529 research outputs found
Signal estimation in on/off measurements including event-by-event variables
No full textSignal estimation in the presence of background noise is a common problem in several scientific disciplines. An "on/off"measurement is performed when the background itself is not known, being estimated from a background control sample. The "frequentist"and Bayesian approaches for signal estimation in on/off measurements are reviewed and compared, focusing on the weakness of the former and on the advantages of the latter in correctly addressing the Poissonian nature of the problem. In this work, we devise a novel reconstruction method, Bayesian analysis including single-event likelihoods (dubbed BASiL), for estimating the signal rate based on the Bayesian formalism. It uses information on event-by-event individual parameters and their distribution for the signal and background population. Events are thereby weighted according to their likelihood of being a signal or a background event and background suppression can be achieved without performing fixed fiducial cuts. Throughout the work, we maintain a general notation that allows us to apply the method generically and provides a performance test using real data and simulations of observations with the MAGIC telescopes, as a demonstration of the performance for Cherenkov telescopes. BASiL allows one to estimate the signal more precisely, avoiding loss of exposure due to signal extraction cuts. We expect its applicability to be straightforward in similar cases
Excess estimation in On/Off measurements including single-event variables
No full textSignal estimation in the presence of background noise is a common problem in many scientific disciplines. An "On/Off"measurement is when the background itself is imprecisely measured, which is the case for instance of observations performed in astronomy. We propose a new method for estimating the signal rate based on the Bayesian formalism. It uses information on single-event variables and their distribution for the signal and background population. Events are thereby weighted according to their likelihood of being a signal or a background event and background suppression can be achieved without performing data selection cuts. Simulating "On/Off"measurements from imaging atmospheric Cherenkov observations, we conclude that this new method is capable of increasing the resolution of the signal estimation, in particular for background dominated observations
Physics holo.lab learning experience: using smartglasses for augmented reality labwork to foster the concepts of heat conduction
No full textDeciphering the gamma-ray sky: study of the gamma-Cygni SNR using a novel likelihood analysis technique for the MAGIC telescopes
No full textThis thesis presents a novel spatial likelihood analysis for Imaging Atmospheric Cherenkov Telescopes (IACTs) and its use to analyse observations of the gamma-Cygni supernova remnant (SNR) with the MAGIC telescopes, a system of two IACTs. SNRs are the prime candidate source for the origin of the galactic component of cosmic rays (CRs). These objects are sufficiently extended to be resolved with gamma-ray telescopes. This allows the determination of different acceleration regions of a source, but poses issues for the current analysis approach for IACT data. IACTs detect the Cherenkov light generated in air showers, which are cascades of energetic particle that result from the interaction of gamma-rays with the molecules in the atmosphere. Currently, the emission from a source is determined using the aperture photometry approach, in which the number of gamma-ray events from the source region is compared against a source-free background control region. In