Botella, F. J., Cornet-Gomez, F., Miro, C., & Nebot, M. (2024). New physics hints from τ scalar interactions and (g-2)e,μ. J. Phys. G, 51(2), 025001–20pp.
Abstract: We consider a flavour conserving two Higgs doublet model that consists of a type I (or X) quark sector and a generalized lepton sector where the Yukawa couplings of the charged leptons to the new scalars are not proportional to the lepton masses. The model, previously proposed to solve both muon and electron g – 2 anomalies simultaneously, is also capable to accommodate the ATLAS excess in pp -> S -> tau(+)tau(-) with gluon-gluon fusion production in the invariant mass range [0.2; 0.6] TeV, including all relevant low and high energy constraints. The excess is reproduced taking into account the new contributions from the scalar H, the pseudoscalar A, or both. In particular, detailed numerical analyses favoured the solution with a significant hierarchy among the vevs of the two Higgs doublets, t(beta)similar to 10, and light neutral scalars satisfying m(A) > m(H) with sizable couplings to tau leptons. In this region of the parameter space, the muon g – 2 anomaly receives one and two-loop (Barr Zee) contributions of similar size, while the electron anomaly is explained at two loops. An analogous ATLAS excess in b-associated production and the CMS excess in ditop production are also studied. Further New Physics prospects concerning the anomalous magnetic moment of the tau lepton and the implications of the CDF M-W measurement on the final results are discussed.
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Dhani, P. K., Rodrigo, G., & Sborlini, G. F. R. (2023). Triple-collinear splittings with massive particles. J. High Energy Phys., 12(12), 188–20pp.
Abstract: We analyze in detail the most singular behaviour of processes involving triple-collinear splittings with massive particles in the quasi-collinear limit, and present compact expressions for the splitting amplitudes and the corresponding splitting kernels at the squared-amplitude level. Our expressions fully agree with well-known triple-collinear splittings in the massless limit, which are used as a guide to achieve the final expressions. These results are important to quantify dominant mass effects in many observables, and constitute an essential ingredient of current high-precision computational frameworks for collider phenomenology.
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Martin-Luna, P., Esperante, D., Prieto, A. F., Fuster-Martinez, N., Rivas, I. G., Gimeno, B., et al. (2024). Simulation of electron transport and secondary emission in a photomultiplier tube and validation. Sens. Actuator A-Phys., 365, 114859–10pp.
Abstract: The electron amplification and transport within a photomultiplier tube (PMT) has been investigated by developing an in-house Monte Carlo simulation code. The secondary electron emission in the dynodes is implemented via an effective electron model and the Modified Vaughan's model, whereas the transport is computed with the Boris leapfrog algorithm. The PMT gain, rise time and transit time have been studied as a function of supply voltage and external magnetostatic field. A good agreement with experimental measurements using a Hamamatsu R13408-100 PMT was obtained. The simulations have been conducted following different treatments of the underlying geometry: three-dimensional, two-dimensional and intermediate (2.5D). The validity of these approaches is compared. The developed framework will help in understanding the behavior of PMTs under highly intense and irregular illumination or varying external magnetic fields, as in the case of prompt gamma-ray measurements during pencil-beam proton therapy; and aid in optimizing the design of voltage dividers with behavioral circuit models.
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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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