SLIDE 1
Bradley J. Kavanagh University of Nottingham
arXiv:1207.2039 with Anne M. Green
detection analysis Bradley J. Kavanagh University of Nottingham - - PowerPoint PPT Presentation
detection analysis Bradley J. Kavanagh University of Nottingham - - PowerPoint PPT Presentation
Improving dark matter direct detection analysis Bradley J. Kavanagh University of Nottingham arXiv:1207.2039 with Anne M. Green Speed dependence Speed dependence dR ~ ( ) v min dE R Speed dependence dR ~ ( ) v min dE
SLIDE 2
SLIDE 3
Speed dependence
) ( ~
min
v dE dR
R
SLIDE 4
Speed dependence
) ( ~
min
v dE dR
R
SLIDE 5
Speed dependence
) (v f
) ( ~
min
v dE dR
R
SLIDE 6
Speed dependence
) (v f
) ( min v
- )
( ~
min
v dE dR
R
SLIDE 7
Speed parametrisation method
- A. H. G. Peter – arXiv:0910.4765, arXiv:1103.5145
SLIDE 8
Speed parametrisation method
Model independent
method - empirical parametrisation of f(v)
- A. H. G. Peter – arXiv:0910.4765, arXiv:1103.5145
SLIDE 9
Speed parametrisation method
Model independent
method - empirical parametrisation of f(v)
Series of constant bins –
bin values used as additional parameters
- A. H. G. Peter – arXiv:0910.4765, arXiv:1103.5145
SLIDE 10
Speed parametrisation method
Model independent
method - empirical parametrisation of f(v)
Series of constant bins –
bin values used as additional parameters
Should be acceptable for
small numbers of events
- A. H. G. Peter – arXiv:0910.4765, arXiv:1103.5145
SLIDE 11
Speed parametrisation method
Model independent
method - empirical parametrisation of f(v)
Series of constant bins –
bin values used as additional parameters
Should be acceptable for
small numbers of events
Unfortunately – IT
DOESN’T WORK!
- A. H. G. Peter – arXiv:0910.4765, arXiv:1103.5145
SLIDE 12
Speed parametrisation method
Model independent
method - empirical parametrisation of f(v)
Series of constant bins –
bin values used as additional parameters
Should be acceptable for
small numbers of events
Unfortunately – IT
DOESN’T WORK!
Still leads to a bias in the
reconstructed mass and cross-section
- A. H. G. Peter – arXiv:0910.4765, arXiv:1103.5145
SLIDE 13
What goes wrong?
SLIDE 14
What goes wrong?
- We’re attempting to reconstruct the event rate as a
function of recoil energy
SLIDE 15
What goes wrong?
2 2
~ v E
N R
- 2
min
2
N R NE
m v
- We’re attempting to reconstruct the event rate as a
function of recoil energy
- Bins in velocity space correspond to bins in energy space,
with width:
SLIDE 16
What goes wrong?
2 2
~ v E
N R
- 2
min
2
N R NE
m v
- We’re attempting to reconstruct the event rate as a
function of recoil energy
- Bins in velocity space correspond to bins in energy space,
with width:
- By going to lower masses, we can reduce the size of bins in
energy space. This allows us to get a better fit to the data with our empirical parametrisation
SLIDE 17
What goes wrong?
2 2
~ v E
N R
- 2
min
2
N R NE
m v
- We’re attempting to reconstruct the event rate as a
function of recoil energy
- Bins in velocity space correspond to bins in energy space,
with width:
- By going to lower masses, we can reduce the size of bins in
energy space. This allows us to get a better fit to the data with our empirical parametrisation
- Instead parametrise the momentum:
SLIDE 18
What goes wrong?
2 2
~ v E
N R
- v
p
N
- )
( ) ( p f v f
- 2
min
2
N R NE
m v
- We’re attempting to reconstruct the event rate as a
function of recoil energy
- Bins in velocity space correspond to bins in energy space,
with width:
- By going to lower masses, we can reduce the size of bins in
energy space. This allows us to get a better fit to the data with our empirical parametrisation
- Instead parametrise the momentum:
SLIDE 19
What goes wrong?
2 2
~ v E
N R
- v
p
N
- 2
~ p ER
- )
( ) ( p f v f
- 2
min
2
N R NE
m v
- We’re attempting to reconstruct the event rate as a
function of recoil energy
- Bins in velocity space correspond to bins in energy space,
with width:
- By going to lower masses, we can reduce the size of bins in
energy space. This allows us to get a better fit to the data with our empirical parametrisation
- Instead parametrise the momentum:
SLIDE 20
Momentum parametrisation v p
N
SLIDE 21
Momentum parametrisation v p
N
- 50 GeV
benchmark SHM
SLIDE 22
Momentum parametrisation v p
N
- 50 GeV
benchmark SHM
SLIDE 23
Reconstructing f(v)
SLIDE 24
Reconstructing f(v)
- Reconstructing f(v)
is complicated (errors strongly correlated)
SLIDE 25
Reconstructing f(v)
- Reconstructing f(v)
is complicated (errors strongly correlated)
- Simple estimates
lead to consistent results
SLIDE 26
Reconstructing f(v)
- Reconstructing f(v)
is complicated (errors strongly correlated)
- Simple estimates
lead to consistent results
SLIDE 27
Reconstructing f(v)
- Reconstructing f(v)
is complicated (errors strongly correlated)
- Simple estimates
lead to consistent results
- Small statistics
means discriminating between underlying f(v) is difficult
SLIDE 28
Conclusion
SLIDE 29
Conclusion
Hope to extract WIMP parameters from DM
direct detection
SLIDE 30
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
SLIDE 31
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail
SLIDE 32
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail Instead parametrise the momentum reduced
bias and more accurate errors
SLIDE 33
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail Instead parametrise the momentum reduced
bias and more accurate errors
Drawbacks
SLIDE 34
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail Instead parametrise the momentum reduced
bias and more accurate errors
Drawbacks
- cannot yet distinguish between different underlying f(v)
SLIDE 35
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail Instead parametrise the momentum reduced
bias and more accurate errors
Drawbacks
- cannot yet distinguish between different underlying f(v)
- Experiments not sensitive to all speeds/momenta can
- nly place limits on σp
SLIDE 36
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail Instead parametrise the momentum reduced
bias and more accurate errors
Drawbacks
- cannot yet distinguish between different underlying f(v)
- Experiments not sensitive to all speeds/momenta can
- nly place limits on σp
SLIDE 37
Conclusion
Hope to extract WIMP parameters from DM
direct detection
Need to account for uncertainties owing to poor
understanding of f(v)
Naïve attempts to parametrise f(v) fail Instead parametrise the momentum reduced
bias and more accurate errors
Drawbacks
- cannot yet distinguish between different underlying f(v)
- Experiments not sensitive to all speeds/momenta can
- nly place limits on σp
Future – extending to directional detectors which
give full 3D information about f(v)
SLIDE 38