twitter: @nicoleshearer93 cern@school: analysing data … · 2015-12-04 · data management with...

N. Shearer a,b & T. Whyntie b,c

a Durham University, b Langton Star Centre, c QMUL

CERN@school Research Symposium 2014, University of SurreyMonday 8th September 2014 #CERNatschool

CERN@school: analysing datafrom the LUCID experiment

Twitter: @nicoleshearer93@twhyntie

N. Shearer & T. Whyntie CERN@school Research Symposium #CERNatschool 2

Acknowledgments

Copyright © CERN@school 2014This work is licensed under the Creative Commons Attribution License.See http://cernatschool.web.cern.ch/content/license for more information.

This work was supported by the Royal Commission of theExhibition of 1851 and the IOP Top50 Work Placementsscheme. For further details, see:• http://www.royalcommission1851.org/• http://www.iop.org/careers/top50/

http://cernatschool.web.cern.ch/content/license

http://www.royalcommission1851.org/

http://www.iop.org/careers/top50/

http://www.royalcommission1851.org/

http://www.iop.org/careers/top50/

Outline of the workshop• Introduction

- The Timepix detector, LUCID, TechDemoSat-1, the science.

• The DAQMAP• Analysing LUCID data with the DAQMAP

- Particle identification; cluster variables; cluster validation.

• Some worked examples- International Space Station data; simulated LUCID data.

• Summary and conclusions


The Timepix detector; the LUCID experiment; TechDemoSat-1; scientific aims and objectives


Introduction

The Timepix hybrid silicon pixel detector


256 pixels, 14.08mm

256

pixe

ls, 1

4.08

mm

Active sensor area ~1.98cm2

CERN@school is based around the Timepixhybrid silicon pixel detector:• Developed by the Medipix2

Collaboration [Llopart et al. 2007].• 300 μm thick silicon sensor bump-

bonded to a Timepix readout chip.• 256 × 256 pixels of pitch 55 μm provide

65,536 readout channels from the 1.98cm2 sensor element.

• It can be used to detect ionising radiation, make energy measurements (when calibrated) and perform particle identification (to an extent).

Huge thanks to Michael Campbell et al.

http://medipix.web.cern.ch/

http://dx.doi.org/10.1016/j.nima.2007.08.079

The Timepix hybrid silicon pixel detector


ab

b

g

256 pixels, 14.08mm

256

pixe

ls, 1

4.08

mm

Active sensor area ~1.98cm2

The Langton Ultimate Cosmic ray Intensity Detector experiment features five Timepixdetectors in an open-faced cube, housed in a ~0.68 mm aluminium “dome” (not pictured), to measure the LEO radiation environment.

Data taking/transmitting capabilities:

• Max. shutter frequency: ~4Hz

• Transmission: 80Mbs-1 (20Mbs-1);

• Storage: 2GB;

• Operational 2 out of every 8 days.

The LUCID experiment



The LUCID detector arrangement

TPX0

TPX3 TPX1

TPX2

TPX4

LUCID launched aboard TechDemoSat-1 from Baikonur Cosmodrome aboard a Soyuz 2b launch vehicle in July 2014.

• TechDemoSat-1 is an “in-orbit test facility” from SSTL supported by the UK’s Technology Strategy Board;

• Many scientific payloads from UK academic institutions/labs, of which LUCID is one.

Orbit parameters

• Altitude: 635km;

• Orbit: sun synchronous;

• Inclination: 98.4°;

• LTDN: starts at 0900, with drift of 40 minutes per 6 months.

• Dominant radiation sources: trapped protons and electrons, outer electron belts, South Atlantic Anomaly (SAA).

TechDemoSat-1


• Directionality of particles:- Pattern recognition; estimates of angles of

incidence; inter-detector track reconstruction.• Particle spectra:

- Measure particle intensities as a function of space and time;

- Determine dose (J/kg) and the LET spectrum.• Solar flares/SPEs:

- Investigate the difference in time between electron and proton surges;

- Log solar activity for the mission life time (which is more than 50% of the solar cycle);

- Investigate the Forbrush Decrease.

