Use of transverse beam polarization to probe anomalous VVH interactions at a Linear Collider

We investigate use of transverse beam polarization in probing anomalous coupling of a Higgs boson to a pair of vector bosons, at the International Linear Collider (ILC). We consider the most general form of VVH (V = W/Z) vertex consistent with Lorentz invariance and investigate its effects on the process e+ e- --> f bar{f} H, f being a light fermion. Constructing observables with definite CP and naive time reversal (tilde T) transformation properties, we find that transverse beam polarization helps us to improve on the sensitivity of one part of the anomalous ZZH coupling that is odd under CP. Even more importantly it provides the possibility of discriminating from each other, two terms in the general ZZH vertex, both of which are even under CP and tilde T. Use of transverse beam polarization when combined with information from unpolarized and linearly polarized beams therefore, allows one to have completely independent probes of all the different parts of a general ZZH vertex.Comment: 15 pages, 3 figures, published versio

Effects of polarisation on study of anomalous VVH interactions at a Linear Collider

We investigate the use of beam polarisation as well as final state $\tau$ polarisation effects in probing the interaction of the Higgs boson with a pair of heavy vector bosons in the process $e^+ e^- \to f \bar f H$, where $f$ is any light fermion. The sensitivity of the International Linear Collider (ILC) operating at $\sqrt s=500$ GeV, to such $VVH$($V = W/Z$) couplings is examined in a model independent way. The effects of ISR and beamstrahlung are discussed.Comment: To appear in the proceedings of 2007 International Linear Collider Workshop (LCWS07 and ILC07), Hamburg, Germany, 30 May - 3 Jun 2007. 4 pages, LaTeX, 1 eps figure. requires ilcws07.cls. included in submissio

A Short Review on Machine Learning in Space Science and Exploration

Machine learning is revolutionizing space exploration by tackling massive datasets, empowering astronauts, and driving scientific breakthroughs. From Deep Space 1's autonomous navigation to the James Webb Space Telescope's AI-assisted exoplanet discovery, Machine learning is transforming the present and shaping the future. With missions like NASA's Parker Solar Probe and the development of AI-powered monitoring systems and astro robots, the possibilities for unravelling the cosmos and democratizing space exploration are limitless. The future of space exploration lies in harnessing the power of ML to unlock the universe's secrets and make them accessible to all

The Potential of Machine Learning for Future Mars Exploration

The pursuit of understanding Mars, our neighboring planet, is rife with challenges that range from treacherous conditions for potential human astronauts to the vast distances that complicate communication. However, a beacon of hope emerges in the form of machine learning, a technological frontier that promises to transform the landscape of Martian exploration. As we embark on this interplanetary journey, the recognition of machine learning's potential is growing. It offers innovative solutions to some of the most pressing challenges, ushering in a new era of autonomous exploration. Imagine rovers and orbiter spacecraft equipped with the ability to analyze Martian data on-site, reducing the need for slow communications with Earth. This revolutionary approach is already in action with rovers like Curiosity, where machine learning enables self-directed exploration and continuous data analysis on the Martian surface. The applications of machine learning extend beyond mere autonomy. They hold the promise of addressing communication limitations, providing greater operational autonomy, and unlocking the mysteries that shroud the Red Planet. From identifying sources of atmospheric gases, such as oxygen and methane, to interpreting geological features like cloud distributions and weather patterns, machine learning is proving itself to be a versatile and indispensable tool in unraveling the complexities of Mars. Venturing deeper into the Martian climate, machine learning becomes a powerful ally. By leveraging this technology to analyze climate data, we have the potential to generate predictive models crucial for planning future surface missions and assessing the habitability of Mars. Additionally, the application of machine learning on Earth offers a unique opportunity to decode uncertainties related to Martian atmospheric interactions, the dynamics of dust storms, and conditions beneath the surface. Anticipating the wealth of data that future Mars missions will yield, the integration of machine learning emerges as a game-changer. Its efficiency in discerning intricate patterns within extensive datasets has the potential to revolutionize our scientific understanding of Mars. As we delve deeper into the mysteries of the Red Planet, machine learning stands as a pivotal catalyst, promising not just incremental but transformative discoveries. It becomes the linchpin in our ongoing quest to answer the age-old question: Did life ever exist on Mars? In the realm of Martian exploration, machine learning is proving to be the technological cornerstone that propels us towards unprecedented scientific revelations

Signatures of anomalous VVH interactions at a linear collider

We examine, in a model independent way, the sensitivity of a Linear Collider to the couplings of a light Higgs boson to gauge bosons. Including the possibility of CP violation, we construct several observables that probe the different anomalous couplings possible. For an intermediate mass Higgs, a collider operating at a center of mass energy of 500 GeV and with an integrated luminosity of 500 fb$^{-1}$ is shown to be able to constrain the $ZZH$ vertex at the few per cent level, and with even higher sensitivity in certain directions. However, the lack of sufficient number of observables as well as contamination from the $ZZH$ vertex limits the precision with which the $WWH$ coupling can be measured.Comment: Typeset in RevTeX4, 16 pages, 12 figures; V2: minor changes in title and Sec. II and III; V3: version appeared in PRD with minor correctio