the case of superimposed emission regions, an event count cannot be attributed to one emission region. Furthermore, extended objects or objects of complex morphology make the definition of the source region a difficult task. These issues can be overcome by a spatial likelihood analysis of the skymaps of IACTs. In this approach, a user-defined source template is convolved with the instrument response functions (IRFs) and the "realistic" model fitted to the event count maps via a Poissonian likelihood fit. The data analyses of space-based gamma-ray telescopes, such as the Fermi Large Area Telescope (LAT), are based on this technique. For IACTs the determination of the IRFs, however, is a challenging task: because the atmosphere is part of the detector, the IRFs cannot be measured in the laboratory but need to be computed from Monte-Carlo events for each observation individually. This thesis presents SkyPrism, a software package performing such an analysis on MAGIC data including the accurate determination of the IRFs. Using SkyPrism it was possible to analyse observations of the ~7000 year old gamma-Cygni SNR taken with MAGIC between 2015 and 2017. CRs are accelerated and confined in the shock region by magnetic turbulences ahead and behind the shock, making the level of turbulence an important ingredient of the acceleration process. Only a small high energetic fraction of CRs may escape the fast shocks of young SNRs (10000 years) almost all CRs have already escaped. I studied the escape of CRs from the shock into the interstellar medium using 85 hours of MAGIC data and 9 years Fermi-LAT data covering the energy range from 5 GeV to 5TeV. Using the theoretical model of the diffusive shock acceleration, I determined that the maximum energy of the CRs confined in the shock region decreases faster with the lifetime of the SNR than expected and that the level of turbulence is not constant over the lifetime of the SNR
Spatial likelihood analysis for MAGIC telescope data. From instrument response modelling to spectral extraction
No full textContext. The increase in sensitivity of Imaging Atmospheric Cherenkov Telescopes (IACTs) has lead to numerous detections of extended γ-ray sources at TeV energies, sometimes of sizes comparable to the instrument's field of view (FoV). This creates a demand for advanced and flexible data analysis methods, able to extract source information by utilising the photon counts in the entire FoV. Aims. We present a new software package, "SkyPrism", aimed at performing 2D (3D if energy is considered) fits of IACT data, possibly containing multiple and extended sources, based on sky images binned in energy. Though the development of this package was focused on the analysis of data collected with the MAGIC telescopes, it can further be adapted to other instruments, such as the future Cherenkov Telescope Array (CTA). Methods. We have developed a set of tools that, apart from sky images (count maps), compute the instrument response functions (IRFs) of MAGIC (effective exposure throughout the FoV, point spread function (PSF), energy resolution and background shape), based on the input data, Monte-Carlo simulations and the pointing track of the telescopes. With this information, the presented package can perform a simultaneous maximum likelihood fit of source models of arbitrary morphology to the sky images providing energy spectra, detection significances, and upper limits. Results. We demonstrate that the SkyPrism tool accurately reconstructs the MAGIC PSF, on and off-axis performance as well as the underlying background. We further show that for a point source analysis with MAGIC's default observational settings, SkyPrism gives results compatible with those of the standard tools while being more flexible and widely applicable
