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Birnick, Johann ; Bläsius, Thomas ; Friedrich, Tobias ; Naumann, Felix ; Papenbrock, Thorsten ; Schirneck, Friedrich Martin
Unique column combinations (UCCs) are a fundamental concept in relational databases. They identify entities in the data and support various data management activities. Still, UCCs are usually not explicitly defined and need to be discovered. State-of-the-art data profiling algorithms are able to efficiently discover UCCs in moderately sized datasets, but they tend to fail on large and, in particular, on wide datasets due to run time and memory limitations. <br /> In this paper, we introduce HPIValid, a novel UCC discovery algorithm that implements a faster and more resource-saving search strategy. HPIValid models the metadata discovery as a hitting set enumeration problem in hypergraphs. In this way, it combines efficient discovery techniques from data profiling research with the most recent theoretical insights into enumeration algorithms. Our evaluation shows that HPIValid is not only orders of magnitude faster than related work, it also has a much smaller memory footprint.
Primary keys (PKs) and foreign keys (FKs) are important elements of relational schemata in various applications, such as query optimization and data integration. However, in many cases, these constraints are unknown or not documented. Detecting them manually is time-consuming and even infeasible in large-scale datasets. We study the problem of discovering primary keys and foreign keys automatically and propose an algorithm to detect both, namely Holistic Primary Key and Foreign Key Detection (HoPF). PKs and FKs are subsets of the sets of unique column combinations (UCCs) and inclusion dependencies (INDs), respectively, for which efficient discovery algorithms are known. Using score functions, our approach is able to effectively extract the true PKs and FKs from the vast sets of valid UCCs and INDs. Several pruning rules are employed to speed up the procedure. We evaluate precision and recall on three benchmarks and two real-world datasets. The results show that our method is able to retrieve on average 88% of all primary keys, and 91% of all foreign keys. We compare the performance of HoPF with two baseline approaches that both assume the existence of primary keys.
How inclusive are we?
(2022)
ACM SIGMOD, VLDB and other database organizations have committed to fostering an inclusive and diverse community, as do many other scientific organizations. Recently, different measures have been taken to advance these goals, especially for underrepresented groups. One possible measure is double-blind reviewing, which aims to hide gender, ethnicity, and other properties of the authors. <br /> We report the preliminary results of a gender diversity analysis of publications of the database community across several peer-reviewed venues, and also compare women's authorship percentages in both single-blind and double-blind venues along the years. We also obtained a cross comparison of the obtained results in data management with other relevant areas in Computer Science.
Introducing the CTA concept
(2013)
