Galactic cosmic rays on extrasolar Earth-like planets: II. Atmospheric implications

(abridged abstract) Theoretical arguments indicate that close-in terrestial exoplanets may have weak magnetic fields. As described in the companion article (Paper I), a weak magnetic field results in a high flux of galactic cosmic rays to the top of the planetary atmosphere. We investigate effects that may result from a high flux of galactic cosmic rays both throughout the atmosphere and at the planetary surface. Using an air shower approach, we calculate how the atmospheric chemistry and temperature change under the influence of galactic cosmic rays for Earth-like (N_2-O_2 dominated) atmospheres. We evaluate the production and destruction rate of atmospheric biosignature molecules. We derive planetary emission and transmission spectra to study the influence of galactic cosmic rays on biosignature detectability. We then calculate the resulting surface UV flux, the surface particle flux, and the associated equivalent biological dose rates. We find that up to 20% of stratospheric ozone is destroyed by cosmic-ray protons. The reduction of the planetary ozone layer leads to an increase in the weighted surface UV flux by two orders of magnitude under stellar UV flare conditions. The resulting biological effective dose rate is, however, too low to strongly affect surface life. We also examine the surface particle flux: For a planet with a terrestrial atmosphere, a reduction of the magnetic shielding efficiency can increase the biological radiation dose rate by a factor of two. For a planet with a weaker atmosphere (with a surface pressure of 97.8 hPa), the planetary magnetic field has a much stronger influence on the biological radiation dose, changing it by up to two orders of magnitude.Comment: 14 pages, 9 figures, published in A&

Galactic cosmic rays on extrasolar Earth-like planets I. Cosmic ray flux

(abridged abstract) Theoretical arguments indicate that close-in terrestial exoplanets may have weak magnetic fields, especially in the case of planets more massive than Earth (super-Earths). Planetary magnetic fields, however, constitute one of the shielding layers that protect the planet against cosmic-ray particles. In particular, a weak magnetic field results in a high flux of Galactic cosmic rays that extends to the top of the planetary atmosphere. We wish to quantify the flux of Galactic cosmic rays to an exoplanetary atmosphere as a function of the particle energy and of the planetary magnetic moment. We numerically analyzed the propagation of Galactic cosmic-ray particles through planetary magnetospheres. We evaluated the efficiency of magnetospheric shielding as a function of the particle energy (in the range 16 MeV $\le$ E $\le$ 524 GeV) and as a function of the planetary magnetic field strength (in the range 0 ${M}_\oplus$ $\le$ {M} $\le$ 10 ${M}_\oplus$). Combined with the flux outside the planetary magnetosphere, this gives the cosmic-ray energy spectrum at the top of the planetary atmosphere as a function of the planetary magnetic moment. We find that the particle flux to the planetary atmosphere can be increased by more than three orders of magnitude in the absence of a protecting magnetic field. For a weakly magnetized planet (${M}=0.05\,{M}_{\oplus}$), only particles with energies below 512 MeV are at least partially shielded. For a planet with a magnetic moment similar to Earth, this limit increases to 32 GeV, whereas for a strongly magnetized planet ($M=10.0\,{M}_{\oplus}$), partial shielding extends up to 200 GeV. We find that magnetic shielding strongly controls the number of cosmic-ray particles reaching the planetary atmosphere. The implications of this increased particle flux are discussed in a companion article.Comment: 10 pages, 9 figures; accepted in A&

Flexible Coloring

Motivated by reliability considerations in data deduplication for storage systems, we introduce the problem of flexible coloring. Given a hypergraph H and the number of allowable colors k, a flexible coloring of H is an assignment of one or more colors to each vertex such that, for each hyperedge, it is possible to choose a color from each vertex’s color list so that this hyperedge is strongly colored (i.e., each vertex has a different color). Different colors for the same vertex can be chosen for different incident hyperedges (hence the term flexible). The goal is to minimize color consumption, namely, the total number of colors assigned, counting multiplicities. Flexible coloring is NP-hard and trivially s − (s−1)k n approximable, where s is the size of the largest hyperedge, and n is the number of vertices. Using a recent result by Bansal and Khot, we show that if k is constant, then it is UGC-hard to approximate to within a factor of s − ε, for arbitrarily small constant ε> 0. s − (s−1)k k ′ Lastly, we present an algorithm with an approximation ratio, where k ′ is number of colors used by a strong coloring algorithm for H. Keywords: graph coloring, hardness of approximatio

Revisiting the cosmic-ray induced Venusian radiation dose in the context of habitability

The Atmospheric Radiation Interaction Simulator (AtRIS) was used to model the altitude-dependent Venusian absorbed dose and the Venusian dose equivalent. For the first time, we modeled the dose rates for different shape-, size-, and composition-mimicking detectors (phantoms): a CO$_2$-based phantom, a water-based microbial cell, and a phantom mimicking human tissue. Based on a new model approach, we give a reliable estimate of the altitude-dependent Venusian radiation dose in water-based microorganisms here for the first time. These microorganisms are representative of known terrestrial life. We also present a detailed analysis of the influence of the strongest ground-level enhancements measured at the Earth's surface, and of the impact of two historic extreme solar events on the Venusian radiation dose. Our study shows that because a phantom based on Venusian air was used, and because furthermore, the quality factors of different radiation types were not taken into account, previous model efforts have underestimated the radiation hazard for any putative Venusian cloud-based life by up to a factor of five. However, because we furthermore show that even the strongest events would not have had a hazardous effect on putative microorganisms within the potentially habitable zone (51 km - 62 km), these differences may play only a minor role

Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks

The increased attention to regulating the outputs of deep generative models, driven by growing concerns about privacy and regulatory compliance, has highlighted the need for effective control over these models. This necessity arises from instances where generative models produce outputs containing undesirable, offensive, or potentially harmful content. To tackle this challenge, the concept of machine unlearning has emerged, aiming to forget specific learned information or to erase the influence of undesired data subsets from a trained model. The objective of this work is to prevent the generation of outputs containing undesired features from a pre-trained GAN where the underlying training data set is inaccessible. Our approach is inspired by a crucial observation: the parameter space of GANs exhibits meaningful directions that can be leveraged to suppress specific undesired features. However, such directions usually result in the degradation of the quality of generated samples. Our proposed method, known as 'Adapt-then-Unlearn,' excels at unlearning such undesirable features while also maintaining the quality of generated samples. This method unfolds in two stages: in the initial stage, we adapt the pre-trained GAN using negative samples provided by the user, while in the subsequent stage, we focus on unlearning the undesired feature. During the latter phase, we train the pre-trained GAN using positive samples, incorporating a repulsion regularizer. This regularizer encourages the model's parameters to be away from the parameters associated with the adapted model from the first stage while also maintaining the quality of generated samples. To the best of our knowledge, our approach stands as first method addressing unlearning in GANs. We validate the effectiveness of our method through comprehensive experiments.Comment: 15 pages, 12 figure