. Tothill, G. Wong, A deep spectromorphological study of the γ-ray emission surrounding the young massive stellar cluster Westerlund 1. A&A 666, A124 (2022). https://doi.org/10.1051/0004-6361/202244323, 2207.10921

  • H.E.S.S. Collaboration, HESS J1809−193: a halo of escaped electrons around a pulsar wind nebula? A&A 672, A103 (2023). https://doi.org/10.1051/0004-6361/202245459, 2302.13663

  • A.M. Hillas, Angular and energy distributions of charged particles in electron-photon cascades in air. J. Phys. G Nucl. Phys. 8, 1461 (1982). https://doi.org/10.1088/0305-4616/8/10/016

    Article  ADS  Google Scholar 

  • A.M. Hillas, Cerenkov light images of EAS produced by primary gamma rays and nuclei, in Proceedings of 19th International Cosmic Ray Conference vol. 3 (1985), pp. 445–448. https://ui.adsabs.harvard.edu/abs/1985ICRC....3..445H

  • J. Hinton, The Southern wide-field gamma-ray observatory: status and prospects, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 023. https://doi.org/10.22323/1.395.0023, 2111.13158

  • W. Hofmann, I. Jung, A. Konopelko, H. Krawczynski, H. Lampeitl, G. Pühlhofer, Comparison of techniques to reconstruct VHE gamma-ray showers from multiple stereoscopic Cherenkov images. Astropart. Phys. 12, 135–143 (1999). https://doi.org/10.1016/S0927-6505(99)00084-5, astro-ph/9904234

  • J. Holder, Atmospheric Cherenkov gamma-ray telescopes, in The WSPC Handbook of Astronomical Instrumentation Volume 5: Gamma-Ray and Multimessenger Astronomical Instrumentation, ed. by D.N. Burrows (World Scientific, 2021). https://doi.org/10.1142/9446-vol5, 1510.05675

  • T.L. Holch, I. Shilon, M. Büchele, T. Fischer, S. Funk, N. Groeger, D. Jankowsky, T. Lohse, U. Schwanke, P. Wagner, Probing convolutional neural networks for event reconstruction in gamma-ray astronomy with Cherenkov telescopes, in Proceedings of 35th International Cosmic Ray Conference (ICRC2017), vol. 301 (2017), p. 795. https://doi.org/10.22323/1.301.0795, 1711.06298

  • M. Holler, J.P. Lenain, M. de Naurois, R. Rauth, D.A. Sanchez, A run-wise simulation and analysis framework for Imaging Atmospheric Cherenkov Telescope arrays. Astropart. Phys. 123, 102491 (2020). https://doi.org/10.1016/j.astropartphys.2020.102491, 2007.01697

  • B. Hona, Matched runs method to study extended regions of gamma-ray emission, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 729. https://doi.org/10.22323/1.395.0729, 2108.07663

  • M. Holler, D. Berge, C. van Eldik, J.P. Lenain, V. Marandon, T. Murach, M. de Naurois, R.D. Parsons, H. Prokoph, D. Zaborov, Observations of the Crab Nebula with H.E.S.S. phase II, in Proceedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236, (2015) p. 847. https://doi.org/10.22323/1.236.0847, 1509.02902

  • J.J.M. in ’t Zand, E. Bozzo, J. Qu et al., Observatory science with eXTP. Sci. China Phys. Mech. Astron. 62, 29506 (2019). https://doi.org/10.1007/s11433-017-9186-1, 1812.04023

  • V. Joshi, J. Hinton, H. Schoorlemmer, R. López-Coto, R. Parsons, A template-based γ-ray reconstruction method for air shower arrays. J. Cosmol. Astropart. Phys. 2019(1), 012 (2019). https://doi.org/10.1088/1475-7516/2019/01/012, 1809.07227

  • G. Kanbach, D.L. Bertsch, C.E. Fichtel, R.C. Hartman, S.D. Hunter, D.A. Kniffen, B.W. Hughlock, A. Favale, R. Hofstadter, E.B. Hughes, The project EGRET (energetic gamma-ray experiment telescope) on NASA’s Gamma-Ray Observatory GRO. Space Sci. Rev. 49, 69–84 (1989). https://doi.org/10.1007/BF00173744