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C. ; Rataj, M. ; Raue, M. ; Reardon, P. ; Reimann, O. ; Reimer, A. ; Reimer, O. ; Reitberger, K. ; Renaud, M. ; Renner, S. ; Reville, B. ; Rhode, W. ; Ribo, M. ; Ribordy, M. ; Richer, M. G. ; Rico, J. ; Ridky, J. ; Rieger, F. ; Ringegni, P. ; Ripken, J. ; Ristori, P. R. ; Riviere, A. ; Rivoire, S. ; Rob, L. ; Roeser, U. ; Rohlfs, R. ; Rojas, G. ; Romano, Patrizia ; Romaszkan, W. ; Romero, G. E. ; Rosen, S. ; Lees, S. Rosier ; Ross, D. ; Rouaix, G. ; Rousselle, J. ; Rousselle, S. ; Rovero, A. C. ; Roy, F. ; Royer, S. ; Rudak, B. ; Rulten, C. ; Rupinski, M. ; Russo, F. ; Ryde, F. ; Sacco, B. ; Saemann, E. O. ; Saggion, A. ; Safiakian, V. ; Saito, K. ; Saito, T. ; Saito, Y. ; Sakaki, N. ; Sakonaka, R. ; Salini, A. ; Sanchez, F. ; Sanchez-Conde, M. ; Sandoval, A. ; Sandaker, H. ; Sant'Ambrogio, E. ; Santangelo, Andrea ; Santos, E. M. ; Sanuy, A. ; Sapozhnikov, L. ; Sarkar, S. ; Sartore, N. ; Sasaki, H. ; Satalecka, K. ; Sawada, M. ; Scalzotto, V. ; Scapin, V. ; Scarcioffolo, M. ; Schafer, J. ; Schanz, T. ; Schlenstedt, S. ; Schlickeiser, R. ; Schmidt, T. ; Schmoll, J. ; Schovanek, P. ; Schroedter, M. ; Schultz, C. ; Schultze, J. ; Schulz, A. ; Schure, K. ; Schwab, T. ; Schwanke, U. ; Schwarz, J. ; Schwarzburg, S. ; Schweizer, T. ; Schwemmer, S. ; Segreto, A. ; Seiradakis, J. -H. ; Sembroski, G. H. ; Seweryn, K. ; Sharma, M. ; Shayduk, M. ; Shellard, R. C. ; Shi, J. ; Shibata, T. ; Shibuya, A. ; Shum, E. ; Sidoli, L. ; Sidz, M. ; Sieiro, J. ; Sikora, M. ; Silk, J. ; Sillanpaa, A. ; Singh, B. B. ; Sitarek, J. ; Skole, C. ; Smareglia, R. ; Smith, A. ; Smith, D. ; Smith, J. ; Smith, N. ; Sobczynska, D. ; Sol, H. ; Sottile, G. ; Sowinski, M. ; Spanier, F. ; Spiga, D. ; Spyrou, S. ; Stamatescu, V. ; Stamerra, A. ; Starling, R. ; Stawarz, L. ; Steenkamp, R. ; Stegmann, Christian ; Steiner, S. ; Stergioulas, N. ; Sternberger, R. ; Sterzel, M. ; Stinzing, F. ; Stodulski, M. ; Straumann, U. ; Strazzeri, E. ; Stringhetti, L. ; Suarez, A. ; Suchenek, M. ; Sugawara, R. ; Sulanke, K. -H. ; Sun, S. ; Supanitsky, A. D. ; Suric, T. ; Sutcliffe, P. ; Sykes, J. ; Szanecki, M. ; Szepieniec, T. ; Szostek, A. ; Tagliaferri, G. ; Tajima, H. ; Takahashi, H. ; Takahashi, K. ; Takalo, L. ; Takami, H. ; Talbot, C. ; Tammi, J. ; Tanaka, M. ; Tanaka, S. ; Tasan, J. ; Tavani, M. ; Tavernet, J. -P. ; Tejedor, L. A. ; Telezhinsky, Igor O. ; Temnikov, P. ; Tenzer, C. ; Terada, Y. ; Terrier, R. ; Teshima, M. ; Testa, V. ; Tezier, D. ; Thuermann, D. ; Tibaldo, L. ; Tibolla, O. ; Tiengo, A. ; Tluczykont, M. ; Todero Peixoto, C. J. ; Tokanai, F. ; Tokarz, M. ; Toma, K. ; Torii, K. ; Tornikoski, M. ; Torres, D. F. ; Torres, M. ; Tosti, G. ; Totani, T. ; Toussenel, C. ; Tovmassian, G. ; Travnicek, P. ; Trifoglio, M. ; Troyano, I. ; Tsinganos, K. ; Ueno, H. ; Umehara, K. ; Upadhya, S. S. ; Usher, T. ; Uslenghi, M. ; Valdes-Galicia, J. F. ; Vallania, P. ; Vallejo, G. ; van Driel, W. ; van Eldik, C. ; Vandenbrouke, J. ; Vanderwalt, J. ; Vankov, H. ; Vasileiadis, G. ; Vassiliev, V. ; Veberic, D. ; Vegas, I. ; Vercellone, S. ; Vergani, S. ; Veyssiere, C. ; Vialle, J. P. ; Viana, A. ; Videla, M. ; Vincent, P. ; Vincent, S. ; Vink, J. ; Vlahakis, N. ; Vlahos, L. ; Vogler, P. ; Vollhardt, A. ; von Gunten, H. P. ; Vorobiov, S. ; Vuerli, C. ; Waegebaert, V. ; Wagner, R. ; Wagner, R. G. ; Wagner, S. ; Wakely, S. P. ; Walter, R. ; Walther, T. ; Warda, K. ; Warwick, R. ; Wawer, P. ; Wawrzaszek, R. ; Webb, N. ; Wegner, P. ; Weinstein, A. ; Weitzel, Q. ; Welsing, R. ; Werner, M. ; Wetteskind, H. ; White, R. ; Wierzcholska, A. ; Wiesand, S. ; Wilkinson, M. ; Williams, D. A. ; Willingale, R. ; Winiarski, K. ; Wischnewski, R. ; Wisniewski, L. ; Wood, M. ; Woernlein, A. ; Xiong, Q. ; Yadav, K. K. ; Yamamoto, H. ; Yamamoto, T. ; Yamazaki, R. ; Yanagita, S. ; Yebras, J. M. ; Yelos, D. ; Yoshida, A. ; Yoshida, T. ; Yoshikoshi, T. ; Zabalza, V. ; Zacharias, M. ; Zajczyk, A. ; Zanin, R. ; Zdziarski, A. ; Zech, Alraune ; Zhao, A. ; Zhou, X. ; Zietara, K. ; Ziolkowski, J. ; Ziolkowski, P. ; Zitelli, V. ; Zurbach, C. ; Zychowski, P.
