Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning

Valeria Amaro; Stefano Cavuoti; Massimo Brescia; Giuseppe Riccio; Crescenzo Tortora; Maurizio D'Addona; Michele Delli Veneri; Nicola R. Napolitano; Mario Radovich; Giuseppe Longo

doi:10.1007/978-3-030-65867-0_11

arXiv · 2007.01840

Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning

Valeria Amaro·Stefano Cavuoti·Massimo Brescia·Giuseppe Riccio·Crescenzo Tortora·Maurizio D'Addona·Michele Delli Veneri·Nicola R. Napolitano·Mario Radovich·Giuseppe Longo

View original source ↗Read source document ↗Export metadata ↓

Abstract

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of $\simeq 75\%$ and an improvement of $\simeq 6\%$ for NMAD, with respect to the original data set, preserving the $\simeq 93\%$ of its content.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Valeria Amaro, Stefano Cavuoti, Massimo Brescia, Giuseppe Riccio, Crescenzo Tortora, Maurizio D'Addona, Michele Delli Veneri, Nicola R. Napolitano, Mario Radovich, Giuseppe Longo. 2020-07-03. Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning. https://doi.org/10.1007/978-3-030-65867-0_11

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Performance characterization of a new Structural and Thermal Architecture for a future spaceborne Closed-Cycle Dilution Refrigerator

A Structural and Thermal Model (STM) has been developed to support the new spaceborne Closed-Cycle Dilution Refrigerator (CCDR), which aims to provide continuous cooling at 100~mK for long-duration astrophysical missions. The STM is based on a hexapod architecture that ensures both thermal decoupling and mechanical robustness during launch. In this paper, we present the characterization of its thermal and mechanical performances. A dedicated experimental setup was used to investigate the thermal behavior of the STM across a broad temperature range. The study reveals limitations of the collar design, with incomplete power interception from thermal boundary resistances and vibration test failure traced to defective strut gluing. These results guide the next STM iteration with optimized collar and strut assembly for reliable CCDR operation in space.

astro-ph.IM↗

arXiv2026

The Simons Observatory: Development of a Pipeline to Detect Rapid Transients in Time-Ordered Data

We introduce a method for detecting astrophysical transients evolving on timescales of milliseconds to minutes using cosmic microwave background (CMB) survey telescopes. While previous transient searches in CMB data operate in map space, our pipeline directly processes the raw time-ordered data, enabling sensitivity to fast, dynamic signals. We integrate our detection approach into the Simons Observatory time-domain pipeline and assess the performance by injecting symmetric, stellar flare-like light curves into simulated observations. For events flaring with a timescale of 0.5 s, the pipeline detects $\gtrsim90$ % of events at flux densities of 800, 1150, 1650, and 4250\,mJy when measured in the 93, 145, 225, and 280 GHz bands respectively. At a fixed peak flux density, the pipeline more readily detects longer flares. The limiting flux density for 90 % completeness is four times lower for a $\ge5$ s flare than for a 0.5 s flare, while the flux density limits for $\gtrsim50$ % detection efficiency are comparable to the rms noise of the time-ordered data. We are able to determine the position of detected events in each observing band, with a positional uncertainty at the detection threshold comparable to the telescope resolution at that band. These results demonstrate the readiness of this pipeline for incorporation into upcoming Simons Observatory data analyses.

astro-ph.IM↗

arXiv2026

Thermal conductivity of various CFRPs from 100 mK to 20 K

Carbon-fiber-reinforced polymers (CFRPs) are some of the most useful materials for building spacecraft and aerospace tools. They are especially valuable for systems that work at extremely cold (cryogenic) temperatures because they are strong, lightweight, and don't transfer heat easily. In this study, researchers measured how well heat moves through several different types of carbon fiber samples, specifically T300, T700, HS40, M55J, and IMA, at different fiber layouts and densities. These measurements were taken at ultra-cold temperatures ranging from 100 mK to 20 K. The team used a newly developed analysis method to calculate the thermal conductivity for each sample. Finally, they shared how each material behaved at different temperatures and compared their findings to previous research.

astro-ph.IM↗