No Arabic abstract
The search for life on the planets outside the Solar System can be broadly classified into the following: looking for Earth-like conditions or the planets similar to the Earth (Earth similarity), and looking for the possibility of life in a form known or unknown to us (habitability). The two frequently used indices, ESI and PHI, describe heuristic methods to score similarity/habitability in the efforts to categorize different exoplanets or exomoons. ESI, in particular, considers Earth as the reference frame for habitability and is a quick screening tool to categorize and measure physical similarity of any planetary body with the Earth. The PHI assesses the probability that life in some form may exist on any given world, and is based on the essential requirements of known life: a stable and protected substrate, energy, appropriate chemistry and a liquid medium. We propose here a different metric, a Cobb-Douglas Habitability Score (CDHS), based on Cobb-Douglas habitability production function (CD-HPF), which computes the habitability score by using measured and calculated planetary input parameters. The proposed metric, with exponents accounting for metric elasticity, is endowed with verifiable analytical properties that ensure global optima, and is scalable to accommodate finitely many input parameters. The model is elastic, does not suffer from curvature violations and, as we discovered, the standard PHI is a special case of CDHS. Computed CDHS scores are fed to K-NN (K-Nearest Neighbour) classification algorithm with probabilistic herding that facilitates the assignment of exoplanets to appropriate classes via supervised feature learning methods, producing granular clusters of habitability. The proposed work describes a decision-theoretical model using the power of convex optimization and algorithmic machine learning.
Martian subsurface habitability and astrobiology can be evaluated via a lava tube cave, without drilling. MACIE addresses two key goals of the Decadal Survey (2013-2022) and three MEPAG goals. New advances in robotic architectures, autonomous navigation, target sample selection, and analysis will enable MACIE to explore the Martian subsurface.
Habitability has been generally defined as the capability of an environment to support life. Ecologists have been using Habitat Suitability Models (HSMs) for more than four decades to study the habitability of Earth from local to global scales. Astrobiologists have been proposing different habitability models for some time, with little integration and consistency between them and different in function to those used by ecologists. In this white paper, we suggest a mass-energy habitability model as an example of how to adapt and expand the models used by ecologists to the astrobiology field. We propose to implement these models into a NASA Habitability Standard (NHS) to standardize the habitability objectives of planetary missions. These standards will help to compare and characterize potentially habitable environments, prioritize target selections, and study correlations between habitability and biosignatures. Habitability models are the foundation of planetary habitability science. The synergy between the methods used by ecologists and astrobiologists will help to integrate and expand our understanding of the habitability of Earth, the Solar System, and exoplanets.
The Los Alamos National Laboratory designed and built Mars Odyssey Neutron Spectrometer (MONS) has been in excellent health operating from February 2002 to the present. MONS measures the neutron leakage albedo from galactic cosmic ray bombardment of Mars. These signals can indicate the presence of near-surface water deposits on Mars, and can also be used to study properties of the seasonal polar CO$_2$ ice caps. This work outlines a new analysis of the MONS data that results in new and extended time-series maps of MONS thermal and epithermal neutron data. The new data are compared to previous publications on the MONS instrument. We then present preliminary results studying the inter-annual variability in the polar regions of Mars based on 8 Mars-Years of MONS data from the new dataset.
The search for exoplanetary life must encompass the complex geological processes reflected in an exoplanets atmosphere, or we risk reporting false positive and false negative detections. To do this, we must nurture the nascent discipline of exogeoscience to fully integrate astronomers, astrophysicists, geoscientists, oceanographers, atmospheric chemists and biologists. Increased funding for interdisciplinary research programs, supporting existing and future multidisciplinary research nodes, and developing research incubators is key to transforming true exogeoscience from an aspiration to a reality.
We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.