| Name | astroblast JSON |
| Version |
2.0.0
JSON |
| download |
| home_page | None |
| Summary | Synchtrotron peak estimator for blazars |
| upload_time | 2024-04-08 20:18:14 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
blazar
synchrotron
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# BlaST
BlaST (**Bla**zar **S**ynchrotron **T**ool) is a tool to estimate the
synchrotron peak of blazars given its spectral energy distribution. The package is available on pip
and can simply be installed via
```
pip install astroblast
```
Running the tool is as simple as typing
```
blast sed.txt
```
where `sed.txt` is a output file generated by the *VOUBlazar* tool and might look something like the following:
```
1 matched source 227.16230 -49.88400 99
Frequency nufnu nufnu unc. nufnu unc. start time end time Catalog Reference
Hz erg/cm2/s upper lower MJD MJD
---------------------------------------------------------------------------------------------------------------------------
2.418E+17 2.185E-13 3.139E-13 1.230E-13 55000.0000 55000.0000 RASS Boller et al. 2016, A&A, 103, 1
2.418E+17 5.085E-13 6.281E-13 3.889E-13 58150.0000 58150.1016 OUSXB Giommi et al. 2019, Accepted for publication in A&A
```
This gives the following output:
```
12.42 (+/- 0.71)
```
The first value is the synchrotron peak and the second the 95% prediction interval both in `log10`. It is possible to specify a different prediction interval width by the `-w` argument, followed by the width in sigmas:
```
blast sed.txt -w 1.0
```
BlaST also supports bulk estimation, e.g. estimating a whole catalogue, by providing a directory or zip file containing
the seds as well as an output file in which the results will be written to as csv:
```
blast seds.zip -o estimates.csv
```
With V2 you now also have the option to estimate the synchrotron peak flux (`erg/cm2/s`), again in `log10`.
You enable this with the `-f` flag:
```
-12.39 (+/- 0.63) @ 12.39 (+/- 0.39)
```
## Changes from v1 to v2
With v2 there has been some major changes. Some are listed in the following:
- **New data set** (courtesy of N. Krieger)
The data set has been refined resulting in a new set independent of the previous
one. This one now also includes labels for the flux.
- **Flux estimate**
Biggest difference is the estimation of the synchrotron peak's flux by an
independent ensemble of neural networks.
- **Save models in ONNX format**
Previously, the tool needed pytorch installed, which is quite large. We moved to
ONNX runtime to make the installation slimmer.
## How it works
BlaST consists of an ensemble of similar neural networks power by [pytorch](https://pytorch.org/) based on the method presented in
[Lakshminarayanan et. al. 2016][2]. The data set (see `SEDs.zip`) is based on the three catalogues [4LAC][3], [3HSP][4]
and [5BZCat][5] and divided into 5 bags. For each bag an ensemble of 5 models are trained totalling 25 independent models.
The bagging allows the reapply the ensemble on the training data as an out-of-bag estimation, i.e. excluding the
ensemble members that were trained on a training set without that specific bag. The results are shown in `estimates.csv`.
For specific details, e.g. how the data was enhanced and how the bias in the data set was handles, take a look in
`BlaST.ipynb` which is the notebook used to train the models. Note that if you want to run that notebook you'll also
need to install [PyTorch Lightning](https://www.pytorchlightning.ai/)
## Performance
The performance was evaluated using out-of-bag estimates on the training data
including a 95% prediction interval. Metrics are shown as median and 25%/75%
quantile.
Since the model for predicting the peak frequency and peak flux are independent,
metrics are shown for both.
| Metric | Value | Freq | Flux |
|----------|--------|-------|-------|
| Abs Err | Median | 0.163 | 0.104 |
| | 25% | 0.075 | 0.049 |
| | 75% | 0.314 | 0.186 |
| PI Width | Median | 1.267 | 1.087 |
| | 25% | 1.009 | 0.971 |
| | 75% | 1.625 | 1.198 |
| Interval | Median | 1.400 | 1.088 |
| Score | 25% | 1.010 | 0.972 |
| | 75% | 1.636 | 1.199 |
### Peak Frequency Estimate
The following show the prediction histogram with the median as black line, as well as the 90% and 10% quantile shown as
dotted lines
The next shows the prediction interval (95%) widths. The percentages above and below show the respective amount of samples
outside their intervals.
