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We consider the lifetime of rectangular vertical soap films and we explore the influence of relative humidity and both dimensions on the stability of large soap films, reaching heights of up to 1.2 m. Using an automated rupture detection system, we achieve a robust statistical measurement of their lifetimes and we also measure the film thinning dynamics. We demonstrate that drainage has a negligible impact on the film stability as opposed to evaporation. To do so, we compare the measured lifetimes with predictions from the Boulogne \& Dollet model \cite{BoulogneDollet2018}, originally designed to describe the convective evaporation of hydrogels. Interestingly, we show that this model can predict a maximum film lifetime for all sizes.

Photometric redshift estimation plays a crucial role in modern cosmological surveys for studying the universe's large-scale structures and the evolution of galaxies. Deep learning has emerged as a powerful method to produce accurate photometric redshift estimates from multi-band images of galaxies. Here, we introduce a multimodal approach consisting of the parallel processing of several subsets of image bands prior, the outputs of which are then merged for further processing through a convolutional neural network (CNN). We evaluate the performance of our method using three surveys: the Sloan Digital Sky Survey (SDSS), The Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) and Hyper Suprime-Cam (HSC). By improving the model's ability to capture information embedded in the correlation between different bands, our technique surpasses the state-of-the-art photometric redshift precision. We find that the positive gain does not depend on the specific architecture of the CNN and that it increases with the number of photometric filters available., Comment: Accepted in A&A

We release photometric redshifts, reaching $\sim$0.7, for $\sim$14M galaxies at $r\leq 20$ in the 11,500 deg$^2$ of the SDSS north and south galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on $ugriz$ stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA and BOSS surveys. Representative training sets of $\sim$370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of Luminous Red Galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signaling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias and rate of catastrophic failures of the median point estimate are of order $\sigma_{\rm MAD}=0.014$, <$\Delta z_{\rm norm}$>$=0.0015$, $\eta(|\Delta z_{\rm norm}|>0.05)=4\%$ on a representative test sample at $r<19.8$, out-performing currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members. The CNN input and output are available at: https://deepdip.iap.fr/treyer+2023., Comment: Submitted to MNRAS

The last decades witnessed the emergence of the field of correlated disordered media, a great challenge offering a large panel of new perspectives for applications in theoretical modelling and material fabrication. The efficient design of structures with a controlled level of spatial correlation is a central challenge in this field, in a context where existing techniques generally rely on gradient descent on non-convex functions and on the use of stochastic methods to explore vast design spaces more efficiently. In this work, we propose a new generative technique based on Alan Turing's morphogenesis theory for designing correlated disordered materials. Inspired by the structuring of living organisms, this technique relies on the definition of simple local interactions guiding the self-organization of a generated medium. The decentralization of design constraints and the elimination of cost function minimization make this approach natively scalable to the design of large domains with controlled levels of disorder. As a validation, the morphogenetic generation of stealthy hyperuniform disordered structures is exploited to reproduce an experiment of isotropic electromagnetic bandgap synthesis in the microwave range using a low refractive index contrast.

Coarsening of two-phase systems is crucial for the stability of dense particle packings such as alloys, foams, emulsions or supersaturated solutions. Mean field theories predict an asymptotic scaling state with a broad particle size distribution. Aqueous foams are good model systems for investigations of coarsening-induced structures, because the continuous liquid as well as the dispersed gas phases are uniform and isotropic. We present coarsening experiments on wet foams, with liquid fractions up to their unjamming point and beyond, that are performed under microgravity to avoid gravitational drainage. As time elapses, a self-similar regime is reached where the normalized bubble size distribution is invariant. Unexpectedly, the distribution features an excess of small \textit{roaming} bubbles, mobile within the network of \textit{jammed} larger bubbles. These roaming bubbles are reminiscent of rattlers in granular materials (grains not subjected to contact forces). We identify a critical liquid fraction $\phi^*$, above which the bubble assembly unjams and the two bubble populations merge into a single narrow distribution of bubbly liquids. Unexpectedly, $\phi^*$ is larger than the random close packing fraction of the foam $\phi_{rcp}$. This is because, between $\phi_{rcp}$ and $\phi^*$, the large bubbles remain connected due to a weak adhesion between bubbles. We present models that identify the physical mechanisms explaining our observations. We propose a new comprehensive view of the coarsening phenomenon in wet foams. Our results should be applicable to other phase-separating systems and they may also help to control the elaboration of solid foams with hierarchical structures.

