News from the experiments
ANTARES/KM3NeT had a 2-week Collaboration Meeting (7-18 June, via zoom) hosted by LPC Caen, France. The main focus was on finalizing results for ICRC.
Two new institutions have joined KM3NeT as new associate members:
Laboratoire d’Informatique & Systemes, Univ. Toulon, France, interested in bioacoustics data,
Instituto de Ciencias del Mar (ICM), Barcelona, Spain, interested in bioluminescence studies.
Baikal GVD had its collaboration meeting from May 31 to June 3, focusing on the evaluation of the last deployment season, technological solution for future deployments, and – of course – also to the preparation of ICRC contributions.
For everybody who is looking for an actual display of Baikal-GVD (status since April 2021), here is it:
IceCube (apart of also preparing ICRC) has launched a new public newsletter, see the first edition at https://app.explore.wisc.edu/e/es?s=1427524768&e=604627&elqTrackId=51e861781d16471282f1c296cabd15a9&elq=c3bd221edf564f7bbff95bb16e6cceaf&elqaid=17524&elqat=1
to which you can subscribe at https://explore.wisc.edu/icecube-sub-1.
Advertised on this page and very worth watching: https://www.youtube.com/watch?v=n9UJOflxNwU
The Baikal collaboration has submitted a paper Measuring muon tracks in Baikal-GVD using a fast reconstruction algorithm to EPJ-C (posted at arXiv:2106.06288).
GNN Monthly readers may remember the key result of the analysis (September/October edition 2020), shown (with slightly different absolute numbers due to tighter cuts) in the figure below: the zenith angle distribution of 44 neutrino candidates, based on data from 323 cluster × days taken during low-light background periods. This fits well to MC expectations: 43.6±6.6 events from atmospheric neutrinos and <1 mis-reconstructed down-going muon (the absolute numbers changed a bit compared to last year due to tighter cuts). The algorithm is currently used for the physics analysis of GVD data and is well suited for fast online data analysis applications. More sophisticated (but also more time consuming) methods are under development and being optimized, with improved background rejection allowing a more efficient analysis of data taken during high-noise periods.
Meanwhile there is much effort to combine the data from different clusters. This, of course, will considerably extend the sensitivity towards more horizontal tracks.