Star Formation Rates for photometric samples of galaxies using machine learning methods

Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised Machine Learning models. We demonstrate that, with such approach, accurate multi-band photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky survey Data Release 7. The catalogue is available through the Vizier facility at the following link ftp://cdsarc.u-strasbg.fr/pub/cats/J/MNRAS/486/1377

Photometric SFR using machine learning

Star formation rates (SFRs) are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based on the photometric estimation of global SFRs for large samples of galaxies, by using methods such as automatic parameter space optimisation, and supervised machine learning models. We demonstrate that, with such approach, accurate multiband photometry allows to estimate reliable SFRs. We also investigate how the use of photometric rather than spectroscopic redshifts, affects the accuracy of derived global SFRs. Finally, we provide a publicly available catalogue of SFRs for more than 27 million galaxies extracted from the Sloan Digital Sky Survey Data Release 7. The catalogue will be made available through the Vizier facility

Cellular-Automata model for dense-snow avalanches

This paper introduces a three-dimensional model for simulating dense-snow avalanches, based on the numerical method of cellular automata. This method allows one to study the complex behavior of the avalanche by dividing it into small elements, whose interaction is described by simple laws, obtaining a reduction of the computational power needed to perform a three-dimensional simulation. Similar models by several authors have been used to model rock avalanches, mud and lava flows, and debris avalanches. A peculiar aspect of avalanche dynamics, i.e., the mechanisms of erosion of the snowpack and deposition of material from the avalanche is taken into account in the model. The capability of the proposed approach has been illustrated by modeling three documented avalanches that occurred in Susa Valley (Western Italian Alps). Despite the qualitative observations used for calibration, the proposed method is able to reproduce the correct three-dimensional avalanche path, using a digital terrain model, and the order of magnitude of the avalanche deposit volume

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

Annealing Effects on PECVD-grown Si rich aSiNx Thin Films

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TCO Optimization in Si Heterojunction Solar Cells on p-type Wafers with n-SiOx Emitter☆

Abstract Silicon heterojunction solar cells have largely demonstrated their suitability to reach high efficiencies. We have here focused on p-type c-Si wafers as absorber, considering that they share more than 90% of the solar cell market. To overcome some of the issues encountered in the conventional (n)a-Si:H/(p)c-Si configuration, we have implemented a mixed phase n-type silicon oxide (n-SiOx) emitter in order to gain from the wider bandgap and lower activation energy of this material with respect to (n)a-Si:H. The workfunction of the transparent conductive oxide layer (WTCO) plays also a key role, as it may induce an unfavourable band bending at the interface with the emitter. We have here focused on AZO, a promising alternative to ITO. Different layers with varying WTCO were prepared, by changing relevant deposition parameters, and were tested into solar cells. The experimental results have been explained with the aid of numerical simulations. Finally, for the n-SiOx/(p)c-Si heterojunction with optimized WTCO a potential conversion efficiency well over 23% has been estimated

Modification of amorphous and microcrystalline silicon film properties after irradiation with MeV and GeV protons

It is well known that the degree of crystallinity has a prominent influence on the stability of Silicon under proton irradiation. Amorphous silicon films are much more stable than mono- or polycrystalline silicon substrates or microcrystalline silicon thin films. In particular it has been shown, that in a micromorph tandem solar cell irradiated with protons in the lower MeV energy range only the microcrystalline diode showed a pronounced decrease in photocurrent after irradiation1. The proton irradiation induced damage in thick crystalline silicon samples has a maximum at beam energies between 1MeV and 4MeV and decreases for further increasing proton energies. However, irradiating an amorphous silicon/crystalline silicon heterojunction solar cell with a relatively dose of 24GeV, we observed a very strong drop in conversion efficiency with only minor recovery after sample annealing. In literature it has been reported 2, that the degradation of amorphous silicon is negligible for proton energies above 100MeV. In order to clarify to which extent also the thin film top layer of the hetero solar cell is affected by the proton irradiation, we exposed a variety of thin film silicon samples either to a 1.7MeV beam with a dose of 5.1012 protons/cm2 or to a 24GeV beam with a dose of 5 .1013 protons/cm2. The investigated intrinsic, p-type and n-type amorphous and microcrystalline silicon films have been deposited by conventional plasma deposition under variation of the silane / hydrogen gas phase ratio. Raman measurements have been done in order to determine the order of crystallinity obtained under various deposition conditions. We observed even at 24GeV a clear modification in the electrical characteristics of the films. Temperature dependent measurements of the dark current revealed in particular for all doped samples a significant increase of the activation energy, that might be explained by a decrease of the dopant efficiency, while for intrinsic a-Si:H layers the increasing activation energy is due to deep defect creation

Procedure Based on External Quantum Efficiency for Reliable Characterization of Perovskite Solar Cells

Perovskite solar cells PSCs have the potential for widespread application, but challenges remain for a reliable characterization of their performance. Standardized protocols for measuring and reporting are still debated. Focusing on the short circuit current density J SC , current voltage characteristics J V and external quantum efficiency EQE are collected to estimate the parameter. Still, they often provide a mismatch above 1 amp; 8201;mA amp; 8201;cm amp; 8722;2, resulting in a possible 5 or higher error. Combining experimental data and optical simulations, it is demonstrated that the EQE can provide a reliable estimate of the J SC that could otherwise easily be overestimated by J V. With access to the internally transmitted light through simulations, an upper limit for EQE is defined depending on the front layers. Details on the origin of the spectral shape and contributions to the optical losses are obtained with further optical simulations, providing hints for cell optimization to achieve a photocurrent gain. The authors use solution processed n i p PSCs with triple cation mixed halide absorbers as demonstrators and ultimately come to the proposal of an upgrade of the present best practices in PSC efficiency measurements. Still, the approach and conclusions are general and apply to cells with all designs and chemical formulation

SKA Science Data Challenge 2: analysis and results

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarise the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data product representing a 2000~h SKA MID spectral line observation from redshifts 0.25 to 0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, `reproducibility awards' were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy -- which combined predictions from two independent machine learning techniques to yield a 20 percent improvement in overall performance -- underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical datasets.Comment: Under review by MNRAS; 28 pages, 16 figure