    Article  ADS  Google Scholar 

  • M.P. Kertzman, G.H. Sembroski, Computer simulation methods for investigating the detection characteristics of TeV air Cherenkov telescopes. Nucl. Instr. Methods Phys. Res. A 343, 629–643 (1994). https://doi.org/10.1016/0168-9002(94)90247-X

    Article  ADS  Google Scholar 

  • S. Koldobskiy, M. Kachelrieß, A. Lskavyan, A. Neronov, S. Ostapchenko, D.V. Semikoz, Energy spectra of secondaries in proton-proton interactions. PRD 104, 123027 (2021). https://doi.org/10.1103/PhysRevD.104.123027, 2110.00496

  • J. Knödlseder, M. Mayer, C. Deil, J.B. Cayrou, E. Owen, N. Kelley-Hoskins, C.C. Lu, R. Buehler, F. Forest, T. Louge, H. Siejkowski, K. Kosack, L. Gerard, A. Schulz, P. Martin, D. Sanchez, S. Ohm, T. Hassan, S. Brau-Nogué, GammaLib and ctools: a software framework for the analysis of astronomical gamma-ray data. A&A 593, A1 (2016). https://doi.org/10.1051/0004-6361/201628822, 1606.00393

  • J. Knödlseder, L. Tibaldo, D. Tiziani, A. Specovius, J. Cardenzana, M. Mayer, N. Kelley-Hoskins, L. Di Venere, S. Bonnefoy, A. Ziegler, S. Eschbach, P. Martin, T. Louge, F. Brun, M. Haupt, R. Bühler, Analysis of the HESS public data release with ctools. A&A 632, A102 (2019). https://doi.org/10.1051/0004-6361/201936010, 1910.09456

  • M. Krause, E. Pueschel, G. Maier, Improved γ/hadron separation for the detection of faint γ-ray sources using boosted decision trees. Astropart. Phys. 89, 1–9 (2017). https://doi.org/10.1016/j.astropartphys.2017.01.004, 1701.06928

  • L. Kuiper, W. Hermsen, G. Cusumano, R. Diehl, V. Schönfelder, A. Strong, K. Bennett, M.L. McConnell, The Crab Pulsar in the 0.75–30 MeV range as seen by CGRO COMPTEL. A coherent high-energy picture from soft X-rays up to high-energy gamma-rays. A&A 378, 918–935 (2001). https://doi.org/10.1051/0004-6361:20011256, astro-ph/0109200

  • S. Le Bohec, B. Degrange, M. Punch, A. Barrau, R. Bazer-Bachi, H. Cabot, L.M. Chounet, G. Debiais, J.P. Dezalay, A. Djannati-Atai, D. Dumora, P. Espigat, B. Fabre, P. Fleury, G. Fontaine, R. George, C. Ghesquiere, P. Goret, C. Gouiffes, I.A. Grenier, L. Iacoucci, I. Malet, C. Meynadier, F. Munz, T.A. Palfrey, E. Pare, Y. Pons, J. Quebert, K. Ragan, C. Renault, M. Rivoal, L. Rob, P. Schovanek, D. Smith, J.P. Tavernet, J. Vrana, A new analysis method for very high definition Imaging Atmospheric Cherenkov Telescopes as applied to the CAT telescope. Nucl. Instr. Methods Phys. Res. A 416, 425–437 (1998). https://doi.org/10.1016/S0168-9002(98)00750-5, astro-ph/9804133

  • M. Lemoine-Goumard, B. Degrange, M. Tluczykont, Selection and 3D-reconstruction of gamma-ray-induced air showers with a stereoscopic system of atmospheric Cherenkov telescopes. Astropart. Phys. 25, 195–211 (2006). https://doi.org/10.1016/j.astropartphys.2006.01.005, astro-ph/0601373

  • R.W. Lessard, J.H. Buckley, V. Connaughton, S. LeBohec, A new analysis method for reconstructing the arrival direction of TeV gamma rays using a single imaging atmospheric Cherenkov telescope. Astropart. Phys. 15, 1–18 (2001). https://doi.org/10.1016/S0927-6505(00)00133-X, astro-ph/0005468

  • LHAASO Collaboration, Observation of the Crab Nebula with LHAASO-KM2A – a performance study. Chin. Phys. C. 45, 025002 (2021). https://doi.org/10.1088/1674-1137/abd01b, 2010.06205

  • LHAASO Collaboration, Peta-electron volt gamma-ray emission from the Crab Nebula. Science 373, 425–430 (2021). https://doi.org/10.1126/science.abg5137, 2111.06545