MAGIC observations of the diffuse γ-ray emission in the vicinity of the Galactic center
No full textAims. In the presence of a sufficient amount of target material, γ-rays can be used as a tracer in the search for sources of Galactic cosmic rays (CRs). Here we present deep observations of the Galactic center (GC) region with the MAGIC telescopes and use them to infer the underlying CR distribution and to study the alleged PeV proton accelerator at the center of our Galaxy.Methods. We used data from ≈100 h observations of the GC region conducted with the MAGIC telescopes over five years (from 2012 to 2017). Those were collected at high zenith angles (58−70 deg), leading to a larger energy threshold, but also an increased effective collection area compared to low zenith observations. Using recently developed software tools, we derived the instrument response and background models required for extracting the diffuse emission in the region. We used existing measurements of the gas distribution in the GC region to derive the underlying distribution of CRs. We present a discussion of the associated biases and limitations of such an approach.Results. We obtain a significant detection for all four model components used to fit our data (Sgr A*, “Arc”, G0.9+0.1, and an extended component for the Galactic Ridge). We observe no significant difference between the γ-ray spectra of the immediate GC surroundings, which we model as a point source (Sgr A*) and the Galactic Ridge. The latter can be described as a power-law with index 2 and an exponential cut-off at around 20 TeV with the significance of the cut-off being only 2σ. The derived cosmic-ray profile hints to a peak at the GC position and with a measured profile index of 1.2 ± 0.3 is consistent with the 1/r radial distance scaling law, which supports the hypothesis of a CR accelerator at the GC. We argue that the measurements of this profile are presently limited by our knowledge of the gas distribution in the GC vicinity.Key words: gamma rays: general / gamma rays: ISM / Galaxy: center / cosmic rays⋆ Tables and sky maps are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/642/A190⋆⋆ Corresponding authors: Christian Fruck, Ievgen Vovk, Yuki Iwamura and Marcel Strzys (e-mail: [email protected])
Pybkgmodel - a background modelling toolbox for the CTA
No full textDespite the advancement in background rejection techniques, observation of the very-high-energy gamma-ray sky by imaging atmospheric Cherenkov telescopes (IACTs) are subject to an irreducible background from gamma-like hadron- or electron-induced air showers. The determination of this residual background is crucial for accurate spectral and spatial measurements. The Cherenkov Telescope Array (CTA) will become the next generation of IACTs. To unveil its full potential, the improved reconstruction performance of CTA needs to be coupled with a reliable background estimate across the entire field of view. This may become especially important in the case of the planned surveys of large areas of the sky. In this contribution we will present pybkgmodel, an open-source python software package developed for CTA. It aims at providing in a consistent way the various background modelling methods, based on the experience from current IACTs such as H.E.S.S, MAGIC, and VERITAS. It is designed as a toolbox allowing a user to easily choose the optimal reconstruction approach for various target regions or a combination of several algorithms. We will introduce the design of the package as well as demonstrate its functionality using data for the CTA Large-Sized Telescope prototype (LST-1). © Copyright owned by the author(s) under the terms of the Creative Commons
