# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP is distributed in the hope that it will be useful. A Data Envelopment Analysis (Computer) Program This page describes the computer program DEAP Version 2.1 which was written by Tim Coelli. This program is used to construct DEA frontiers for the calculation of technical and cost efficiencies and also for the calculation of Malmquist TFP Indices.
Newer version available (1.3.0) Last released:
Distributed Evolutionary Algorithms in Python
Project description
# DEAP
[![Build status](https://travis-ci.org/DEAP/deap.svg?branch=master)](https://travis-ci.org/DEAP/deap) [![Download](https://img.shields.io/pypi/dm/deap.svg)](https://pypi.python.org/pypi/deap) [![Join the chat at https://gitter.im/DEAP/deap](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/DEAP/deap?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of
ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and [SCOOP](http://pyscoop.org).
DEAP includes the following features:
* Genetic algorithm using any imaginable representation
* List, Array, Set, Dictionary, Tree, Numpy Array, etc.
* Genetic programing using prefix trees
* Loosely typed, Strongly typed
* Automatically defined functions
* Evolution strategies (including CMA-ES)
* Multi-objective optimisation (NSGA-II, SPEA2, MO-CMA-ES)
* Co-evolution (cooperative and competitive) of multiple populations
* Parallelization of the evaluations (and more)
* Hall of Fame of the best individuals that lived in the population
* Checkpoints that take snapshots of a system regularly
* Benchmarks module containing most common test functions
* Genealogy of an evolution (that is compatible with [NetworkX](http://networkx.lanl.gov))
* Examples of alternative algorithms : Particle Swarm Optimization, Differential Evolution, Estimation of Distribution Algorithm
## Downloads
Following acceptation of [PEP 438](http://www.python.org/dev/peps/pep-0438/) by the Python community, we have moved DEAP's source releases on [PyPI](https://pypi.python.org).
You can find the most recent releases at: https://pypi.python.org/pypi/deap/.
## Documentation
See the [DEAP User's Guide](http://deap.readthedocs.org/) for DEAP documentation.
In order to get the tip documentation, change directory to the `doc` subfolder and type in `make html`, the documentation will be under `_build/html`. You will need [Sphinx](http://sphinx.pocoo.org) to build the documentation.
### Notebooks
Also checkout our new [notebook examples](https://github.com/DEAP/notebooks). Using [IPython's](http://ipython.org/) notebook feature you'll be able to navigate and execute each block of code individually and tell what every line is doing. Either, look at the notebooks online using the notebook viewer links at the botom of the page or download the notebooks, navigate to the you download directory and run
```bash
ipython notebook --pylab inline
```
## Installation
We encourage you to use easy_install or pip to install DEAP on your system. Other installation procedure like apt-get, yum, etc. usually provide an outdated version.
```bash
pip install deap
```
The latest version can be installed with
```bash
pip install git+https://github.com/DEAP/deap@master
```
If you wish to build from sources, download or clone the repository and type
```bash
python setup.py install
```
## Build Status
DEAP build status is available on Travis-CI https://travis-ci.org/DEAP/deap.
## Requirements
The most basic features of DEAP requires Python2.6. In order to combine the toolbox and the multiprocessing module Python2.7 is needed for its support to pickle partial functions. CMA-ES requires Numpy, and we recommend matplotlib for visualization of results as it is fully compatible with DEAP's API.
Since version 0.8, DEAP is compatible out of the box with Python 3. The installation procedure automatically translates the source to Python 3 with 2to3.
## Example
The following code gives a quick overview how simple it is to implement the Onemax problem optimization with genetic algorithm using DEAP. More examples are provided [here](http://deap.readthedocs.org/en/master/examples/index.html).
```python
import random
from deap import creator, base, tools, algorithms
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register('attr_bool', random.randint, 0, 1)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
def evalOneMax(individual):
return sum(individual),
toolbox.register('evaluate', evalOneMax)
toolbox.register('mate', tools.cxTwoPoint)
toolbox.register('mutate', tools.mutFlipBit, indpb=0.05)
toolbox.register('select', tools.selTournament, tournsize=3)
population = toolbox.population(n=300)
NGEN=40
for gen in range(NGEN):
offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = fit
population = toolbox.select(offspring, k=len(population))
top10 = tools.selBest(population, k=10)
```
## How to cite DEAP
Authors of scientific papers including results generated using DEAP are encouraged to cite the following paper.
