Operations and performance of the PACS instrument 3He sorption cooler on board of the Herschel space observatory

Sauvage, M.; Okumura, K.; Klaas, U.; Müller, T.; Moór, A.; Poglitsch, A.; Feuchtgruber, H.; Duband, L.

doi:10.1007/s10686-014-9388-z

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Hora et al 2012;Yee et al 2017), and the Herschel mission ended on 29 April 2013 when the satellite ran out of its helium coolant (e.g. Sauvage et al 2014)). Nevertheless, the aforementioned analysis suggests that bands near 3.6 µm and 24 µm would be informative if the other bands are comparable to those employed here.…”

Section: Gradient Boostingmentioning

confidence: 99%

Protostellar classification using supervised machine learning algorithms

Miettinen

2018

Astrophys Space Sci

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Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy distribution (SED) analysis. An alternative approach is provided by supervised machine learning algorithms, which can be trained to classify large samples of YSOs much faster than via SED analysis. We attempt to classify a sample of Orion YSOs (the parent sample size is 330) into different classes, where each source has already been classified using multiwavelength SED analysis. We used eight different learning algorithms to classify the target YSOs, namely a decision tree, random forest, gradient boosting machine (GBM), logistic regression, naïve Bayes classifier, k-nearest neighbour classifier, support vector machine, and neural network. The classifiers were trained and tested by using a 10-fold cross-validation procedure. As the learning features, we employed ten different continuum flux densities spanning from the near-infrared to submillimetre wavebands (λ = 3.6 − 870 µm). With a classification accuracy of 82% (with respect to the SED-based classes), a GBM algorithm was found to exhibit the best performance. The lowest accuracy of 47% was obtained with a naïve Bayes classifier. Our analysis suggests that the inclusion of the 3.6 µm and 24 µm flux densities is useful to maximise the YSO classification accuracy. Although machine learning has the potential to provide a rapid and fairly reliable way to classify YSOs, an SED analysis is still needed to derive the physical properties of the sources (e.g. dust temperature and mass), and to create the labelled training data. The machine learning classification accuracies can be improved with respect O. Miettinen 1 Digia Plc/Avarea Oy, Rautatieläisenkatu 6, FI-00520 Helsinki, Finland to the present results by using larger data sets, more detailed missing value imputation, and advanced ensemble methods (e.g. extreme gradient boosting). Overall, the application of machine learning is expected to be very useful in the era of big astronomical data, for example to quickly assemble interesting target source samples for follow-up studies.

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Section: Gradient Boostingmentioning

confidence: 99%

Protostellar classification using supervised machine learning algorithms

Miettinen

2018

Astrophys Space Sci

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“…Hereafter, these half-data maps are referred to as jackknife maps or map pairs, and full-data maps as co-added. Note, one of the 12 scans is discarded because of a voltage bias swell (or "cooler-burp" Sauvage et al 2014) that occurred during a cooler cycle, which if included would lead to large-scale striping. The result is that one of the jackknife maps is not fully sampled; this is taken into account in all stages of bandpower and uncertainty estimation.…”

Section: Herschel/ Spire Datamentioning

confidence: 99%

Measurements of the Cross Spectra of the Cosmic Infrared and Microwave Backgrounds from 95 to 1200 GHz

Viero,

Reichardt,

Benson

et al. 2018

Preprint

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We present measurements of the power spectra of cosmic infrared background (CIB) and cosmic microwave background (CMB) fluctuations in six frequency bands. Maps at the lower three frequency bands, 95, 150, and 220 GHz (3330, 2000, 1360 µm) are from the South Pole Telescope, while the upper three frequency bands, 600, 857, and 1200 GHz (500, 350, 250 µm) are observed with Herschel/SPIRE. From these data, we produce 21 angular power spectra (six auto-and fifteen cross-frequency) spanning the multipole range 600 ≤ ≤ 11,000. Our measurements are the first to cross-correlate measurements near the peak of the CIB spectrum with maps at 95 GHz, complementing and extending the measurements from Planck Collaboration et al. ( 2014) at 143 -857 GHz. The observed fluctuations originate largely from clustered, infrared-emitting, dusty star-forming galaxies, the CMB, and to a lesser extent radio galaxies, active galactic nuclei, and the Sunyaev-Zel'dovich effect.

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Refrigeration Below 1 Kelvin

Cao

2021

J Low Temp Phys

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Operations and performance of the PACS instrument 3He sorption cooler on board of the Herschel space observatory

Cited by 4 publications

References 11 publications

Protostellar classification using supervised machine learning algorithms

Protostellar classification using supervised machine learning algorithms

Measurements of the Cross Spectra of the Cosmic Infrared and Microwave Backgrounds from 95 to 1200 GHz

Refrigeration Below 1 Kelvin

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