Working group report: Neutrino and astroparticle physics

This is the report of neutrino and astroparticle physics working group at WHEPP-7. Discussions and work on CP violation in long baseline neutrino experiments, ultra high energy neutrinos, supernova neutrinos and water Cerenkov detectors are discussed.     

Chemical reaction studies in CH4/Ar and CH4/N2 gas mixtures of a dielectric barrier discharge

Chemical reactions in a dielectric barrier discharge at medium pressure of 250-300 mbar have been studied in CH(4)/Ar and CH(4)/N(2) gas mixtures by means of mass spectrometry. The main reaction scheme is production of H(2) by fragmentation of CH(4), but also production of higher order hydrocarbon molecules such as C(n)H(m) with n up to 9 including formation of different functional CN groups is observed. Formation of C(2)H(2), C(2)H(4), and C(2)H(6) molecules has been investigated in some detail. Significant differences are noted in comparison to a theoretical estimate.     

Scaling of decay lengths for surface states in the gaps of one-dimensional semi-infinite superlattices

We look at some one-dimensional semi-infinite superlattices with an underlying Hamiltonian that is of the nearest neighbour, tight binding type. A real space rescaling procedure which is exact in one dimension is applied to obtain the location of the subbands. It has been found that these subbands never overlap in 1D, and we interpret this as a band repulsion effect. Relevance in the case of a disordered system where this band repulsion crosses over to the well-known level repulsion is discussed. Then with a proper matching at the boundary we solve for the sets of denumerably infinite number of decaying solutions (the surface states) in the gaps. These types of states have been proposed quite some time ago. We look at detail theirexact analytical solutions in 1D and find that their decay lengths near the band edges diverge as |E–E _b|^–v, wherev=1/2 andE _b is the nearest band edge. The decay lengths and their divergence exponent match extremely well with those obtained from transfer matrix method. Some recent experiments on quantum well structures seem to have observed such states.     

A periodic first principle study to design microporous crystal 12MO, 7Al2O3 for selective and active O radicals encaging

Periodic first principle calculation correlates the role of metal substitution (Sr and Ba in place of Ca) on selective encaging of active O⁻ radicals inside the microporous 12MO, 7Al₂O₃ crystal structure. We have exchanged Ca by Sr and Ba and as well extracted electron stepwise to monitor selectivity of different anion encaging inside the same structure type. Ca favors O²⁻ encaging and shows no electron transition when neutral, whereas Sr shows no transition in absence of unpaired electron and can successfully trap O⁻, Ba though less active than the other metal substituents shows oxygen encaging at its zero and mono-positive state.     

Convenient route to enantiopure aryl cyclopentanes via Diels-Alder reaction of asymmetric dienes. Total synthesis of (+)-herbertene and (+)-cuparene

A general route for the synthesis of highly substituted aryl cyclopentanes has been developed involving Diels-Alder reaction of asymmetric dienes prepared from (+)-camphoric acid followed by aromatization of the resulting cyclohexene derivatives. Employing this protocol enantiospecific synthesis of (+)-herbertene and (+)-cuparene has been accomplished.     

The k-essence scalar field in the context of Supernova Ia observations

We study a framework where the hypothesis of a minimum length in space-time is complemented with the notion of reference frame invariance. It turns out natural to interpret the action of the obtained reference frame transformations in the context of doubly special relativity. As a consequence of this formalism we find interesting connections between the minimum length properties and the modified velocity-energy relation for ultra-relativistic particles. For example, we can predict the ratio between the minimum lengths in space and time using the results from OPERA on superluminal neutrinos.     

H2-release from alcohols,diols, and compounds with amino functionality promoted by titanium(II) sandwich complex, [Cp2Ti]: a theoretical approach

Structural Chemistry - The role of Ti(II) sandwich complex, [Cp2Ti], generated by the combination of [Cp2TiCl2] and two equivalents of nBuLi, in situ, in toluene has been investigated theoretically...     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.