```xml
@article{DEAP_JMLR2012,
author = ' F'elix-Antoine Fortin and Franc{c}ois-Michel {De Rainville} and Marc-Andr'e Gardner and Marc Parizeau and Christian Gagn'e ',
title = { {DEAP}: Evolutionary Algorithms Made Easy },
pages = { 2171--2175 },
volume = { 13 },
month = { jul },
year = { 2012 },
journal = { Journal of Machine Learning Research }
}
```
## Publications on DEAP
* François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné, 'DEAP -- Enabling Nimbler Evolutions', SIGEVOlution, vol. 6, no 2, pp. 17-26, February 2014. [Paper](http://goo.gl/tOrXTp)
* Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné, 'DEAP: Evolutionary Algorithms Made Easy', Journal of Machine Learning Research, vol. 13, pp. 2171-2175, jul 2012. [Paper](http://goo.gl/amJ3x)
* François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné, 'DEAP: A Python Framework for Evolutionary Algorithms', in !EvoSoft Workshop, Companion proc. of the Genetic and Evolutionary Computation Conference (GECCO 2012), July 07-11 2012. [Paper](http://goo.gl/pXXug)
## Projects using DEAP
* S. Chardon, B. Brangeon, E. Bozonnet, C. Inard (2016), Construction cost and energy performance of single family houses : From integrated design to automated optimization, Automation in Construction, Volume 70, p.1-13.
* B. Brangeon, E. Bozonnet, C. Inard (2016), Integrated refurbishment of collective housing and optimization process with real products databases, Building Simulation Optimization, pp. 531–538 Newcastle, England.
* Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). Automating biomedical data science through tree-based pipeline optimization. Applications of Evolutionary Computation, pages 123-137.
* Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. Proceedings of GECCO 2016, pages 485-492.
* Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J, Muller EB, Schürmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017 https://github.com/BlueBrain/BluePyOpt
* Lara-Cabrera, R., Cotta, C. and Fernández-Leiva, A.J. (2014). Geometrical vs topological measures for the evolution of aesthetic maps in a rts game, Entertainment Computing,
* Macret, M. and Pasquier, P. (2013). Automatic Tuning of the OP-1 Synthesizer Using a Multi-objective Genetic Algorithm. In Proceedings of the 10th Sound and Music Computing Conference (SMC). (pp 614-621).
* Fortin, F. A., Grenier, S., & Parizeau, M. (2013, July). Generalizing the improved run-time complexity algorithm for non-dominated sorting. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (pp. 615-622). ACM.
* Fortin, F. A., & Parizeau, M. (2013, July). Revisiting the NSGA-II crowding-distance computation. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (pp. 623-630). ACM.
* Marc-André Gardner, Christian Gagné, and Marc Parizeau. Estimation of Distribution Algorithm based on Hidden Markov Models for Combinatorial Optimization. in Comp. Proc. Genetic and Evolutionary Computation Conference (GECCO 2013), July 2013.
* J. T. Zhai, M. A. Bamakhrama, and T. Stefanov. 'Exploiting Just-enough Parallelism when Mapping Streaming Applications in Hard Real-time Systems'. Design Automation Conference (DAC 2013), 2013.
* V. Akbarzadeh, C. Gagné, M. Parizeau, M. Argany, M. A Mostafavi, 'Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage', Accepted in IEEE Transactions on Instrumentation and Measurement, 2012.
* M. Reif, F. Shafait, and A. Dengel. 'Dataset Generation for Meta-Learning'. Proceedings of the German Conference on Artificial Intelligence (KI'12). 2012.
* M. T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani. 'Pareto-Efficient Hybridization for Multi-Objective Recommender Systems'. Proceedings of the Conference on Recommanders Systems (!RecSys'12). 2012.
* M. Pérez-Ortiz, A. Arauzo-Azofra, C. Hervás-Martínez, L. García-Hernández and L. Salas-Morera. 'A system learning user preferences for multiobjective optimization of facility layouts'. Pr,oceedings on the Int. Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO'12). 2012.
* Lévesque, J.C., Durand, A., Gagné, C., and Sabourin, R., Multi-Objective Evolutionary Optimization for Generating Ensembles of Classifiers in the ROC Space, Genetic and Evolutionary Computation Conference (GECCO 2012), 2012.
* Marc-André Gardner, Christian Gagné, and Marc Parizeau, 'Bloat Control in Genetic Programming with Histogram-based Accept-Reject Method', in Proc. Genetic and Evolutionary Computation Conference (GECCO 2011), 2011.