We report foam coarsening studies which were performed in the International Space Station (ISS) to suppress drainage due to gravity. Foams and bubbly liquids with controlled liquid fractions $\phi$ between 15 and 50\% were investigated to study the transition between bubble growth laws previously reported near the dry limit $\phi \rightarrow 0$ and the dilute limit $\phi \rightarrow 1$ (Ostwald ripening). We determined the coarsening rates; for the driest foams and the bubbly liquids, they are in close agreement with theoretical predictions. We observe a sharp cross-over between the respective laws at a critical value $\phi^*$. At liquid fractions beyond this transition, neighboring bubbles are no longer all in contact, like at a jamming transition. Remarkably $\phi^*$ is significantly larger than the random close packing volume fraction of the bubbles $\phi_{\text{rcp}}$ which was determined independently. We attribute the differences between $\phi^*$ and $\phi_{\text{rcp}}$ to a weakly adhesive bubble interaction that we have studied in complementary ground-based experiments.

Big bubbles are largely used in physics laboratories to study 2D turbulence, surface wavers, fundamental properties of soap systems... On a more artistic point of view, blowing big bubbles is part of many artistic shows. Both communities usually wan to get reasonably stable foam films. The purpose of this article is to propose the main physical ingredients allowing to identify a good recipe for making stable films and bubbles. We propose controlled experiments, to measure both the easiness to generate a bubble and its stability for different stabilizing solutions, which we choose by adding one by one the ingredients contained in an artist's recipe. The main results are that (i) the surfactant concentration must be not too high (ii) the solution must contain some long flexible polymer chains to allow an easy bubble generation and (iii) the addition of glycerol allows a better bubble stability by avoiding evaporation. We finally propose an efficient recipe, which takes into account all these considerations.

Production, drainage and stability of foams films, i.e. films in contact with their menisci, are fascinating problems that remain still unsolved. In this article, we propose to explore the regime of large velocities and large film sizes. This one is not accessible in experiments classically conducted in the literature, and allows us to study the regime of large extension and large extension rates. With our setup, we make soap films up to two meters high by pulling a horizontal fishing line driven by belts out of a soapy solution at velocities ranging from 20~cm/s to 250~cm/s. We characterize the thickness profile of the central part of the film that behaves like a rubber band under tension. We show that its thickness profile is well described by a static model in which a homogeneous elastic film is stretched by its own weight. This leads to an exponential thickness profile with a characteristic length given by a competition between gravity and surface elasticity. The prefactor is fixed by the shape and area of the film, governed by the fishing line motion but also by a continuous extraction of foam film from the lateral menisci, thicker than the central part, and that progressively invades the film from its lateral boundaries. The model we propose captures the subtle interplay between gravity, film elasticity and film extraction and leads to predictions in good agreement with our experimental data.

The paper describes a study of wet foams in microgravity whose bubble size distribution evolves due to diffusive gas exchange. We focus on the comparison between the size of bubbles determined from images of the foam surface and the size of bubbles in the bulk foam, determined from Diffuse Transmission Spectroscopy (DTS). Extracting the bubble size distribution from images of a foam surface is difficult so we have used three different procedures : manual analysis, automatic analysis with a customized Python script and machine learning analysis. Once various pitfalls were identified and taken into account, all the three procedures yield identical results within error bars. DTS only allows the determination of an average bubble radius which is proportional to the photon transport mean free path $\ell^*$. The relation between the measured diffuse transmitted light intensity and {$\ell^*$} previously derived for slab-shaped samples of infinite lateral extent does not apply to the cuboid geometry of the cells used in the microgravity experiment. A new more general expression of the diffuse intensity transmitted with specific optical boundary conditions has been derived and applied to determine the average bubble radius. The temporal evolution of the average bubble radii deduced from DTS and of the same average radii of the bubbles measured at the sample surface are in very good agreement throughout the coarsening. Finally, ground experiments were performed to compare bubble size distributions in a bulk wet foam and at its surface at times so short that diffusive gas exchange is insignificant. They were found to be similar, confirming that bubbles seen at the surface are representative of the bulk foam bubbles.

Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses. In this work, we investigate two major forms of biases, i.e., class-dependent residuals and mode collapse, in a case study of estimating photometric redshifts as a classification problem using Convolutional Neural Networks (CNNs) and galaxy images with spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multi-channel outputs, balancing the training data and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshifts or photometric redshifts, and the biases with respect to these two definitions are incompatible and should be treated in a split way. We suggest that resolving biases in the spectroscopic space is a prerequisite for resolving biases in the photometric space. Experiments show that our methods possess a better capability in controlling biases compared to benchmark methods, and exhibit robustness under varying implementing and training conditions provided with high-quality data. Our methods have promises for future cosmological surveys that require a good constraint of biases, and may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance trade-off and the demand on sufficient statistics suggest the need for developing better methodologies and optimizing data usage strategies., Comment: 29 pages, 12+11 figures, 2+3 tables; accepted in Astronomy & Astrophysics

In this paper, we investigate the thinning dynamics of evaporating surfactant-stabilised surface bubbles by considering the role of the physical-chemistry of solutions used in the liquid bath. We study the impact of the surfactant concentration below and above the cmc (critical micelle concentration) and the role of ambient humidity. First, in a humidity-saturated atmosphere, we show that if the initial thickness depends on the surfactant concentration and is limited by the surface elasticity, the drainage dynamics are very well described from the capillary and gravity contributions. These dynamics are independent of the surfactant concentration. In a second part, our study reveals that the physical-chemistry impacts the thinning dynamics through evaporation. We include in the model the additional contribution due to evaporation, which shows a good description of the experimental data below the cmc. Above the cmc, although this model is unsatisfactory at short times, the dynamics at long times is correctly rendered and we establish that the increase of the surfactant concentration decreases the impact of evaporation. Finally, the addition of a hygroscopic compound, glycerol, can be also rationalized by our model. We demonstrate that glycerol decreases the bubble thinning rate at ambient humidity, thus increasing their stability., Comment: 7 pages, 5 figures. Submitted

The prediction of the lifetime of surface bubbles necessitates a better understanding of the thinning dynamics of the bubble cap. In 1959, Mysel \textit{et al.} \cite{mysels1959soap}, proposed that \textit{marginal regeneration} i.e. the rise of patches, thinner than the film should be taken into account to describe the film drainage. Nevertheless, an accurate description of these buoyant patches and of their dynamics as well as a quantification of their contribution to the thinning dynamics is still lacking. In this paper, we visualize the patches, and show that their rising velocities and sizes are in good agreement with models respectively based on the balance of gravitational and surface viscous forces and on a Rayleigh-Taylor like instability \cite{Seiwert2017,Shabalina2019}. Our results suggest that, in an environment saturated in humidity, the drainage induced by their dynamics correctly describes the film drainage at the apex of the bubble within the experimental error bars. We conclude that the film thinning of soap bubbles is indeed controlled, to a large extent, by \textit{marginal regeneration} in the absence of evaporation.

Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project., Comment: 113 pages, White paper

Artists, using an empirical knowledge, manage to generate and play with giant soap films and bubbles. Until now, scientific studies of soap films generated at a controlled velocity and without any feeding from the top, studied films of a few square centimeters. The present work aims to present a new setup to generate and characterize giant soap films (2~m $\times$ 0.7~m). Our setup is enclosed in a humidity-controlled box of 2.2~m high, 1~m long and 0.75~m large. Soap films are entrained by a fishing line withdrawn out of a bubbling solution at various velocities. We measure the maximum height of the generated soap films, as well as their lifetime, thanks to an automatic detection. This is allowed by light-sensitive resistors collecting the light reflected on the soap films and ensures robust statistical measurements. In the meantime, thickness measurements are performed with a UV-VIS-spectrometer, allowing us to map the soap films thickness over time.

Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images. However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects. These limitations might be harmful for subsequent scientific applications in astrophysics. Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation. In this work, we propose a two-way image translation model using GANs that exploits both paired and unpaired images in a semi-supervised manner, and introduce a noise emulating module that is able to learn and reconstruct noise characterized by high-frequency features. By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global and local properties effectively and outperforms benchmark image translation models. To our best knowledge, this work is the first attempt to apply semi-supervised methods and noise reconstruction techniques in astrophysical studies., Comment: Accepted at ICPR 2020

We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the characterization and the classification of light curves. It takes light curves as input, without any additional features. PELICAN can deal with the sparsity and the irregular sampling of light curves. It is designed to remove the problem of non-representativeness between the training and test databases coming from the limitations of the spectroscopic follow-up. We applied our methodology on different supernovae light curve databases. First, we evaluated PELICAN on the Supernova Photometric Classification Challenge for which we obtained the best performance ever achieved with a non-representative training database, by reaching an accuracy of 0.811. Then we tested PELICAN on simulated light curves of the LSST Deep Fields for which PELICAN is able to detect 87.4% of supernovae Ia with a precision higher than 98%, by considering a non-representative training database of 2k light curves. PELICAN can be trained on light curves of LSST Deep Fields to classify light curves of LSST main survey, that have a lower sampling rate and are more noisy. In this scenario, it reaches an accuracy of 96.5% with a training database of 2k light curves of the Deep Fields. It constitutes a pivotal result as type Ia supernovae candidates from the main survey might then be used to increase the statistics without additional spectroscopic follow-up. Finally we evaluated PELICAN on real data from the Sloan Digital Sky Survey. PELICAN reaches an accuracy of 86.8% with a training database composed of simulated data and a fraction of 10% of real data. The ability of PELICAN to deal with the different causes of non-representativeness between the training and test databases, and its robustness against survey properties and observational conditions, put it on the forefront of the light curves classification tools for the LSST era.

Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophysical models, and thus, for the classification, they usually use a machine learning approach in two-steps, which consists in, first, extracting features, and second, using a classifier. In this paper, we are specifically studying the supernovae phenomenon and especially the binary classification "I.a supernovae versus not-I.a supernovae". We present two Convolutional Neural Networks (CNNs) defeating the current state-of-the-art. The first one is adapted to time series and thus to the treatment of supernovae light-curves. The second one is based on a Siamese CNN and is suited to the nature of data, i.e. their sparsity and their weak quantity (small learning database)., Comment: IS&T International Symposium on Electronic Imaging, EI'2019, Color Imaging XXIV: Displaying, Processing, Hardcopy, and Applications, Burlingame (suburb of San Francisco), California USA, 13 - 17 January, 2019, 8 pages. The CNN is downloadable there: https://github.com/Anzzy30/SupernovaeClassification

Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. Photometric redshifts can be efficiently combined with the cosmic web (CW) extracted from overlapping spectroscopic surveys to improve their accuracy. We apply a similar method using a new generation of photometric redshifts based on a convolution neural network (CNN). The CNN is trained on the SDSS images with the main galaxy sample (SDSS-MGS, $r \leq 17.8$) and the GAMA spectroscopic redshifts up tor $\sim 19.8$. The mapping of the CW is obtained with 680,000 spectroscopic redshifts from the MGS and BOSS surveys. The redshift probability distribution functions (PDF), which are well calibrated (unbiased and narrow, $\leq 120$ Mpc), intercept a few CW structure along the line of sight. Combining these PDFs with the density field distribution provides new photometric redshifts, $z_{web}$, whose accuracy is improved by a factor of two (i.e.,${\sigma} \sim 0.004(1+z)$) for galaxies with $r \leq 17.8$. For half of them, the distance accuracy is better than 10 cMpc. The narrower the original PDF, the larger the boost in accuracy. No gain is observed for original PDFs wider than 0.03. The final $z_{web}$ PDFs also appear well calibrated. The method performs slightly better for passive galaxies than star-forming ones, and for galaxies in massive groups since these populations better trace the underlying large-scale structure. Reducing the spectroscopic sampling by a factor of 8 still improves the photometric redshift accuracy by 25%. Extending the method to galaxies fainter than the MGS limit still improves the redshift estimates for 70% of the galaxies, with a gain in accuracy of 20% at low $z$ where the resolution of the CW is the highest.

