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Author Stoppa, F.; Bhattacharyya, S.; Ruiz de Austri, R.; Vreeswijk, P.; Caron, S.; Zaharijas, G.; Bloemen, S.; Principe, G.; Malyshev, D.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G.
Title (up) AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information Type Journal Article
Year 2023 Publication Astronomy & Astrophysics Abbreviated Journal Astron. Astrophys.
Volume 680 Issue Pages A109 - 16pp
Keywords methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics
Abstract Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Address [Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl
Corporate Author Thesis
Publisher Edp Sciences S A Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6361 ISBN Medium
Area Expedition Conference
Notes WOS:001131898100001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5888
Permanent link to this record
 
 
Author Stoppa, F.; Ruiz de Austri, R.; Vreeswijk, P.; Bhattacharyya, S.; Caron, S.; Bloemen, S.; Zaharijas, G.; Principe, G.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G.
Title (up) AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation Type Journal Article
Year 2023 Publication Astronomy & Astrophysics Abbreviated Journal Astron. Astrophys.
Volume 680 Issue Pages A108 - 14pp
Keywords astronomical databases: miscellaneous; methods: data analysis; stars: imaging; techniques: image processing
Abstract Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
Address [Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl
Corporate Author Thesis
Publisher Edp Sciences S A Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6361 ISBN Medium
Area Expedition Conference
Notes WOS:001131898100003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5887
Permanent link to this record
 
 
Author Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Bhattacharyya, S.; Caron, S.; Johannesson, G.; Ruiz de Austri, R.; van den Oetelaar, C.; Zaharijas, G.; Groot, P.J.; Cator, E.; Nelemans, G.
Title (up) AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian Type Journal Article
Year 2022 Publication Astronomy & Astrophysics Abbreviated Journal Astron. Astrophys.
Volume 662 Issue Pages A109 - 8pp
Keywords astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing
Abstract Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
Address [Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl
Corporate Author Thesis
Publisher Edp Sciences S A Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-6361 ISBN Medium
Area Expedition Conference
Notes WOS:000818665600009 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5291
Permanent link to this record
 
 
Author Hall, O. et al; Agramunt, J.; Algora, A.; Domingo-Pardo, C.; Morales, A.I.; Rubio, B.; Tain, J.L.; Tolosa-Delgado, A.
Title (up) beta-delayed neutron emission of r-process nuclei at the N=82 shell closure Type Journal Article
Year 2021 Publication Physics Letters B Abbreviated Journal Phys. Lett. B
Volume 816 Issue Pages 136266 - 7pp
Keywords beta-delayed neutron emission; r-processimportant
Abstract Theoretical models of beta-delayed neutron emission are used as crucial inputs in r-process calculations. Benchmarking the predictions of these models is a challenge due to a lack of currently available experimental data. In this work the beta-delayed neutron emission probabilities of 33 nuclides in the important mass regions south and south-west of Sn-132 are presented, 16 for the first time. The measurements were performed at RIKEN using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The P-1n values presented constrain the predictions of theoretical models in the region, affecting the final abundance distribution of the second r-process peak at A approximate to 130.
Address [Hall, O.; Davinson, T.; Bruno, C. G.; Griffin, C. J.; Kahl, D.] Univ Edinburgh, Sch Phys & Astron, Edinburgh EH9 3FD, Midlothian, Scotland, Email: oscar.hall@ed.ac.uk
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0370-2693 ISBN Medium
Area Expedition Conference
Notes WOS:000647421500016 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4819
Permanent link to this record
 
 
Author SCiMMA and SNEWS Collaborations (Baxter, A.L. et al); Colomer, M.
Title (up) Collaborative experience between scientific software projects using Agile Scrum development Type Journal Article
Year 2022 Publication Software-Practice & Experience Abbreviated Journal Softw.-Pract. Exp.
Volume 52 Issue Pages 2077-2096
Keywords Agile; cyberinfrastructure; multimessenger astrophysics; scientific computing; software development
Abstract Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open-source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.
Address [Baxter, Amanda L.; Clark, Michael; Kopec, Abigail; Lang, Rafael F.; Li, Shengchao; Linvill, Mark W.; Milisavljevic, Danny; Weil, Kathryn E.] Purdue Univ, Dept Phys & Astron, W Lafayette, IN 47907 USA, Email: adepoian@purdue.edu;
Corporate Author Thesis
Publisher Wiley Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0038-0644 ISBN Medium
Area Expedition Conference
Notes WOS:000830363800001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5305
Permanent link to this record
 