The final one shows the prediction error and the prediction interval distribution using a moving window and smoothed using cubic splines.
### Peak Flux Estimate
Same plots, but now for the peak flux estimate.
## Use BlaST in code
BlaST can also be imported as python package:
```python
from blast import *
sed, pos = parse_sed('sed.txt', position=True) #reads the file
bag = get_bag(pos) #Returns bag if sed was part of training
bins = bin_data(sed)
# estimate peak frequency
estimator = PeakFrequencyEstimator()
peak, err = estimator(bins, bag)
# estimate peak flux
estimator = PeakFluxEstimator()
peak, err = estimator(bins, bag)
```
## References
- [*Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles*, Lakshminarayanan et. al., 2016][2]
- [*The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope*, The Fermit-LAT collaboration, 2019][3]
- [*The 3HSP catalogue of Extreme & High Synchrotron Peaked Blazars*, Chang et. al., 2019][4]
- [*The 5th edition of the Roma-BZCAT. A short presentation*, Massaro et. al., 2015][5]
[2]: https://arxiv.org/abs/1612.01474 "Lakshminarayanan et. al., 2016"
[3]: https://arxiv.org/abs/1905.10771 "The Fermit-LAT collaboration, 2019"
[4]: https://arxiv.org/abs/1909.08279 "Chang et. al., 2019"
[5]: https://www.ssdc.asi.it/bzcat/ "Massaro et. al. 2015"
Raw data
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"description": "# BlaST\r\n\r\nBlaST (**Bla**zar **S**ynchrotron **T**ool) is a tool to estimate the\r\nsynchrotron peak of blazars given its spectral energy distribution. The package is available on pip\r\nand can simply be installed via\r\n\r\n```\r\npip install astroblast\r\n```\r\n\r\nRunning the tool is as simple as typing\r\n\r\n```\r\nblast sed.txt\r\n```\r\n\r\nwhere `sed.txt` is a output file generated by the *VOUBlazar* tool and might look something like the following:\r\n\r\n```\r\n 1 matched source 227.16230 -49.88400 99\r\n Frequency nufnu nufnu unc. nufnu unc. start time end time Catalog Reference\r\n Hz erg/cm2/s upper lower MJD MJD \r\n---------------------------------------------------------------------------------------------------------------------------\r\n 2.418E+17 2.185E-13 3.139E-13 1.230E-13 55000.0000 55000.0000 RASS Boller et al. 2016, A&A, 103, 1 \r\n 2.418E+17 5.085E-13 6.281E-13 3.889E-13 58150.0000 58150.1016 OUSXB Giommi et al. 2019, Accepted for publication in A&A \r\n```\r\n\r\nThis gives the following output:\r\n\r\n```\r\n12.42 (+/- 0.71)\r\n```\r\n\r\nThe first value is the synchrotron peak and the second the 95% prediction interval both in `log10`. It is possible to specify a different prediction interval width by the `-w` argument, followed by the width in sigmas:\r\n\r\n```\r\nblast sed.txt -w 1.0\r\n```\r\n\r\nBlaST also supports bulk estimation, e.g. estimating a whole catalogue, by providing a directory or zip file containing\r\nthe seds as well as an output file in which the results will be written to as csv:\r\n\r\n```\r\nblast seds.zip -o estimates.csv\r\n```\r\n\r\nWith V2 you now also have the option to estimate the synchrotron peak flux (`erg/cm2/s`), again in `log10`.\r\nYou enable this with the `-f` flag:\r\n\r\n```\r\n-12.39 (+/- 0.63) @ 12.39 (+/- 0.39)\r\n```\r\n\r\n## Changes from v1 to v2\r\n\r\nWith v2 there has been some major changes. Some are listed in the following:\r\n\r\n- **New data set** (courtesy of N. Krieger) \r\nThe data set has been refined resulting in a new set independent of the previous\r\none. This one now also includes labels for the flux.