The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project.
The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity-duplicates-into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. <br /> We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.
Aliu, E. ; Archambault, S. ; Aune, T. ; Behera, B. ; Beilicke, M. ; Benbow, W. ; Berger, K. ; Bird, R. ; Bouvier, A. ; Buckley, J. H. ; Bugaev, V. ; Byrum, K. ; Cerruti, M. ; Chen, X. ; Ciupik, L. ; Connolly, M. P. ; Cui, W. ; Duke, C. ; Dumm, J. ; Errando, M. ; Falcone, A. ; Federici, S. ; Feng, Q. ; Finley, J. P. ; Fortin, P. ; Fortson, L. ; Furniss, A. ; Galante, N. ; Gillanders, G. H. ; Griffin, S. ; Griffiths, S. T. ; Grube, J. ; Gyuk, G. ; Hanna, D. ; Holder, J. ; Hughes, G. ; Humensky, T. B. ; Kaaret, P. ; Kertzman, M. ; Khassen, Y. ; Kieda, D. ; Krawczynski, H. ; Krennrich, F. ; Lang, M. J. ; Madhavan, A. S. ; Maier, G. ; Majumdar, P. ; McCann, A. ; Moriarty, P. ; Mukherjee, R. ; Nieto, D. ; Ong, R. A. ; Otte, A. N. ; Park, N. ; Perkins, J. S. ; Pohl, M. ; Popkow, A. ; Prokoph, H. ; Quinn, J. ; Ragan, K. ; Rajotte, J. ; Reyes, L. C. ; Reynolds, P. T. ; Richards, G. T. ; Roache, E. ; Rousselle, J. ; Sembroski, G. H. ; Sheidaei, F. ; Skole, C. ; Smith, A. W. ; Staszak, D. ; Stroh, M. ; Telezhinsky, Igor O. ; Theiling, M. ; Tucci, J. V. ; Tyler, J. ; Varlotta, A. ; Vincent, S. ; Wakely, S. P. ; Weinstein, A. ; Welsing, R. ; Williams, D. A. ; Zajczyk, A. ; Zitzer, B. ; Abramowski, Attila ; Aharonian, Felix A. ; Benkhali, Faical Ait ; Akhperjanian, A. G. ; Angüner, Ekrem Oǧuzhan ; Anton, Gisela ; Balenderan, Shangkari ; Balzer, Arnim ; Barnacka, Anna ; Becherini, Yvonne ; Tjus, J. Becker ; Bernlöhr, K. ; Birsin, E. ; Bissaldi, E. ; Biteau, Jonathan ; Boettcher, Markus ; Boisson, Catherine ; Bolmont, J. ; Bordas, Pol ; Brucker, J. ; Brun, Francois ; Brun, Pierre ; Bulik, Tomasz ; Carrigan, Svenja ; Casanova, Sabrina ; Cerruti, M. ; Chadwick, Paula M. ; Chalme-Calvet, R. ; Chaves, Ryan C. G. ; Cheesebrough, A. ; Chretien, M. ; Colafrancesco, Sergio ; Cologna, Gabriele ; Conrad, Jan ; Couturier, C. ; Dalton, M. ; Daniel, M. K. ; Davids, I. D. ; Degrange, B. ; Deil, C. ; deWilt, P. ; Dickinson, H. J. ; Djannati-Ataï, A. ; Domainko, W. ; Dubus, G. ; Dutson, K. ; Dyks, J. ; Dyrda, M. ; Edwards, T. ; Egberts, Kathrin ; Eger, P. ; Espigat, P. ; Farnier, C. ; Fegan, S. ; Feinstein, F. ; Fernandes, M. V. ; Fernandez, D. ; Fiasson, A. ; Fontaine, G. ; Foerster, A. ; Fuessling, M. ; Gajdus, M. ; Gallant, Y. A. ; Garrigoux, T. ; Giavitto, G. ; Giebels, B. ; Glicenstein, J. F. ; Grondin, M. -H. ; Grudzinska, M. ; Haeffner, S. ; Hahn, J. ; Harris, J. ; Heinzelmann, G. ; Henri, G. ; Hermann, G. ; Hervet, O. ; Hillert, A. ; Hinton, James Anthony ; Hofmann, W. ; Hofverberg, P. ; Holler, M. ; Horns, D. ; Jacholkowska, A. ; Jahn, C. ; Jamrozy, M. ; Janiak, M. ; Jankowsky, F. ; Jung, I. ; Kastendieck, M. A. ; Katarzynski, K. ; Katz, U. ; Kaufmann, S. ; Khelifi, B. ; Kieffer, M. ; Klepser, S. ; Klochkov, D. ; Kluzniak, W. ; Kneiske, T. ; Kolitzus, D. ; Komin, Nu. ; Kosack, K. ; Krakau, S. ; Krayzel, F. ; Krueger, P. P. ; Laffon, H. ; Lamanna, G. ; Lefaucheur, J. ; Lemiere, A. ; Lemoine-Goumard, M. ; Lenain, J. -P. ; Lennarz, D. ; Lohse, T. ; Lopatin, A. ; Lu, C. -C. ; Marandon, V. ; Marcowith, Alexandre ; Marx, R. ; Maurin, G. ; Maxted, N. ; Mayer, M. ; McComb, T. J. L. ; Mehault, J. ; Menzler, U. ; Meyer, M. ; Moderski, R. ; Mohamed, M. ; Moulin, Emmanuel ; Murach, T. ; Naumann, C. L. ; de Naurois, M. ; Niemiec, J. ; Nolan, S. J. ; Oakes, L. ; Ohm, S. ; Wilhelmi, E. de Ona ; Opitz, B. ; Ostrowski, M. ; Oya, I. ; Panter, M. ; Parsons, R. D. ; Arribas, M. Paz ; Pekeur, N. W. ; Pelletier, G. ; Perez, J. ; Petrucci, P. -O. ; Peyaud, B. ; Pita, S. ; Poon, H. ; Puehlhofer, G. ; Punch, M. ; Quirrenbach, A. ; Raab, S. ; Raue, M. ; Reimer, A. ; Reimer, O. ; Renaud, M. ; de los Reyes, R. ; Rieger, F. ; Rob, L. ; Romoli, C. ; Rosier-Lees, S. ; Rowell, G. ; Rudak, B. ; Rulten, C. B. ; Sahakian, V. ; Sanchez, David M. ; Santangelo, Andrea ; Schlickeiser, R. ; Schuessler, F. ; Schulz, A. ; Schwanke, U. ; Schwarzburg, S. ; Schwemmer, S. ; Sol, H. ; Spengler, G. ; Spies, F. ; Stawarz, L. ; Steenkamp, R. ; Stegmann, Christian ; Stinzing, F. ; Stycz, K. ; Sushch, Iurii ; Szostek, A. ; Tavernet, J. -P. ; Tavernier, T. ; Taylor, A. M. ; Terrier, R. ; Tluczykont, M. ; Trichard, C. ; Valerius, K. ; van Eldik, C. ; Vasileiadis, G. ; Venter, C. ; Viana, A. ; Vincent, P. ; Voelk, H. J. ; Volpe, F. ; Vorster, M. ; Wagner, S. J. ; Wagner, P. ; Ward, M. ; Weidinger, M. ; Weitzel, Q. ; White, R. ; Wierzcholska, A. ; Willmann, P. ; Woernlein, A. ; Wouters, D. ; Zacharias, M. ; Zajczyk, A. ; Zdziarski, A. A. ; Zech, Alraune ; Zechlin, H. -S.
MDedup
(2020)
Duplicate detection is an integral part of data cleaning and serves to identify multiple representations of same real-world entities in (relational) datasets. Existing duplicate detection approaches are effective, but they are also hard to parameterize or require a lot of pre-labeled training data. Both parameterization and pre-labeling are at least domain-specific if not dataset-specific, which is a problem if a new dataset needs to be cleaned.
For this reason, we propose a novel, rule-based and fully automatic duplicate detection approach that is based on matching dependencies (MDs). Our system uses automatically discovered MDs, various dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules. Once trained, the model can select useful MDs for duplicate detection on any new dataset. To increase the generally low recall of MD-based data cleaning approaches, we propose an additional boosting step. Our experiments show that this approach reaches up to 94% F-measure and 100% precision on our evaluation datasets, which are good numbers considering that the system does not require domain or target data-specific configuration.
