Aplicación del método Delphi para validar la concepción didáctica del

In this section, different tests are carried out to compare the performance of the eSASS source detection pipeline with the best wavelet-based source detection algorithm found in Section4.2, mr_filter+SE. For this analysis, the simulated images of the Intermediate field (∼ 4 ks in exposure time) of the January 2013 release are used. The eSASS version used for this exercise is fits_090304, which has the fol- lowing task versions: ermask version 1.1, erbox version 1.4, erbackmap version 1.4, ermldet version 1.6. The version of the joint procedure mr_filter+SE is the same as stated in Section4.2.2. A list of the most relevant parameters used in each procedure is shown in Tables4.4and4.5.

4.3 Comparison of source detection algorithms for eROSITA images eSASS (fits_090304)

Parameter Value Parameter Value erbox(local mode) ermldet

emin 500 emin 500 emax 2000 emax 2000 ecf 1.0 ecf 1.0 nruns 1 likemin 15.0 likemin 10.0 extlikemin 0.0 boxsize 2 cutrad 14.0 multrad 14.0 erbox(map mode) extmin 1.5

nruns 2 extmax 30.0

extentmodel beta erbackmap thres_col like nsplinenodes 8 thres_val 30.0

nmaxfit 1

nmulsou 1

Table 4.4: Relevant parameters of the eSASS detection pipeline (version fits_090304).

mr_filter+SE Parameter Value mr_filter Threshold 10−3 Minimum scale 1 Maximum scale 9 SExtractor Detection threshold 3σ Minimum detection area 32 Deblending subthresholds 64 Min. deblending contrast 0.001 Table 4.5:Relevant parameters of the mr_- filter+SEdetection algorithm.

Synthetic simulations with only point-like sources

In this section, different performance tests are carried out on the simulations that include only point-like sources, i.e. on the “AGN+background” simulation. As a reminder, sources detected by the eSASS detection pipeline are considered as point-like sources if they have an extension likelihood value equals zero. For mr_filter+SE it is still assumed that the detected sources correspond to the simulated ones. The positional accuracy was first determined. The images obtained from the survey mode have a final averaged PSF across the entire image. Then, it is expected that all the sources are affected by the same positional shift. Given the broad eROSITA PSF in survey mode (2800_{), the initial searching radius is} set to 4000 _{for both detection algorithms. For each simulated object, the nearest detected source inside} this radius is searched and cross-correlated with the source input list. The results show that more than ∼ 97% of the cross-identified point-like sources lie within 3000. Then, this value is adopted as search radius of point-like sources.

The point-like source detection efficiency as function of input flux is shown in Fig. 4.13. The plot shows that both detection algorithms, eSASS and mr_filter+SE, have a similar performance. Both procedures detect more than 90% of the sources above 2.0 × 10−14 _{erg s}−1 _cm−2_{. Figure 4.13 does} not display any error bars because the Release January 2013 only offers a single “AGN+background” simulation per field.

Another issue that has to be taken into account is the number of missed (non-detected) and false (spuri- ous) sources detected by each algorithm. As mention earlier, a good source detection algorithm identi- fies as many as possible true sources with a low or null contamination by false detections. Valtchanov et al. (2001) have presented a number of issues that can originate false detections and lower the source detection efficiency:

1. False detections represent non-simulated objects or two or more simulated sources that are blen- ded into a single detected object. In this latter case, the blended object becomes a false detection

Figure 4.13: Detection efficiency as a function of input flux for point-sources for eSASS and mr_filter+SE pipelines in ∼ 4 ks exposure (Intermediate field). It is not possible to add error bars since the analysis has been done over only one simulation (see text for more details).

if it is not in the input list or if its determined centroid has shifted beyond the searching radius. 2. It can happen that in the source cross-identification process the closest detected source to the input

source position is not the simulated source.

3. Simulated objects can be missed by the detection algorithm because they are located in regions with high noise properties.

It is not easy to disentangle all the above issues, especially for faint sources which tend to be confused with the background and, therefore, missed. Moreover, these kinds of sources are more prone to blend- ing effects if they are close to each other. Taking all these into account, the closest detected source to the input source will be considered as the true match, and the rest of detected sources as spurious detections.

The results on the false detection rate are: ∼ 13 false detections per square degree for eSASS, and ∼ 15 false detections per square degree for mr_filter+SE. These false detection rates represent a high level of contamination. For an all-sky survey, like the eROSITA one, the number of false detections would be too large, making very difficult and unreliable the use of such samples for any scientific purpose. In order to overcome this problem one can further explore the output parameter space of ermldet, to look for parameters that can help to distinguish between true and false detected sources, and, therefore, obtain cleaner samples. In the case of mr_filter+SE a further implementation of a maximum likelihood fitting method can reduce the contamination rate.

Synthetic simulations with point-like and extended sources

In this second exercise, simulations with point-like and extended sources are used. The images are created by merging the “AGN+background” with the “cluster only” simulations. As explained in Sec-

tion 4.3.1, each “cluster only” simulation contains the same kind of galaxy cluster, i.e. with the same

flux and core radius. In this way, one can obtain enough statistics on the detection efficiency of each type of galaxy cluster. The objective of this exercise is to estimate the capabilities of the eSASS and mr_filter+SEalgorithms to detect and identify extended objects embedded in a point-like field. The point-like sources can change the local and global background properties and, therefore, lead to source

4.3 Comparison of source detection algorithms for eROSITA images

Figure 4.14:Detection efficiency as a function of input flux for extended sources by mr_filter+SE (left) and eSASS (right) with maximal contamination in ∼ 4 ks exposures (Intermediate field). Each colour represents a different galaxy cluster core radius (in arcsec).

confusion effects. Moreover, the presence of a point-like source near to an extended one can lead to source confusion, to a non-source detection or to a source misclassification. This can affect faint exten- ded sources, even when the nearby point-like source is also faint.

As shown in Section4.2.4, the recovered position of extended sources is usually more displaced from the simulated position than the determined position of point-like sources. Moreover, the fainter the extended source the larger is the displacement from the simulated position. This issue will become clear in the next section. For this exercise, a search radius of 6000_{is chosen in both detection algorithms.} The extended source detection efficiency as a function of input flux for both algorithms is shown in Fig.4.14. There are just four different fluxes for galaxy clusters in the Release January 2013 simulations, hence the only four points in the plot. The error bars display the standard deviation. Again, for mr_- filter+SE the closest detected source to the input position is assumed to be the true match, while for eSASS only sources with extension likelihood greater than zero are considered as extended sources. Both algorithms show a good performance in detecting bright galaxy clusters (> 5×10−14_{erg s}−1_cm−2_), especially the ones that are more concentrated, i.e. with small core radius (< 7000_{). However, fainter} (10−14_{erg s}−1_cm−2_{) or larger (rc ≤ 70}00_{) extended sources are more difficult to detect with the eSASS} pipeline. mr_filter+SE shows a better performance at this flux, but the lack of an extended source classifier in this detection procedure makes difficult to conclude if such results really reflect the true extended source detection efficiency.