TY  - JOUR
A1  - Abdalla, Hassan E.
A1  - Abramowski, Attila
A1  - Aharonian, Felix A.
A1  - Benkhali, Faiçal Ait
A1  - Akhperjanian, A. G.
A1  - Angüner, Ekrem Oǧuzhan
A1  - Arrieta, M.
A1  - Aubert, Pierre
A1  - Backes, Michael
A1  - Balzer, Arnim
A1  - Barnard, Michelle
A1  - Becherini, Yvonne
A1  - Tjus, Julia Becker
A1  - Berge, David
A1  - Bernhard, Sabrina
A1  - Bernlöhr, K.
A1  - Birsin, E.
A1  - Blackwell, R.
A1  - Bottcher, Markus
A1  - Boisson, Catherine
A1  - Bolmont, J.
A1  - Bordas, Pol
A1  - Bregeon, Johan
A1  - Brun, Francois
A1  - Brun, Pierre
A1  - Bryan, Mark
A1  - Bulik, Tomasz
A1  - Capasso, M.
A1  - Carr, John
A1  - Casanova, Sabrina
A1  - Chakraborty, N.
A1  - Chalme-Calvet, R.
A1  - Chaves, Ryan C. G.
A1  - Chen, Andrew
A1  - Chevalier, J.
A1  - Chretien, M.
A1  - Colafrancesco, Sergio
A1  - Cologna, Gabriele
A1  - Condon, B.
A1  - Conrad, Jan
A1  - Couturier, C.
A1  - Cui, Y.
A1  - Davids, I. D.
A1  - Degrange, B.
A1  - Deil, Christoph
A1  - deWilt, P.
A1  - Djannati-Atai, Arache
A1  - Domainko, Wilfried
A1  - Donath, Axel
A1  - Dubus, Guillaume
A1  - Dutson, Kate
A1  - Dyks, J.
A1  - Dyrda, M.
A1  - Edwards, T.
A1  - Egberts, Kathrin
A1  - Eger, P.
A1  - Ernenwein, J. -P.
A1  - Eschbach, S.
A1  - Farnier, C.
A1  - Fegan, Stuart
A1  - Fernandes, M. V.
A1  - Fiasson, A.
A1  - Fontaine, G.
A1  - Foerster, A.
A1  - Funk, S.
A1  - Füßling, Matthias
A1  - Gabici, Stefano
A1  - Gajdus, M.
A1  - Gallant, Y. A.
A1  - Garrigoux, T.
A1  - Giavitto, Gianluca
A1  - Giebels, B.
A1  - Glicenstein, J. F.
A1  - Gottschall, Daniel
A1  - Goyal, A.
A1  - Grondin, M. -H.
A1  - Grudzinska, M.
A1  - Hadasch, Daniela
A1  - Hahn, J.
A1  - Hawkes, J.
A1  - Heinzelmann, G.
A1  - Henri, Gilles
A1  - Hermann, G.
A1  - Hervet, Olivier
A1  - Hillert, A.
A1  - Hinton, James Anthony
A1  - Hofmann, Werner
A1  - Hoischen, Clemens
A1  - Holler, M.
A1  - Horns, D.
A1  - Ivascenko, Alex
A1  - Jacholkowska, A.
A1  - Jamrozy, Marek
A1  - Janiak, M.
A1  - Jankowsky, D.
A1  - Jankowsky, Felix
A1  - Jingo, M.
A1  - Jogler, Tobias
A1  - Jouvin, Lea
A1  - Jung-Richardt, Ira
A1  - Kastendieck, M. A.
A1  - Katarzynski, Krzysztof
A1  - Katz, Uli
A1  - Kerszberg, D.
A1  - Khelifi, B.
A1  - Kieffer, M.
A1  - King, J.
A1  - Klepser, S.
A1  - Klochkov, Dmitry
A1  - Kluzniak, W.
A1  - Kolitzus, D.
A1  - Komin, Nu.
A1  - Kosack, K.
A1  - Krakau, S.
A1  - Kraus, Michael
A1  - Krayzel, F.
A1  - Kruger, P. P.
A1  - Laffon, H.
A1  - Lamanna, G.