* Vahab Akbarzadeh, Albert Ko, Christian Gagné, and Marc Parizeau, 'Topography-Aware Sensor Deployment Optimization with CMA-ES', in Proc. of Parallel Problem Solving from Nature (PPSN 2010), Springer, 2010.
* DEAP is used in [TPOT](https://github.com/rhiever/tpot), an open source tool that uses genetic programming to optimize machine learning pipelines.
* DEAP is also used in ROS as an optimization package http://www.ros.org/wiki/deap.
* DEAP is an optional dependency for [PyXRD](https://github.com/mathijs-dumon/PyXRD), a Python implementation of the matrix algorithm developed for the X-ray diffraction analysis of disordered lamellar structures.
* DEAP is used in [glyph](https://github.com/Ambrosys/glyph), a library for symbolic regression with applications to [MLC](https://en.wikipedia.org/wiki/Machine_learning_control).
If you want your project listed here, send us a link and a brief description and we'll be glad to add it.
[![Build status](https://travis-ci.org/DEAP/deap.svg?branch=master)](https://travis-ci.org/DEAP/deap) [![Download](https://img.shields.io/pypi/dm/deap.svg)](https://pypi.python.org/pypi/deap) [![Join the chat at https://gitter.im/DEAP/deap](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/DEAP/deap?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of
ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and [SCOOP](http://pyscoop.org).
DEAP includes the following features:
* Genetic algorithm using any imaginable representation
* List, Array, Set, Dictionary, Tree, Numpy Array, etc.
* Genetic programing using prefix trees
* Loosely typed, Strongly typed
* Automatically defined functions
* Evolution strategies (including CMA-ES)
* Multi-objective optimisation (NSGA-II, SPEA2, MO-CMA-ES)
* Co-evolution (cooperative and competitive) of multiple populations
* Parallelization of the evaluations (and more)
* Hall of Fame of the best individuals that lived in the population
* Checkpoints that take snapshots of a system regularly
* Benchmarks module containing most common test functions
* Genealogy of an evolution (that is compatible with [NetworkX](http://networkx.lanl.gov))
* Examples of alternative algorithms : Particle Swarm Optimization, Differential Evolution, Estimation of Distribution Algorithm
## Downloads
Following acceptation of [PEP 438](http://www.python.org/dev/peps/pep-0438/) by the Python community, we have moved DEAP's source releases on [PyPI](https://pypi.python.org).
You can find the most recent releases at: https://pypi.python.org/pypi/deap/.
## Documentation
See the [DEAP User's Guide](http://deap.readthedocs.org/) for DEAP documentation.
In order to get the tip documentation, change directory to the `doc` subfolder and type in `make html`, the documentation will be under `_build/html`. You will need [Sphinx](http://sphinx.pocoo.org) to build the documentation.
### Notebooks
Also checkout our new [notebook examples](https://github.com/DEAP/notebooks). Using [IPython's](http://ipython.org/) notebook feature you'll be able to navigate and execute each block of code individually and tell what every line is doing. Either, look at the notebooks online using the notebook viewer links at the botom of the page or download the notebooks, navigate to the you download directory and run
```bash
ipython notebook --pylab inline
```
## Installation
We encourage you to use easy_install or pip to install DEAP on your system. Other installation procedure like apt-get, yum, etc. usually provide an outdated version.
```bash
pip install deap
```
The latest version can be installed with
```bash
pip install git+https://github.com/DEAP/deap@master
```
If you wish to build from sources, download or clone the repository and type
```bash
python setup.py install
```
## Build Status
DEAP build status is available on Travis-CI https://travis-ci.org/DEAP/deap.
## Requirements
The most basic features of DEAP requires Python2.6. In order to combine the toolbox and the multiprocessing module Python2.7 is needed for its support to pickle partial functions. CMA-ES requires Numpy, and we recommend matplotlib for visualization of results as it is fully compatible with DEAP's API.
Since version 0.8, DEAP is compatible out of the box with Python 3. The installation procedure automatically translates the source to Python 3 with 2to3.
## Example
The following code gives a quick overview how simple it is to implement the Onemax problem optimization with genetic algorithm using DEAP. More examples are provided [here](http://deap.readthedocs.org/en/master/examples/index.html).
