Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

Mountrichas, G.; Corral, A.; Masoura, V. A.; Georgantopoulos, I.; Ruiz, Ángel Liceras; Georgakakis, A.; Carrera, Francisco J.; Fotopoulou, S.

doi:10.1051/0004-6361/201731762

Cited by 22 publications

(26 citation statements)

References 40 publications

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“…This is taken into account when computing photo-z via SED-fitting by adopting a prior in absolute magnitude (e.g., Salvato et al 2009Salvato et al , 2011Fotopoulou et al 2012;Hsu et al 2014, A17). More recently, the separation of the sources in these two subgroups is becoming the standard also when computing photo-z via ML (e.g., Mountrichas et al 2017;Ruiz et al 2018). One limitation of this method is that it relies on images that are affected by the quality of the seeing, which can alter the morphological classification of the sources.…”

Section: Point-like Vs Extendedmentioning

confidence: 99%

Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

Brescia

Salvato

Cavuoti

et al. 2019

Monthly Notices of the Royal Astronomical Society

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With the launch of eROSITA, successfully occurred on July 13th, 2019, we are facing the challenge of computing reliable photometric redshifts for 3 million of AGNs over the entire sky, having available only patchy and inhomogeneous ancillary data. While we have a good understanding of the photo-z quality obtainable for AGN using SEDfitting technique, we tested the capability of Machine Learning (ML), usually reliable in computing photo-z for QSO in wide and shallow areas with rich spectroscopic samples. Using MLPQNA as example of ML, we computed photo-z for the X-ray selected sources in Stripe 82X, using the publicly available photometric and spectroscopic catalogues. Stripe 82X is at least as deep as eROSITA will be and wide enough to include also rare and bright AGNs. In addition, the availability of ancillary data mimics what can be available in the whole sky. We found that when optical, NIR and MIR data are available, ML and SED-fitting perform comparably well in terms of overall accuracy, realistic redshift probability density functions and fraction of outliers, although they are not the same for the two methods. The results could further improve if the photometry available is accurate and including morphological information. Assuming that we can gather sufficient spectroscopy to build a representative training sample, with the current photometry coverage we can obtain reliable photo-z for a large fraction of sources in the Southern Hemisphere well before the spectroscopic follow-up, thus timely enabling the eROSITA science return. The photo-z catalogue is released here.

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Section: Point-like Vs Extendedmentioning

confidence: 99%

Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

Brescia

Salvato

Cavuoti

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…We used the training sample presented in Mountrichas et al (2017) for the sources in the ARCHES catalogue (SDSS training sample). This sample contains sources from XXL (Menzel et al 2016), XWAS (Esquej et al 2013), COSMOS (Brusa et al 2010), XMS (Barcons et al 2007) and XBS (Della Ceca et al 2004), all of them X-rays surveys with a high level of spectroscopic identification.…”

Section: Training Samplesmentioning

confidence: 99%

“…In addition, it also contains 1500 SDSS-DR13 sources spectroscopically identified as QSO with X-ray counterparts. Even though these QSO are optically selected instead of X-ray selected, adding them does not bias our photoz derivation significantly (Mountrichas et al 2017). The final training sample is composed of 5157 objects with SDSS photometric data, 3129 with also NIR data (UKIDSS or 2MASS) and 4718 with MIR data (AllWISE).…”

Section: Training Samplesmentioning

confidence: 99%

“…This was due to lack of large enough X-ray spectroscopic training sets. Mountrichas et al (2017) presented a catalogue with ∼ 1000 X-ray sources in the X-ATLAS field using, for the first time, a machine learning technique (TPZ; Carrasco Kind & Brunner 2013). Their training, spectroscopic sample consists of 5157 sources primarily in the XMM-XXL field (Liu et al 2016;Menzel et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Here we present the XMM-Newton Photo-Z CATalogue (XMMPZCAT). Using the training set presented in Mountrichas et al (2017), we estimate photometric redshifts for ∼ 100 000…”

Section: Introductionmentioning

confidence: 99%

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XMMPZCAT: A catalogue of photometric redshifts for X-ray sources

Ruiz

Corral

Mountrichas

et al. 2018

A&A

Self Cite

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Aims. The third version of the XMM-Newton serendipitous catalogue (3XMM), containing almost half million sources, is now the largest X-ray catalogue. However, its full scientific potential remains untapped due to the lack of distance information (i.e. redshifts) for the majority of its sources. Here we present XMMPZCAT, a catalogue of photometric redshifts (photo-z) for 3XMM sources. Methods. We searched for optical counterparts of 3XMM-DR6 sources outside the Galactic plane in the SDSS and Pan-STARRS surveys, with the addition of near-(NIR) and mid-infrared (MIR) data whenever possible (2MASS, UKIDSS, VISTA-VHS, and AllWISE). We used this photometry data set in combination with a training sample of 5157 X-ray selected sources and the MLZ-TPZ package, a supervised machine learning algorithm based on decision trees and random forests for the calculation of photo-z. Results. We have estimated photo-z for 100 178 X-ray sources, about 50% of the total number of 3XMM sources (205 380) in the XMM-Newton fields selected to build this catalogue (4208 out of 9159). The accuracy of our results highly depends on the available photometric data, with a rate of outliers ranging from 4% for sources with data in the optical+NIR+MIR, up to ∼ 40% for sources with only optical data. We also addressed the reliability level of our results by studying the shape of the photo-z probability density distributions.Article number, page 1 of 16 arXiv:1807.04526v1 [astro-ph.GA] 12 Jul 2018 A&A proofs: manuscript no. xmmpzcat 3XMM sources with at least optical photometry available (SDSS or Pan-STARRS). We provide estimations of the accuracy of our photo-z estimations as well as the percentage of outliers.The structure of the paper is as follows: in Sect. 2 we describe the 3XMM sample as well as the catalogues used to obtain photometric information for our X-ray sources. In Sect. 3 we present the methods used for the cross-match among the various datasets and in Sect. 4 we briefly describe the TPZ algorithm and the training sample. The results are presented in Sect. 5, while we summarise the main conclusions of our analysis in Sect. 6. DataOptical photometry is needed to derive photometric redshifts, so we explored several wide-angle surveys to maximise the number of counterparts of our X-ray sources. We also complemented these data with photometry in the near-and mid-infrared to increase the accuracy of the photometric redshifts. A brief description of all catalogues we used is presented below. 3XMM3XMM-DR6 catalogue ) was released in July 2016. It contains 9160 observations, covering an energy interval from 0.2 keV to 12 keV. The net sky area observed is ∼ 1032 deg 2 . 468 440 unique X-ray sources are included in the catalogue with a median flux in the total energy band of ∼ 2.4 × 10 −14 erg cm −2 s −1 . SDSSData release 13 of the Sloan digital sky survey (SDSS-DR13;Albareti et al. 2017) is the first data release of the fourth phase of the SDSS and covers 14 555 deg 2 , more than one-third of the entire celestial sphere. This release does not...

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Learning in Big Data: Introduction to Machine Learning

Bouchefry¹,

Souza²

2020

Knowledge Discovery in Big Data From Astronomy and Earth Observation

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Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

Cited by 22 publications

References 40 publications

Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

XMMPZCAT: A catalogue of photometric redshifts for X-ray sources

Learning in Big Data: Introduction to Machine Learning

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