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	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	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
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	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	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
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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	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
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	Author	Ruiz de Austri, R.; Perez de los Heros, C.
	Title	Impact of nucleon matrix element uncertainties on the interpretation of direct and indirect dark matter search results			Type	Journal Article
	Year	2013	Publication	Journal of Cosmology and Astroparticle Physics	Abbreviated Journal	J. Cosmol. Astropart. Phys.
	Volume	11	Issue	11	Pages	049 - 19pp
	Keywords	dark matter theory; dark matter experiments; supersymmetry and cosmology
	Abstract	We study in detail the impact of the current uncertainty in nucleon matrix elements on the sensitivity of direct and indirect experimental techniques for dark matter detection. We perform two scans in the framework of the cMSSM: one using recent values of the pion-sigma term obtained from Lattice QCD, and the other using values derived from experimental measurements. The two choices correspond to extreme values quoted in the literature and reflect the current tension between different ways of obtaining information about the structure of the nucleon. All other inputs in the scans, astrophysical and from particle physics, are kept unchanged. We use two experiments, XENON100 and IceCube, as benchmark cases to illustrate our case. We find that the interpretation of dark matter search results from direct detection experiments is more sensitive to the choice of the central values of the hadronic inputs than the results of indirect search experiments. The allowed regions of cMSSM parameter space after including XENON100 constrains strongly differ depending on the assumptions on the hadronic matrix elements used. On the other hand, the constraining potential of IceCube is almost independent of the choice of these values.
	Address	[Ruiz de Austri, R.] IFIC UV CSIC, Inst Fis Corpuscular, Valencia, Spain, Email: rruiz@ific.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	1475-7516	ISBN		Medium
	Area		Expedition		Conference
	Notes	WOS:000327843900050			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	1674
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	Author	Roszkowski, L.; Ruiz de Austri, R.; Trotta, R.; Tsai, Y.L.S.; Varley, T.A.
	Title	Global fits of the nonuniversal Higgs model			Type	Journal Article
	Year	2011	Publication	Physical Review D	Abbreviated Journal	Phys. Rev. D
	Volume	83	Issue	1	Pages	015014 - 19pp
	Keywords
	Abstract	We carry out global fits to the nonuniversal Higgs Model (NUHM), applying all relevant present-day constraints. We present global probability maps for the NUHM parameters and observables (including collider signatures, direct, and indirect detection quantities), both in terms of posterior probabilities and in terms of profile likelihood maps. We identify regions of the parameter space where the neutralino dark matter in the model is either binolike, or else higgsinolike with mass close to 1 TeV and a spin-independent scattering cross section similar to 10(-9)-10(-8) pb. We trace the occurrence of the higgsinolike region to be a consequence of a mild focusing effect in the running of one of the Higgs masses, the existence of which in the NUHM we identify in our analysis. Although the usual binolike neutralino is more prominent, higgsinolike dark matter cannot be excluded, however its significance strongly depends on the prior and statistics used to assess it. We note that, despite experimental constraints often favoring different regions of parameter space to the constrained minimal supersymmetric standard model, most observational consequences appear fairly similar, which will make it challenging to distinguish the two models experimentally.
	Address	[Roszkowski, Leszek; Tsai, Yue-Lin Sming; Varley, Tom A.] Univ Sheffield, Dept Phys & Astron, Sheffield S3 7RH, S Yorkshire, England, Email: L.Roszkowski@sheffield.ac.uk
	Corporate Author				Thesis
	Publisher	Amer Physical Soc	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1550-7998	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISI:000286765800007			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	586
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	Author	Roszkowski, L.; Ruiz de Austri, R.; Trotta, R.
	Title	Efficient reconstruction of constrained MSSM parameters from LHC data: A case study			Type	Journal Article
	Year	2010	Publication	Physical Review D	Abbreviated Journal	Phys. Rev. D
	Volume	82	Issue	5	Pages	055003 - 12pp
	Keywords
	Abstract	We present an efficient method of reconstructing the parameters of the constrained MSSM from assumed future LHC data, applied both on their own right and in combination with the cosmological determination of the relic dark matter abundance. Focusing on the ATLAS SU3 benchmark point, we demonstrate that our simple Gaussian approximation can recover the values of its parameters remarkably well. We examine two popular noninformative priors and obtain very similar results, although when we use an informative, naturalness-motivated prior, we find some sizeable differences. We show that a further strong improvement in reconstructing the SU3 parameters can by achieved by applying additional information about the relic abundance at the level of WMAP accuracy, although the expected data from Planck will have only a very limited additional impact. Further external data may be required to break some remaining degeneracies. We argue that the method presented here is applicable to a wide class of low-energy effective supersymmetric models, as it does not require one to deal with purely experimental issues, e.g., detector performance, and has the additional advantages of computational efficiency. Furthermore, our approach allows one to distinguish the effect of the model's internal structure and of the external data on the final parameters constraints.