```python
import random
from deap import creator, base, tools, algorithms
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register('attr_bool', random.randint, 0, 1)
toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
def evalOneMax(individual):
return sum(individual),
toolbox.register('evaluate', evalOneMax)
toolbox.register('mate', tools.cxTwoPoint)
toolbox.register('mutate', tools.mutFlipBit, indpb=0.05)
toolbox.register('select', tools.selTournament, tournsize=3)
population = toolbox.population(n=300)
NGEN=40
for gen in range(NGEN):
offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)
fits = toolbox.map(toolbox.evaluate, offspring)
for fit, ind in zip(fits, offspring):
ind.fitness.values = fit
population = toolbox.select(offspring, k=len(population))
top10 = tools.selBest(population, k=10)
```
## How to cite DEAP
Authors of scientific papers including results generated using DEAP are encouraged to cite the following paper.
```xml
@article{DEAP_JMLR2012,
author = ' F'elix-Antoine Fortin and Franc{c}ois-Michel {De Rainville} and Marc-Andr'e Gardner and Marc Parizeau and Christian Gagn'e ',
title = { {DEAP}: Evolutionary Algorithms Made Easy },
pages = { 2171--2175 },
volume = { 13 },
month = { jul },
year = { 2012 },
journal = { Journal of Machine Learning Research }
}
```
## Publications on DEAP
* François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné, 'DEAP -- Enabling Nimbler Evolutions', SIGEVOlution, vol. 6, no 2, pp. 17-26, February 2014. [Paper](http://goo.gl/tOrXTp)
* Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau and Christian Gagné, 'DEAP: Evolutionary Algorithms Made Easy', Journal of Machine Learning Research, vol. 13, pp. 2171-2175, jul 2012. [Paper](http://goo.gl/amJ3x)
* François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau and Christian Gagné, 'DEAP: A Python Framework for Evolutionary Algorithms', in !EvoSoft Workshop, Companion proc. of the Genetic and Evolutionary Computation Conference (GECCO 2012), July 07-11 2012. [Paper](http://goo.gl/pXXug)
## Projects using DEAP
* S. Chardon, B. Brangeon, E. Bozonnet, C. Inard (2016), Construction cost and energy performance of single family houses : From integrated design to automated optimization, Automation in Construction, Volume 70, p.1-13.
* B. Brangeon, E. Bozonnet, C. Inard (2016), Integrated refurbishment of collective housing and optimization process with real products databases, Building Simulation Optimization, pp. 531–538 Newcastle, England.
* Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). Automating biomedical data science through tree-based pipeline optimization. Applications of Evolutionary Computation, pages 123-137.
* Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. Proceedings of GECCO 2016, pages 485-492.
* Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J, Muller EB, Schürmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017 https://github.com/BlueBrain/BluePyOpt
* Lara-Cabrera, R., Cotta, C. and Fernández-Leiva, A.J. (2014). Geometrical vs topological measures for the evolution of aesthetic maps in a rts game, Entertainment Computing,
* Macret, M. and Pasquier, P. (2013). Automatic Tuning of the OP-1 Synthesizer Using a Multi-objective Genetic Algorithm. In Proceedings of the 10th Sound and Music Computing Conference (SMC). (pp 614-621).
* Fortin, F. A., Grenier, S., & Parizeau, M. (2013, July). Generalizing the improved run-time complexity algorithm for non-dominated sorting. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (pp. 615-622). ACM.
* Fortin, F. A., & Parizeau, M. (2013, July). Revisiting the NSGA-II crowding-distance computation. In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (pp. 623-630). ACM.
* Marc-André Gardner, Christian Gagné, and Marc Parizeau. Estimation of Distribution Algorithm based on Hidden Markov Models for Combinatorial Optimization. in Comp. Proc. Genetic and Evolutionary Computation Conference (GECCO 2013), July 2013.
* J. T. Zhai, M. A. Bamakhrama, and T. Stefanov. 'Exploiting Just-enough Parallelism when Mapping Streaming Applications in Hard Real-time Systems'. Design Automation Conference (DAC 2013), 2013.
* V. Akbarzadeh, C. Gagné, M. Parizeau, M. Argany, M. A Mostafavi, 'Probabilistic Sensing Model for Sensor Placement Optimization Based on Line-of-Sight Coverage', Accepted in IEEE Transactions on Instrumentation and Measurement, 2012.
* M. Reif, F. Shafait, and A. Dengel. 'Dataset Generation for Meta-Learning'. Proceedings of the German Conference on Artificial Intelligence (KI'12). 2012.
* M. T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani. 'Pareto-Efficient Hybridization for Multi-Objective Recommender Systems'. Proceedings of the Conference on Recommanders Systems (!RecSys'12). 2012.