Search for Gamma-Ray Spectral Lines from Dark Matter Annihilation up to 100 TeV toward the Galactic Center with MAGIC
No full textAbe, H. et al.--Full list of authors: Abe, H.; Abe, S.; Acciari, V. A.; Aniello, T.; Ansoldi, S.; Antonelli, L. A.; Engels, A. Arbet; Arcaro, C.; Artero, M.; Asano, K.; Baack, D.; Babic, A.; Baquero, A.; Almeida, U. Barres de; Barrio, J. A.; Batkovic, I.; Baxter, J.; Gonzalez, J. Becerra; Bednarek, W.; Bernardini, E.; Bernardos, M.; Berti, A.; Besenrieder, J.; Bhattacharyya, W.; Bigongiari, C.; Biland, A.; Blanch, O.; Bonnoli, G.; Bosnjak, Z.; Burelli, I.; Busetto, G.; Carosi, R.; Carretero-Castrillo, M.; Ceribella, G.; Chai, Y.; Chilingarian, A.; Cikota, S.; Colombo, E.; Contreras, J. L.; Cortina, J.; Covino, S.; D'Amico, G.; D'Elia, V.; Da Vela, P.; Dazzi, F.; De Angelis, A.; De Lotto, B.; Del Popolo, A.; Delfino, M.; Delgado, J.; Mendez, C. Delgado; Depaoli, D.; Di Pierro, F.; Di Venere, L.; Espineira, E. Do Souto; Prester, D. Dominis; Donini, A.; Dorner, D.; Doro, M.; Elsaesser, D.; Emery, G.; Ramazani, V. Fallah; Farina, L.; Fattorini, A.; Font, L.; Fruck, C.; Fukami, S.; Fukazawa, Y.; Lopez, R. J. Garcia; Garczarczyk, M.; Gasparyan, S.; Gaug, M.; Paiva, J. G. Giesbrecht; Giglietto, N.; Giordano, F.; Gliwny, P.; Godinovic, N.; Green, J. G.; Green, D.; Hadasch, D.; Hahn, A.; Hassan, T.; Heckmann, L.; Herrera, J.; Hrupec, D.; Huetten, M.; Imazawa, R.; Inada, T.; Iotov, R.; Ishio, K.; Martinez, I. Jimenez; Jormanainen, J.; Kerszberg, D.; Kobayashi, Y.; Kubo, H.; Kushida, J.; Lamastra, A.; Lelas, D.; Leone, F.; Lindfors, E.; Linhoff, L.; Lombardi, S.; Longo, F.; Lopez-Coto, R.; Lopez-Moya, M.; Lopez-Oramas, A.; Loporchio, S.; Lorini, A.; Lyard, E.; Fraga, B. Machado de Oliveira; Majumdar, P.; Makariev, M.; Maneva, G.; Mang, N.; Manganaro, M.; Mangano, S.; Mannheim, K.; Mariotti, M.; Martinez, M.; Aguilar, A. Mas; Mazin, D.; Menchiari, S.; Mender, S.; Micanovic, S.; Miceli, D.; Miener, T.; Miranda, J. M.; Mirzoyan, R.; Molina, E.; Mondal, H. A.; Moralejo, A.; Morcuende, D.; Moreno, V.; Nakamori, T.; Nanci, C.; Nava, L.; Neustroev, V.; Rosillo, M. Nievas; Nigro, C.; Nilsson, K.; Nishijima, K.; Ekoume, T. Njoh; Noda, K.; Nozaki, S.; Ohtani, Y.; Oka, T.; Otero-Santos, J.; Paiano, S.; Palatiello, M.; Paneque, D.; Paoletti, R.; Paredes, J. M.; Pavletic, L.; Persic, M.; Pihet, M.; Podobnik, F.; Moroni, P. G. Prada; Prandini, E.; Principe, G.; Priyadarshi, C.; Puljak, I.; Rhode, W.; Ribo, M.; Rico, J.; Righi, C.; Rugliancich, A.; Sahakyan, N.; Saito, T.; Sakurai, S.; Satalecka, K.; Saturni, F. G.; Schleicher, B.; Schmidt, K.; Schmuckermaier, F.; Schubert, J. L.; Schweizer, T.; Sitarek, J.; Sliusar, V.; Sobczynska, D.; Spolon, A.; Stamerra, A.; Striskovic, J.; Strom, D.; Strzys, M.; Suda, Y.; Suric, T.; Takahashi, M.; Takeishi, R.; Tavecchio, F.; Temnikov, P.; Terauchi, K.; Terzic, T.; Teshima, M.; Tosti, L.; Truzzi, S.; Tutone, A.; Ubach, S.; van Scherpenberg, J.; Acosta, M. Vazquez; Ventura, S.; Verguilov, V.; Viale, I; Vigorito, C. F.; Vitale, V.; Vovk, I.; Walter, R.; Will, M.; Wunderlich, C.; Yamamoto, T.; Zaric, D.; Hiroshima, N.; Kohri, K.; MAGIC Collaboration.Linelike features in TeV γ rays constitute a “smoking gun” for TeV-scale particle dark matter and new physics. Probing the Galactic Center region with ground-based Cherenkov telescopes enables the search for TeV spectral features in immediate association with a dense dark matter reservoir at a sensitivity out of reach for satellite γ -ray detectors, and direct detection and collider experiments. We report on 223 hours of observations of the Galactic Center region with the MAGIC stereoscopic telescope system reaching γ -ray energies up to 100 TeV. We