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From: Srinidhi Bheesette <srinidhi.bheesette_at_cern.ch>

Date: Sat, 11 Mar 2017 13:01:15 +1300

Hi Andrew,

Thanks for your reply. I have modified my geometry as shown below:

[image: Inline images 1]

(sorry the conversion layer is not to scale)

I am running 100000 primaries and 5 cycles. Averaging over 5 cycles and

calculating the variance and the std deviation (sqrt(variance) using the

formula below:

[image: Inline images 3]

Where,

· N = number of batches/cycles = 5

· ni = number of primaries in each batch/cycle = 100000

· n= number of primaries in the N batches = 100000*5 = 500000

primaries

And I have plot the averages and the std dev below:

[image: Inline images 2]

I am sorry I donot have something to compare against expect this reference

plot:

[image: Inline images 1]

where the author of the paper has plotted the detection efficiency of

poly-ethylene conversion layer of various thickness to fast neutron

(1-20MeV).

I did perform simulations by modifying the thickness of the conversion

layer and got this graph.

[image: Inline images 2]

I have got a few doubts:

1. How do I make the plot smoother?

2. How do I justify that my results are valid? Are there are variable to

be calculated to confirm the certainty of the measurements?

Your help would be greatly appreciated.

Thanks in advance.

Cheers,

Shri

(Srinidhi Bheesette)

On 9 March 2017 at 15:46, Andrew Davis <andrew.davis_at_wisc.edu> wrote:

*> Hi Shri
*

*>
*

*> It looks much better, that looks like the correct way to plot them to me.
*

*>
*

*> Your setup is quite thin if I recall, so running more histories would seem
*

*> quite sensible. One typically expects that the statistical error goes as
*

*> 1/sqrt(N) where N is the number of particles simulated, so a factor of 10
*

*> more particles will reduce your error by a factor of around 3.
*

*>
*

*> In terms of your 3 sigma reference, you usually worry about 3 sigma when
*

*> comparing to other data, for example under certain circumstances you might
*

*> expect 99% of data points to lie within 3sigma of your calculated data, in
*

*> your case do you have something to compare against?
*

*>
*

*> Thanks
*

*>
*

*> Andy
*

*>
*

*>
*

*> On Mar 8, 2017, at 4:15 PM, Srinidhi Bheesette <srinidhi.bheesette_at_cern.ch>
*

*> wrote:
*

*>
*

*> Hi Lungi,
*

*>
*

*> Thanks for your reply.
*

*>
*

*> I ran 5 cycles for each input file with 100000 primaries and calculated
*

*> the error using the formula which you pointed out. I see that the plot is
*

*> much better and more smoother now.
*

*>
*

*> <image.png>
*

*>
*

*> I have shown the error (std(variance)) on the plot. Do you think this is
*

*> the correct way of representing them?
*

*>
*

*> What are the ways of further reducing errors and achieving 3 sigma in this
*

*> case? It is based on the Z score calculated using the formula [Z=
*

*> (avg-mean)/std].
*

*>
*

*> Thanks in advance.
*

*>
*

*> Cheers,
*

*> Shri
*

*>
*

*> (Srinidhi Bheesette)
*

*>
*

*> On 6 March 2017 at 22:10, Luigi Salvatore Esposito <
*

*> luigi.salvatore.esposito_at_cern.ch> wrote:
*

*>
*

*>> Dear Srhi,
*

*>> if you run parallel runs, then each FLUKA input file needs to have its
*

*>> own initialisation of the random generator,
*

*>> i.e. different WHAT(2) of the RANDOMIZe card (see Note 5 in the manual of
*

*>> the RANDOMIZe card). The user should take care of it.
*

*>>
*

*>> On the other hand, cycles within the same run have automatically
*

*>> independent histories since FLUKA will use the last random number seed
*

*>> from the previous cycle.
*

*>>
*

*>> You have to average over cycles. The post-processing utilities in FLUKA
*

*>> make this job for you.
*

*>> Again, as I already recommended in my previous email, please refer to the
*

*>> slides about statistics from the last FLUKA course:
*

*>>
*

*>> https://indico.cern.ch/event/540415/contributions/2194806/at
*

*>> tachments/1285749/1912258/09_Statistics_and_sampling_2015.pdf
*

*>>
*

*>> in particular slide 32 and following. You can assume batch = cycle.
*

*>> Best regards, luigi
*

*>>
*

*>>
*

*>> On 6 Mar 2017, at 06:29, Srinidhi Bheesette <srinidhi.bheesette_at_cern.ch>
*