LUCID: scientific aims and objectives


NASA

• Charge deposited from a single particle in the detector may spread into more than one pixel.- Multiple interactions can also occur

in the silicon leading to many pixels being “hit” by one initial particle.

• A cluster is a group of pixels that are all next to each other.- The shape of the cluster can give us

clues as to which particle we’ve detected.

Cluster analysis with LUCID


• In order to make useful measurements with LUCID, we will need to be able to identify the different types of particles seen in Low Earth Orbit.

• The problem is we’re not quite sure what to expect…- This is where you come in!- Your contributions will count

towards LUCID publications.

Cluster analysis with LUCID


Data management with the DAQMAP; logging on to the DAQMAP.


The DAQMAP

Data management with the DAQMAP


The Data AcQuisition, Management, Analysis and Presentation system:• Based on the same technology as Galaxy Zoo –

Ruby on Rails app (Hobo gemset) running on an AWS Ubuntu 12.04 LTS server (PhusionPassenger deployment);

• Data uploaded and downloaded via zip files, filtered and processed server-side. Users can inspect frames and clusters using client-side Java display powered by Raphaël.

• Users organised into research groups. Functionality also available for monitoring usage, Support Tickets, reporting and equipment inventories.

http://raphaeljs.com/

• You have/will receive an invite email with a invitation link:- Enter a new password;- You can reset your password if you

forget it.• To log on once you have accepted

your DAQMAP invitation:- http://cern-at-school.org/DAQMAP

Logging on to the DAQMAP


http://cern-at-school.org/DAQMAP

Particles and their corresponding clusters; cluster variables; the standard particles; spotting anomalies and resolving them; creating cluster validation reports.


Cluster validation with the DAQMAP

• Alpha- Generate lots of charge from the point that they hit,

forming a dense, circular cluster.• Beta (electron or positron)

- Scatter through material, leaving elongated tracks.• Gamma (photon)

- Hits at a single point with minimal charge distribution, corresponding to a small round cluster.

• Muon- Clusters that are much longer and straighter than

those corresponding to beta particles.

Particles and their corresponding clusters


• Proton- Very energetic particles that don’t scatter much in

the sensor material, forming very long, straight clusters. Thicker than muons.

• “Curly” beta- Betas that scatter through the sensor material,

leaving curly, elongated tracks.

Particles and their corresponding clusters


• In scientific research, a variable is a characteristic of a set of data that is liable to change.

• There are a number of variables associated with clusters that we find in this project.- These include; cluster size, total counts, cluster radius, cluster density and

cluster linearity.

• Finding values for each of these variables can help us to decide the type of radiation particle (alpha, beta, gamma or muon) that a cluster corresponds to.

Cluster variables


• The cluster size, 𝑁ℎ, is the total number of hit pixels in a cluster.

• A smaller cluster size would probably correspond to a gamma particle, whereas a larger cluster size could be caused by an alpha, beta or muon particle.

• The cluster on the right has a size of 8.- 8 pixels have been hit by the radiation.- It looks as though it is a beta cluster.

Cluster size


• When hit, each pixel detects a particular number of counts and passes this value to the computer.- The number of counts depends on the charge deposited by the radiation

particle, which is dependent upon its type and intensity.

• The program sums the number of counts from each pixel to calculate a value for the total counts.

Total counts


• The cluster radius is the distance between the centre of a cluster and the furthest pixel hit.

• The centre of the cluster has coordinates of the mean of the 𝑥 and 𝑦values of every pixel within the cluster.- The centre can be found using the equation:- 𝑥𝑖 and 𝑦𝑖 are the coordinates of individual pixels.

• The code then loops through the coordinates of each pixel in the cluster, calculating the distance from the centre to each pixel and takes the furthest distance as the cluster radius.

