Janelle Henrich

Mentored by: 

Antonio Faraone and 

Paul Kienzle
Overview:

1. An Introduction to Cold Neutron Scattering and the Related Data

2. An Examination of the Current Data Needs Facing Users

3. A Summary of the Progress Towards a Solution

4. The Next Steps
Neutron scattering is a tool 

to study material dynamics 

through the interaction of 

neutrons with the 

material’s atoms

Delta E

Plot of Energies

Detector

NeutronBeam

1. Pynn, Roger. Scattering Interactions. Digital image. Neutron Scattering - A Primer. N.p., n.d. Web. 03 Aug. 2015. 
Material dynamics is the study 

of atom movement within a 

material

The dynamics when in the time 

domain can be modelled by a 

negative exponential curve

Inverse Fourier Transform of Convoluted Data with Noise

Time

2. Hattemer, Greg. “Polymer-Grafted Nanoparticle Simulation”. Online Video Clip YouTube. 14 Oct 

2013. Web. 3 Aug. 2016
Each cold neutron instrument’s unique 

specifications allow for the study of 

different sections of the material’s 

dynamics

High Flux Backscattering 

Spectrometer

Disk Chopper Spectrometer
The study of dynamics using neutron scattering 

has a variety of applications for both research and 

industry purposes
The curves resulting from 

measurements on 

separate instruments 

should be consistent with 

each other

Water confined in Carbon Porous Matrix

PEO + LiClO4

3. Ganapatibhotla, Lalitha and Janna Maranas. "Structure and Mobility of PEO/LiClO4 Solid Polymer Electrolytes Filled with Al2O3 Nanoparticles." The Journal of Physical Chemistry. Web. 31 July 2015.
The Inverse Fourier Transform converts the data 

into a form suitable for directly visualizing the 

dynamics

Energy Time
The current processing algorithms contained 

within DAVE were used to test the new way of 

processing data.

Time

From DAVE: From my program:
The primary difference 

between DAVE and the code 

used for this project is the 

treatment of the Inverse 

Fourier Transform

Discrete Inverse Fourier Transform:

&

Trapezoidal Rule:

&

The Fast Fourier Transform

Continuous Inverse Fourier Transform:

f(t) = 1

2ππ ∫−∞

∞ FF ωω eeiiiiiidd

VS.
Using code, simulated data mimic the real data in 

order to investigate the reasoning behind the 

mismatched curves SSMM(QQ, EE) is the convolution of a material dynamics and 

the instrumental resolution
SSMM(QQ, EE) is the convolution of a material dynamics 

and the instrumental resolution

Take the Inverse Fourier Transform

Divide by the Resolution

I(QQ, TT) is the function describing the material dynamics 
1. Cutoff decreases the peak and increases oscillation 

2. Background again decreases the peak and increases oscillation

3. Resolution creates an offset, but there must be a significant change 

in sigma 

Through the manipulation of simulated data, the 

potential for additional unforeseen instrumental 

effects was tested

I(Q,T)
Using code, simulated data mimic the real data in 

order to investigate the reasoning behind the 

mismatched curves SSMM(QQ, EE) is the convolution of a material dynamics and 

the instrumental resolution
1. Cutoff decreases the peak and increases oscillation 

2. Background again decreases the peak and increases oscillation

3. Resolution creates an offset, but there must be a significant change 

in sigma 

Through the manipulation of simulated data, the 

potential for additional unforeseen instrumental 

effects was tested

I(Q,T)
I(Q,T)
I(Q,T)

Divided by predicted resolution

Divided by predicted resolution times 5

Divided by predicted resolution

Divided by predicted resolution times .5
The lack of rebinning is leading to initially 

surprising results.

The Fast Fourier Transform

vs.

Trapezoidal Rule
Further 

investigation 

into this 

problem needs 

to be done 

particularly into 

the results of 

the nonrebinned data

Next Steps:

Artificial Data Real Data

Asymmetry in the 

Errors Add in Error Bars

Smoothing

Investigate the non-rebinned

data
• Antonio Faraone

• Paul Kienzle

• Julie Borchers

• The SURF program

• NSF