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Strege, C., Bertone, G., Besjes, G. J., Caron, S., Ruiz de Austri, R., Strubig, A., et al. (2014). Profile likelihood maps of a 15-dimensional MSSM. J. High Energy Phys., 09(9), 081–59pp.
Abstract: We present statistically convergent profile likelihood maps obtained via global fits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters (the MSSM-15), based on over 250M points. We derive constraints on the model parameters from direct detection limits on dark matter, the Planck relic density measurement and data from accelerator searches. We provide a detailed analysis of the rich phenomenology of this model, and determine the SUSY mass spectrum and dark matter properties that are preferred by current experimental constraints. We evaluate the impact of the measurement of the anomalous magnetic moment of the muon (g – 2) on our results, and provide an analysis of scenarios in which the lightest neutralino is a subdominant component of the dark matter. The MSSM-15 parameters are relatively weakly constrained by current data sets, with the exception of the parameters related to dark matter phenomenology (M-1, M-2, mu), which are restricted to the sub-TeV regime, mainly due to the relic density constraint. The mass of the lightest neutralino is found to be < 1.5TeV at 99% C.L., but can extend up to 3 TeV when excluding the g – 2 constraint from the analysis. Low-mass bino-like neutralinos are strongly favoured, with spin-independent scattering cross-sections extending to very small values, similar to 10(-20) pb. ATLAS SUSY null searches strongly impact on this mass range, and thus rule out a region of parameter space that is outside the reach of any current or future direct detection experiment. The best-fit point obtained after inclusion of all data corresponds to a squark mass of 2.3 TeV, a gluino mass of 2.1 TeV and a 130 GeV neutralino with a spin-independent cross-section of 2.4 x 10(-10) pb, which is within the reach of future multi-ton scale direct detection experiments and of the upcoming LHC run at increased centre-of-mass energy.
Keywords: Supersymmetry Phenomenology
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Stoppa, F., Vreeswijk, P., Bloemen, S., Bhattacharyya, S., Caron, S., Johannesson, G., et al. (2022). AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian. Astron. Astrophys., 662, A109–8pp.
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.
Keywords: astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing
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Stoppa, F., Ruiz de Austri, R., Vreeswijk, P., Bhattacharyya, S., Caron, S., Bloemen, S., et al. (2023). AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation. Astron. Astrophys., 680, A108–14pp.
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.
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Stoppa, F., Bhattacharyya, S., Ruiz de Austri, R., Vreeswijk, P., Caron, S., Zaharijas, G., et al. (2023). AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information. Astron. Astrophys., 680, A109–16pp.
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.
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Staub, F., Porod, W., & Herrmann, B. (2010). The electroweak sector of the NMSSM at the one-loop level. J. High Energy Phys., 10(10), 040–50pp.
Abstract: We present the electroweak spectrum for the Next-to-Minimal Supersymmetric Standard Model at the one-loop level, e. g. the masses of Higgs bosons, sleptons, charginos and neutralinos. For the numerical evaluation we present a mSUGRA variant with non-universal Higgs mass parameters squared and we compare our results with existing ones in the literature. Moreover, we briefly discuss the implications of our results for the calculation of the relic density.
Keywords: Supersymmetry Phenomenology
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Stadler, J., Boehm, C., & Mena, O. (2019). Comprehensive study of neutrino-dark matter mixed damping. J. Cosmol. Astropart. Phys., 08(8), 014–23pp.
Abstract: Mixed damping is a physical effect that occurs when a heavy species is coupled to a relativistic fluid which is itself free streaming. As a cross-case between collisional damping and free-streaming, it is crucial in the context of neutrino-dark matter interactions. In this work, we establish the parameter space relevant for mixed damping, and we derive an analytical approximation for the evolution of dark matter perturbations in the mixed damping regime to illustrate the physical processes responsible for the suppression of cosmological perturbations. Although extended Boltzmann codes implementing neutrino-dark matter scattering terms automatically include mixed damping, this effect has not been systematically studied. In order to obtain reliable numerical results, it is mandatory to reconsider several aspects of neutrino-dark matter interactions, such as the initial conditions, the ultra-relativistic fluid approximation and high order multiple moments in the neutrino distribution. Such a precise treatment ensures the correct assessment of the relevance of mixed damping in neutrino-dark matter interactions.
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Stadler, J., Boehm, C., & Mena, O. (2020). Is it mixed dark matter or neutrino masses? J. Cosmol. Astropart. Phys., 01(1), 039–18pp.
