Machine learning astrophysics from 21 cm lightcones: Impact of network architectures and signal contamination

Imaging the cosmic 21 cm signal will map out the first billion years of our Universe. The resulting 3D lightcone (LC) will encode the properties of the unseen first galaxies and physical cosmology. Here, we build on previous work using neural networks (NNs) to infer astrophysical parameters directly from 21 cm LC images. We introduce recurrent neural networks (RNNs), capable of efficiently characterizing the evolution along the redshift axis of 21 cm LC images. Using a large database of simulated cosmic 21 cm LCs, we compare the relative performance in parameter estimation of different network architectures. These including two types of RNNs, which differ in their complexity, as well as a more traditional convolutional neural network (CNN). For the ideal case of no instrumental effects, our simplest and easiest to train RNN performs the best, with a mean squared parameter estimation error (MSE) that is lower by a factor of 2 compared with the other architectures studied here, and a factor of 8 lower than the previously-studied CNN. We also corrupt the cosmic signal by adding noise expected from a 1000 h integration with the Square Kilometre Array, as well as excising a foreground-contaminated 'horizon wedge'. Parameter prediction errors increase when the NNs are trained on these contaminated LC images, though recovery is still good even in the most pessimistic case (with R2 0.5-0.95). However, we find no notable differences in performance between network architectures on the contaminated images. We argue this is due to the size of our data set, highlighting the need for larger data sets and/or better data augmentation in order to maximize the potential of NNs in 21 cm parameter estimation

A mafic-ultramafic cumulate sequence derived from boninite-type melts (Niagara Icefalls, northern Victoria Land, Antarctica)

The layered sequence from Niagara Icefalls (northern Victoria Land, Antarctica) is related to the Cambrian-Early Ordovician Ross Orogeny. The sequence consists of dunites, harzburgites, orthopyroxenites, melagabbronorites and gabbronorites of cumulus origin. The Mg# of olivine, spinel, orthopyroxene and clinopyroxene from these rocks yields positive correlations, thus indicating formation from melts that mainly evolved through fractional crystallisation. The following fractionation sequence was identified: olivine (up to 94 mol% forsterite) + Cr-rich spinel -> olivine + orthopyroxene +/- spinel -> orthopyroxene -> orthopyroxene + anorthite-rich plagioclase +/- clinopyroxene. Clinopyroxenes retain the peculiar trace element signature of boninite melts, such as extremely low concentrations of HREE and HFSE, and LILE enrichment over REE and HFSE. U-Pb isotope data on zircons separated from a gabbronorite have allowed us to constrain the age of emplacement of the Niagara Icefalls sequence at similar to 514 Ma. The occurrence of inherited zircons dated at similar to 538 Ma indicates that the boninitic melts experienced, at least locally, crustal contamination. The Niagara Icefalls sequence can be related to a regional scale magmatic event that affected the eastern margin of the Gondwana supercontinent in the Middle Cambrian. We propose that the formation of the sequence was associated with the development of an embryonic back-arc basin in an active continental margin

Implementing sustainability in laboratory activities: A case study on aluminum titanium nitride based thin film magnetron sputtering deposition onto commercial laminated steel

The goal of the study was to identify the environmental hotspots of an experimental research work at lab scale consisting in the physical vapor deposition magnetron sputtering of aluminum titanium nitride based thin coatings onto commercial laminated steel. The findings can provide useful insights for supporting the design of future experimental research campaigns, or instrumentation set-ups, with lower environmental impacts. Results highlighted that the main driver of impacts in the analyzed laboratory activities was the electricity used for instruments operations, in particular for the vacuum keeping. Thus, several optimization strategies were evaluated to reduce the overall electricity consumption, and to improve the environmental profile of experimental activities

Sopj: A scalable online provenance join for data integration

Data integration is a technique used to combine different sources of data together to provide an unified view among them. MOMIS[1] is an open-source data integration framework developed by the DBGroup1. The goal of our work is to make MOMIS be able to scale-out as the input data sources increase without introducing noticeable performance penalty. In particular, we present a full outer join method capable to efficiently integrate multiple sources at the same time by using data streams and provenance information. To evaluate the scalability of this innovative approach, we developed a join engine employing a distributed data processing framework. Our solution is able to process input data sources in the form of continuous stream, execute the join operation on-the-fly and produce outputs as soon as they are generated. In this way, the join can return partial results before the input streams have been completely received or processed optimizing the entire execution

A new breakthrough in the P recovery from sewage sludge ash by thermochemical processes

For the first time, the thermochemical treatment of sewage sludge ash made by using microwaves, associated with a devoted patented chamber, was realised. It promotes the formation of bioavailable CaNaPO4 compound, offering a new breakthrough in recovering phosphorus from sewage sludge ash and providing new possibilities in terms of sustainability

Impurities removal by laser blow-off from in-vacuum optical surfaces on RFX-mod experiment

An in situ window cleaning system by laser blow-off through optical fiber has been developed on the basis of a feasibility study previously presented. The beam generated by a Q-switched Nd:YAG laser is launched in a vacuum box into a high damage threshold optical fiber through a lens. The fiber output is focused on the impurities-coated surface of a vacuum window exposed to the plasma of the RFX-mod experiment, and it is remotely controlled with an xy motion system to scan the entire surface. We first investigate the energy density threshold necessary to ablate the deposited impurity substrate on removed dirty windows: above threshold, a single laser pulse recovers 3c95% of the window transmission before its exposure to the plasma, while below it the efficiency of the cleaning process is too poor. The system so conceived was then used to clean the three collection windows of the Main Thomson scattering diagnostic on RFX-mod. We also present results obtained applying the same technique to the SiO-protected Al mirror used for the Zeff diagnostic: an energy threshold for efficient impurity removal without mirror damage is first identified, then ablation tests are executed and analyzed in terms of recovered reflectivity. The SIMS technique is used both with windows and mirror to study the composition of surfaces before and after the ablation

Single wall carbon nanohorns coated with anatase titanium oxide

Single wall carbon nanohorns (SWCNHs) were coated with anatase titanium oxide thin films by metal-organic chemical vapour deposition with titanium tetraisopropoxide Ti(OiPr)4 as the precursor. The pristine SWCNHs and the new hybrid material SWCNHs/ TiO2 were characterized by transmission electron microscopy, Raman spectroscopy, X-ray diffraction, thermogravimetric thermal analysis and inductively coupled plasma-mass spectrometry, showing that the deposition process does not alter the typical structures of the SWCNHs. Finally, it is shown how the hydrophilic properties of the titanium oxide coating allowed a stable dispersion of SWCNHs/TiO2 in water, opening new perspectives for water based nanofluids, biological sensing or drug delivery systems