Neutrino Observations and Instrument Response Function
Neutrinos from distant sources as a source of information about Dark Matter
Since various dark matter searches are only still growing. One of the most promising approaches is a combined analysis
of different experiments, so-called multi-messenger astronomy (MMA). MMA includes three types of signals:
electromagnetic, gravitational and neutrinos. The coincidence of these signals allows to significantly eliminate the
background and shed light on the origin of the investigated phenomenon.
The common task for neutrino astronomy is a source identification. The neutrino flux can be distributed from different
source types (point, extended or diffuse sources). Dark matter decay or annihilation can also participate as such
sources. The energy of the expected neutrino flux should be higher than 100 GeV for successful detection in neutrino
telescopes. The complementary electromagnetic experiments give information about the energy spectrum shape and shrink
the possible space of searches. In turn the gravitational experiments give a time window for the arrival of a neutrino
signal. The main advantage of neutrinos and their detection is a robust determination of the source position because
neutrinos are weakly interacting particles and their tracks can be barely deflected during the transverses of the space.
Experiments
The existence of high energy neutrinos was proven by the IceCube experiment. The new generation of neutrino telescopes will provide a more detailed analysis especially in MMA. One of the most promising upcoming experiments is the KM3NeT (cubic kilometer neutrino telescope) that is located in the Mediterranean Sea. The high energy part of the detector is called ARCA (Astroparticle Research with Cosmics in Abyss). In KM3NeT/ARCA, neutrinos are detected by measuring the Cherenkov light induced by charged secondary particles emerging from a neutrino interaction in the sea water, which serves as target material and Cherenkov radiator as well as a shield for downgoing atmospheric muons. The light is detected by photomultiplier tubes (PMTs) arranged in glass spheres that withstand the water pressure (digital optical modules, DOM). Each optical module carries 31 3-inch PMTs optimizing the photo-cathode area, the directional sensitivity, the angular coverage per DOM, and the photon counting capability. The DOMs of the KM3NeT/ARCA detector are arranged along flexible strings with a total height of about 700 m. KM3NeT/ARCA will consist of two building blocks of 115 strings each, with 18 DOMs per string, vertically spaced by 36 m. Each block will have a roughly circular footprint with an average distance between strings of about 90 m. The two blocks together will cover an instrumented volume of about 1 km3. They will be deployed and anchored in the Capo Passero site, at a depth of 3500 m, and will be connected to the shore station via a 100 km electro-optical cable to transfer power and data between shore and the detector.
Data
For sensitivity/discovery calculations data are produced as MC-simulations stored in ROOT files. One of the easiest
access to data is using the km3io package, which can be installed via pip install km3io command. It allows us to not use
internal collaboration software.
Another part of the data consists from .fits files which represent the Instrument Response Function (IRF).
These IRFs files can be created from ROOT files using km3irf.
IRFs and km3irf
What is IRF? It is a property of a neutrino telescope. At the same time it contains information about the physical characteristics of the detector, such as angular resolution, energy resolution, effective area or volume of the detector. Moreover it gives the signal and background events quantification. Which can be used for further likelihood analysis.
Due to similarities between gamma and neutrino astronomy, it is reasonable to have a tool, that represents IRF in a common way. gammapy defines IRF as a set of next components:
- Effective area (Aeff)
- Point Spread Function (PSF)
- Energy dispersion (Edisp)
- Background
km3irf adapts the same approach. While the package deep integration and compatibility with gammapy, the package itself is independent from gammapy.
Package can build IRFs files from original KM3NeT data or operates with existing .fits of IRFs.
Ideally the package should provide next modules:
- Event list
- IRFs
- Datasets
For currently created IRFs files informative representation can be done using the standard for gammapy peek() function.
Look up the CODE
