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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Barrientos, L., Borja-Lloret, M., Casaña, J. V., Dendooven, P., Garcia Lopez, J. G., Hueso-Gonzalez, F., et al. (2024). Gamma-ray sources imaging and test-beam results with MACACO III Compton camera. Phys. Medica, 117, 103199–10pp.
Abstract: Hadron therapy is a radiotherapy modality which offers a precise energy deposition to the tumors and a dose reduction to healthy tissue as compared to conventional methods. However, methods for real-time monitoring are required to ensure that the radiation dose is deposited on the target. The IRIS group of IFIC-Valencia developed a Compton camera prototype for this purpose, intending to image the Prompt Gammas emitted by the tissue during irradiation. The system detectors are composed of Lanthanum (III) bromide scintillator crystals coupled to silicon photomultipliers. After an initial characterization in the laboratory, in order to assess the system capabilities for future experiments in proton therapy centers, different tests were carried out in two facilities: PARTREC (Groningen, The Netherlands) and the CNA cyclotron (Sevilla, Spain). Characterization studies performed at PARTREC indicated that the detectors linearity was improved with respect to the previous version and an energy resolution of 5.2 % FWHM at 511 keV was achieved. Moreover, the imaging capabilities of the system were evaluated with a line source of 68Ge and a point-like source of 241Am-9Be. Images at 4.439 MeV were obtained from irradiation of a graphite target with an 18 MeV proton beam at CNA, to perform a study of the system potential to detect shifts at different intensities. In this sense, the system was able to distinguish 1 mm variations in the target position at different beam current intensities for measurement times of 1800 and 600 s.
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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., Cabrera Urban, S., et al. (2023). Comparison of inclusive and photon-tagged jet suppression in 5.02 TeV Pb+Pb collisions with ATLAS. Phys. Lett. B, 846, 138154–27pp.
Abstract: Parton energy loss in the quark-gluon plasma (QGP) is studied with a measurement of photon-tagged jet production in 1.7 nb-1 of Pb+Pb data and 260 pb-1 of pp data, both at root sNN = 5.02 TeV, with the ATLAS detector. The process pp -> gamma +jet+X and its analogue in Pb+Pb collisions is measured in events containing an isolated photon with transverse momentum (pT) above 50 GeV and reported as a function of jet pT. This selection results in a sample of jets with a steeply falling pT distribution that are mostly initiated by the showering of quarks. The pp and Pb+Pb measurements are used to report the nuclear modification factor, RAA, and the fractional energy loss, Sloss, for photon-tagged jets. In addition, the results are compared with the analogous ones for inclusive jets, which have a significantly smaller quark-initiated fraction. The RAA and Sloss values are found to be significantly different between those for photon-tagged jets and inclusive jets, demonstrating that energy loss in the QGP is sensitive to the colour-charge of the initiating parton. The results are also compared with a variety of theoretical models of colour-charge-dependent energy loss.
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ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2024). Measurement of the tt cross section and its ratio to the Z production cross section using pp collisions at √s=13.6 TeV with the ATLAS detector. Phys. Lett. B, 848, 138376–25pp.
Abstract: The inclusive top-quark-pair production cross section rrtt and its ratio to the Z-boson production cross section have been measured in proton-proton collisions at root s = 13.6 TeV, using 29 fb-1 of data collected in 2022 with the ATLAS experiment at the Large Hadron Collider. Using events with an opposite-charge electron-muon pair and b-tagged jets, and assuming Standard Model decays, the top-quark-pair production cross section is measured to be rrtt = 850 +/- 3(stat.) +/- 18(syst.) +/- 20(lumi.) pb. The ratio of the ttand the Z-boson production cross sections is also measured, where the Z-boson contribution is determined for inclusive e+e- and mu+mu- events in a fiducial phase space. The relative uncertainty on the ratio is reduced compared to the ttcross section, thanks to the cancellation of several systematic uncertainties. The result for the ratio, Rtt/Z = 1.145 +/- 0.003(stat.) +/- 0.021(syst.) +/- 0.002(lumi.) is consistent with the Standard Model prediction using the PDF4LHC21 PDF set.
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