Cluster radius


• As the most elongated cluster, a muon generally results in the largest cluster radius, followed by beta, alpha then gamma particles.

• The cluster on the right has a radius of 2.34 pixels.- This suggests that it could correspond to a

beta particle.

Cluster radius


• The cluster density , 𝜌, is the number of hits per unit area, calculated as if the cluster is circular.-

- where 𝑟𝑐 is the cluster radius and 𝜋𝑟𝑐2 is the area of a circle.

• Using this equation:- 𝜌 ≈ 1 would be a roughly circular cluster.- A circular cluster with 𝜌 ≥ 1 would correspond to an

alpha or gamma particle.- A straighter cluster with 𝜌 ≤ 1 corresponds to a beta

particle or a muon.

Cluster density


With a cluster size of 8and a radius of 2.34,this cluster would havea density of 0.43.

• The linearity of a cluster describes how straight it is.- This variable is key to determine whether a cluster was produced by a muon or

beta particle.- The straighter the cluster, the more likely it is to have been produced by a

muon.

• The linearity of a cluster is calculated by fitting a straight line through it and measuring how much the pixel coordinates deviate from this straight line.- The amount by which each point deviates is called its ‘residual’. Summing the

residuals and dividing by the cluster size, 𝑁ℎ, gives a value for cluster linearity.- The smaller the cluster linearity value, the ‘straighter’ the cluster.

Cluster linearity


• Here is an example:

• The cluster on the left is straighter because none of the pixels deviate from the straight red line fitted through it, meaning the sum of the residuals would be 0. This means it is more likely to correspond to a muon, whereas the cluster on the right is more likely to be produced by an beta particle

Cluster linearity


Gamma, 𝜸Cluster size: 3Cluster radius: 0.75Cluster density: 1.70Linearity: 0.30

Remember the different types of cluster we saw? Here are values for some of their variables, to be explained in more detail in the next few slides:

Particles, clusters and their variables


Muon, 𝝁Cluster size: 16Cluster radius: 7.13Cluster density: 0.10Linearity: 0.26

Beta, 𝜷Cluster size: 8Cluster radius: 2.34Cluster density: 0.43Linearity: 0.39

Alpha, 𝜶Cluster size: 61Cluster radius: 4.24Cluster density: 1.08Linearity: 1.84

Cluster density: 1.08

Dividing the cluster size (61) by the area of the cluster (as if it was a circle of radius 4.24) results in a cluster density of 1.08 pixels per unit area.

A detailed look at a typical alpha cluster


Linearity: 1.84

The linearity value is large because many pixels are far from the straight line of best fit.

Cluster radius: 4.24Dividing the radius by the length of a single pixel gives a value of 4.24 pixels for the radius of the cluster.

Cluster size: 61

There are 61 pixels (the small squares) within the cluster.

1 pixel

4.24 pixels

Cluster size: 8There are 8 pixels in this cluster.

A detailed look at a typical beta cluster


Cluster radius: 2.3

Dividing the radius line by the length of a single pixel gives 2.3 pixels for the cluster radius.

Linearity: 0.39A low linearity value implies that the hit pixels could be fit to a straight line, but there is still too much deviation for the cluster to be caused by a muon.



1 pixel

2.3 pixels

Cluster size: 3There are 3 pixels in this cluster.

A detailed look at a typical gamma cluster


Cluster radius: 0.75Dividing the radius line by the length of a single pixel gives 0.75 pixels for the cluster radius.

Linearity: 0.3A low linearity value implies that the hit pixels could be fit to a straight line, but there is too much deviation here for the cluster to be caused by a muon.



1 pixel 0.75 pixels

Cluster size: 16

There are 16 pixels in this cluster.

A detailed look at a typical muon cluster



Linearity: 0.26

A low linearity value implies that the hit pixels could be fit to a straight line; this is low enough to assume that the cluster was produced by a muon.