Abstract: In this paper, we explore a scenario where the dark matter is a mixture of interacting and non interacting species. Assuming dark matter-photon interactions for the interacting species, we find that the suppression of the matter power spectrum in this scenario can mimic that expected in the case of massive neutrinos. Our numerical studies include present limits from Planck Cosmic Microwave Background data, which render the strength of the dark matter photon interaction unconstrained when the fraction of interacting dark matter is small. Despite the large entangling between mixed dark matter and neutrino masses, we show that future measurements from the Dark Energy Instrument (DESI) could help in establishing the dark matter and the neutrino properties simultaneously, provided that the interaction rate is very close to its current limits and the fraction of interacting dark matter is at least of O (10%). However, for that region of parameter space where a small fraction of interacting DM coincides with a comparatively large interaction rate, our analysis highlights a considerable degeneracy between the mixed dark matter parameters and the neutrino mass scale.
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Srivastava, R., Ternes, C. A., Tortola, M., & Valle, J. W. F. (2018). Testing a lepton quarticity flavor theory of neutrino oscillations with the DUNE experiment. Phys. Lett. B, 778, 459–463.
Abstract: Oscillation studies play a central role in elucidating at least some aspects of the flavor problem. Here we examine the status of the predictions of a lepton quarticity flavor theory of neutrino oscillations against the existing global sample of oscillation data. By performing quantitative simulations we also determine the potential of the upcoming DUNE experiment in narrowing down the currently ill-measured oscillation parameters theta(23) and delta(CP). We present the expected improved sensitivity on these parameters for different assumptions.
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Soriano, A., Gonzalez, A., Orero, A., Moliner, L., Carles, M., Sanchez, F., et al. (2011). Attenuation correction without transmission scan for the MAMMI breast PET. Nucl. Instrum. Methods Phys. Res. A, 648, S75–S78.
Abstract: Whole-body Positron Emission Tomography (PET) scanners are required in order to span large Fields of View (FOV). Therefore, reaching the sensitivity and spatial resolution required for early stage breast tumor detection is not straightforward. MAMMI is a dedicated breast PET scanner with a ring geometry designed to provide PET images with a spatial resolution as high as 1.5 mm, being able to detect small breast tumors ( < 1 cm). The patient lays down in prone position during the scan, thus making possible to image the whole breast, up to regions close to the base of the pectoral without the requirement of breast compression. Attenuation correction (AC) for PET data improves the image quality and the quantitative accuracy of radioactivity distribution determination. In dedicated, high resolution breast cancer scanners, this correction would enhance the proper diagnosis in early disease stages. In whole-body PET scanners, AC is usually taken into account with the use of transmission scans, either by external radioactive rod sources or by Computed Tomography (CT). This considerably increases the radiation dose administered to the patient and time needed for the exploration. In this work we propose a method for breast shape identification by means of PET image segmentation. The breast shape identification will be used for the determination of the AC. For the case of a specific breast PET scanner the procedure we propose should provide AC similar to that obtained by transmission scans as we take advantage of the breast anatomical simplicity. Experimental validation of the proposed approach with a dedicated breast PET prototype is also presented. The main advantage of this method is an important dose reduction since the transmission scan is not required.
Keywords: Breast PET; Attenuation correction
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Solevi, P., Muñoz, E., Solaz, C., Trovato, M., Dendooven, P., Gillam, J. E., et al. (2016). Performance of MACACO Compton telescope for ion-beam therapy monitoring: first test with proton beams. Phys. Med. Biol., 61(14), 5149–5165.
Abstract: In order to exploit the advantages of ion-beam therapy in a clinical setting, delivery verification techniques are necessary to detect deviations from the planned treatment. Efforts are currently oriented towards the development of devices for real-time range monitoring. Among the different detector concepts proposed, Compton cameras are employed to detect prompt gammas and represent a valid candidate for real-time range verification. We present the first on-beam test of MACACO, a Compton telescope (multi-layer Compton camera) based on lanthanum bromide crystals and silicon photo-multipliers. The Compton telescope was first characterized through measurements and Monte Carlo simulations. The detector linearity was measured employing Na-22 and Am-Be sources, obtaining about 10% deviation from linearity at 3.44 MeV. A spectral image reconstruction algorithm was tested on synthetic data. Point-like sources emitting gamma rays with energy between 2 and 7 MeV were reconstructed with 3-5 mm resolution. The two-layer Compton telescope was employed to measure radiation emitted from a beam of 150 MeV protons impinging on a cylindrical PMMA target. Bragg-peak shifts were achieved via adjustment of the PMMA target location and the resulting measurements used during image reconstruction. Reconstructed Bragg peak profiles proved sufficient to observe peak-location differences within 10 mm demonstrating the potential of the MACACO Compton Telescope as a monitoring device for ion-beam therapy.
Keywords: ion-beam therapy; range verification; prompt gamma; Compton camera; GATE
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