 
Author Aliaga, R.J.; Herrero-Bosch, V.; Capra, S.; Pullia, A.; Duenas, J.A.; Grassi, L.; Triossi, A.; Domingo-Pardo, C.; Gadea, R.; Gonzalez, V.; Huyuk, T.; Sanchis, E.; Gadea, A.; Mengoni, D.
Title (up) Conceptual design of the TRACE detector readout using a compact, dead time-less analog memory ASIC Type Journal Article
Year 2015 Publication Nuclear Instruments & Methods in Physics Research A Abbreviated Journal Nucl. Instrum. Methods Phys. Res. A
Volume 800 Issue Pages 34-39
Keywords Analog memory; Dead time; Detector readout; Front-end electronics; Switched Capacitor Array (SCA); Triggerless data acquisition
Abstract The new TRacking Array for light Charged particle Ejectiles (TRACE) detector system requires monitorization and sampling of all pulses in a large number of channels with very strict space and power consumption restrictions for the front-end electronics and cabling, Its readout system is to be based on analog memory ASICs with 64 channels each that sample a 1 μs window of the waveform of any valid pulses at 200 MHz while discarding any other signals and are read out at 50 MHz with external ADC digitization. For this purpose, a new, compact analog memory architecture is described that allows pulse capture with zero dead time in any channel while vastly reducing the total number of storage cells, particularly for large amounts of input channels. This is accomplished by partitioning the typical Switched Capacitor Array structure into two pipelined, asymmetric stages and introducing FIFO queue-like control circuitry for captured data, achieving total independence between the capture and readout operations.
Address [Aliaga, R. J.; Domingo-Pardo, C.; Hueyuek, T.; Gadea, A.] Inst Fis Corpuscular, Paterna 46980, Spain, Email: raalva@ific.uv.es
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0168-9002 ISBN Medium
Area Expedition Conference
Notes WOS:000361878200006 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 2407
Permanent link to this record
 
 
Author DUNE Collaboration (Abud, A.A. et al); Antonova, M.; Barenboim, G.; Cervera-Villanueva, A.; De Romeri, V.; Fernandez Menendez, P.; Garcia-Peris, M.A.; Izmaylov, A.; Martin-Albo, J.; Masud, M.; Mena, O.; Molina Bueno, L.; Novella, P.; Rubio, F.C.; Sorel, M.; Ternes, C.A.; Tortola, M.; Valle, J.W.F.
Title (up) Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC Type Journal Article
Year 2022 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 17 Issue 1 Pages P01005 - 111pp
Keywords Noble liquid detectors (scintillation, ionization, double-phase); Photon detectors for UV; visible and IR photons (solid-state) (PIN diodes, APDs, Si-PMTs, G-APDs, CCDs, EBCCDs, EMCCDs, CMOS imagers, etc); Scintillators; scintillation and light emission processes (solid, gas and liquid scintillators); Time projection Chambers (TPC)
Abstract The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 x 6 x 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.
Address [Fani, M.; Isenhower, L.] Abilene Christian Univ, Abilene, TX 79601 USA, Email: Stefania.Bordoni@cern.ch
Corporate Author Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:000757487100001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5131
Permanent link to this record
 
 
Author Oliveira, C.A.B.; Sorel, M.; Martin-Albo, J.; Gomez-Cadenas, J.J.; Ferreira, A.L.; Veloso, J.F.C.A.
Title (up) Energy resolution studies for NEXT Type Journal Article
Year 2011 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 6 Issue Pages P05007 - 13pp
Keywords Scintillators, scintillation and light emission processes (solid, gas and liquid scintillators); Detector modelling and simulations II (electric fields, charge transport, multiplication and induction, pulse formation, electron emission etc); Large detector systems for particle and astroparticle physics; Time projection chambers
Abstract This work aims to present the current state of simulations of electroluminescence (EL) produced in gas-based detectors with special interest for NEXT – Neutrino Experiment with a Xenon TPC. NEXT is a neutrinoless double beta decay experiment, thus needs outstanding energy resolution which can be achieved by using electroluminescence. The process of light production is reviewed and properties such as EL yield and associated fluctuations, excitation and electroluminescence efficiencies, and energy resolution, are calculated. An EL production region with a 5 mm width gap between two infinite parallel planes is considered, where a uniform electric field is produced. The pressure and temperature considered are 10 bar and 293 K, respectively. The results show that, even for low values of VUV photon detection efficiency, good energy resolution can be achieved: below 0.4% (FWHM) at Q(beta beta) = 2.458 MeV.
Address [Oliveira, CAB; Ferreira, AL; Veloso, JFCA] Univ Aveiro, Dept Phys, i3N, P-3810193 Aveiro, Portugal, Email: carlos.oliveira@ua.pt
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:000294491900008 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ elepoucu @ Serial 747
Permanent link to this record
 
 
Author Folgado, M.G.; Sanz, V.
Title (up) Exploring the political pulse of a country using data science tools Type Journal Article
Year 2022 Publication Journal of Computational Social Science Abbreviated Journal J. Comput. Soc. Sci.
Volume 5 Issue Pages 987-1000
Keywords Politics; Spain; Sentiment analysis; Artificial Intelligence; Machine learning; Neural networks; Natural Language Processing (NLP)
Abstract In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.
Address [Folgado, Miguel G.; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Valencia 46980, Spain, Email: migarfol@upvnet.upv.es;
Corporate Author Thesis
Publisher Springernature Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2432-2717 ISBN Medium
Area Expedition Conference
Notes WOS:000742263500002 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5077
Permanent link to this record
 
 
Author Ortiz Arciniega, J.L.; Carrio, F.; Valero, A.
Title (up) FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions Type Journal Article
Year 2019 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 14 Issue Pages P09002 - 13pp
Keywords Data processing methods; Pattern recognition; cluster finding; calibration and fitting methods; Simulation methods and programs
Abstract The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.
Address [Ortiz Arciniega, J. L.] Univ Valencia, Avinguda Univ S-N, Burjassot, Spain, Email: orarjo@alumni.uv.es
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:000486990000002 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 4150
Permanent link to this record