  • T. Li, Y. Ma, Analysis methods for results in gamma-ray astronomy. ApJ. 272, 317–324 (1983). https://doi.org/10.1086/161295

    Article  ADS  Google Scholar 

  • S. Lombardi for the MAGIC Collaboration, Advanced stereoscopic gamma-ray shower analysis with the MAGIC telescopes, in Proceedings of 32nd International Cosmic Ray Conference (ICRC2011) (2011). https://doi.org/10.48550/arXiv.1109.6195

  • E. Lyard, R. Walter, V. Sliusar, N. Produit, Probing Neural Networks for the Gamma/Hadron Separation of the Cherenkov Telescope Array. J. Phys. Conf. Ser. 1525, 012084 (2020). https://doi.org/10.1088/1742-6596/1525/1/012084, 1907.02428

  • MAGIC Collaboration, Unfolding of differential energy spectra in the MAGIC experiment. Nucl. Instr. Methods Phys. Res. A 583, 494–506 (2007). https://doi.org/10.1016/j.nima.2007.09.048, 0707.2453

  • MAGIC Collaboration, Implementation of the random forest method for the imaging atmospheric Cherenkov telescope MAGIC. Nucl. Instr. Methods Phys. Res. A 588, 424–432 (2008). https://doi.org/10.1016/j.nima.2007.11.068, 0709.3719

  • MAGIC Collaboration, Improving the performance of the single-dish Cherenkov telescope MAGIC through the use of signal timing. Astropart. Phys. 30, 293–305 (2009). https://doi.org/10.1016/j.astropartphys.2008.10.003, 0810.3568

  • MAGIC Collaboration, The major upgrade of the MAGIC telescopes, Part I: the hardware improvements and the commissioning of the system. Astropart. Phys. 72, 61–75 (2016a). https://doi.org/10.1016/j.astropartphys.2015.04.004, 1409.6073

  • MAGIC Collaboration, The major upgrade of the MAGIC telescopes, Part II: a performance study using observations of the Crab Nebula. Astropart. Phys. 72, 76–94 (2016b). https://doi.org/10.1016/j.astropartphys.2015.02.005, 1409.5594

  • D. Malyshev, M. Chernyakova, Constraints on the spectrum of HESS J0632+057 from Fermi-LAT data. MNRAS 463, 3074–3077 (2016). https://doi.org/10.1093/mnras/stw2173, 1601.08216

  • D. Malyshev, A.A. Zdziarski, M. Chernyakova, High-energy gamma-ray emission from Cyg X-1 measured by Fermi and its theoretical implications. MNRAS 434, 2380–2389 (2013). https://doi.org/10.1093/mnras/stt1184, 1305.5920

  • S. Mangano, C. Delgado, M.I. Bernardos, M. Lallena, J.J. Rodríguez Vázquez, Extracting gamma-ray information from images with convolutional neural network methods on simulated Cherenkov telescope array data, in Artificial Neural Networks in Pattern Recognition, ed. by L. Pancioni, F. Schwenker, E. Trentin (Springer International Publishing, 2018), pp. 243–254. https://doi.org/10.1007/978-3-319-99978-4_19, 1810.00592

  • J.R. Mattox, D.L. Bertsch, J. Chiang, B.L. Dingus, S.W. Digel, J.A. Esposito, J.M. Fierro, R.C. Hartman, S.D. Hunter, G. Kanbach, D.A. Kniffen, Y.C. Lin, D.J. Macomb, Mayer-H.A. Hasselwander, P.F. Michelson, C. von Montigny, R. Mukherjee, P.L. Nolan, P.V. Ramanamurthy, E. Schneid, P. Sreekumar, D.J. Thompson, T.D. Willis, The likelihood analysis of EGRET data. ApJ. 461, 396–407 (1996). https://doi.org/10.1086/177068

  • J. McEnery, A. van der Horst, A. Dominguez et al., All-sky medium energy gamma-ray observatory: exploring the extreme multimessenger universe, in Bulletin of the American Astronomical Society, vol. 51 (2019), p. 245. 1907.07558

    Google Scholar 

  • M. Meyer, D. Horns, H.S. Zechlin, The Crab Nebula as a standard candle in very high-energy astrophysics. A&A 523, A2 (2010). https://doi.org/10.1051/0004-6361/201014108, 1008.4524

