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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	Beenakker, W.; Caron, S.; Kip, J.; Ruiz de Austri, R.; Zhang, Z.
	Title	New energy spectra in neutrino and photon detectors to reveal hidden dark matter signals			Type	Journal Article
	Year	2023	Publication	Journal of High Energy Physics	Abbreviated Journal	J. High Energy Phys.
	Volume	11	Issue	11	Pages	028 - 13pp
	Keywords
	Abstract	Neutral particles capable of travelling cosmic distances from a source to detectors on Earth are limited to photons and neutrinos. Examination of the Dark Matter annihilation/decay spectra for these particles reveals the presence of continuum spectra (e.g. due to fragmentation and W or Z decay) and peaks (due to direct annihilations/decays). However, when one explores extensions of the Standard Model (BSM), unexplored spectra emerge that differ significantly from those of the Standard Model (SM) for both neutrinos and photons. In this paper, we argue for the inclusion of important spectra that include peaks as well as previously largely unexplored entities such as boxes and combinations of box, peak and continuum decay spectra.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	6085
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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	Caron, S.; Eckner, C.; Hendriks, L.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G.
	Title	Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess			Type	Journal Article
	Year	2023	Publication	Journal of Cosmology and Astroparticle Physics	Abbreviated Journal	J. Cosmol. Astropart. Phys.
	Volume	06	Issue	6	Pages	013 - 56pp
	Keywords	dark matter simulations; gamma ray experiments; Machine learning; millisecond pulsars
	Abstract	The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.
	Address	[Caron, Sascha; Hendriks, Luc] Radboud Univ Nijmegen, Theoret High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: scaron@nikhef.nl;
	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:001025516000009			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	5576
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	Author	Caron, S.; Ruiz de Austri, R.; Zhang, Z.Y.
	Title	Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories?			Type	Journal Article
	Year	2023	Publication	Journal of High Energy Physics	Abbreviated Journal	J. High Energy Phys.
	Volume	03	Issue	3	Pages	004 - 37pp
	Keywords	Specific BSM Phenomenology; Supersymmetry
	Abstract	Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.
	Address	[Caron, Sascha; Zhang, Zhongyi] Radboud Univ Nijmegen, High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: scaron@nikhef.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	1029-8479	ISBN		Medium
	Area		Expedition		Conference
	Notes	WOS:000943095100001			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	5494
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	Author	van Beekveld, M.; Beenakker, W.; Caron, S.; Kip, J.; Ruiz de Austri, R.; Zhang, Z.Y.
	Title	Non-standard neutrino spectra from annihilating neutralino dark matter			Type	Journal Article
	Year	2023	Publication	Scipost Physics Core	Abbreviated Journal	SciPost Phys. Core
	Volume	6	Issue	1	Pages	006 - 23pp
	Keywords
	Abstract	Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation.
	Address	[van Beekveld, Melissa] Univ Oxford, Rudolf Peierls Ctr Theoret Phys, Clarendon Lab, Parks Rd, Oxford OX1 3PU, England, Email: melissa.vanbeekveld@physics.ox.ac.uk;
	Corporate Author				Thesis
	Publisher	Scipost Foundation	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	WOS:000928492200001			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	5480
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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	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	van Beekveld, M.; Caron, S.; Hendriks, L.; Jackson, P.; Leinweber, A.; Otten, S.; Patrick, R.; Ruiz de Austri, R.; Santoni, M.; White, M.
	Title	Combining outlier analysis algorithms to identify new physics at the LHC			Type	Journal Article
	Year	2021	Publication	Journal of High Energy Physics	Abbreviated Journal	J. High Energy Phys.
	Volume	09	Issue	9	Pages	024 - 33pp
	Keywords	Phenomenological Models; Supersymmetry Phenomenology
	Abstract	The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a beta-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.
	Address	[van Beekveld, Melissa] Clarendon Lab, Rudolf Peierls Ctr Theoret Phys, 20 Pks Rd, Oxford OX1 3PU, England, 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:000695421600003			Approved	no
	Is ISI	yes	International Collaboration	yes
	Call Number	IFIC @ pastor @			Serial	4973
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	Author	van Beekveld, M.; Caron, S.; Ruiz de Austri, R.
	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	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
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