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First, we tested how varying the amount of a single template type affects the marginalisation process of the PDF and what is the effect on the resulting redshift estimations. For this, we increased the ratio of templates that were defined as elliptical up to 50 times, increasing the template set from 75 up to ~1100. The resulting photometric redshift estimations worsened at most 1% and thus we conclude that the number of templates of any certain spectral type has negligible effect of the outcome. This is mainly because the ratio of templates with different spectral types only affect redshift estimations for galaxies with PDFs having multiple peaks (75% of the test catalogue is single peaked) with sufficient strength. Therefore, a potential template bias that would be caused by the fact that DEEP2 and DEEP3 spectroscopic samples were selected to prefer higher-redshift sources should not affect our results. This type of template selection effect could be further reduced by introducing a template prior that would weight each template type by its occurrence in the selection. Erinevate väiksemate parandustega, nagu näiteks õhukompressorite väljavahetuse ja soojustagastusega ventilatsiooni paigaldamisega, oleme kokku oma tootmises vähendanud 1600 MWh soojust ehk umbes 500 tonni CO2-e. Most typically, photo- z methods have been applied to broadband filter data ( Ilbert et al. 2006; Tanaka et al. 2018; Lee & Chary 2020, and many others). In such cases, redshifts are primarily derived from large-scale features such as the 4000 Å, Balmer, or Lyman breaks if they are present in between the filter passband ranges. Thereby, a redshift accuracy of up to d z≈ 0.03(1 + z) has been achieved. Using narrow-band filters, this limit can be considerably extended by a more precise localisation of the continuum breaks as well as by the possibility of detecting individual spectral lines: JPLUS/SPLUS ( Izquierdo-Villalba et al. 2019), miniJPAS ( Bonoli et al. 2021), and PAU ( Eriksen et al. 2020; Rodriguez et al. 2020). Photometric correction term C (defined in Eq. (15)) for each passband given in wavelengths. This is the average correction value over the four different tiles in miniJPAS data. In addition to the template quality and representativeness, also the size of the template library can be optimised. A higher number of templates increases the redshift accuracy of brighter sources, while also lowering the accuracy of fainter ones due to over-fitting. Therefore, the optimal number of templates for different brightness sub-samples are different. The results of the corresponding test are described in Sect. 6.1.1. For the brighter ( r< 22 mag) subset, we find that a library of around 75 templates gives an optimal result.

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Distribution of spectroscopic redshifts and r-band magnitudes of the miniJPAS sources in the test catalogue. The dashed line represent the magnitude cut of the brighter sub-sample. Põltsamaalt on jõudnud turule uue põlvkonna valmissupid ja -toidud: tippkokkade retsepte ja kohalikku toorainet kasutades valminud eestlaste lemmikmaitsed, mis on kvaliteetsed ja ilma lisaaineteta. Põltsamaa valmissupid ja -toidud on praktilises portsjonpakendis ja neid on imelihtne valmistada: eemalda kate, soojenda ja valmis. Ei ole vaja vett lisada ega ümber valada. Valmis 3 minutiga, koristatud 3 sekundiga. Based on the calculated merit values (see lower panel of Fig. 11), we find that the optimal number of selected templates for bright galaxies is 75, although the differences between other number of chosen templates are not that big for number of templates above 50. In this work, we were satisfied with only one set of 75 templates as we mostly use the magnitude cut of r< 22 mag. But if larger sets of observations should become available in the future, it might be viable to construct different template selections for each brightness range. Methods. TOPz relies on template-based photo- z estimation with some added J-PAS specific features and possibilities. We present TOPz performance on data from the miniJPAS survey, a precursor to the J-PAS survey with an identical filter system. First, we generated spectral templates based on the miniJPAS sources using the synthetic galaxy spectrum generation software CIGALE. Then we applied corrections to the input photometry by minimising systematic offsets from the template flux in each filter. To assess the accuracy of the redshift estimation, we used spectroscopic redshifts from the DEEP2, DEEP3, and SDSS surveys, available for 1989 miniJPAS galaxies with r < 22 mag AB. We also tested how the choice and number of input templates, photo- z priors, and photometric corrections affect the TOPz redshift accuracy.Meie kõrvitsad, kapsad, kartulid, peedid ja porgandid tulevad kõik Eesti kohalikelt kasvatajatelt. Suurema osa Põltsamaa kurkidest ning suvikõrvitsatest kasvatavad kohalikud talupidajad, kuid väikese osa ostame ka välismaalt. Aasta jooksul ostame talunikelt erinevaid köögivilju kokku pea 5000 tonni. The J-PAS optical system comprises of observations that range from 3400 to 11 000 Å. Since several sources are expected to lie well beyond z = 1, we require the templates to cover at least the 1000 to 11 000 Å range. The minimally required spectral resolution is not determined by the width of the passbands (FWHM ~ 145 Å) but rather by the sharp edges of the throughput curves. These edges are measured to be around 10 Å wide, so a template spectral sampling rate of ≤ 10 Å would be needed.