Kruse, Sebastian ; Kaoudi, Zoi ; Quiane-Ruiz, Jorge-Arnulfo ; Chawla, Sanjay ; Naumann, Felix ; Contreras-Rojas, Bertty
Data analytics are moving beyond the limits of a single data processing platform. A cross-platform query optimizer is necessary to enable applications to run their tasks over multiple platforms efficiently and in a platform-agnostic manner. For the optimizer to be effective, it must consider data movement costs across different data processing platforms. In this paper, we present the graph-based data movement strategy used by RHEEM, our open-source cross-platform system. In particular, we (i) model the data movement problem as a new graph problem, which we prove to be NP-hard, and (ii) propose a novel graph exploration algorithm, which allows RHEEM to discover multiple hidden opportunities for cross-platform data processing.
Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is CSV. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard CSV formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic lpollutionz process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.
Abramowski, Attila ; Aharonian, Felix A. ; Benkhali, Faical Ait ; Akhperjanian, A. G. ; Angüner, Ekrem Oǧuzhan ; Anton, Gisela ; Backes, Michael ; Balenderan, Shangkari ; Balzer, Arnim ; Barnacka, Anna ; Becherini, Yvonne ; Tjus, J. Becker ; Bernlöhr, K. ; Birsin, E. ; Bissaldi, E. ; Biteau, Jonathan ; Boettcher, Markus ; Boisson, Catherine ; Bolmont, J. ; Bordas, Pol ; Brucker, J. ; Brun, Francois ; Brun, Pierre ; Bulik, Tomasz ; Carrigan, Svenja ; Casanova, Sabrina ; Chadwick, Paula M. ; Chalme-Calvet, R. ; Chaves, Ryan C. G. ; Cheesebrough, A. ; Chretien, M. ; Colafrancesco, Sergio ; Cologna, Gabriele ; Conrad, Jan ; Couturier, C. ; Cui, Y. ; Dalton, M. ; Daniel, M. K. ; Davids, I. D. ; Degrange, B. ; Deil, C. ; deWilt, P. ; Dickinson, H. J. ; Djannati-Ataï, A. ; Domainko, W. ; Dubus, G. ; Dutson, K. ; Dyks, J. ; Dyrda, M. ; Edwards, T. ; Egberts, Kathrin ; Eger, P. ; Espigat, P. ; Farnier, C. ; Fegan, S. ; Feinstein, F. ; Fernandes, M. V. ; Fernandez, D. ; Fiasson, A. ; Fontaine, G. ; Foerster, A. ; Füßling, Matthias ; Gajdus, M. ; Gallant, Y. A. ; Garrigoux, T. ; Giavitto, G. ; Giebels, B. ; Glicenstein, J. F. ; Grondin, M. -H. ; Grudzinska, M. ; Haeffner, S. ; Hahn, J. ; Harris, J. ; Heinzelmann, G. ; Henri, G. ; Hermann, G. ; Hervet, O. ; Hillert, A. ; Hinton, James Anthony ; Hofmann, W. ; Hofverberg, P. ; Holler, Markus ; Horns, D. ; Jacholkowska, A. ; Jahn, C. ; Jamrozy, M. ; Janiak, M. ; Jankowsky, F. ; Jung, I. ; Kastendieck, M. A. ; Katarzynski, K. ; Katz, U. ; Kaufmann, S. ; Khelifi, B. ; Kieffer, M. ; Klepser, S. ; Klochkov, D. ; Kluzniak, W. ; Kneiske, T. ; Kolitzus, D. ; Komin, Nu. ; Kosack, K. ; Krakau, S. ; Krayzel, F. ; Krueger, P. P. ; Laffon, H. ; Lamanna, G. ; Lefaucheur, J. ; Lemiere, A. ; Lemoine-Goumard, M. ; Lenain, J. -P. ; Lohse, T. ; Lopatin, A. ; Lu, C. -C. ; Marandon, V. ; Marcowith, Alexandre ; Marx, R. ; Maurin, G. ; Maxted, N. ; Mayer, Markus ; McComb, T. J. L. ; Mehault, J. ; Meintjes, P. J. ; Menzler, U. ; Meyer, M. ; Moderski, R. ; Mohamed, M. ; Moulin, Emmanuel ; Murach, T. ; Naumann, C. L. ; de Naurois, M. ; Niemiec, J. ; Nolan, S. J. ; Oakes, L. ; Odaka, H. ; Ohm, S. ; Wilhelmi, E. de Ona ; Opitz, B. ; Ostrowski, M. ; Oya, I. ; Panter, M. ; Parsons, R. D. ; Arribas, M. Paz ; Pekeur, N. W. ; Pelletier, G. ; Perez, J. ; Petrucci, P. -O. ; Peyaud, B. ; Pita, S. ; Poon, H. ; Puehlhofer, G. ; Punch, M. ; Quirrenbach, A. ; Raab, S. ; Raue, M. ; Reichardt, I. ; Reimer, A. ; Reimer, O. ; Renaud, M. ; Reyes, R. de