Lasma Alberte, Paolo Creminelli, Andrei Khmelnitsky, David Pirtskhalava, Enrico Trincherini

We propose a technically natural scenario whereby an initially large cosmological constant (c.c.) is relaxed down to the observed value due to the dynamics of a scalar evolving on a very shallow potential. The model crucially relies on a sector that violates the null energy condition (NEC) and gets activated only when the Hubble rate becomes sufficiently small --- of the order of the present one. As a result of NEC violation, this low-energy universe evolves into inflation, followed by reheating and the standard Big Bang cosmology. The symmetries of the theory force the c.c. to be the same before and after the NEC-violating phase, so that a late-time observer sees an effective c.c. of the correct magnitude. Importantly, our model allows neither for eternal inflation nor for a set of possible values of dark energy, the latter fixed by the parameters of the theory.

Leonardo Di G. Sigalotti, Otto Rendón, Jaime Klapp, Carlos A. Vargas, Kilver Campos

In this paper the problem of consistency of smoothed particle hydrodynamics (SPH) is solved. A novel error analysis is developed in $n$-dimensional space using the Poisson summation formula, which enables the treatment of the kernel and particle approximation errors in combined fashion. New consistency integral relations are derived for the particle approximation which correspond to the cosine Fourier transform of the classically known consistency conditions for the kernel approximation. The functional dependence of the error bounds on the SPH interpolation parameters, namely the smoothing length $h$ and the number of particles within the kernel support ${\cal{N}}$ is demonstrated explicitly from which consistency conditions are seen to follow naturally. As ${\cal{N}}\to\infty$, the particle approximation converges to the kernel approximation independently of $h$ provided that the particle mass scales with $h$ as $m\propto h^{\beta}$, with $\beta >n$. This implies that as $h\to 0$, the joint limit $m\to 0$, ${\cal{N}}\to\infty$, and $N\to\infty$ is necessary for complete convergence to the continuum, where $N$ is the total number of particles. The analysis also reveals the presence of a dominant error term of the form $(\ln {\cal{N}})^{n}/{\cal{N}}$, which tends asymptotically to $1/{\cal{N}}$ when ${\cal{N}}\gg 1$, as it has long been conjectured based on the similarity between the SPH and the quasi-Monte Carlo estimates.

Arata Aoki, Jiro Soda

The ultralight axion with mass around $10^{-23}$ eV is known as a candidate of dark matter. A peculiar feature of the ultralight axion is oscillating pressure in time, which produces oscillation of gravitational potentials. Since the solar system moves through the dark matter halo at the velocity of about $v \sim 300 \, \text{km} / \text{s} = 10^{-3}$, there exists axion wind, which looks like scalar gravitational waves for us. Hence, there is a chance to detect ultralight axion dark matter with a wide mass range by using laser interferometer detectors. We calculate the detector signal induced by the oscillating pressure of the ultralight axion field, which would be detected by future laser interferometer experiments. We also argue that the detector signal can be enhanced due to the resonance in modified gravity theory explaining the dark energy.

A. Möller, V. Ruhlmann-Kleider, C. Leloup, J. Neveu, N. Palanque-Delabrouille, J. Rich, R. Carlberg, C. Lidman, C. Pritchet

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2<z<1.1$). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of $0.98$. We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than $5\%$. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high-$z$ SN survey with application to real SN data.