1 pixel

7.13 pixels

Cluster size: 68


A detailed look at a typical proton cluster


Cluster radius: 19.77

Dividing the radius line by the length of a single pixel gives 19.77 pixels for the cluster radius.

Linearity: 0.45

A low linearity value implies that the hit pixels could be fit to a straight line; this is low enough to assume that the cluster was produced by a proton.



1 pixel

19.77 pixels

Cluster size: 20


A detailed look at a curly electron cluster



Linearity: 2.24A high linearity value implies that the hit pixels can’t really be fit to a straight line; this is high enough to assume that the cluster was not produced by a muon or proton.



1 pixel5.27 pixels

NoNo

Cluster type identification


1 ≤ 𝑁ℎ ≤ 4 𝑁ℎ ≤ 7 𝜌 ≥ 1

0 ≤ 𝑟 ≤ 0.75

𝜸

𝜷

Yes

No

No

Yes

Yes

No

Yes

Yes

No

𝑟 ≤ 1.42

𝜶

𝜷

𝜶

𝜷Linearity ≤ 0.3No

𝝁Linearity ≤ 0.3

𝝁

Yes

Yes

This flow diagram represents the cluster algorithm. It can be used to determine the type of particle causing a cluster, knowing a few values for cluster variables.

• There is currently an algorithm that identifies which particle a cluster corresponds to.- Occasionally a cluster will look a bit different to anything the algorithm

expects and it will be identified incorrectly.

• This is where you come in. We are always looking to improve the identification algorithm.- To do this we need people to compare results of the clustering algorithm

with their own judgement to help find the incorrectly identified clusters.

• The next few slides give some examples of mis-identified clusters that you might see on the DAQMAP.

Spotting anomalies and finding solutions


• Problem- The algorithm identifies this less straight and more blob-

like beta cluster as an alpha cluster.• Cluster properties

- Cluster size: 5; Cluster radius: 1.26.- The algorithm identifies clusters with a size less than 7

and radius less than 1.42 as an alpha cluster!• Potential solutions

- An alpha cluster wouldn’t be this small – change the size limit for an alpha cluster on the algorithm.

- An alpha cluster would be more circular - include a step in the algorithm that checks how round the cluster is.



• Problem- The algorithm identifies this muon cluster as a beta

cluster.- The algorithm doesn’t currently have a system in place for

spotting muon clusters.• Cluster properties

- Cluster radius: 129.83; cluster linearity: 0.312.• Potential solutions

- Include a step in the algorithm that takes into account linearity of the cluster. A cluster should be classed as a muon if it over a particular straightness limit.

- Muon clusters should also have a large radius.



• Problem- This double alpha cluster is identified as a beta cluster.- As the two clusters are touching each other, they are

recognised by the algorithm as one cluster.• Cluster properties

- Cluster size: 104; cluster radius: 8.55; cluster density: 0.45.• Potential solutions

- Include a step in the algorithm that recognises if there are two distinct sections to the cluster.

- Note that a beta cluster is unlikely to have such a large size.



• Problem- Occasionally particles will be detected that are not alpha,

beta, gamma or muon particles.- The algorithm mis-identifies them, for example it classed

the cluster on the right as a beta cluster.• Cluster properties

- Unusual clusters could have a range of properties, for example this large one has a strange three-pronged track .

• Potential solutions- If many similar particles are spotted, alterations to the

algorithm could be made to find them automatically.- The algorithm could be adjusted to recognise ‘unusual’

particles that don’t fit into any other categories.



• To find the mis-identified clusters, we need people to create cluster reports that explain whether a cluster has been identified correctly.- Humans are much more intelligent than computers and are more likely to

notice if something is wrong.

• This can be done using the DAQMAP- http://www.cern-at-school.org/DAQMAP/clusterreports

• Click on ‘Create a new report’ (just above the table of reports)- This brings up a random cluster for you to look at and decide whether it

has been identified correctly.

