Euclid preparation: XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for galaxy clusters. / Methods. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. Such colour selections are expressed as a function of the lens redshift. / Results. We calibrated galaxy selections using simulated ground-based griz and EuclidYEJEHE photometry. Both selections produce a purity higher than 97%. The griz selection completeness ranges from 30% to 84% in the lens redshift range zl ∈ [0.2, 0.8]. With the full grizYEJEHE selection, the completeness improves by up to 25 percentage points, and the zl range extends up to zl = 1.5. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on griz bands provides excellent results on real external datasets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The griz selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percentage points higher completeness. The combination of colour and photo-z selections applied to simulated Euclid data yields up to 95% completeness, while the purity decreases down to 92% at high zl. We show that the calibrated colour selections provide robust results even when observations from a single band are missing from the ground-based data. Finally, we show that colour selections do not disrupt the shear calibration for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on the shear calibration

Euclid preparation: LXVI. Impact of line-of-sight projections on the covariance between galaxy cluster multi-wavelength observable properties: Insights from hydrodynamic simulations

\ua9 The Authors 2025.Context. Cluster cosmology can benefit from combining multi-wavelength studies. In turn, these studies benefit from a characterisation of the correlation coefficients among different mass-observable relations. Aims. In this work, we aim to provide information on the scatter, skewness, and covariance of various mass-observable relations in galaxy clusters in cosmological hydrodynamic simulations. This information will help future analyses improve the general approach to accretion histories and projection effects, as well as to model mass-observable relations for cosmology studies. Methods. We identified galaxy clusters in Magneticum Box2b simulations with masses of M200c > 1014 M⊙ at redshifts of z = 0.24 and z = 0.90. Our analysis included Euclid-derived properties such as richness, stellar mass, lensing mass, and concentration. Additionally, we investigated complementary multi-wavelength data, including X-ray luminosity, integrated Compton-y parameter, gas mass, and temperature. We then examined the impact of projection effects on mass-observable residuals and correlations. Results. We find that at intermediate redshift (z = 0.24), projection effects have the greatest impact of lensing concentration, richness, and gas mass in terms of the scatter and skewness of the log-residuals of scaling relations. The contribution of projection effects can be significant enough to boost a spurious hot-versus cold-baryon correlations and consequently hide underlying correlations due to halo accretion histories. At high redshift (z = 0.9), the richness has a much lower scatter (of log-residuals), while the quantity that is most impacted by projection effects is the lensing mass. The lensing concentration reconstruction, in particular, is affected by deviations of the reduced-shear profile shape from that derived using a Navarro-Frenk-White (NFW) profile; the amount of interlopers in the line of sight, on the other hand, is not as important

Retroperitoneal malignant fibrous histiocytoma. A case report

[No abstract available

Benefits of thermal load forecasts in balancing load fluctuations through thermal storage

Planning and managing the operation of cogenerative plants (CHP) is increasingly becoming an industrial necessity because of the participation of CHP plants in the day-ahead energy market and the need to deal with heat demand fluctuations. Short-term operational planning usually considers power and heat demand forecast, which can widely fluctuate daily and seasonally, to maximise the net revenue and fulfil the heat requirements. In this framework, a Thermal Storage (TS) can balance day-night fluctuations due to outdoor temperature, as well as unexpected energy surplus and deficiencies caused by heat demand forecast errors, thus giving the CHP plant more operational flexibility. In this paper, the capability of a TS to balance thermal load fluctuations and forecast errors is investigated when a Machine Learning (ML) thermal load forecast for short-term predictions up to 48 h is used. The TS is modelled as a layered storage tank with perfectly mixed layers. Weather and consumption data from 2018 to 2020 related to a large greenhouse powered by a CHP plant located in Tuscany were used as a case study to perform the training and validation of the forecast model as well as to analyse the TS capability in balancing fluctuations with volumes ranging from 500 up to 10,000 m3. Several ML algorithms were used and compared against a naive prediction based on load persistency. Support Vector Machine (SVM) resulted as the best-performing algorithm. Using SVM leads to better exploitation of the TS capacity, compared to persistence, leading to a more regular State of Charge (SOC) trend and allowing the system to operate within expected conditions up to 80 % of the year. In contrast, a more naive forecast approach brings to relevant volume size increase to achieve equal performance. Finally, a more accurate forecast reduced the TS size to 50 %, potentially cutting the investment and operational costs compared to the load persistency forecast strategy