	Address	[Roszkowski, Leszek] Univ Sheffield, Dept Phys & Astron, Sheffield S3 7RH, S Yorkshire, England, Email: L.Roszkowski@sheffield.ac.uk
	Corporate Author				Thesis
	Publisher	Amer Physical Soc	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1550-7998	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISI:000281517100002			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ elepoucu @			Serial	385
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	Author	Pato, M.; Baudis, L.; Bertone, G.; Ruiz de Austri, R.; Strigari, L.E.; Trotta, R.
	Title	Complementarity of dark matter direct detection targets			Type	Journal Article
	Year	2011	Publication	Physical Review D	Abbreviated Journal	Phys. Rev. D
	Volume	83	Issue	8	Pages	083505 - 11pp
	Keywords
	Abstract	We investigate the reconstruction capabilities of the dark matter mass and spin-independent cross section from future ton-scale direct detection experiments using germanium, xenon, or argon as targets. Adopting realistic values for the exposure, energy threshold, and resolution of dark matter experiments which will come online within 5 to 10 years, the degree of complementarity between different targets is quantified. We investigate how the uncertainty in the astrophysical parameters controlling the local dark matter density and velocity distribution affects the reconstruction. For a 50 GeV WIMP, astrophysical uncertainties degrade the accuracy in the mass reconstruction by up to a factor of similar to 4 for xenon and germanium, compared to the case when astrophysical quantities are fixed. However, the combination of argon, germanium, and xenon data increases the constraining power by a factor of similar to 2 compared to germanium or xenon alone. We show that future direct detection experiments can achieve self-calibration of some astrophysical parameters, and they will be able to constrain the WIMP mass with only very weak external astrophysical constraints.
	Address	[Pato, Miguel; Bertone, Gianfranco] Univ Zurich, Inst Theoret Phys, CH-8057 Zurich, Switzerland, Email: pato@iap.fr
	Corporate Author				Thesis
	Publisher	Amer Physical Soc	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1550-7998	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISI:000289353200003			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	605
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	Author	Panes, B.; Eckner, C.; Hendriks, L.; Caron, S.; Dijkstra, K.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G.
	Title	Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge			Type	Journal Article
	Year	2021	Publication	Astronomy & Astrophysics	Abbreviated Journal	Astron. Astrophys.
	Volume	656	Issue		Pages	A62 - 18pp
	Keywords	catalogs; gamma rays: general; astroparticle physics; methods: numerical; methods: data analysis; techniques: image processing
	Abstract	Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
	Address	[Panes, Boris] Pontificia Univ Catolica Chile, Ave Vicuna Mackenna 4860, Macul, Region Metropol, Chile, Email: bapanes@gmail.com
	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:000725877600001			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	5053
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	Author	Otten, S.; Rolbiecki, K.; Caron, S.; Kim, J.S.; Ruiz de Austri, R.; Tattersall, J.
	Title	DeepXS: fast approximation of MSSM electroweak cross sections at NLO			Type	Journal Article
	Year	2020	Publication	European Physical Journal C	Abbreviated Journal	Eur. Phys. J. C
	Volume	80	Issue	1	Pages	12 - 9pp
	Keywords
	Abstract	We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for pp ->(chi) over tilde (+)(1)(chi) over tilde (-)(1), (chi) over tilde (0)(2)(chi) over tilde (0)(2) and (chi) over tilde (0)(2)(chi) over tilde (+/-)(1) as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below 0.5% allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of O(10(7)) from O(min) to O(mu s) per evaluation.
	Address	[Otten, Sydney; Caron, Sascha] Radboud Univ Nijmegen, IMAPP, Nijmegen, Netherlands, Email: Sydney.Otten@ru.nl
	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:000513271500001			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	4279
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	Author	Otten, S.; Caron, S.; de Swart, W.; van Beekveld, M.; Hendriks, L.; van Leeuwen, C.; Podareanu, D.; Ruiz de Austri, R.; Verheyen, R.
	Title	Event generation and statistical sampling for physics with deep generative models and a density information buffer			Type	Journal Article
	Year	2021	Publication	Nature Communications	Abbreviated Journal	Nat. Commun.
	Volume	12	Issue	1	Pages	2985 - 16pp
	Keywords
	Abstract	Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
	Address	[Otten, Sydney; Caron, Sascha; de Swart, Wieske; van Beekveld, Melissa; Hendriks, Luc; Verheyen, Rob] Radboud Univ Nijmegen, Inst Math Astro & Particle Phys IMAPP, Nijmegen, Netherlands, Email: Sydney.Otten@ru.nl
	Corporate Author				Thesis
	Publisher	Nature Research	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2041-1723	ISBN		Medium
	Area		Expedition		Conference
	Notes	WOS:000658761600003			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	4862
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