Bayesian and Machine Learning Methods in the Big Data era for astronomical imaging

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Article

Bayesian and Machine Learning Methods in the Big

Data era for astronomical imaging

Fabrizia Guglielmetti1, Philipp Arras2, Michele Delli Veneri3, Torsten Enßlin2, Giuseppe Longo4,

Lukasz Tychoniec1, Eric Villard1

1European Southern Observatory, Karl-Schwarzschild-Str. 2, Garching D-85748, Germany

2Max Planck Institute for Astrophysics, Karl-Schwarzschild-Str.1, Garching D-85748, Germany

3University of Naples "Federico II" Department of Electrical Engineering and Information Technology, Via

Claudio 21, Napoli I-80125, Italy

4University of Naples "Federico II" Department of Physics "Ettore Pancini", Via Cinthiaaug 21, Napoli I-80126,

Italy

*Fabrizia Guglielmetti; fgugliel@eso.org

† Submitted to International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and

Engineering, IHP, Paris, July 18-22, 2022.

Received: June 2022; Accepted: September 2022; Published: date

Abstract:

The Atacama Large Millimeter/submillimeter Array with the planned electronic upgrades

will deliver an unprecedented amount of deep and high resolution observations. Wider ﬁelds of view are

possible with the consequential cost of image reconstruction. Alternatives to commonly used applications

in image processing have to be sought and tested. Advanced image reconstruction methods are critical

to meet the data requirements needed for operational purposes. Astrostatistics and astroinformatics

techniques are employed. Evidence is given that these interdisciplinary ﬁelds of study applied to synthesis

imaging meet the Big Data challenges and have the potentials to enable new scientiﬁc discoveries in

radio astronomy and astrophysics.

Keywords: Inverse Problems; Bayesian Inference; Machine Learning; Image Analysis; Radio Astronomy

1. Introduction

The Atacama Large Millimeter/submillimeter Array (ALMA) [

] is an aperture synthesis telescope

consisting of 66 high-precision antennas. Sensitive and high-resolution imaging is accomplished employing

up to ﬁfty antennas, characterized by 12-meter dishes (12-m Array). The remaining sixteen antennas

compose the ALMA Compact Array (ACA), tailored for wide-ﬁeld imaging. ACA is characterized by

four 12-m antennas for total power observations and twelve 7-m dishes (7-m Array) for interferometric

observations.

Each antenna is equipped with eight different receiver bands, covering a wavelength range from 3.6

(ALMA band 3) to 0.32 mm (ALMA band 10), corresponding to a frequency range of 84-950 GHz.

Antennas of the 12-m Array can be positioned in a number of different conﬁgurations with longest

baselines ranging 0.16-16.2 km, which are crucial in determining the image quality and spatial resolution:

at the highest frequencies in the most extended conﬁgurations, the spatial angular resolution reaches 5

mas at 950 GHz [

]. The Array is capable of providing single ﬁeld and mosaics of pointings. To make

interferometric images, signals from each antenna pair are compared 10

times per second within the

ALMA correlator. Equipped with a set of correlator modes, ALMA allows both continuum and spectral

line observations simultaneously.

ALMA is undergoing further developments to boost the Full Operation capabilities. In the near future,

ALMA band 1 [

] and band 2 [

] will be installed on each antenna broadening the receiver bandwidth

arXiv:2210.01444v1 [astro-ph.IM] 4 Oct 2022

2 of 9

Figure 1.

On the left, the ALMA correlator in the ALMA Array Operation Site (AOS) Technical Building

is composed by four identical quadrants with over 134

processors, performing up to 17

operations/s (Image credit: ESO). On the right, a panoramic view of the ALMA Array, located at an

elevation of 5000 m on the Chajnantor Plateau in the Chilean Andes. The AOS is the small building left of

picture centre. The tight clump of antennas near the image centre is the ACA (Image credit: JAO).

to cover a total wavelength range of 8.5-0.32 mm (35-950 GHz). Moreover, the ALMA2030 Development

Roadmap [

] has been approved to keep ALMA as a world leading facility. The vision of ALMA2030

accounts for: (1) Broaden the instantaneous bandwidth of the receivers, upgrade the associated electronics

and the correlator to process the entire bandwidth; (2) Improve the ALMA Archive for the end users; (3)

Extension of the maximum baseline length by a factor 2-3. Another innovative aspect is the design of an

array conﬁguration employing all 66 antennas. These advancements will enable the following key science

drivers: Origins of the Planets, Origins of Chemical Complexity (with improved continuum imaging) and

Origins of the Galaxies. For instance, the study of the Sunyaev–Zel’dovich effect will be enabled to probe

the physics of galaxy clusters with the goal of detecting cluster substructures through high resolution and

high-sensitivity observations.

Currently ALMA is generating 1 TB of scientiﬁc data daily. Within the next decade, at least one order

of magnitude of increased daily data rate is foreseen [

]. The planned electronic upgrades (receivers and

correlator) will improve ALMA sensitivity and observing efﬁciency. In terms of imaging products, ALMA

will produce single ﬁeld and mosaic cubes of at least two orders of magnitude larger than the current cube

size in the GB regime. Since the number of observed spectral lines at once will be duplicated, advanced

algorithms are needed to provide shorter processing time while handling larger data volume. Additionally,

the imaging algorithms must provide robust and reliable results to reduce human intervention. Sparse

sampling, sky and instrumental responses, pervasive presence of noise increase complexities to the

demanding task of image reconstruction.

The ALMA development study “Bayesian Adaptive Interferometric Image Reconstruction Methods” is

providing an initial exploration of concepts that may be of interest to ALMA development in the long

term. Using real and simulated data sets, we investigate how to employ Bayesian and Machine Learning

techniques to tackle the mentioned challenges. Speciﬁcally for real ALMA data we make use of Science

Veriﬁcation (SV) data. SV is a process by which data quality is assured for scientiﬁc analysis. Observations

of a small number of selected astronomical objects are taken with a low number (

≥

7) of antennas. For

ALMA simulated data we make use of the Common Astronomy Software Applications (CASA) [

], the

software package ordinarly used to calibrate, image and simulate ALMA data. The performance of

a Bayesian and a supervised Machine Learning (ML) techniques is discussed in view of the pipeline

developments in the ALMA2030 era.

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ArticleBayesianandMachineLearningMethodsintheBigDataeraforastronomicalimagingFabriziaGuglielmetti1,PhilippArras2,MicheleDelliVeneri3,TorstenEnßlin2,GiuseppeLongo4,LukaszTychoniec1,EricVillard11EuropeanSouthernObservatory,Karl-Schwarzschild-Str.2,GarchingD-85748,Germany2MaxPlanckInstituteforAstrophys...

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