arXiv subjects

A. Poliszczuk

Publications and source records attributed to A. Poliszczuk.

4 recordsLinked to original sources

arXiv2026

Enhancing the sensitivity of next-generation X-ray imaging detectors with artificial intelligence and advanced event reconstruction algorithms

Advanced algorithms incorporating artificial intelligence and machine learning (AI/ML) enhance the sensitivity of X-ray imaging detectors and the scientific capabilities of future X-ray missions. In orbit, current instruments are limited in their sensitivity by (1) the instrumental background, induced by cosmic rays which produce signals that can be confused for genuine, astrophysical X-rays, and (2) the ability to reconstruct the detected photon events, degrading the quantum efficiency and energy resolution at the lowest energies, where much discovery space resides. We report on the development of prototype algorithms designed to operate on the raw frame-level data to provide improved identification of particle-induced background events and enhanced energy reconstruction. These algorithms consider the contextual information from all signals in a frame, and are built upon physics-motivated models of charge diffusion and signal generation within the detector. Using high fidelity simulations, we show that following recent developments, prototype ML algorithms can reduce the unrejected particle background by up to 68 per cent compared with traditional filtering methods when operated in an aggressive mode suitable for source detection in imaging surveys, or up to 40 per cent in a conservative mode designed to prioritize accurate measurements of the spectrum. We find that next-generation event reconstruction algorithms improve the sensitivity and energy resolution of CCD-like detectors at event energies below 1keV, and can aid in background filtering, and reduce the impact of photon pile-up. We present new laboratory data that demonstrates the performance of the algorithm on the MIT-LL CCID-93 CCD detector. Together with the capabilities of next-generation high-speed, low-noise detectors, these algorithms can satisfy the requirements for future X-ray flagship missions.

astro-ph.IM↗

arXiv2024

Augmenting astronomical X-ray detectors with AI for enhanced sensitivity and reduced background

Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events.

astro-ph.IM↗

arXiv2022

North Ecliptic Pole merging galaxy catalogue

We aim to generate a catalogue of merging galaxies within the 5.4 sq. deg. North Ecliptic Pole over the redshift range $0.0 < z < 0.3$. To do this, imaging data from the Hyper Suprime-Cam are used along with morphological parameters derived from these same data. The catalogue was generated using a hybrid approach. Two neural networks were trained to perform binary merger non-merger classifications: one for galaxies with $z < 0.15$ and another for $0.15 \leq z < 0.30$. Each network used the image and morphological parameters of a galaxy as input. The galaxies that were identified as merger candidates by the network were then visually checked by experts. The resulting mergers will be used to calculate the merger fraction as a function of redshift and compared with literature results. We found that 86.3% of galaxy mergers at $z < 0.15$ and 79.0% of mergers at $0.15 \leq z < 0.30$ are expected to be correctly identified by the networks. Of the 34 264 galaxies classified by the neural networks, 10 195 were found to be merger candidates. Of these, 2109 were visually identified to be merging galaxies. We find that the merger fraction increases with redshift, consistent with literature results from observations and simulations, and that there is a mild star-formation rate enhancement in the merger population of a factor of $1.102 \pm 0.084$.

astro-ph.GA↗

arXiv2021

Optically-detected galaxy cluster candidates in the $AKARI$ North Ecliptic Pole field based on photometric redshift from Subaru Hyper Suprime-Cam

Galaxy clusters provide an excellent probe in various research fields in astrophysics and cosmology. However, the number of galaxy clusters detected so far in the $AKARI$ North Ecliptic Pole (NEP) field is limited. In this work, we provide galaxy cluster candidates in the $AKARI$ NEP field with the minimum requisites based only on coordinates and photometric redshift (photo-$z$) of galaxies. We used galaxies detected in 5 optical bands ($g$, $r$, $i$, $z$, and $Y$) by the Subaru Hyper Suprime-Cam (HSC), assisted with $u$-band from Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam, and IRAC1 and IRAC2 bands from the $Spitzer$ space telescope for photo-$z$ estimation. We calculated the local density around every galaxy using the 10$^{th}$-nearest neighbourhood. Cluster candidates were determined by applying the friends-of-friends algorithm to over-densities. 88 cluster candidates containing 4390 member galaxies below redshift 1.1 in 5.4 deg$^2$ have been detected. The reliability of our method was examined through false detection tests, redshift uncertainty tests, and applications on the COSMOS data, giving false detection rates of 0.01 to 0.05 and recovery rate of 0.9 at high richness. 3 X-ray clusters previously observed by $ROSAT$ and $Chandra$ were recovered. The cluster galaxies show higher stellar mass and lower star formation rate (SFR) compared to the field galaxies in two-sample Z-tests. These cluster candidates are useful for environmental studies of galaxy evolution and future astronomical surveys in the NEP, where $AKARI$ has performed unique 9-band mid-infrared photometry for tens of thousands of galaxies.

astro-ph.GA↗