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NRCan DND Carleton U Project on. Efficacy of Muon Detection for Solar Flare Early Warning. Canadian Muon Workshop St-Émile-de-Suffolk, Québec, Canada October 17-19, 2011. Outline. Motivation Objective Why muons Deliverance Tools Perspectives.

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NRCan DND Carleton U

Project on

Efficacy of Muon Detection

for Solar Flare Early Warning

Canadian Muon WorkshopSt-Émile-de-Suffolk, Québec, Canada October 17-19, 2011


  • Motivation

  • Objective

  • Why muons

  • Deliverance

  • Tools

  • Perspectives

Motivation 1/2

Extreme space weather is mostly due to such solar disturbances

that lead to Coronal Mass Ejection (CME), a large release of

charged particles from the Sun. CMEs typically take 2-3 days

to reach the Earth where they cause geomagnetic storms.

They produce “geomagnetically” induced currents (GICs) in

conductors, especially high voltage transmission lines. GICs

can cause severe damage to the critical components of the

electrical power grid (e.g. during the Quebec blackout of 1989).

Many of these critical components do not have spares, can

require one year to be manufactured and require significant

expertise to be installed. Thus extreme space weather conditions

can have severe impacts on Canada’s critical infrastructure (CI).

Therefore it is very important to provide sufficient early warning of

an arrival of CME; it would allow CI operators (Hydro One, Hydro

Quebec, Telesat, NORAD, etc.) to take protective measures.

Motivation 2/2

Unfortunately, today a reliable early warning of CMEs is not

available. For example, the NASA ACE satellite can provide

some data up to 30 minutes warning but it is not sufficient for

CI operators.

Improved warnings of approaching CMEs could be obtained by

monitoring muons produced by galactic cosmic rays (GCRs) in

the Earth’s upper atmosphere.


To improve the protection of Canadian critical infrastructure from solar disturbances using ground-based measurements of cosmic-ray-produced muons.

Why muons 1/3

The cosmic rays are deflected away from the Earth by the magnetic field of CME, therefore a sudden

decrease in the flux of muons from a specific direction can indicate that a CME is approaching the Earth.

Why muons 2/3

Muon detectors respond to higher-energy (50 GeV) cosmic rays than neutron monitors (Munakata et al., 2000), therefore the cosmic ray precursors of large geomagnetic storms might be observed by them much earlier.

Why muons 3/3

Relativistic muons have relatively long lifetimes (the proper half-life being 2.2 µs) and can reach the ground preserving the incident direction of the initiating primary particles. Therefore one can measure the cosmic rays intensity in various directions with a

multi-directional detector at a single location

(Okazaki et al., 2008).


Performance requirements for a muon telescope (or network of telescopes)

Assessment of different muon detector systems

Tools: LC precursor 1/7

To identify precursors of geomagnetic storms we can follow an approach based on an analysis of loss-cone (LC) events mentioned in the talk given by

Dr. K. Munakata.

To provide an accurate analysis of LC events and improve the precursor observations, it is necessary to properly remove the contribution from the diurnal anisotropy (DA).

Tools: LC precursor 2/7


To derive an anisotropy we fit function

of muons

to the observed hourly count rate

at universal time t

in the j-th directional channel of the

i-th muon detector;

is the local time at the location

of the i-th detector,


Tools: LC precursor 3/7

The coupling coefficients

relate the



observed muon intensity to the primary cosmic ray intensity in free space (Dorman, 1963), (Fujimoto, et al., 1984), (Baker, et al., 1989), (Kuwabara, et al., 2004).

The best-fit parameters

denote three components of the anisotropy which are defined in a local geographical coordinate system (GEO) and along with

are determined

by minimizing S defined as follows

Tools: LC precursor 4/7


is hourly residual of the best fitting at

the time

, M is the total number of hours used

for the best fit calculations and

is the count rate

error for the (i,j) directional channel.

Tools: LC precursor 5/7

- 12-hours trailing moving averages (TMAs)

Tools: LC precursor 6/7

To remove from the data the contribution from the DA for precise analysis of the LC precursor, we subtract

from the observed

; to suppress the

statistical fluctuations and improve a visualization of

the precursor signatures, we divide the result by

Since the difference

is calculated using TMAs,

it is not affected by the variation occurring after time t (Fushishita et al., 2010).

Tools: LC precursor 7/7

The results of computations should model a distribution

of the observed muon intensity similar to one shown

in talk by Dr. K. Munakata yesterday and in today’s talks

by Dr. L. Dorman and Dr. E. Eroshenko.

Also, based on the same model and following the method

in (Fushishita et al., 2010), it could be possible to

estimate an anisotropy in terms of differences between

the anisotropy coefficients and their TMAs.


As a particular muon detector, one can orient on Forewarn tracking system constructed in Carleton University (Ottawa, Canada) by Prof. J. Armitage et al. (2011). Today the system is able to track cosmic-ray muons by providing the hit position and the angular distributions in two directions.


The long-term goal is to provide Canadian contribution to the Global Muon Detector Network.


J. Armitage, J. Botte, K. Boudjemline, and A. Robichaud (2011). FOREWARN Detector Conctruction, Report, Department of Physics, Carleton University (Ottawa, Canada), August 17, 2011, 12 p.

L.I. Dorman (1963) Cosmic Rays Variations and Space Explorations, Nauka, Moscow.

A. Fushishita, et al. (2010) Precursors of the Forbush decrease on 2006 December 14 observed with the global muon detector network (GMDN), The Astrophysical Journal, 715, pp. 1239–1247, doi:10.1088/0004-637X/715/2/1239.

F. Jansen & J. Behrens (2008) Cosmic rays and space situational awareness in Europe,, 6p.

T. Kuwabara, et al. (2004) Geometry of an interplanetary CME on October 29, 2003 deduced from cosmic rays, Geophysical Research Letters 31 (19) L19803, 5p.

K. Munakata, J.W. Bieber and S. Yasue, et al. (2000) Precursors of geomagnetic storms observed by the muon detector network, J. Geophys. Res., 105, pp. 27,457–27,468. Y. Okazaki, et al. (2008) Drift Effects and the Cosmic Ray Density Gradient in a Solar Rotation Period: First Observation with the Global Muon Detector Network (GMDN),

The Astrophysical Journal, 681, pp. 693–707.

M. Rockenbach, et al. (2011) Geomagnetic storm's precursors observed from 2001 to 2007 with the Global Muon Detector Network (GMDN), Geophysical Research Letters, 38, L16108, 4 p., doi:10.1029/2011GL048556.

S. Yasue, et al. (2003) Design of a Recording System for a Muon Telescope Using FPGA and VHDL, Proc. 28th Int. Cosmic Ray Conf., Universal Academy Press, 3461.

From “Cosmic rays and space situational awareness in Europe” (2008) by F. Jansen & J. Behrens (