A1  - Lau, Jeanie
A1  - Lees, J. -P.
A1  - Lefaucheur, J.
A1  - Lefranc, V.
A1  - Lemiere, A.
A1  - Lemoine-Goumard, M.
A1  - Lenain, J. -P.
A1  - Leser, Eva
A1  - Lohse, Thomas
A1  - Lorentz, M.
A1  - Lui, R.
A1  - Lypova, Iryna
A1  - Marandon, Vincent
A1  - Marcowith, Alexandre
A1  - Mariaud, C.
A1  - Marx, R.
A1  - Maurin, G.
A1  - Maxted, N.
A1  - Mayer, Michael
A1  - Meintjes, Petrus Johannes
A1  - Menzler, U.
A1  - Meyer, Manuel
A1  - Mitchell, A. M. W.
A1  - Moderski, R.
A1  - Mohamed, M.
A1  - Mora, K.
A1  - Moulin, Emmanuel
A1  - Murach, T.
A1  - de Naurois, Mathieu
A1  - Niederwanger, F.
A1  - Niemiec, J.
A1  - Oakes, L.
A1  - Odaka, Hirokazu
A1  - Ohm, Stefan
A1  - Oettl, S.
A1  - Ostrowski, M.
A1  - Oya, I.
A1  - Padovani, Marco
A1  - Panter, M.
A1  - Parsons, R. D.
A1  - Arribas, M. Paz
A1  - Pekeur, N. W.
A1  - Pelletier, G.
A1  - Petrucci, P. -O.
A1  - Peyaud, B.
A1  - Pita, S.
A1  - Poon, Helen
A1  - Prokhorov, Dmitry
A1  - Prokoph, Heike
A1  - Puehlhofer, Gerd
A1  - Punch, Michael
A1  - Quirrenbach, Andreas
A1  - Raab, S.
A1  - Reimer, Anita
A1  - Reimer, Olaf
A1  - Renaud, M.
A1  - de los Reyes, R.
A1  - Rieger, Frank
A1  - Romoli, Carlo
A1  - Rosier-Lees, S.
A1  - Rowell, G.
A1  - Rudak, B.
A1  - Rulten, C. B.
A1  - Sahakian, V.
A1  - Salek, David
A1  - Sanchez, David A.
A1  - Santangelo, Andrea
A1  - Sasaki, Manami
A1  - Schlickeiser, Reinhard
A1  - Schussler, F.
A1  - Schulz, Andreas
A1  - Schwanke, U.
A1  - Schwemmer, S.
A1  - Seyffert, A. S.
A1  - Shafi, N.
A1  - Simoni, R.
A1  - Sol, H.
A1  - Spanier, Felix
A1  - Spengler, G.
A1  - Spiess, F.
A1  - Stawarz, Lukasz
A1  - Steenkamp, R.
A1  - Stegmann, Christian
A1  - Stinzing, F.
A1  - Stycz, K.
A1  - Sushch, Iurii
A1  - Tavernet, J. -P.
A1  - Tavernier, T.
A1  - Taylor, A. M.
A1  - Terrier, R.
A1  - Tluczykont, Martin
A1  - Trichard, C.
A1  - Tuffs, R.
A1  - van der Walt, Johan
A1  - van Eldik, Christopher
A1  - van Soelen, Brian
A1  - Vasileiadis, Georges
A1  - Veh, J.
A1  - Venter, C.
A1  - Viana, A.
A1  - Vincent, P.
A1  - Vink, Jacco
A1  - Voisin, F.
A1  - Voelk, Heinrich J.
A1  - Vuillaume, Thomas
A1  - Wadiasingh, Z.
A1  - Wagner, Stefan J.
A1  - Wagner, P.
A1  - Wagner, R. M.
A1  - White, R.
A1  - Wierzcholska, Alicja
A1  - Willmann, P.
A1  - Woernlein, A.
A1  - Wouters, Denis
A1  - Yang, R.
A1  - Zabalza, Victor
A1  - Zaborov, D.
A1  - Zacharias, M.
A1  - Zdziarski, A. A.
A1  - Zech, Andreas
A1  - Zefi, F.