los ; Rieger, F. ; Rob, L. ; Romoli, C. ; Rosier-Lees, S. ; Rowell, G. ; Rudak, B. ; Rulten, C. B. ; Sahakian, V. ; Sanchez, David M. ; Santangelo, Andrea ; Schlickeiser, R. ; Schuessler, F. ; Schulz, A. ; Schwanke, U. ; Schwarzburg, S. ; Schwemmer, S. ; Sol, H. ; Spengler, G. ; Spies, F. ; Stawarz, L. ; Steenkamp, R. ; Stegmann, Christian ; Stinzing, F. ; Stycz, K. ; Sushch, Iurii ; Tavernet, J. -P. ; Tavernier, T. ; Taylor, A. M. ; Terrier, R. ; Tluczykont, M. ; Trichard, C. ; Valerius, K. ; van Eldik, C. ; van Soelen, B. ; Vasileiadis, G. ; Venter, C. ; Viana, A. ; Vincent, P. ; Voelk, H. J. ; Volpe, F. ; Vorster, M. ; Vuillaume, T. ; Wagner, S. J. ; Wagner, P. ; Wagner, R. M. ; Ward, M. ; Weidinger, M. ; Weitzel, Q. ; White, R. ; Wierzcholska, A. ; Willmann, P. ; Woernlein, A. ; Wouters, D. ; Yang, R. ; Zabalza, V. ; Zacharias, M. ; Zdziarski, A. A. ; Zech, Alraune ; Zechlin, H. -S. ; Acero, F. ; Casandjian, J. M. ; Cohen-Tanugi, J. ; Giordano, F. ; Guillemot, L. ; Lande, J. ; Pletsch, H. ; Uchiyama, Y.
Aims. Previous observations with the High Energy Stereoscopic System (H.E.S.S.) have revealed an extended very-high-energy (VHE; E > 100 GeV) gamma-ray source, HESS J1834-087, coincident with the supernova remnant (SNR) W41. The origin of the gamma-ray emission was investigated in more detail with the H.E.S.S. array and the Large Area Telescope (LAT) onboard the Fermi Gamma-ray Space Telescope.
Methods. The gamma-ray data provided by 61 h of observations with H.E.S.S., and four years with the Fermi LAT were analyzed, covering over five decades in energy from 1.8 GeV up to 30 TeV. The morphology and spectrum of the TeV and GeV sources were studied and multiwavelength data were used to investigate the origin of the gamma-ray emission toward W41.
Results. The TeV source can be modeled with a sum of two components: one point-like and one significantly extended (sigma(TeV) = 0.17 degrees +/- 0.01 degrees), both centered on SNR W41 and exhibiting spectra described by a power law with index Gamma(TeV) similar or equal to 2.6. The GeV source detected with Fermi LAT is extended (sigma(GeV) = 0.15 degrees +/- 0.03 degrees) and morphologically matches the VHE emission. Its spectrum can be described by a power-law model with an index Gamma(GeV) = 2.15 +/- 0.12 and smoothly joins the spectrum of the whole TeV source. A break appears in the gamma-ray spectra around 100 GeV. No pulsations were found in the GeV range.
Conclusions. Two main scenarios are proposed to explain the observed emission: a pulsar wind nebula (PWN) or the interaction of SNR W41 with an associated molecular cloud. X-ray observations suggest the presence of a point-like source (a pulsar candidate) near the center of the remnant and nonthermal X-ray diffuse emission that could arise from the possibly associated PWN. The PWN scenario is supported by the compatible positions of the TeV and GeV sources with the putative pulsar. However, the spectral energy distribution from radio to gamma-rays is reproduced by a one-zone leptonic model only if an excess of low-energy electrons is injected following a Maxwellian distribution by a pulsar with a high spin-down power (> 10(37) erg s(-1)). This additional low-energy component is not needed if we consider that the point-like TeV source is unrelated to the extended GeV and TeV sources. The interacting SNR scenario is supported by the spatial coincidence between the gamma-ray sources, the detection of OH (1720 MHz) maser lines, and the hadronic modeling.