  • T. Miener, D. Nieto, A. Brill, S.T. Spencer, J.L. Contreras, Reconstruction of stereoscopic CTA events using deep learning with CTLearn, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 730. https://doi.org/10.22323/1.395.0730, 2109.05809

  • T. Miener, D. Nieto, R. López-Coto, J.L. Contreras, J.G. Green, D. Green, E. Mariotti, The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn (2022). arXiv e-prints. https://doi.org/10.48550/arXiv.2211.16009

  • N. Milke, M. Doert, S. Klepser, D. Mazin, V. Blobel, W. Rhode, Solving inverse problems with the unfolding program TRUEE: examples in astroparticle physics. Nucl. Instr. Methods Phys. Res. A 697, 133–147 (2013). https://doi.org/10.1016/j.nima.2012.08.105, 1209.3218

  • A.M.W. Mitchell, Optical efficiency calibration for inhomogeneous IACT arrays and a detailed study of the highly extended pulsar wind nebula HESS J1825−137. Ph.D. thesis, Ruprecht-Karls-Universität Heidelberg, 2016. https://hdl.handle.net/11858/00-001M-0000-002B-1DD2-F

  • A. Mitchell, S. Caroff, J. Hinton, L. Mohrmann, Detection of extended TeV emission around the Geminga pulsar with H.E.S.S., in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2023), p. 780. https://doi.org/10.22323/1.395.0780, 2108.02556

  • L. Mohrmann, A. Specovius, D. Tiziani, S. Funk, D. Malyshev, K. Nakashima, C. van Eldik, Validation of open-source science tools and background model construction in γ-ray astronomy. A&A 632, A72 (2019). https://doi.org/10.1051/0004-6361/201936452, 1910.08088

  • A.A. Moiseev, R.C. Hartman, J.F. Ormes, D.J. Thompson, M.J. Amato, T.E. Johnson, K.N. Segal, D.A. Sheppard, The anti-coincidence detector for the GLAST large area telescope. Astropart. Phys. 27, 339–358 (2007). https://doi.org/10.1016/j.astropartphys.2006.12.003, astro-ph/0702581

  • T. Murach, M. Gajdus, R.D. Parsons, A neural network-based reconstruction algorithm for monoscopically detected air showers observed with the HESS experiment, in Proceedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236 (2015), p. 1022. https://doi.org/10.22323/1.236.1022, 1509.00794

  • D. Nieto Castaño, A. Brill, B. Kim, T.B. Humensky, Exploring deep learning as an event classification method for the Cherenkov Telescope Array, in Proceedings of 35th International Cosmic Ray Conference (ICRC2017), vol. 301 (2017), p. 809. https://doi.org/10.22323/1.301.0809, 1709.05889

  • C. Nigro, C. Deil, R. Zanin, T. Hassan, J. King, J.E. Ruiz, L. Saha, R. Terrier, K. Brügge, M. Nöthe, R. Bird, T.T.Y. Lin, J. Aleksić, C. Boisson, J.L. Contreras, A. Donath, L. Jouvin, N. Kelley-Hoskins, B. Khelifi, K. Kosack, J. Rico, A. Sinha, Towards open and reproducible multi-instrument analysis in gamma-ray astronomy. A&A 625, A10 (2019). https://doi.org/10.1051/0004-6361/201834938, 1903.06621

  • C. Nigro, T. Hassan, L. Olivera-Nieto, Evolution of Data Formats in Very-High-Energy Gamma-Ray Astronomy. Universe 7, 374 (2021). https://doi.org/10.3390/universe7100374, 2109.14661

  • S. Ohm, C. van Eldik, K. Egberts, γ/hadron separation in very-high-energy γ-ray astronomy using a multivariate analysis method. Astropart. Phys. 31, 383–391 (2009). https://doi.org/10.1016/j.astropartphys.2009.04.001, 0904.1136

  • E. Orlando, E. Bottacini, A.A. Moiseev et al., Exploring the MeV sky with a combined coded mask and Compton telescope: the Galactic Explorer with a Coded aperture mask Compton telescope (GECCO). J. Cosmol. Astropart. Phys. 2022, 036 (2022). https://doi.org/10.1088/1475-7516/2022/07/036, 2112.07190