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In Sect. 5.2, we noted that ~22% of the galaxies in the test catalogue fall outside the colour region that our templates cover. We also noted that these galaxies are fainter on average, having a median brightness of r = 21.57 mag compared to r = 21.21 mag of those galaxies that are inside the region. We find that at a fixed brightness level, the number of galaxies that reach the J-PAS accuracy goal is similar between galaxies outside the colour region and the remaining galaxies. This shows that, although the broadband colours of the templates are somewhat more restricted than those of the observed galaxies, the templates are accurate enough to yield reliable redshift estimates from the full J-PAS filter set. The most probable explanation is that the accuracy of photo- z for fainter galaxies is, due to their larger photometric uncertainties, mostly defined by the detection of emission lines and not the template broadband colours themselves. Super-sharp photos often pop off the screen with more impact and emotion, so you can often make your photos more powerful by increasing sharpness. However, people are also very sensitive to “unnaturally sharp” photos with haloing and fringing caused by traditional sharpening. Sharpen AI is an intelligent image sharpener specifically trained to give your photos that extra pop while remaining natural. We tested whether the inclusion of a simple prior as described in Sect. 5.4 make a notable impact on the redshift estimation accuracy. Distribution of the redshift accuracy of the sources with and without prior can be seen in Fig. 13. In general, the shape of the accuracy distribution remains roughly the same. The minor differences are due to galaxies with the redshift PDF shapes that are most affected by the prior. The number in the top left corner of each panel shows the ratio of galaxies that achieve the J-PAS target accuracy and the number below that is the fraction of outliers. As can be seen, there is an improvement of both indicators where the number of outliers is reduced by almost 10%. Thus, we conclude that applying even a simple prior is an improvement to the overall redshift estimation quality. 6.1.3 Impact of photometric corrections

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A number of required and optional inputs (see Sect. 3.1) affect the resulting photometric redshift estimations with TOPz. Unfortunately, the effects of these inputs are often degenerate and a set of inputs that improve the results separately might not do so when combined. TOPz is most strongly affected by the input templates, observational data quality, the accuracy of the uncertainty estimates, and photo- z priors. where the weights w gal are based on the normalised apparent magnitude of the galaxy. To lower the effect of the more noisy fainter galaxies, the weight values used in this paper were between 3 and 1. This means that the brightest galaxy has a weight value of 3 and the faintest galaxy a weight value of 1 with other galaxies having weight values linearly distributed between those two. This ensures that when the values change between different template sets V, the brighter galaxies would contribute more to the total change than the fainter galaxies.

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For characterising the brightness of the sources and applying luminosity cuts we have used the r-band MAG_AUTO magnitudes. The specific luminosity cut values are described in the text. For each photometric redshift value, we also give an ‘odds’ estimate which is the relative area of the PDF within a user-defined fixed range centred on the estimated redshift value. Odds value close to one means that the PDF is narrowly condensed around the highest PDF value whereas a low odds value means that the PDF is broad and the estimated redshift is of a lower probability. Although a single redshift value is usually the most preferred output of a redshift catalogue, the underlying redshift PDFs contain more useful information. As one of the TOPz outputs is the full redshift PDF of every template-galaxy pair, we can conduct some statistical tests on the whole catalogue to determine how well-behaved our redshift PDFs are in terms of statistics.Kogu meie tootmises kasutatav elektrienergia on 100% roheelekter, mida saadakse peamiselt hüdro-, tuule ja päikesenergiast. Support for rotating videos 90, 180, and 270 degrees-corrects videos with missing metadata for rotation. Isikuandmete esitamine on vabatahtlik ning teil on õigus piirata töötlemist või avaldada vastuseisu töötlemisele. Figure 18 shows how the improvements described in Sect. 6.1 affect photometric redshift estimations. The results improve progressively when we use the full base template set (blue), enable photometric corrections (orange), apply the reduced template selection (green), and finally enable priors along with the previous steps (red). The grey line shows the photo- z estimations from miniJPAS database and the dashed vertical line marks the target accuracy of J-PAS redshifts. Every added step increases the number of galaxies achieving J-PAS photo- z target accuracy with the biggest improvement coming from implementing the photometric corrections (see Sect. 5.3). Overall, TOPz results are on par with previous miniJPAS photo- z estimations showing the accuracy that template-based photometric redshift methods can reach given the quality of the miniJPAS photometry. Meie hoidised ja valmistoidud on klaaspurkides, millest suurem osa on toodetud Eestis. Tehase lähedus vähendab transpordiga seotud süsinikujalajälge. Klaas ja metall (purkide kaaned) on hästi ringlussevõetavad materjalid, mida saab palju kordi taaskasutada. Meie moosipurkidel on 2022 aasta sügisest lihtsalt eemaldatavad etiketid. Kui viid klaaspurgid oma kodu lähedal olevasse pakendi- või klaasikonteinerisse, annad neile uue elu.

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Depending on the observational uncertainties and the spectral characteristics of the galaxy, the PDF shapes can vary dramatically. Therefore, depending on the goal and on the actual data, other kinds of ‘best’ redshift estimators can be used, for example taking into account the area under the likelihood curve up to some distance from the peak. Currently, two of such additional estimators have been implemented in TOPz, described below. TOPZ!’i pakend on välja töötatud mikrolaineahjus soojendamiseks. Antud pakendit tavaahju või lahtisele leegile panna ei soovita. Uute toodete arendamisel peame soola-, suhkru- ja rasvasisaldust silmas juba arenduse algfaasis. Lisaks tegeleme lisandväärtusega toodete arendamisega.

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The distributions of spectroscopic redshifts and r-band magnitudes of the test catalogue sources can be seen in Fig. 3. The statistical fluctuations of redshifts are visible, hinting that a larger test sample would be desirable for a comprehensive assessment of the redshift estimates. For example, the red-shift histogram (green barplot on the right panel) shows peaks at around redshift 0.25 and 0.4 that are most likely due to larger clusters or superclusters in the line of sight of the observations. Such concentrations may bias the final template selection.