Consistency Tests for Comparing Astrophysical Models and Observations

In astronomy, there is an opportunity to enhance the practice of validating models through statistical techniques, specifically to account for measurement error uncertainties. While models are commonly used to describe observations, there are instances where there is a lack of agreement between the two. This can occur when models are derived from incomplete theories, when a better-fitting model is not available or when measurement uncertainties are not correctly considered. However, with the application of specific tests that assess the consistency between observations and astrophysical models in a model-independent way, it is possible to address this issue. The consistency tests (ConTESTs) developed in this paper use a combination of non-parametric methods and distance measures to obtain a test statistic that evaluates the closeness of the astrophysical model to the observations. To draw conclusions on the consistency hypothesis, a simulation-based methodology is performed. In particular, we built two tests for density models and two for regression models to be used depending on the case at hand and the power of the test needed. We used ConTEST to examine synthetic examples in order to determine the effectiveness of the tests and provide guidance on using them while building a model. We also applied ConTEST to various astronomy cases, identifying which models were consistent and, if not, identifying the probable causes of rejection

AutoSourceID-FeatureExtractor

Aims: In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources\u27 features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data. Methods: The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment. Results: We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities

The Gravitational Wave Universe Toolbox: A software package to simulate observation of the Gravitational Wave Universe with different detectors

Context. As the importance of Gravitational Wave (GW) Astrophysics increases rapidly, astronomers in different fields and with different backgrounds can have the need to get a quick idea of which GW source populations can be detected by which detectors and with what measurement uncertainties. Aims. The GW-Toolbox is an easy-to-use, flexible tool to simulate observations on the GW universe with different detectors, including ground-based interferometers (advanced LIGO, advanced VIRGO, KAGRA, Einstein Telescope, and also customised designs), space-borne interferometers (LISA and a customised design), pulsar timing arrays mimicking the current working ones (EPTA, PPTA, NANOGrav, IPTA) and future ones. We include a broad range of sources such as mergers of stellar mass compact objects, namely black holes, neutron stars and black hole-neutron stars; and supermassive black hole binaries mergers and inspirals, Galactic double white dwarfs in ultra-compact orbit, extreme mass ratio inspirals and Stochastic GW backgrounds. Methods. We collect methods to simulate source populations and determine their detectability with the various detectors. The paper aims at giving a comprehensive description on the algorithm and functionality of the GW-Toolbox. Results. The GW-Toolbox produces results that are consistent with more detailed calculations of the different source classes and can be accessed with a website interface (gw-universe.org) or as a python package (https://bitbucket.org/radboudradiolab/gwtoolbox). In the future, it will be upgraded with more functionality.Comment: 21 pages, 26 figures, 4 tables. Submitted to Astronomy & Astrophysics; Website url: gw-universe.or

The GW-Universe Toolbox II: constraining the binary black hole population with second and third generation detectors

We employ the Gravitational Waves-Universe toolbox to generate synthetic catalogues of detections of stellar mass binary black hole (BBH) mergers. Using the catalogues, we study how GW observations of BBHs can be used to constrain the merger rate as function of redshift and masses. We study advanced LIGO (aLIGO) and Einstein Telescope (ET) as two representatives for the 2nd and 3rd generation GW observatories. We also simulate the observations from a detector that is half as sensitive as the ET in design which represents an early phase of ET. Two methods are used to obtain the constraints on the source population properties from the catalogues: the first assumes a parametric differential merger rate model and applies a Bayesian inference; The other is non-parametric and uses weighted Kernel density estimators. The results show the overwhelming advantages of the 3rd generation detector over the 2nd generation for the study of BBH population properties, especially at a redshifts higher than $\sim2$, where the merger rate is believed to peak. With the LIGO catalogue, the parameteric Bayesian method can still give some constraints on the merger rate density and mass function beyond its detecting horizon, while the non-parametric method lose the constraining ability completely there. These two methods represent the two extreme situations of general population reconstruction. We also find that, despite the numbers of detection of the half-ET can be easily compatible with full ET after a longer observation duration, the catalogue from the full ET can still give much better constraints on the population properties, due to its smaller uncertainties on the physical parameters of the GW events.Comment: 8 pages, 10 figures, submitted to A&

Investigating the VHE Gamma-ray Sources Using Deep Neural Networks

The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0∘<l<20∘, |b|<4∘) for energies ranging from 30 GeV to 100 TeV. Dividing the source extensions ranging from 0.03∘ to 1∘ in three different classes, we find that using a simple and light convolutional neural network achieves 97% global accuracy in separating the extended sources from the point-like sources. The neural net architecture including other data pre-processing codes is available online

A Novel CMOS Sensor for Position Detection

A novel architecture of optical sensor developed for flying-spot active triangulation will be presented. The architecture implements a spot position circulation based on a two steps procedure allowing for increased readout speed and color detection. The proposed sensor has been fully integrated in standard CMOS technology and is currently under test. Preliminary experimental results will be presente

The Gravitational Wave Universe Toolbox

Context. As the importance of gravitational wave (GW) astrophysics increases rapidly, astronomers interested in GWs who are not experts in this field sometimes need to get a quick idea of what GW sources can be detected by certain detectors, and the accuracy of the measured parameters. Aims. The GW-Toolbox is a set of easy-to-use, flexible tools to simulate observations of the GW universe with different detectors, including ground-based interferometers (advanced LIGO, advanced VIRGO, KAGRA, Einstein Telescope, Cosmic Explorer, and also customised interferometers), space-borne interferometers (LISA and a customised design), and pulsar timing arrays mimicking the current working arrays (EPTA, PPTA, NANOGrav, IPTA) and future ones. We include a broad range of sources, such as mergers of stellar-mass compact objects, namely black holes, neutron stars, and black hole–neutron star binaries, supermassive black hole binary mergers and inspirals, Galactic double white dwarfs in ultra-compact orbit, extreme-mass-ratio inspirals, and stochastic GW backgrounds. Methods. We collected methods to simulate source populations and determine their detectability with various detectors. Our aim is to provide a comprehensive description of the methodology and functionality of the GW-Toolbox. Results. The GW-Toolbox produces results that are consistent with previous findings in the literature, and the tools can be accessed via a website interface or as a Python package. In the future, this package will be upgraded with more functions

AutoSourceID-Light. Fast Optical Source Localization via U-Net and Laplacian of Gaussian

$\textbf{Aims}$. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. $\textbf{Methods}$. We show that the AutoSourceID-Light algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter (Chen et al. 1987) enables outstanding performances in the localization of sources. A U-Net (Ronneberger et al. 2015) network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. $\textbf{Results}$. Application on optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with the widely used SExtractor (Bertin & Arnouts 1996) and show the out-performances of our method. AutoSourceID-Light rapidly detects more sources not only in low and mid crowded fields, but particularly in areas with more than 150 sources per square arcminute