A1  - Ziegler, A.
A1  - Zywucka, Natalia
T1  - Search for Dark Matter Annihilations towards the Inner Galactic Halo from 10 Years of Observations with HESS
JF  - Physical review letters
N2  - The inner region of the Milky Way halo harbors a large amount of dark matter (DM). Given its proximity, it is one of the most promising targets to look for DM. We report on a search for the annihilations of DM particles using gamma-ray observations towards the inner 300 pc of the Milky Way, with the H.E.S.S. array of ground-based Cherenkov telescopes. The analysis is based on a 2D maximum likelihood method using Galactic Center (GC) data accumulated by H.E.S.S. over the last 10 years (2004-2014), and does not show any significant gamma-ray signal above background. Assuming Einasto and Navarro-Frenk-White DM density profiles at the GC, we derive upper limits on the annihilation cross section <sigma nu >. These constraints are the strongest obtained so far in the TeV DM mass range and improve upon previous limits by a factor 5. For the Einasto profile, the constraints reach <sigma nu > values of 6 x 10(-26) cm(3) s(-1) in the W+W- channel for a DM particle mass of 1.5 TeV, and 2 x 10(-26) cm(3) s(-1) in the tau(+)tau(-) channel for a 1 TeV mass. For the first time, ground-based gamma-ray observations have reached sufficient sensitivity to probe <sigma nu > values expected from the thermal relic density for TeV DM particles.
Y1  - 2016
U6  - https://doi.org/10.1103/PhysRevLett.117.111301
SN  - 0031-9007
SN  - 1079-7114
VL  - 117
PB  - American Physical Society
CY  - College Park
ER  - 
TY  - JOUR
A1  - Huang, Daniel Zhengyu
A1  - Huang, Jiaoyang
A1  - Reich, Sebastian
A1  - Stuart, Andrew M.
T1  - Efficient derivative-free Bayesian inference for large-scale inverse problems
JF  - Inverse problems : an international journal of inverse problems, inverse methods and computerised inversion of data
N2  - We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need for repeated evaluations of an expensive forward model. 
This renders most Markov chain Monte Carlo approaches infeasible, since they typically require O(10(4)) model runs, or more. 
Moreover, the forward model is often given as a black box or is impractical to differentiate. 
Therefore derivative-free algorithms are highly desirable. We propose a framework, which is built on Kalman methodology, to efficiently perform Bayesian inference in such inverse problems. 
The basic method is based on an approximation of the filtering distribution of a novel mean-field dynamical system, into which the inverse problem is embedded as an observation operator. 
Theoretical properties are established for linear inverse problems, demonstrating that the desired Bayesian posterior is given by the steady state of the law of the filtering distribution of the mean-field dynamical system, and proving exponential convergence to it. 
This suggests that, for nonlinear problems which are close to Gaussian, sequentially computing this law provides the basis for efficient iterative methods to approximate the Bayesian posterior. 
Ensemble methods are applied to obtain interacting particle system approximations of the filtering distribution of the mean-field model; and practical strategies to further reduce the computational and memory cost of the methodology are presented, including low-rank approximation and a bi-fidelity approach. 
The effectiveness of the framework is demonstrated in several numerical experiments, including proof-of-concept linear/nonlinear examples and two large-scale applications: learning of permeability parameters in subsurface flow; and learning subgrid-scale parameters in a global climate model. 

Moreover, the stochastic ensemble Kalman filter and various ensemble square-root Kalman filters are all employed and are compared numerically. 
The results demonstrate that the proposed method, based on exponential convergence to the filtering distribution of a mean-field dynamical system, is competitive with pre-existing Kalman-based methods for inverse problems.
KW  - inverse problem
KW  - uncertainty quantification
KW  - Bayesian inference
KW  - derivative-free optimization
KW  - mean-field dynamical system
KW  - interacting particle system
KW  - ensemble Kalman filter
Y1  - 2022
U6  - https://doi.org/10.1088/1361-6420/ac99fa
SN  - 0266-5611
SN  - 1361-6420
VL  - 38
IS  - 12
PB  - IOP Publ. Ltd.
CY  - Bristol
ER  -