*>> wrote:
*

*>>
*

*>> Hi Andrew,
*

*>>
*

*>> 1. Let say I increase the number of primaries in the input file to
*

*>> 1000000. Then how do I split the jobs?
*

*>> Should create 5 different input files with different random number and
*

*>> run it once or run each run (again with different random seed in each) for
*

*>> 5 cycles?
*

*>> eg: My input file has 100000 primaries specified. I created 5 different
*

*>> input files with 5 different random seeds and am running each 5 cycles for
*

*>> each input file, and am scoring USRBIN every time.
*

*>>
*

*>> So at the end of the whole process, I get 5 fort files (from 5 cycles)
*

*>> for each input file (hence run) and since there are 5 runs = 5* 5 = 25 fort
*

*>> files.
*

*>>
*

*>> Should should be the averaging be done in this scenario over runs or over
*

*>> cycles?
*

*>>
*

*>> Please help.
*

*>>
*

*>> Cheers,
*

*>> Shri
*

*>>
*

*>> (Srinidhi Bheesette)
*

*>>
*

*>> On 1 March 2017 at 15:26, Andrew Davis <andrew.davis_at_wisc.edu> wrote:
*

*>>
*

*>>> Hi Srinidhi
*

*>>>
*

*>>> There are a number of issues assuming that your input deck is
*

*>>> representative
*

*>>>
*

*>>> 1) Your START card only contains a single history to run, from a single
*

*>>> run of 1 primary you cannot make any assertions regarding the results of
*

*>>> your calculation since you have a large statistical uncertainty. You should
*

*>>> likely run several million histories with (at least) 5 independent random
*

*>>> number seeds using the already established tools to perform the averaging.
*

*>>> 2) You used a USRBIN score (with 1 bin for x-y-z) to determine the
*

*>>> energy deposition, you might as well use a USRTRACK score, not an issue
*

*>>> really, but I may’ve chosen a USRTRACK instead
*

*>>> 3) Your plot doesn’t include any statistical error, its hard judge the
*

*>>> accuracy of a Monte Carlo calculation without any mention of the
*

*>>> statistical error.
*

*>>>
*

*>>> Thanks
*

*>>>
*

*>>> Andy
*

*>>>
*

*>>> On Feb 28, 2017, at 4:57 PM, Srinidhi Bheesette <
*

*>>> srinidhi.bheesette_at_cern.ch> wrote:
*

*>>>
*

*>>> Hi all,
*

*>>>
*

*>>> I have simulated a pixel detector (just a silicon sensor layer) in FLUKA
*

*>>> and bombarding it with point neutron source at different energies from
*

*>>> 1-25.5MHz with a step size of 0.5MHz. I run a single simulation.
*

*>>>
*

*>>> I then score the energy deposited in the detector (one value for one
*

*>>> run) and then plot a graph of the energy deposited on the x-axis the source
*

*>>> energy on the y-axis shown below:
*

*>>>
*

*>>> <image.png>
*

*>>>
*

*>>> The energy seems to increase till 20MeV but then start fluctuating from
*

*>>> 20.5 to 25.5MeV and also a spike at 17MeV
*

*>>>
*

*>>> Am I missing some cards in my input file (attached) for activating some
*

*>>> feature for neutron energies after 20MeV?
*

*>>>
*

*>>> Please help.
*

*>>>
*

*>>> Cheers,
*

*>>> Shri
*

*>>>
*

*>>> (Srinidhi Bheesette)
*

*>>> <sensor-22.5MeV.inp>
*

*>>>
*

*>>>
*

*>>>
*

*>>
*

*>>
*

*>
*

*>
*

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Received on Sat Mar 11 2017 - 02:40:35 CET

Date: Sat, 11 Mar 2017 13:01:15 +1300

Hi Andrew,

Thanks for your reply. I have modified my geometry as shown below:

[image: Inline images 1]

(sorry the conversion layer is not to scale)

I am running 100000 primaries and 5 cycles. Averaging over 5 cycles and

calculating the variance and the std deviation (sqrt(variance) using the

formula below:

[image: Inline images 3]

Where,

· N = number of batches/cycles = 5

· ni = number of primaries in each batch/cycle = 100000

· n= number of primaries in the N batches = 100000*5 = 500000

primaries

And I have plot the averages and the std dev below:

[image: Inline images 2]

I am sorry I donot have something to compare against expect this reference

plot:

[image: Inline images 1]

where the author of the paper has plotted the detection efficiency of

poly-ethylene conversion layer of various thickness to fast neutron

(1-20MeV).

I did perform simulations by modifying the thickness of the conversion

layer and got this graph.

[image: Inline images 2]

I have got a few doubts:

1. How do I make the plot smoother?

2. How do I justify that my results are valid? Are there are variable to

be calculated to confirm the certainty of the measurements?

Your help would be greatly appreciated.

Thanks in advance.

Cheers,

Shri

(Srinidhi Bheesette)

On 9 March 2017 at 15:46, Andrew Davis <andrew.davis_at_wisc.edu> wrote:

__________________________________________________________________________

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(image/png attachment: image.png)

(image/png attachment: 02-image.png)

(image/png attachment: 03-image.png)

(image/png attachment: 04-image.png)

(image/png attachment: 05-image.png)

*
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