arXiv subjects

A. Mazumder

Publications and source records attributed to A. Mazumder.

4 recordsLinked to original sources

arXiv2026

MIGHTEE: The Host-Galaxy Associated Catalogue of the Radio Sources in MIGHTEE Continuum Data Release 1

Radio continuum surveys provide samples of active galactic nuclei (AGN) and star forming galaxies (SFGs) to high redshifts, free of biases due to dust obscuration. However, radio detected sources require multi-wavelength counterparts to understand their intrinsic properties (e.g. redshift, stellar mass) and to study the evolution of star formation and AGN activity. In this work we present host galaxy counterparts for the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey continuum Data Release 1 in regions with the best ancillary data (totalling 7.5 sq. deg). We combine statistical cross-matching and visual inspection to identify Ks-band selected host galaxies, and additionally combine multiple radio components into single physical objects, where needed. This results in a combined radio catalogue of ~66 000 sources, with host counterparts and redshifts identified for ~95 per cent of sources in the COSMOS field, ~91 per cent in XMM-LSS and ~90 per cent in CDFS-DEEP. This includes a significant fraction of sources with spectroscopic redshifts within the COSMOS field (~50 per cent), with ~30 and ~20 per cent in the XMM-LSS and CDFS-DEEP fields respectively. Using the cross-matched catalogue, we make an initial identification of radio-excess and star forming galaxies based on comparisons of the radio luminosities to host star formation rates. Using this split as a proxy for radio loud AGN or SFGs, we present expectations for the redshift distributions of these sources, finding broad agreement with those from deep radio luminosity functions and simulated catalogues.

astro-ph.GA↗

arXiv2025

Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

We present and analyse the results of the Science data challenge 3a (SDC3a, https://sdc3.skao.int/challenges/foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is $4.2_{-4.2}^{+20} \times 10^{-4}\,\rm{K}^2h^{-3}$cMpc$^{3}$ across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically under-estimated, sometimes very significantly. The most accurate error bars account for $60 \pm 20$\% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation.

astro-ph.IM↗

arXiv2023

SKA Science Data Challenge 2: analysis and results

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarise the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data product representing a 2000~h SKA MID spectral line observation from redshifts 0.25 to 0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, `reproducibility awards' were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy -- which combined predictions from two independent machine learning techniques to yield a 20 percent improvement in overall performance -- underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical datasets.

astro-ph.IM↗

arXiv2021

Estimation of temperature-dependent growth profiles for the assessment of time of hatching in forensic entomology

Forensic entomology contributes important information to crime scene investigations. In this paper, we propose a method to estimate the hatching time of larvae (or maggots) based on their lengths, the temperature profile at the crime scene and experimental data on larval development. This requires the estimation of a time-dependent growth curve from experiments where larvae have been exposed to a relatively small number of constant temperature profiles. Since the temperature influences the developmental speed, a crucial step is the time alignment of the curves at different temperatures. We propose a model for time varying temperature profiles based on the local growth rate estimated from the experimental data. This allows us to estimate the most likely hatching time for a sample of larvae from the crime scene. Asymptotic properties are provided for the estimators of the growth curves and the hatching time. We explore via simulations the robustness of the method to errors in the estimated temperature profile. We also apply the methodology to data from two criminal cases from the United Kingdom.

stat.AP↗