Measurement of the atmospheric muon depth intensity relation with the NEMO Phase-2 tower

The results of the analysis of the data collected with the NEMO Phase-2 tower, deployed at 3500 m depth about 80 km off-shore Capo Passero (Italy), are presented. Cherenkov photons detected with the photomultipliers tubes were used to reconstruct the tracks of atmospheric muons. Their zenith-angle distribution was measured and the results compared with Monte Carlo simulations. An evaluation of the systematic effects due to uncertainties on environmental and detector parameters is also included. The associated depth intensity relation was evaluated and compared with previous measurements and theoretical predictions. With the present analysis, the muon depth intensity relation has been measured up to 13 km of water equivalent.Comment: submitted to Astroparticle Physic

Deep sea tests of a prototype of the KM3NeT digital optical module

The first prototype of a photo-detection unit of the future KM3NeT neutrino telescope has been deployed in the deepwaters of the Mediterranean Sea. This digital optical module has a novel design with a very large photocathode area segmented by the use of 31 three inch photomultiplier tubes. It has been integrated in the ANTARES detector for in-situ testing and validation. This paper reports on the first months of data taking and rate measurements. The analysis results highlight the capabilities of the new module design in terms of background suppression and signal recognition. The directionality of the optical module enables the recognition of multiple Cherenkov photons from the same (40)Kdecay and the localisation of bioluminescent activity in the neighbourhood. The single unit can cleanly identify atmospheric muons and provide sensitivity to the muon arrival directions

Long term monitoring of the optical background in the Capo Passero deep-sea site with the NEMO tower prototype