improved the sensitivity to spectral lines at high energies using large-zenith-angle observations and a novel background modeling method within a maximum-likelihood analysis in the energy domain. No linelike spectral feature is found in our analysis. Therefore, we constrain the cross section for dark matter annihilation into two photons to ⟨σv⟩ ≲ 5 × 10−28 cm3 s−1 at 1 TeV and ⟨σv⟩ ≲ 1 × 10−25 cm3 s−1 at 100 TeV, achieving the best limits to date for a dark matter mass above 20 TeV and a cuspy dark matter profile at the Galactic Center. Finally, we use the derived limits for both cuspy and cored dark matter profiles to constrain supersymmetric wino models.The financial support of the German BMBF, MPG, and HGF; the Italian INFN and INAF; the Swiss National
Fund SNF; Grants No. PID2019-104114RB-C31, No. PID2019-104114RB-C32, No. PID2019-104114RB-C33, No. PID2019–105510 GB-C31, No. PID2019-107847RB-C41, No. PID2019-107847RB-C42, No. PID2019-107847RB-C44, and No. PID2019–107988 GB-C22 funded by MCIN/AEI/10.13039/501100011033; the Indian Department of Atomic Energy; the Japanese ICRR, the University of Tokyo, JSPS, and MEXT; the Bulgarian Ministry of Education and Science, National RI Roadmap Project DO1-400/18.12.2020; and the Academy of Finland Grant No. 320045 are gratefully acknowledged. This work has also been supported by Centros de Excelencia “Severo Ochoa” y Unidades “María de Maeztu” program of the MCIN/AEI/10.13039/501100011033 (SEV-2016-0588,SEV-2017-0709, CEX2019-000920-S, CEX2019-000918-M, MDM-2015-0509-18-2) and by the CERCA institution of the Generalitat de Catalunya; by the Croatian Science Foundation (HrZZ) Project No. IP-2016-06-9782 and the University of Rijeka Project No. uniri-prirod-18-48; by the DFG Collaborative Research Centers SFB1491 and SFB876/C3; the Polish Ministry of Education and Science Grant No. 2021/WK/08; and by the Brazilian MCTIC, CNPq, and FAPERJ. This work was supported by JSPS Grant-in-Aid for JSPS Research Fellow 19J12715. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 754510.With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (SEV-2016-0588).With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (SEV-2017-0709).With funding from the Spanish government through the "Unit of Excellence Maria de Maeztu" accreditation (CEX2019-000918-M).With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000920-S).With funding from the Spanish government through the "Unit of Excellence Maria de Maeztu" accreditation (MDM-2015-0509).Peer reviewe
Cosmic-ray acceleration and escape from supernova remnant W44 as probed by Fermi -LAT and MAGIC
No full textAbe, S. et al.-- Full list of authors:Abe, S.; Abhir, J.; Abhishek, A.; Acciari, V. A.; Aguasca-Cabot, A.; Agudo, I.; Aniello, T.; Ansoldi, S.; Antonelli, L. A.; Arbet Engels, A.; Arcaro, C.; Asano, K.; Babić, A.; Baquero, A.; Barres de Almeida, U.; Barrio, J. A.; Batković, I.; Bautista, A.; Baxter, J.; Becerra González, J.; Bednarek, W.; Bernardini, E.; Bernete, J.; Berti, A.; Besenrieder, J.; Bigongiari, C.; Biland, A.; Blanch, O.; Bonnoli, G.; Bošnjak, Ž.; Bronzini, E.; Burelli, I.; Busetto, G.; Campoy-Ordaz, A.; Carosi, A.; Carosi, R.; Carretero-Castrillo, M.; Castro-Tirado, A. J.; Cerasole, D.; Ceribella, G.; Chai, Y.; Chilingarian, A.; Cifuentes, A.; Colombo, E.; Contreras, J. L.; Cortina, J.; Covino, S.; D'Amico, G.; D'Elia, V.; Da Vela, P.; Dazzi, F.; De Angelis, A.; De Lotto, B.; de Menezes, R.; Del Popolo, A.; Delfino, M.; Delgado, J.; Delgado Mendez, C.; Di Pierro, F.; Dominis Prester, D.; Donini, A.; Dorner, D.; Doro, M.; Elsaesser, D.; Emery, G.; Escudero, J.; Fariña, L.; Fattorini, A.; Foffano, L.; Font, L.; Fröse, S.; Fukazawa, Y.; García López, R. J.; Garczarczyk, M.; Gasparyan, S.; Gaug, M.; Giesbrecht