  • L. Olivera-Nieto, A.M.W. Mitchell, K. Bernlöhr, J.A. Hinton, Muons as a tool for background rejection in imaging atmospheric Cherenkov telescope arrays. Eur. Phys. J. C 81, 1101 (2021). https://doi.org/10.1140/epjc/s10052-021-09869-0, 2111.12041

  • L. Olivera-Nieto, H.X. Ren, A.M.W. Mitchell, V. Marandon, J.A. Hinton, Background rejection using image residuals from large telescopes in imaging atmospheric Cherenkov telescope arrays. Eur. Phys. J. C 82, 1118 (2022). https://doi.org/10.1140/epjc/s10052-022-11067-5, 2211.13167

  • N. Park, Performance of the VERITAS experiment, in Proceedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236, (2015), p. 771. https://doi.org/10.22323/1.236.0771, 1508.07070

  • R.D. Parsons, J.A. Hinton, A Monte Carlo template based analysis for air-Cherenkov arrays. Astropart. Phys. 56, 26–34 (2014). https://doi.org/10.1016/j.astropartphys.2014.03.002, 1403.2993

  • R.D. Parsons, S. Ohm, Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks. Eur. Phys. J. C 80, 363 (2020). https://doi.org/10.1140/epjc/s10052-020-7953-3, 1910.09435

  • R.D. Parsons, H. Schoorlemmer, Systematic differences due to high energy hadronic interaction models in air shower simulations in the 100 GeV–100 TeV range. PRD 100, 023010 (2019). https://doi.org/10.1103/PhysRevD.100.023010, 1904.05135

  • R.D. Parsons, A.M.W. Mitchell, S. Ohm, Investigations of the systematic uncertainties in convolutional neural network based analysis of atmospheric cherenkov telescope data (2022). arXiv e-prints. https://doi.org/10.48550/arXiv.2203.05315

  • F. Piron, A. Djannati-Ataï, M. Punch, J.P. Tavernet, A. Barrau, R. Bazer-Bachi, L.M. Chounet, G. Debiais, B. Degrange, J.P. Dezalay, P. Espigat, B. Fabre, P. Fleury, G. Fontaine, P. Goret, C. Gouiffes, B. Khelifi, I. Malet, C. Masterson, G. Mohanty, E. Nuss, C. Renault, M. Rivoal, L. Rob, S. Vorobiov, Temporal and spectral gamma-ray properties of Mkn 421 above 250 GeV from CAT observations between 1996 and 2000. A&A 374, 895–906 (2001). https://doi.org/10.1051/0004-6361:20010798, astro-ph/0106196

  • S. Polyakov, A. Demichev, A. Kryukov, E. Postnikov, The use of convolutional neural networks for processing images from multiple IACTs in the TAIGA experiment, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 753. https://doi.org/10.22323/1.395.0753

  • G.P. Rowell, A new template background estimate for source searching in TeV γ-ray astronomy. A&A 410, 389–396 (2003). https://doi.org/10.1051/0004-6361:20031194, astro-ph/0310025

  • M. Shayduk for the CTA Consortium, Optimized next-neighbour image cleaning method for cherenkov telescopes, in Proceedings of 33rd International Cosmic Ray Conference (ICRC2013) (2013). 1307.4939

    Google Scholar 

  • I. Shilon, M. Kraus, M. Büchele, K. Egberts, T. Fischer, T.L. Holch, T. Lohse, U. Schwanke, C. Steppa, S. Funk, Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data. Astropart. Phys. 105, 44–53 (2019). https://doi.org/10.1016/j.astropartphys.2018.10.003, 1803.10698

  • J. Sitarek, TeV instrumentation: current and future. Galaxies 10, 21 (2022). https://doi.org/10.3390/galaxies10010021, 2201.08611

  • J. Sollerman, P. Lundqvist, D. Lindler, R.A. Chevalier, C. Fransson, T.R. Gull, C.S.J. Pun, G. Sonneborn, Observations of the Crab Nebula and Its Pulsar in the Far-Ultraviolet and in the Optical. ApJ. 537, 861–874 (2000). https://doi.org/10.1086/309062, astro-ph/0002374

  • S. Spencer, T. Armstrong, J. Watson, S. Mangano, Y. Renier, G. Cotter, Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array. Astropart. Phys. 129, 102579 (2021). https://doi.org/10.1016/j.astropartphys.2021.102579, 2103.06054

  • M. Tavani, G. Barbiellini, A. Argan et al., The AGILE mission. A&A 502, 995–1013 (2009). https://doi.org/10.1051/0004-6361/200810527, 0807.4254