We apply a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a gaussian process classifier). Indeed, the accuracy of the CNN within |\Delta z|<0.1 can reach 78.09%, within |\Delta z|<0.2 reaches 86.15%, within |\Delta z|<0.3 reaches 91.2% and the value of rms is 0.359. The performance of the KNN decreases for the three |\Delta z| regions, since within the accuracy of |\Delta z|<0.1, |\Delta z|<0.2 and |\Delta z|<0.3 is 73.72%, 82.46% and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases like the Large Synoptic Survey Telescope.

Surface bubbles have attracted much interest in the past decades. In this article, we propose to explore the lifetime and thinning dynamics of centimetric surface bubbles. We study the impact of the bubbles size as well as that of the atmospheric humidity through a careful control and systematic variation of the relative humidity in the measuring chamber. We first adress the question of the drainage under saturated water vapor conditions and show that a model including both capillary and gravity driven drainage provides the best prediction for this process. Additionally, unprecedented statistics on the bubbles lifetimes confirm experimentally that this parameter is set by evaporation to leading order. We make use of a model based on the overall thinning dynamics of the thin film and assume a rupture thickness of the order 10-100 nm to obtain a good representation of these data. For experiments conducted far from saturation, the convective evaporation of the bath is shown to dominate the overall mass loss in the cap film due to evaporation.

A 1-parameter class of quadratic equations of state is confronted with the Hubble diagram of supernovae and Baryonic Acoustic Oscillations. The fit is found to be as good as the one using the LambdaCDM model. The corresponding universe has no initial singularity, only a mild bounce at a temperature well above the Planck temperature., Comment: To the memory of Daniel Kastler and Raymond Stora. 21 pages, 7 figures. arXiv admin note: text overlap with arXiv:1508.00809; version 2: 2 references added; version 3: corrected equations (9), (15) and the second of (36), matches published version

We developed a Deep Convolutional Neural Network (CNN), used as a classifier, to estimate photometric redshifts and associated probability distribution functions (PDF) for galaxies in the Main Galaxy Sample of the Sloan Digital Sky Survey at z < 0.4. Our method exploits all the information present in the images without any feature extraction. The input data consist of 64x64 pixel ugriz images centered on the spectroscopic targets, plus the galactic reddening value on the line-of-sight. For training sets of 100k objects or more ($\geq$ 20% of the database), we reach a dispersion $\sigma_{MAD}$<0.01, significantly lower than the current best one obtained from another machine learning technique on the same sample. The bias is lower than 0.0001, independent of photometric redshift. The PDFs are shown to have very good predictive power. We also find that the CNN redshifts are unbiased with respect to galaxy inclination, and that $\sigma_{MAD}$ decreases with the signal-to-noise ratio (SNR), achieving values below 0.007 for SNR >100, as in the deep stacked region of Stripe 82. We argue that for most galaxies the precision is limited by the SNR of SDSS images rather than by the method. The success of this experiment at low redshift opens promising perspectives for upcoming surveys., Comment: Submitted to A&A, Comments welcome, Model and Example available at "https://github.com/jpasquet/Photoz

We compute the birefringence of light in curved Robertson-Walker spacetimes and propose an exotic formula for redshift based on the internal structure of the spinning photon. We then use the Hubble diagram of supernovae to test this formula., Comment: 16 pages, 2 figures, version 2 matches the published one