A multi-variate approach to discriminate mass on an event-by

A multi-variate approach to discriminate mass on
an event-by-event basis in the highest energy
cosmic rays seen by the Pierre Auger
Observatory
Lorenzo Caccianiga
LPNHE – UPMC Paris 6
10/7/2014
ISCRA 2014 - Erice
1
PIERRE AUGER OBSERVATORY
The largest cosmic rays detector.
Area: 3000 km2
.
Operating since 2004
(completed in 2008)
Total exposure
~ 45000 km2 sr yr
(5T5 0-60°)
First hybrid detector.
Surface Detector (SD) : 1600 Cherenkov detectors (100% duty cycle)
Fluorescence Detector (FD): 4 stations with 6 UV telescopes (13% duty cycle)
2
UHECRs
The sources of ultra-high energy cosmic rays are still unknown.
Above ~50 EeV
cosmic rays should
interact with the
cosmic microwave
background losing
their energy
→GZK
suppression
Due to magnetic
fields deflections
we have to study
the high-end of
the energy
spectrum and
possibly select
low-Z particles
This work is
focused on events
with E>50 EeV
3
COMPOSITION RESULTS
Xmax , the depth of shower maximum, can be observed directly
only by the Fluorescence Detector (duty cycle 13%)
50 EeV
A trend towards
heavier composition
is suggested, but the
interpretation is
model-dependent.
Not enough
Fluorescence Detector
data above 50 EeV.
Cannot rely on FD for
event-by-event studies
4
MASS-SENSITIVE OBSERVABLES
Muons
e.m. component
er
of
P
Nu
mb
- Longitudinal profile
(i.e. number of particles
as a function of shower
depth) in particular its
maximum, Xmax
ar
tic
les
Mass-sensitive observables:
We cannot access directly the primary mass but we can try to
evaluate it through various EAS characteristics:
Xmax
- Shower width (i.e.
Number of particles as a
function of the distance
from the axis)
- Number of muons and depth
of muon production.
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OBSERVABLES: SHOWER DEPTH
Xmax is not directly accessible through Surface Detector
→ it is possible to build Xmax related observables
From timing
information in SD
stations we extract:
- the asymmetry of
the rise time of the
station signal
- the rise time at 1000 m
from the core (so - called
Delta variable), related to
the em/μ ratio
- the curvature of
the shower front
6
OBSERVABLES: SHOWER WIDTH
The information on the shower width can be extracted
by the Lateral Density Function (LDF)
- β: slope of the LDF
- Sb: distance-weighted
sum of the signals
S b=∑
i
[ (
r
S i⋅
1000m
)]
b
- Number of Candidate
Stations (NCS): footprint
of the shower at ground
Lateral density function: signal in the stations as a
function of distance from the shower impact point.
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OBSERVABLES: Nμ
An estimation of the muonic signal can be extracted by
looking at the tank signal traces with algorithms such as the
smoothing techique.
A simulated trace to show the different behavior of the μ and em components.
- Sμ1500 : Signal of μ at 1500 meters. Result of a μ-LDF fit
8
DISCRIMINATING POWER
Variables are dependent on zenith and energy: correct for
dependencies and bin in zenith allow better discrimination
Sb/S1000
β-Fβ
Monte Carlo simulations 0-70° EPOS:
Curv
Sμ1500
NCS*cos(θ)
QGSJet:
Delta
m/q
IRON - PROTON
Energy
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EXAMPLE: BIN 4 38°<θ< 45°
Monte Carlo simulations - EPOS:
Sb/S1000
Curv
β-Fβ
Sμ1500
QGSJet:
NCS*cos(θ)
Delta
IRON - PROTON
m/q
Overall
agreement
between models
and fairly good
separation
But NOT
ENOUGH for
discriminating.
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MULTI VARIATE ANALYSIS
The studied variables can be combined in order to increase
the discriminating power
This can be done through Multi-Variate Analysis (MVA)
CERN ROOT TMVA package
●
●
●
Train different methods on our MC sample (one different MVA in
each zenith angle bin)
Check results in all different zenith angle bins
Fine tuning of the parameters for the most promising methods
● → a Boosted Decision Trees (BDT) method and
● a Multi-Layer Perceptron (MLP, artificial neural network)
11
CAVEAT: MODEL DEPENDENCE
We cannot directly test hadronic interaction at such high energies.
UHECR simulations are based on extrapolations: different models
have been proposed (QGSJET, SIBYLL, EPOS...). Post-LHC models
show much better agreement between each other than before
Signal Test – Proton EPOS
Bkg Test –Iron EPOS
Signal Train – Proton QGSJETII
Bkg Train –Iron QGSJETII 04
OLD
QGSJETII-04 – EPOS LHC
MUCH BETTER AGREEMENT!
Signal Test – Proton EPOS LHC
Signal Train – Proton QGSJETII-04
Bkg Test –Iron EPOS LHC
Bkg Train –Iron QGSJETII 04
NEW
QGSJETII – EPOS 1.99*
*fixed energies
Train QGSJET
Test EPOS.
This check was made with only 5 variables: Sb,β, NCS, m/q,Curv
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MVA-ANN BIN 4
Signal Test – Proton EPOS
Background Test – Iron EPOS
4th Bin
38-45°
Signal Train – Proton QGSJET
Background Train – Iron QGSJET
Efficiency at 90% Proton purity
~75%
PRELIMINARY
Train QGSJET
Test EPOS.
All variables:
Impressive separation but slight model dependence in Irons.
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CONCLUSIONS & OUTLOOK
-The Surface Detector of the Pierre Auger Observatory can access
mass-sensitive variables.
-Their discriminating power can be enhanced by removing their energy
and zenith dependencies, for example by binning in zenith.
-Anyhow one single variable doesn't allow event-by-event mass
discrimination.
-Multi-Variate Analysis is able to improve our mass discrimination
capability in the highest energies cosmic rays. Its main drawback is
that it relies totally on simulations
-New hadronic models show a good agreement on mass-sensitive
variables predictions.
Outlook:
-Add variables (e.g. Muon Production Depth)
-Cross-check with different software for MVA (e.g. R)
-Try binning in energy
-Check distribution of variables in data.
-Apply on Auger highest energy dataset → anisotropy studies on
proton-like sample (and FD cross-check if hybrid candidate is find)
-Extension to horizontal showers (+ ~1/3 Exposure)
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BACKUP SLIDES
MODELS
QGSJetIII-04
EPOS-LHC
Conversion of the Xmax and Xμ max observable to <ln(A)> using two different hadronic
interaction models EPOS-LHC (left) and QGSJetII-04 (right). While QGSJetIII-04 present a
more coherent conversion, EPOS-LHC offers a better description of the rapidity gap
distribution of p-p collision at the LHC. The modification of this distribution in EPOS to
better reproduce the LHC p-p data is believed to be responsible for the shift in Xμ max.
A. Letessier-Selvon for the Pierre Auger Collaboration – ICRC 2013 16
Binned discriminating power
SIG−BKG
2
2
σ
+
σ
√ SIG BKG
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