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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. url  doi
openurl 
  Title 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 (up) 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 Caron, S.; Casas, J.A.; Quilis, J.; Ruiz de Austri, R. url  doi
openurl 
  Title Anomaly-free dark matter with harmless direct detection constraints Type Journal Article
  Year 2018 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 12 Issue 12 Pages (up) 126 - 24pp  
  Keywords Beyond Standard Model; Cosmology of Theories beyond the SM  
  Abstract Dark matter (DM) interacting with the SM fields via a Z-boson (Z-portal') remains one of the most attractive WIMP scenarios, both from the theoretical and the phenomenological points of view. In order to avoid the strong constraints from direct detection and dilepton production, it is highly convenient that the Z has axial coupling to DM and leptophobic couplings to the SM particles, respectively. The latter implies that the associated U(1) coincides with baryon number in the SM sector. In this paper we completely classify the possible anomaly-free leptophobic Z with minimal dark sector, including the cases where the coupling to DM is axial. The resulting scenario is very predictive and perfectly viable from the present constraints from DM detection, EW observables and LHC data (di-lepton, di-jet and mono-jet production). We analyze all these constraints, obtaining the allowed areas in the parameter space, which generically prefer mZ less than or similar to 500 GeV, apart from resonant regions. The best chances to test these viable areas come from future LHC measurements.  
  Address [Caron, S.] Radboud Univ Nijmegen, Fac Sci, Inst Math Astrophys & Particle Phys, Mailbox 79,POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: scaron@cern.ch;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000454274400004 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 3856  
Permanent link to this record
 

 
Author van Beekveld, M.; Caron, S.; Ruiz de Austri, R. url  doi
openurl 
  Title The current status of fine-tuning in supersymmetry Type Journal Article
  Year 2020 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 01 Issue 1 Pages (up) 147 - 41pp  
  Keywords Supersymmetry Phenomenology  
  Abstract In this paper, we minimize and compare two different fine-tuning measures in four high-scale supersymmetric models that are embedded in the MSSM. In addition, we determine the impact of current and future dark matter direct detection and collider experiments on the fine-tuning. We then compare the low-scale electroweak measure with the high-scale Barbieri-Giudice measure. We find that they reduce to the same value when the higgsino parameter drives the degree of fine-tuning. We also find spectra where the high-scale measure turns out to be lower than the low-scale measure. Depending on the high-scale model and fine-tuning definition, we find a minimal fine-tuning of 3-38 (corresponding to O(10-1)%) for the low-scale measure, and 63-571 (corresponding to O(1-0.1)%) for the high-scale measure. We stress that it is too early to conclude on the fate of supersymmetry, based only on the fine-tuning paradigm.  
  Address [van Beekveld, Melissa; Caron, Sascha] Radboud Univ Nijmegen, Theoret High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: mcbeekveld@gmail.com;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000512011100001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 4275  
Permanent link to this record
 

 
Author van Beekveld, M.; Beenakker, W.; Caron, S.; Ruiz de Austri, R. url  doi
openurl 
  Title The case for 100 GeV bino dark matter: a dedicated LHC tri-lepton search Type Journal Article
  Year 2016 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.  
  Volume 04 Issue 4 Pages (up) 154 - 26pp  
  Keywords Supersymmetry Phenomenology  
  Abstract Global fit studies performed in the pMSSM and the photon excess signal originating from the Galactic Center seem to suggest compressed electroweak supersymmetric spectra with a similar to 100 GeV bino-like dark matter particle. We find that these scenarios are not probed by traditional electroweak supersymmetry searches at the LHC. We propose to extend the ATLAS and CMS electroweak supersymmetry searches with an improved strategy for bino-like dark matter, focusing on chargino plus next-to-lightest neutralino production, with a subsequent decay into a tri-lepton final state. We explore the sensitivity for pMSSM scenarios with Delta m = m(NLSP) – m(LSF) similar to(5 – 50) GeV in the root s = 14 TeV run of the LHC. Counterintuitively, we find that the requirement of low missing transverse energy increases the sensitivity compared to the current ATLAS and CMS searches. With 300 fb(-1) of data we expect the LHC experiments to be able to discover these supersymmetric spectra with mass gaps down to Am 9 GeV for DM masses between 40 and 140 GeV. We stress the importance of a dedicated search strategy that targets precisely these favored pMSSM spectra.  
  Address [van Beekveld, Melissa; Beenakker, Wim; Caron, Sascha] Radboud Univ Nijmegen, Inst Math Astrophys & Particle Phys, Heyendaalseweg 135, NL-6525 ED Nijmegen, Netherlands, Email: mcbeekveld@gmail.com;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1029-8479 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000375055200007 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 2648  
Permanent link to this record
 

 
Author Caron, S.; Kim, J.S.; Rolbiecki, K.; Ruiz de Austri, R.; Stienen, B. url  doi
openurl 
  Title The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning Type Journal Article
  Year 2017 Publication European Physical Journal C Abbreviated Journal Eur. Phys. J. C  
  Volume 77 Issue 4 Pages (up) 257 - 25pp  
  Keywords  
  Abstract A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce theATLAS exclusion regions in 19 dimensions with an accuracy of at least 93%. It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/.  
  Address [Caron, Sascha; Stienen, Bob] Radboud Univ Nijmegen, IMAPP, Nijmegen, Netherlands, Email: krolb@fuw.edu.pl  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1434-6044 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000400079300001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 3097  
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