The NEMO Phase-2 tower is the first detector which was operated underwater for more than 1 year at the "record" depth of 3500 m. It was designed and built within the framework of the NEMO (NEutrino Mediterranean Observatory) project. The 380 m high tower was successfully installed in March 2013 80 km offshore Capo Passero (Italy). This is the first prototype operated on the site where the Italian node of the KM3NeT neutrino telescope will be built. The installation and operation of the NEMO Phase-2 tower has proven the functionality of the infrastructure and the operability at 3500 m depth. A more than 1 year long monitoring of the deep water characteristics of the site has been also provided. In this paper the infrastructure and the tower structure and instrumentation are described. The results of long term optical background measurements are presented. The rates show stable and low baseline values, compatible with the contribution of K-40 light emission, with a small percentage of light bursts due to bioluminescence. All these features confirm the stability and good optical properties of the site.Funded by SCOAP3Adrián Martínez, S.; Aiello, S.; Ameli, F.; Anghinolfi, M.; Ardid Ramírez, M.; Barbarino, G.; Barbarito, E.... (2016). Long term monitoring of the optical background in the Capo Passero deep-sea site with the NEMO tower prototype. European Physical Journal C: Particles and Fields. 76(68):1-11. https://doi.org/10.1140/epjc/s10052-016-3908-0S1117668M. Ageron et al., ANTARES: the first undersea neutrino telescope. Nucl. Instr. Methods A 656, 11 (2011)V. Aynutdnov for the Baikal Coll., The BAIKAL neutrino project: results and perspective. Nucl. Instr. Methods. A 628, 115 (2011)A. Achterberg et al., First year performance of the IceCube neutrino telescope. Astropart. Phys. 26, 155 (2006)M.G. Aartsen et al., Evidence for high-energy extraterrestrial neutrinos at the IceCube detector. Science 342, 1242856 (2013)M.G. Aartsen et al., Observation of high-energy astrophysical neutrinos in three years of IceCube data. Phys. Rev. Lett. 113, 101101 (2014)M.G. Aartsen et al., Evidence for astrophysical muon neutrinos from the northern sky with IceCube. Phys. Rev. Lett. 115, 081102 (2015)E. Migneco et al., Status of NEMO. Nucl. Instr. Methods A 567, 444 (2006)E. Migneco et al., Recent achievements of the NEMO project. Nucl. Instr. Methods A 588, 111 (2008)A. Capone et al., Recent results and perspectives od the NEMO project. Nucl. Instr. Methods A 602, 47 (2009)M. Taiuti et al., The NEMO project: a status report. Nucl. Instr. Methods A 626, S25 (2011)S. Aiello et al., Measurement of the atmospheric muon flux of the NEMO Phase-1 detector. Astropart. Phys. 33, 263 (2010)A. Capone et al., Measurements of light transmission in deep sea with the AC9 transmissometer. Nucl. Instr. Methods A 487, 423 (2002)G. Riccobene et al., Deep seawater inherent optical properties in the Southern Ionian Sea. Astropart. Phys. 27, 1 (2007)A. Rubino et al., Abyssal undular vortices in the Eastern Mediterranean basin. Nat. Commun. 3, 834 (2012)KM3NeT web site. www.km3net.orgM. Sedita for the NEMO collaboration, Electro-optical cable and power feeding system for the NEMO Phase-2 project. Nucl. Instr. Methods A 567, 531 (2006)R. Cocimano for the NEMO collaboration, A comparison of AC and DC power feeding systems based on the NEMO experiences. Nucl. Instr. Methods A 602, 171 (2009)A. Orlando for the NEMO collaboration, On line monitoring of the power control and engineering parameters systems of the NEMO Phase-2 tower. Nucl. Instr. Methods. A 602, 180 (2009)M. Musumeci for the NEMO collaboration, Construction and deployment issues for a km {3} 3 underwater detector. Nucl. Instr. Methods. A 567, 545 (2006)S. Aiello et al., The optical modules of the phase-2 of the NEMO project. JINST 8, P07001 (2013)E. Leonora, S. Aiello, Design and assembly of the optical modules for phase-2 of the NEMO project. Nucl. Instr. Methods A 725, 234 (2013)S. Aiello et al., Procedures and results of the measurements on large area photomultipliers for the NEMO project. Nucl. Instr. Methods A 614, 206 (2010)C.A. Nicolau for the NEMO collaboration, An FPGA-based readout electronics for neutrino telescopes. Nucl. Instr. Methods A 567, 552 (2006)M. Cordelli et al., PORFIDO: oceanographic data for neutrino telescopes. Nucl. Instr. Methods A 626–627, S109 (2011)F. Ameli, The data acquisition and transport design for NEMO Phase-1. IEEE Trans. Nucl. Sci. 55(1), 233 (2008)A. D’Amico for the NEMO collaboration, Design of the optical Raman amplifier for the shore station of NEMO Phase-2. Nucl. Instr. Methods A 626–627, S173 (2011)T. Chiarusi for the NEMO collaboration, Scalable TriDAS for the NEMO project. Nucl. Instr. Methods A 630, 107 (2011)S. Viola et al., NEMO-SMO acoustic array: a deep-sea test of a novel acoustic positioning system for a km $$^3$$ 3 -scale underwater neutrino telescope. Nucl. Instr. Methods A 725, 207 (2013)S. Viola et al., in Underwater acoustic positioning system for the SMO and KM3NeT-Italia projects. AIP Conference Proceedings 1630, 134 (2014)M. Circella for the NEMO collaboration, Time calibration of the NEutrino Mediterranean Observatory (NEMO). Nucl. Instr. Methods A 602, 187 (2009)S. Aiello et al., Measurement of the atmospheric muon depth intensity relation with the NEMO phase-2 tower. Astropart. Phys. 66, 1 (2015)C. Hugon for the ANTARES and KM3NeT collaborations, Step by step simulation of phototubes for the KM3NeT and ANTARES optical modules. Nucl. Instr. Methods A 787, 189 (2015)Ch. Tamburini et al., Deep-sea bioluminescence blooms after dense water formation at the ocean surface. PLOS One 8, e67523 (2013

A MULTI-STEP APPROACH TO TIME SERIES ANALYSIS AND GENE EXPRESSION CLUSTERING

ABSTRACT Motivation: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. Results: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering genemicroarray data. Themethod,which provides auser-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. Availability: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

NEC for Gene Expression Analysis

Aim of this work is to apply a novel comprehensive machine learning tool for data mining to preprocessing and interpretation of gene expression data. Furthermore, some visualization facilities are provided. The data mining framework consists of two main parts: preprocessing and clustering-agglomerating phases. To the first phase belong a noise filtering procedure and a non-linear PCA Neural Network for feature extraction. The second phase is used to accomplish an unsupervised clustering based on a hierarchy of two approaches: a Probabilistic Principal Surfaces to obtain the rough regions of interesting points and a Fisher-Negentropy information based approach to agglomerate the regions previously found in order to discover substructures present in the data. Experiments on gene microarray data are made. Several experiments are shown varying the threshold, needed by the agglomerative clustering, to understand the structure of the analyzed data set