Identifying Galaxy Mergers in Observations and Simulations with Deep Learning

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. With one of the main difficulties of merger studies being the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. A convolutional neural network architecture was developed and trained in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalizing prospects for using simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22 figures, 14 tables, 1 appendi

Pressure induced magnetic phase separation in La0.75_{0.75}Ca0.25_{0.25}MnO3_{3} manganite

The pressure dependence of the Curie temperature T$_{C}(P)$ in La$_{0.75}$Ca$_{0.25}$MnO$_{3}$ was determined by neutron diffraction up to 8 GPa, and compared with the metallization temperature T$_{IM}(P)$ \cite{irprl}. The behavior of the two temperatures appears similar over the whole pressure range suggesting a key role of magnetic double exchange also in the pressure regime where the superexchange interaction is dominant. Coexistence of antiferromagnetic and ferromagnetic peaks at high pressure and low temperature indicates a phase separated regime which is well reproduced with a dynamical mean-field calculation for a simplified model. A new P-T phase diagram has been proposed on the basis of the whole set of experimental data.Comment: 5 pages, 4 figure

Ergodicity breaking in strong and network-forming glassy system

The temperature dependence of the non-ergodicity factor of vitreous GeO$_2$, $f_{q}(T)$, as deduced from elastic and quasi-elastic neutron scattering experiments, is analyzed. The data are collected in a wide range of temperatures from the glassy phase, up to the glass transition temperature, and well above into the undercooled liquid state. Notwithstanding the investigated system is classified as prototype of strong glass, it is found that the temperature- and the $q$-behavior of $f_{q}(T)$ follow some of the predictions of Mode Coupling Theory. The experimental data support the hypothesis of the existence of an ergodic to non-ergodic transition occurring also in network forming glassy systems

A muon source based on plasma accelerators

The conceptual design of a compact source of GeV-class muons is presented, based on a plasma based electron-gamma collider. Evaluations of muon flux, spectra and brilliance are presented, carried out with ad-hoc montecarlo simulations of the electron-gamma collisions. These are analyzed in the context of a large spread of the invariant mass in the e-gamma interaction, due to the typical characteristics of plasma self-injected GeV electron beams, carrying large bunch charges with huge energy spread. The availability of a compact point-like muon source, triggerable at nsec level, may open a completely new scenario in the muon radiography application field

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA

Disentangling time-focusing from beam divergence: a novel approach for high-flux thermal neutron spectroscopy at continuous and long-pulse sources

We present the concept of a novel time-focusing technique for neutron spectrometers, which allows to disentangle time-focusing from beam divergence. The core of this approach is a double rotating-crystal monochromator that can be used to extract a larger wavelength band from a white beam, thus providing a higher flux at the sample compared to standard time-of-flight instruments, yet preserving energy resolution and beam collimation. The performances of a spectrometer based on this approach are quantitatively discussed in terms of possible incident wavelengths, flux at the sample and $(Q,E)$-resolution. Analytical estimates suggest flux gains of about one order of magnitude at comparable resolutions in comparison to conventional time-of-flight spectrometers. Moreover, the double monochromator configuration natively shifts the sample away from the source line-of-sight, thus significantly improving the signal-to-noise ratio. The latter, in combination with a system that does not increase the beam divergence, brings the further advantage of a cleaner access to the low-$Q$ region, which is recognized to be of fundamental interest for magnetism and for disordered materials, from glasses to biological systems

Electron beam transfer line design for plasma driven Free Electron Lasers

Plasma driven particle accelerators represent the future of compact accelerating machines and Free Electron Lasers are going to benefit from these new technologies. One of the main issue of this new approach to FEL machines is the design of the transfer line needed to match of the electron-beam with the magnetic undulators. Despite the reduction of the chromaticity of plasma beams is one of the main goals, the target of this line is to be effective even in cases of beams with a considerable value of chromaticity. The method here explained is based on the code GIOTTO [1] that works using a homemade genetic algorithm and that is capable of finding optimal matching line layouts directly using a full 3D tracking code.Comment: 9 Pages, 4 Figures. A related poster was presented at EAAC 201

Quadrupole scan emittance measurements for the ELI-NP compton gamma source

The high brightness electron LINAC of the Compton Gamma Source at the ELI Nuclear Physics facility in Roma- nia is accelerating a train of 32 bunches with a nominal total charge of 250 pC and nominal spacing of 16 ns . To achieve the design gamma flux, all the bunches along the train must have the designed Twiss parameters. Beam sizes are mea- sured with optical transition radiation monitors, allowing a quadrupole scan for Twiss parameters measurements. Since focusing the whole bunch train on the screen may lead to permanent screen damage, we investigate non-conventional scans such as scans around a maximum of the beam size or scans with a controlled minimum spot size. This paper discusses the implementation issues of such a technique in the actual machine layou