* M. Pérez-Ortiz, A. Arauzo-Azofra, C. Hervás-Martínez, L. García-Hernández and L. Salas-Morera. 'A system learning user preferences for multiobjective optimization of facility layouts'. Pr,oceedings on the Int. Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO'12). 2012.
* Lévesque, J.C., Durand, A., Gagné, C., and Sabourin, R., Multi-Objective Evolutionary Optimization for Generating Ensembles of Classifiers in the ROC Space, Genetic and Evolutionary Computation Conference (GECCO 2012), 2012.
* Marc-André Gardner, Christian Gagné, and Marc Parizeau, 'Bloat Control in Genetic Programming with Histogram-based Accept-Reject Method', in Proc. Genetic and Evolutionary Computation Conference (GECCO 2011), 2011.
* Vahab Akbarzadeh, Albert Ko, Christian Gagné, and Marc Parizeau, 'Topography-Aware Sensor Deployment Optimization with CMA-ES', in Proc. of Parallel Problem Solving from Nature (PPSN 2010), Springer, 2010.
* DEAP is used in [TPOT](https://github.com/rhiever/tpot), an open source tool that uses genetic programming to optimize machine learning pipelines.
* DEAP is also used in ROS as an optimization package http://www.ros.org/wiki/deap.
* DEAP is an optional dependency for [PyXRD](https://github.com/mathijs-dumon/PyXRD), a Python implementation of the matrix algorithm developed for the X-ray diffraction analysis of disordered lamellar structures.
* DEAP is used in [glyph](https://github.com/Ambrosys/glyph), a library for symbolic regression with applications to [MLC](https://en.wikipedia.org/wiki/Machine_learning_control).
If you want your project listed here, send us a link and a brief description and we'll be glad to add it.
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DEAP-3600 detector during construction in 2014
DEAP (Dark matter Experiment using Argon Pulse-shape discrimination) is a direct dark matter search experiment which uses liquid argon as a target material. DEAP utilizes background discrimination based on the characteristic scintillation pulse-shape of argon. A first-generation detector (DEAP-1) with a 7 kg target mass was operated at Queen's University to test the performance of pulse-shape discrimination at low recoil energies in liquid argon. DEAP-1 was then moved to SNOLAB, 2 km below Earth's surface, in October 2007 and collected data into 2011.
DEAP-3600 was designed with 3600 kg of active liquid argon mass to achieve sensitivity to WIMP-nucleon scattering cross-sections as low as 10−46 cm2 for a dark matter particle mass of 100 GeV/c2. The DEAP-3600 detector finished construction and began data collection in 2016. An incident with the detector forced a short pause in the data collection in 2016. As of 2019, the experiment is collecting data.
To reach even better sensitivity to dark matter, the Global Argon Dark Matter Collaboration[1] was formed with scientists from DEAP, DarkSide, CLEAN and ArDM experiments. A detector with a liquid argon mass above 20 tonnes (DarkSide-20k) is planned for operation at Laboratori Nazionali del Gran Sasso[2]. Research and development efforts are working towards a next generation detector (ARGO) with a multi-hundred tonne liquid argon target mass designed to reach the neutrino floor, planned to operate at SNOLAB due to its extremely low-background radiation environment.
- 4Collaborating institutions
Argon Scintillation properties and background rejection[edit]
Since liquid argon is a scintillating material a particle interacting with it produces light in proportion to the energy deposited from the incident particle, this is a linear effect for low energies before quenching becomes a major contributing factor. The interaction of a particle with the argon causes ionization and recoiling along the path of interaction. The recoiling argon nuclei undergo recombination or self-trapping, ultimately resulting in the emission of 128nm vacuum ultra-violet (VUV) photons. Additionally liquid argon has the unique property of being transparent to its own scintillation light, this allows for light yields of 10's of thousands of photons produced for every MeV of energy deposited.
The elastic scattering of a WIMP dark matter particle with an argon nucleus is expected to cause the nucleus to recoil. This is expected to be a very low energy interaction (keV) and requires a low detection threshold in order to be sensitive. Due to the necessarily low detection threshold, the number of background events detected is very high. The faint signature of a dark matter particle such as a WIMP will be masked by the many different types of possible background events. A technique for identifying these non-dark matter events is pulse shape discrimination (PSD), which characterizes an event based on the timing signature of the scintillation light from liquid argon.