\r\n- **Flux estimate** \r\nBiggest difference is the estimation of the synchrotron peak's flux by an\r\nindependent ensemble of neural networks.\r\n- **Save models in ONNX format** \r\nPreviously, the tool needed pytorch installed, which is quite large. We moved to\r\nONNX runtime to make the installation slimmer.\r\n\r\n## How it works\r\n\r\nBlaST consists of an ensemble of similar neural networks power by [pytorch](https://pytorch.org/) based on the method presented in \r\n[Lakshminarayanan et. al. 2016][2]. The data set (see `SEDs.zip`) is based on the three catalogues [4LAC][3], [3HSP][4]\r\nand [5BZCat][5] and divided into 5 bags. For each bag an ensemble of 5 models are trained totalling 25 independent models.\r\n\r\nThe bagging allows the reapply the ensemble on the training data as an out-of-bag estimation, i.e. excluding the\r\nensemble members that were trained on a training set without that specific bag. The results are shown in `estimates.csv`.\r\n\r\nFor specific details, e.g. how the data was enhanced and how the bias in the data set was handles, take a look in\r\n`BlaST.ipynb` which is the notebook used to train the models. Note that if you want to run that notebook you'll also\r\nneed to install [PyTorch Lightning](https://www.pytorchlightning.ai/)\r\n\r\n## Performance\r\n\r\nThe performance was evaluated using out-of-bag estimates on the training data\r\nincluding a 95% prediction interval. Metrics are shown as median and 25%/75%\r\nquantile.\r\nSince the model for predicting the peak frequency and peak flux are independent,\r\nmetrics are shown for both.\r\n\r\n| Metric | Value | Freq | Flux |\r\n|----------|--------|-------|-------|\r\n| Abs Err | Median | 0.163 | 0.104 |\r\n| | 25% | 0.075 | 0.049 |\r\n| | 75% | 0.314 | 0.186 |\r\n| PI Width | Median | 1.267 | 1.087 |\r\n| | 25% | 1.009 | 0.971 |\r\n| | 75% | 1.625 | 1.198 |\r\n| Interval | Median | 1.400 | 1.088 |\r\n| Score | 25% | 1.010 | 0.972 |\r\n| | 75% | 1.636 | 1.199 |\r\n\r\n### Peak Frequency Estimate\r\n\r\nThe following show the prediction histogram with the median as black line, as well as the 90% and 10% quantile shown as\r\ndotted lines\r\n\r\n\r\n\r\nThe next shows the prediction interval (95%) widths. The percentages above and below show the respective amount of samples\r\noutside their intervals.\r\n\r\n\r\n\r\nThe final one shows the prediction error and the prediction interval distribution using a moving window and smoothed using cubic splines.\r\n\r\n\r\n\r\n### Peak Flux Estimate\r\n\r\nSame plots, but now for the peak flux estimate.\r\n\r\n\r\n\r\n\r\n\r\n## Use BlaST in code\r\n\r\nBlaST can also be imported as python package:\r\n\r\n```python\r\nfrom blast import *\r\n\r\nsed, pos = parse_sed('sed.txt', position=True) #reads the file\r\nbag = get_bag(pos) #Returns bag if sed was part of training\r\nbins = bin_data(sed)\r\n\r\n# estimate peak frequency\r\nestimator = PeakFrequencyEstimator()\r\npeak, err = estimator(bins, bag)\r\n# estimate peak flux\r\nestimator = PeakFluxEstimator()\r\npeak, err = estimator(bins, bag)\r\n```\r\n\r\n## References\r\n\r\n- [*Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles*, Lakshminarayanan et. al., 2016][2]\r\n- [*The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope*, The Fermit-LAT collaboration, 2019][3]\r\n- [*The 3HSP catalogue of Extreme & High Synchrotron Peaked Blazars*, Chang et. al., 2019][4]\r\n- [*The 5th edition of the Roma-BZCAT. 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