Transition Edge Sensors detector devices, like the core of the X-IFU instrument that will be on-board the Athena X-ray Observatory, produce current pulses as a response to the incident X-ray photons. The reconstruction of these pulses has been traditionally performed by means of a triggering algorithm based on the derivative signal overcoming a threshold (detection) followed by an optimal filtering (to retrieve the energy of each event). However, when the arrival of the photons is very close in time, the triggering algorithm is incapable of detecting all the individual pulses which are thus piled-up. In order to improve the efficiency of the detection and energy-retrieval process, we study here an alternative approach based on Machine Learning techniques to process the pulses. For this purpose, we construct and train a series of Neural Networks (NNs) not only for the detection but also for the recovering of the arrival time and the energy of simulated X-ray pulses. The data set used to train the NNs consists of simulations performed with the sixte/xifusim software package, the Athena/X-IFU official simulator. The performance of our NN classification clearly surpasses the detection performance of the classical triggering approach for the full range of photon energy combinations, showing excellent metrics and very competitive computing efficiency. However, the precision obtained for the recovery of the energy of the photons cannot currently compete with the standard optimal filtering algorithm, despite its much better computing efficiency. ; This paper is supported by European Union's Horizon 2020 research and innovation program under grant agreement No 871158, project AHEAD2020. JP-G and PG acknowledge the project "Machine Learning for the adaptation and improvement of applications" (MALGAMA) under the CSIC Intramural 20152170 program. The authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the ...
Vega-Ferrero, J., et al. ; We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date. ; The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract no. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. ; Peer reviewed
We introduce the THE THREE HUNDRED project, an endeavour to model 324 large galaxy clusters with full-physics hydrodynamical re-simulations. Here we present the data set and study the differences to observations for fundamental galaxy cluster properties and scaling relations.We find that the modelled galaxy clusters are generally in reasonable agreement with observations with respect to baryonic fractions and gas scaling relations at redshift z = 0. However, there are still some (model-dependent) differences, such as central galaxies being too massive, and galaxy colours (g - r) being bluer (about 0.2 dex lower at the peak position) than in observations. The agreement in gas scaling relations down to 1013 h -1 M· between the simulations indicates that particulars of the sub-grid modelling of the baryonic physics only has a weak influence on these relations.We also include - where appropriate - a comparison to three semi-analytical galaxy formation models as applied to the same underlying dark-matter only simulation. All simulations and derived data products are publicly available. ; The work has received financial support from the European Union's Horizon 2020 Research and Innovation programme under the Marie Sklodowskaw-Curie grant agreement number 734374, i.e. the LACEGAL project14. The workshop where this work has been finished was sponsored in part by the Higgs Centre for Theoretical Physics at the University of Edinburgh´ WC, AK, GY and RM are supported by the Ministerio de Economía y Competitividad and the Fondo Europeo de Desarrollo Regional (MINECO/FEDER, UE) in Spain through grant AYA2015-63810-P. WC further thanks TaiLai Cui (崔泰莱) for all the joys. AK is also supported by the Spanish Red Consolider MultiDark FPA2017- 90566-REDC and further thanks Krog for making the days counts. CP acknowledges the Australia Research Council (ARC) Centre of Excellence (CoE) ASTRO 3D through project number CE170100013. PJE is supported by the ARC CoE ASTRO 3D through project number CE170100013. SB acknowledged financial support from PRIN-MIUR grant 2015W7KAWC, the agreement ASI-INAF n.2017-14-H.0, the INFN INDARK grant, the EU H2020 Research and Innovation Programme under the ExaNeSt project (Grant Agreement No. 671553). ER acknowledge the ExaNeSt and Euro Exa projects, funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 671553 and No 754337 and financial contribution from the agreement ASI-INAF n.2017-14-H.0. DS' fellowship is funded by the Spanish Ministry of Economy and Competitiveness (MINECO) under the 2014 Severo Ochoa Predoctoral Training Programme. J.V-F acknowledges the hospitality of the Physics & Astronomy Department at the University of Pennsylvania for hosting him during the preparation of this work. YW is supported by the national science foundation of China (No. 11643005). XY is supported by the National Key Basic Research Program of China (No. 2015CB857002), national science foundation of China (No. 11233005, 11621303). JTA acknowledges support from a postgraduate award from STFC. SAC acknowledges funding from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, PIP-0387), Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT, PICT-2013- 0317), and Universidad Nacional de La Plata (G11-124), Argentina. CVM acknowledges CONICET, Argentina, for their supporting fellowships. ASB, GC and MDP are supported by Sapienza University of Rome-Progetti di Ricerca Anno 2016. ASB also acknowledges funding from Sapienza Universit_a di Roma under minor grant Progetti per Avvio alla Ricerca Anno 2017, prot. AR11715C82402BC7. RC is supported by the MERAC foundation postdoctoral grant awarded to Claudia Lagos and by the Consejo Nacional de Ciencia y Tecnología CONACYT CVU 520137 Scholar 290609 Overseas Scholarship 438594. SE acknowledges fi- nancial contribution from the contracts NARO15 ASI-INAF I/037/12/0, ASI 2015-046-R.0 and ASI-INAF n.2017-14- H.0. SEN is member of the Carrera del Investigador Científico of CONICET. SP is supported by the Fundamental Research Program of Presidium of the RAS #28. JS acknowledges support from the "Centre National d'etudes spatiales" (CNES) postdoctoral fellowship program as well as from the "l'Oreal-UNESCO pour les femmes et la Science" fellowship program