PSD is possible in a liquid argon detector because interactions due to different incident particles such as electrons, high energy photons, alphas, and neutrons create different proportions of excited states of the recoiling argon nuclei, these are known as singlet and triplet states and they decay with characteristic lifetimes of 6 ns and 1300 ns respectively[3]. Interactions from gammas and electrons produce primarily triplet excited states through electronic recoils, while neutron and alpha interactions produce primarily singlet excited states through nuclear recoils. It is expected that WIMP-nucleon interactions also produce a nuclear recoil type signal due to the elastic scattering of the dark matter particle with the argon nucleus.
By using the arrival time distribution of light for an event, it is possible to identify its likely source. This is done quantitatively by measuring the ratio of the light measured by the photo-detectors in a 'prompt' window (<60 ns) over the light measured in a 'late' window (<10,000 ns). In DEAP this parameter is called Fprompt. Nuclear recoil type events have high Fprompt (~0.7) values while electronic recoil events have a low Fprompt value (~0.3). Due to this separation in Fprompt for WIMP-like (Nuclear Recoil) and background-like (Electronic Recoil) events, it is possible to uniquely identify the most dominant sources of background in the detector [4].
The most abundant background in DEAP comes from the beta decay of Argon-39 which has an activity of approximately 1 Bq/kg in atmospheric argon.[5] Discrimination of beta and gamma background events from nuclear recoils in the energy region of interest (near 20 keV of electron energy) is required to be better than 1 in 108 to sufficiently suppress these backgrounds for a dark matter search in liquid atmospheric argon.
DEAP-1[edit]
The first stage of the DEAP project, DEAP-1, was designed in order to characterize several properties of liquid argon, demonstrate pulse-shape discrimination, and refine engineering. This detector was too small to perform dark matter searches.DEAP-1 used 7 kg of liquid argon as a target for WIMP interactions. Two photomultiplier tubes (PMTs) were used to detect the scintillation light produced by a particle interacting with the liquid argon. As the scintillation light produced is of short wavelength (128 nm) a wavelength-shifting film was used to absorb the ultraviolet scintillation light and re-emit in the visible spectrum (440 nm) enabling the light to pass through ordinary windows without any losses and eventually be detected by the PMTs.
DEAP-1 demonstrated good pulse-shape discrimination of backgrounds on the surface and began operation at SNOLAB. The deep underground location reduced unwanted cosmogenic background events. DEAP-1 ran from 2007 to 2011, including two changes in the experimental setup. DEAP-1 characterized background events, determining design improvements needed in DEAP-3600. [6]
DEAP-3600[edit]
The DEAP-3600 detector was designed to use 3600 kg of liquid argon, with a 1000 kg fiducial volume, the remaining volume is used as self-shielding and background veto. This is contained in a ~2 m diameter spherical acrylic vessel, the first of its kind ever created[7]. The acrylic vessel is surrounded by 255 high quantum efficiency photomultiplier tubes (PMTs) to detect the argon scintillation light. The acrylic vessel is housed in a stainless steel shell submerged in a 7.8m diameter shield tank filled with ultra-pure water. The outside of the steel shell has additional 48 veto PMTs to detect Cherenkov radiation produced by incoming cosmic particles, primarily muons.
The materials used in the DEAP detector were required to adhere to strict radio-purity standards to reduce background event contamination. All materials used were assayed to determine levels of radiation present, and inner detector components had strict requirements for radon emanation, which emits alpha radiation from its decay daughters. The inner vessel is coated with wavelength shifting material TPB which was vacuum evaporated onto the surface[8]. TPB is a common wavelength shifting material used in liquid argon and liquid xenon experiments due to its fast re-emission and high light yield, with an emission spectra peaked at 425nm, in the sensitivity region for most PMTs.
The projected sensitivity of DEAP in terms of spin-independent WIMP-nucleus cross-section is 10−46 cm2 at 100 GeV/c2 after three live years of data taking.[6]
Collaborating institutions[edit]
Collaborating institutions include :
This collaboration benefits largely from the experience many of the members and institutions gained on the Sudbury Neutrino Observatory (SNO) project, which studied neutrinos, another weakly interacting particle.
Status of DEAP-3600[edit]
After construction was completed, the DEAP-3600 detector started taking commissioning and calibration data in February 2015 with nitrogen gas purge in the detector.[9] The detector fill was completed and. The European Physical Journal Plus. 133 (131): 131. arXiv:1707.08145. Bibcode:2018EPJP..133..131A. doi:10.1140/epjp/i2018-11973-4.
External links[edit]
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