Paiva, J. G.; Giglietto, N.; Gliwny, P.; Godinović, N.; Gozzini, S. R.; Gradetzke, T.; Grau, R.; Green, J. G.; Günther, P.; Hadasch, D.; Hahn, A.; Hassan, T.; Heckmann, L.; Herrera, J.; Hrupec, D.; Hütten, M.; Imazawa, R.; Ishio, K.; Jiménez Martínez, I.; Jormanainen, J.; Kayanoki, T.; Kerszberg, D.; Kluge, G. W.; Kobayashi, Y.; Kouch, P. M.; Kubo, H.; Kushida, J.; Láinez, M.; Lamastra, A.; Leone, F.; Lindfors, E.; Linhoff, L.; Lombardi, S.; Longo, F.; López-Coto, R.; López-Moya, M.; López-Oramas, A.; Loporchio, S.; Lorini, A.; Lyard, E.; Machado de Oliveira Fraga, B.; Majumdar, P.; Makariev, M.; Maneva, G.; Mang, N.; Manganaro, M.; Mangano, S.; Mannheim, K.; Mariotti, M.; Martínez, M.; Martínez-Chicharro, M.; Mas-Aguilar, A.; Mazin, D.; Menchiari, S.; Mender, S.; Miceli, D.; Miener, T.; Miranda, J. M.; Mirzoyan, R.; Molero González, M.; Molina, E.; Mondal, H. A.; Moralejo, A.; Morcuende, D.; Nakamori, T.; Nanci, C.; Nava, L.; Neustroev, V.; Nickel, L.; Nievas Rosillo, M.; Nigro, C.; Nikolić, L.; Nishijima, K.; Njoh Ekoume, T.; Noda, K.; Nozaki, S.; Ohtani, Y.; Okumura, A.; Otero-Santos, J.; Paiano, S.; Palatiello, M.; Paneque, D.; Paoletti, R.; Paredes, J. M.; Peresano, M.; Persic, M.; Pihet, M.; Pirola, G.; Podobnik, F.; Prada Moroni, P. G.; Prandini, E.; Principe, G.; Priyadarshi, C.; Rhode, W.; Ribó, M.; Rico, J.; Righi, C.; Sahakyan, N.; Saito, T.; Satalecka, K.; Saturni, F. G.; Schleicher, B.; Schmidt, K.; Schmuckermaier, F.; Schubert, J. L.; Schweizer, T.; Sciaccaluga, A.; Silvestri, G.; Sitarek, J.; Sliusar, V.; Sobczynska, D.; Spolon, A.; Stamerra, A.; Strišković, J.; Strom, D.; Strzys, M.; Suda, Y.; Suutarinen, S.; Tajima, H.; Takahashi, M.; Takeishi, R.; Temnikov, P.; Terauchi, K.; Terzić, T.; Teshima, M.; Truzzi, S.; Tutone, A.; Ubach, S.; van Scherpenberg, J.; Vazquez Acosta, M.; Ventura, S.; Viale, I.; Vigorito, C. F.; Vitale, V.; Vovk, I.; Walter, R.; Will, M.; Wunderlich, C.; Yamamoto, T.; Di Tria, R.; Di Venere, L.; Giordano, F.; Bissaldi, E.; Green, D.; Morlino, G.Context. The supernova remnant (SNR) W44 and its surroundings are a prime target for studying the acceleration of cosmic rays (CRs). Several previous studies established an extended gamma-ray emission that is set apart from the radio shell of W44. This emission is thought to originate from escaped high-energy CRs that interact with a surrounding dense molecular cloud complex.Aims. We present a detailed analysis of Fermi-LAT data with an emphasis on the spatial and spectral properties of W44 and its surroundings. We also report the results of the observations performed with the MAGIC telescopes of the northwestern region of W44. Finally, we present an interpretation model to explain the gamma-ray emission of the SNR and its surroundings.Methods. We first performed a detailed spatial analysis of 12 years of Fermi-LAT data at energies above 1 GeV, in order to exploit the better angular resolution, while we set a threshold of 100 MeV for the spectral analysis. We performed a likelihood analysis of 174 hours of MAGIC data above 130 GeV using the spatial information obtained with Fermi-LAT. © The Authors 2025Contributions of the authors: R. Di Tria: Fermi-LAT analysis, paper drafting; L. Di Venere: Project coordination, Fermi-LAT analysis, paper drafting; D. Green: Project coordination, CO data analysis, paper drafting; A. Hahn: MAGIC analysis, paper drafting; G. Morlino: theoretical modeling and interpretation, paper drafting; M. Strzys: MAGIC analysis cross-check, paper drafting; E. Bissaldi: paper drafting; S. R. Gozzini, A. López-Oramas: PIs of MAGIC observation campaigns; the rest of the authors have contributed in one or several of the following ways: design, construction, maintenance