Creating a cluster report


http://www.cern-at-school.org/DAQMAP/clusterreports

• You can also create a report when you are processing or looking through frames of data.- If you spot a cluster which you’re not certain has been identified correctly,

click on it to bring up its own page.- Just above the ‘Cluster properties’ heading, click ‘Validate this cluster’.



• Once you have the ‘New Cluster Report’ page open, scroll to the bottom to add your own report.

• Include a report title that explains what the cluster is and whether it has been correctly identified or not.

• Choose your own classification from the dropdown menu. - At the bottom, include a verdict that tells others

whether the cluster was identified ‘Ok’ by the algorithm or whether it requires further attention.



• In the ‘Notes’ box, expand upon the report title with more information about the identification of the cluster. - If you have ideas for solutions to the mis-identification, include them here.

• It helps to write this section using markdown, a text-to-HTML conversion tool which clearly formats the output of your ‘Notes’ box.- For example, you could type:

• ## This is a heading ; *This text will be italic*- See the introduction to markdown for more information:

• https://help.github.com/articles/markdown-basics

• Save the report by clicking ‘Save Cluster Report’.



https://help.github.com/articles/markdown-basics

Clusters from the International Space Station; clusters from the LUCID Allpix simulations.


Some worked examples

• There are currently five Timepixdetectors onboard the International Space Station (ISS).- Dosimetry research by NASA (Prof.

Larry Pinsky, Uni. Houston).• To find this data on the DAQMAP,

search for “ISS” in the search box on the Data File Index page.

Clusters from the ISS



Clusters from the ISS

Developed by J. Idarraga, M. Benoit et al, Allpix provides a suite of tools to simulate silicon pixel detectors. Customised for LUCID and CERN@school.• Timepix digitizer module models charge

diffusion and charge sharing in the bulk; known to perform less well for alphas, heavy ions, but e- and p+ work well.

• The Time-over-Threshold response is modelled using LUCID’s actual calibration parameters (thanks IEAP, CTU Prague).

• Pixel thresholds idealised for simplicity.

Simulating LUCID with Allpix


The Timepix energy calibration surrogate function.

https://twiki.cern.ch/twiki/bin/view/Main/AllPix

Simulating LUCID with Allpix


PCB mounts for TPX

detectors

Aluminium “dome” (cylinder with roof), thickness ~0.7mm 50mm radius

hemisphere

Timepix detectors (including TPX0, through gap)

The GEANT4 General Particle Source (GPS) allows the user to specify multiple primary particle types, positions, energies, etc.• The SPENVIS differential flux spectra for e, p

at each point in the orbit are used to specify a GPS. Relative intensity set by flux ratio.

• Particles are created on a 5cm radius hemisphere with a cosine distribution (as required for an isotropic environment).

• The number of particles per frame is:

Simulating particles in Allpix


Acquisition time [s] = 0.25 [s]

Integrated flux

Hemisphere radius


Clusters from LUCID simulations

• Look through the data on the DAQMAP to find mis-identified particles.- ISS and LUCID simulated data;- But also background radiation measurements too.

• Data from the satellite will be uploaded to the DAQMAP when it arrives from SSTL.- Make sure you are ready!

Now you try!


• You’re now ready to analyse data from the LUCID experiment for CERN@school!- How to use the DAQMAP;- How to analyse and validate clusters from Timepix detectors.

• Now over to you!- Submit cluster validation reports on the DAQMAP;- Find odd clusters to help develop the algorithm;- Be prepared to analyse the real data from LUCID when it arrives;- Get your name on LUCID publications!

Summary and conclusions


N. Shearer a,b & T. Whyntie b,c

a Durham University, b Langton Star Centre, c QMUL

CERN@school Research Symposium 2014, University of SurreyMonday 8th September 2014 #CERNatschool

Thank you for attending!Any questions?

Twitter: @nicoleshearer93@twhyntie

twitter: @nicoleshearer93 cern@school: analysing data … · 2015-12-04 · data management with...

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