  • Tibet ASγ Collaboration, First detection of photons with energy beyond 100 TeV from an astrophysical source. PRL 123, 051101 (2019). https://doi.org/10.1103/PhysRevLett.123.051101, 1906.05521

  • A. Tziamtzis, P. Lundqvist, A.A. Djupvik, The Crab Pulsar and its pulsar-wind nebula in the optical and infrared. A&A 508, 221–228 (2009). https://doi.org/10.1051/0004-6361/200912031, 0911.0608

  • VERITAS Collaboration, VERITAS: the very energetic radiation imaging telescope array system. Astropart. Phys. 17, 221–243 (2002). https://doi.org/10.1016/S0927-6505(01)00152-9, astro-ph/0108478

  • VERITAS Collaboration, The throughput calibration of the VERITAS telescopes. A&A 658, A83 (2022). https://doi.org/10.1051/0004-6361/202142275, 2111.04676

  • G. Vianello, R. Lauer, P. Younk, L. Tibaldo, J.M. Burgess, H. Ayala Solares, J.P. Harding, C.M. Hui, N. Omodei, H. Zhou, The multi-mission maximum likelihood framework, in Proceedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236 (2015), p. 1042. https://doi.org/10.22323/1.236.1042, 1507.08343

  • S. Vincent, A Monte Carlo template-based analysis for very high definition imaging atmospheric Cherenkov telescopes as applied to the VERITAS telescope array, in Proceedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236 (2015), p. 844. https://doi.org/10.22323/1.236.0844, 1509.01980

  • T. Vuillaume, M. Jacquemont, M. de Bony de Lavergne, D.A. Sanchez, V. Poireau, G. Maurin, A. Benoit, P. Lambert, G. Lamanna, Analysis of the Cherenkov telescope array first large size telescope real data using convolutional neural networks, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 703. https://doi.org/10.22323/1.395.0703, 2108.04130

  • H.J. Völk, K. Bernlöhr, Imaging very high energy gamma-ray telescopes. Exp. Astron. 25, 173–191 (2009). https://doi.org/10.1007/s10686-009-9151-z, 0812.4198

  • I. Vovk, M. Strzys, C. Fruck, Spatial likelihood analysis for MAGIC telescope data: from instrument response modelling to spectral extraction. A&A 619, A7 (2018). https://doi.org/10.1051/0004-6361/201833139, 1806.03167

  • T.C. Weekes, M.F. Cawley, D.J. Fegan, K.G. Gibbs, A.M. Hillas, P.W. Kowk, R.C. Lamb, D.A. Lewis, D. Macomb, N.A. Porter, P.T. Reynolds, G. Vacanti, Observation of TeV gamma rays from the Crab Nebula using the atmospheric Cherenkov imaging technique. ApJ. 342, 379–395 (1989). https://doi.org/10.1086/167599

    Article  ADS  Google Scholar 

  • S.S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 9, 60–62 (1938). https://doi.org/10.1214/aoms/1177732360

    Article  Google Scholar 

  • V. Zabalza, Naima: a Python package for inference of particle distribution properties from nonthermal spectra, in Procedings of 34th International Cosmic Ray Conference (ICRC2015), vol. 236 (2015), p. 922. https://doi.org/10.22323/1.236.0922, 1509.03319

  • R. Zanin for the CTA Consortium, CTA – the World’s largest ground-based gamma-ray observatory, in Proceedings of 37th International Cosmic Ray Conference (ICRC2021), vol. 395 (2021), p. 005. https://doi.org/10.22323/1.395.0005

Download references

Acknowledgements

LM acknowledges helpful discussion with Vincent Marandon and Jim Hinton and thanks Werner Hofmann for reading parts of the manuscript. The authors acknowledge support by the state of Baden-Württemberg through bwHPC. The work of DM was supported by DLR through grant 50OR2104 and by DFG through grant MA 7807/2-1.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Denys Malyshev or Lars Mohrmann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Malyshev, D., Mohrmann, L. (2024). Analysis Methods for Gamma-Ray Astronomy. In: Bambi, C., Santangelo, A. (eds) Handbook of X-ray and Gamma-ray Astrophysics. Springer, Singapore. https://doi.org/10.1007/978-981-19-6960-7_177

Download citation

Publish with us

Policies and ethics