and operation of the instrument(s) used to acquire the data; preparation and/or evaluation of the observation proposals; data acquisition, processing, calibration and/or reduction; production of analysis tools and/or related Monte Carlo simulations; overall discussions about the contents of the draft, as well as related refinements in the descriptions. The Fermi-LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat ‘a l’Energie Atomique and the Centre National de la Recherche Scientifique /Institut National de Physique Nucleaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d’Etudes Spatiales in France. This work performed in part under DOE Contract DE-AC02-76SF00515. We would like to thank the Instituto de Astrofísica de Canarias for the excellent working conditions at the Observatorio del Roque de los Muchachos in La Palma. The financial support of the German BMBF, MPG and HGF; the Italian INFN and INAF; the Swiss National Fund SNF; the grants PID2019-104114RB-C31, PID2019-104114RB-C32, PID2019-104114RB-C33, PID2019-105510GB-C31, PID2019-107847RB-C41, PID2019-107847RB-C42, PID2019-107847RB-C44, PID2019-107988GB-C22, PID2022-136828NB-C41, PID2022-137810NB-C22, PID2022-138172NB-C41, PID2022-138172NB-C42, PID2022-138172NB-C43, PID2022-139117NB-C41, PID2022-139117NB-C42, PID2022-139117NB-C43, PID2022-139117NB-C44 funded by the Spanish MCIN/AEI/ 10.13039/501100011033 and “ERDF A way of making Europe”; the Indian Department of Atomic Energy; the Japanese ICRR, the University of Tokyo, JSPS, and MEXT; the Bulgarian Ministry of Education and Science, National RI Roadmap Project DO1-400/18.12.2020 and the Academy of Finland grant nr. 320045 is gratefully acknowledged. This work was also been supported by Centros de Excelencia “Severo Ochoa” y Unidades “María de Maeztu” program of the Spanish MCIN/AEI/ 10.13039/501100011033 (CEX2019-000920-S, CEX2019-000918-M, CEX2021-001131-S) and by the CERCA institution and grants 2021SGR00426 and 2021SGR00773 of the Generalitat de Catalunya; by the Croatian Science Foundation (HrZZ) Project IP-2022-10-4595 and the University of Rijeka Project uniri-prirod-18-48; by the Deutsche Forschungsgemeinschaft (SFB1491) and by the Lamarr-Institute for Machine Learning and Artificial Intelligence; by the Polish Ministry Of Education and Science grant No. 2021/WK/08; and by the Brazilian MCTIC, CNPq and FAPERJ. We acknowledge the contributions of J. Krause, V. Stamatescu, and P. Colin during the original observation campaigns.With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000920-S).With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2019-000918-M).With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2021-001131-S).Peer reviewe
Excess estimation in On/Off measurements including single-event variables
No full textSignal estimation in the presence of background noise is a common problem in many scientific disciplines. An “On/Off” measurement is when the background itself is imprecisely measured, which is the case for instance of observations performed in astronomy. We propose a new method for estimating the signal rate based on the Bayesian formalism. It uses information on single-event variables and their distribution for the signal and background population. Events are thereby weighted according to their likelihood of being a signal or a background event and background suppression can be achieved without performing data selection cuts. Simulating “On/Off” measurements from imaging atmospheric Cherenkov observations, we conclude that this new method is capable of increasing the resolution of the signal estimation, in particular for background dominated observations
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