Source Extractor Research Articles

Improved Real‐Time Post‐Processing for Quantum Random Number Generators

AbstractRandomness extraction is a key problem in cryptography and theoretical computer science. With the recent rapid development of quantum cryptography, quantum‐proof randomness extraction has also been widely studied, addressing the security issues in the presence of a quantum adversary. In contrast with conventional quantum‐proof randomness extractors characterizing the input raw data as min‐entropy sources, it is found that the input raw data generated by a large class of trusted‐device quantum random number generators can be characterized as the so‐called reverse block source. This fact enables us to design improved extractors. Two novel quantum‐proof randomness extractors for reverse block sources that realize real‐time block‐wise extraction are proposed specifically. In comparison with the general min‐entropy randomness extractors, the designs achieve a significantly higher extraction speed and a longer output data length with the same seed length. In addition, they enjoy the property of online algorithms, which process the raw data on the fly without waiting for the entire input raw data to be available. These features make the design an adequate choice for the real‐time post‐processing of practical quantum random number generators. Applying the extractors to the raw data generated by a widely used quantum random number generator, a simulated extraction speed as high as 300 Gbps is achieved.

Semantic representation learning for a mask-modulated lensless camera by contrastive cross-modal transferring.

Lensless computational imaging, a technique that combines optical-modulated measurements with task-specific algorithms, has recently benefited from the application of artificial neural networks. Conventionally, lensless imaging techniques rely on prior knowledge to deal with the ill-posed nature of unstructured measurements, which requires costly supervised approaches. To address this issue, we present a self-supervised learning method that learns semantic representations for the modulated scenes from implicitly provided priors. A contrastive loss function is designed for training the target extractor (measurements) from a source extractor (structured natural scenes) to transfer cross-modal priors in the latent space. The effectiveness of the new extractor was validated by classifying the mask-modulated scenes on unseen datasets and showed the comparable accuracy to the source modality (contrastive language-image pre-trained [CLIP] network). The proposed multimodal representation learning method has the advantages of avoiding costly data annotation, being more adaptive to unseen data, and usability in a variety of downstream vision tasks with unconventional imaging settings.

Enlightening the Universe behind the Milky Way bulge

Context. The location of the Solar System complicates the detection extragalactic sources beyond the Milky Way plane. The optical observations are hampered in the so-called zone of avoidance (ZOA), where stellar crowding and Galactic absorption are severe. Observations at longer wavelengths are needed in order to discover new background galaxies and complete the census in the ZOA. Aims. The goal of this work is to identify galaxies behind the Milky Way bulge using near-infrared (NIR) data from the VISTA Variables in Vía Láctea (VVV) survey. Methods. To this end, we made use of different VISTA Science Archive (VSA) tools in order to extract relevant information from more than 32 billion catalogued sources in the VVV bulge region. We find that initial photometric restriction on sources from the VSA vvvSource table combined with restrictions on star–galaxy separation parameters obtained from Source Extractor is a successful strategy for achieving acceptable levels of contamination (60%) and high completeness (75%) in the construction of a galaxy target sample. To remove contaminating Galactic sources from the initial target sample, our methodology also incorporates a visual inspection of false-colour RGB images, a crucial quality control carried out following a specifically defined procedure. Results. Under this methodology, we find 14 480 galaxy candidates in the VVV bulge region, making this sample the largest catalogue to date in the ZOA. Moreover, these new sources provide a fresh picture of the Universe hidden behind the curtain of stars, dust, and gas in the unexplored Milky Way bulge region. Conclusions. The results from this work further demonstrate the potential of the VVV/VVVX survey to find and study a large number of galaxies and extragalactic structures obscured by the Milky Way, expanding our knowledge of the Universe in this challenging and impressive region of the sky.

Sky subtraction in an era of low surface brightness astronomy

ABSTRACT The Vera C. Rubin Observatory Wide-Fast Deep sky survey will reach unprecedented surface brightness depths over tens of thousands of square degrees. Surface brightness photometry has traditionally been a challenge. Current algorithms which combine object detection with sky estimation systematically oversubtract the sky, biasing surface brightness measurements at the faint end and destroying or severely compromising low surface brightness light. While it has recently been shown that properly accounting for undetected faint galaxies and the wings of brighter objects can in principle recover a more accurate sky estimate, this has not yet been demonstrated in practice. Obtaining a consistent spatially smooth underlying sky estimate is particularly challenging in the presence of representative distributions of bright and faint objects. In this paper, we use simulations of crowded and uncrowded fields designed to mimic Hyper Suprime-Cam data to perform a series of tests on the accuracy of the recovered sky. Dependence on field density, galaxy type, and limiting flux for detection are all considered. Several photometry packages are utilized: source extractor, gnuastro, and the LSST science pipelines. Each is configured in various modes, and their performance at extreme low surface brightness analysed. We find that the combination of the source extractor software package with novel source model masking techniques consistently produce extremely faint output sky estimates, by up to an order of magnitude, as well as returning high fidelity output science catalogues.

The Fornax Deep Survey with the VST

Context.Low surface brightness (LSB) dwarf galaxies in galaxy clusters are an interesting group of objects as their contribution to the galaxy luminosity function and their evolutionary paths are not yet clear. Increasing the completeness of our galaxy catalogs is crucial for understanding these galaxies, which have effective surface brightnesses below 23 mag arcsec−2(in optical). Progress is continuously being made via the performance of deep observations, but detection depth and the quantification of the completeness can also be improved via the application of novel approaches in object detection. For example, the Fornax Deep Survey (FDS) has revealed many faint galaxies that can be visually detected from the images down to a surface brightness level of 27 mag arcsec−2, whereas traditional detection methods, such as using Source Extractor (SE), fail to find them.Aims.In this work we use a max-tree based object detection algorithm (Max-Tree Objects, MTO) on the FDS data in order to detect previously undetected LSB galaxies. After extending the existing Fornax dwarf galaxy catalogs with this sample, our goal is to understand the evolution of LSB dwarfs in the cluster. We also study the contribution of the newly detected galaxies to the faint end of the luminosity function.Methods.We test the detection completeness and parameter extraction accuracy of MTO using simulated and real images. We then apply MTO to the FDS images to identify LSB candidates. The identified objects are fitted with 2D Sérsic models using GALFIT and classified as imaging artifacts, likely cluster members, or background galaxies based on their morphological appearance, colors, and structure.Results.With MTO, we are able to increase the completeness of our earlier FDS dwarf catalog (FDSDC) 0.5–1 mag deeper in terms of total magnitude and surface brightness. Due to the increased accuracy in measuring sizes of the detected objects, we also add many small galaxies to the catalog that were previously excluded as their outer parts had been missed in detection. We detect 265 new LSB dwarf galaxies in the Fornax cluster, which increases the total number of known dwarfs in Fornax to 821. Using the whole cluster dwarf galaxy population, we show that the luminosity function has a faint-end slope ofα= −1.38 ± 0.02. We compare the obtained luminosity function with different environments studied earlier using deep data but do not find any significant differences. On the other hand, the Fornax-like simulated clusters in the IllustrisTNG cosmological simulation have shallower slopes than found in the observational data. We also find several trends in the galaxy colors, structure, and morphology that support the idea that the number of LSB galaxies is higher in the cluster center due to tidal forces and the age dimming of the stellar populations. The same result also holds for the subgroup of large LSB galaxies, so-called ultra-diffuse galaxies.

Extractors for Small Zero-Fixing Sources

Let V ⊆ [n] be a k-element subset of [n]. The uniform distribution on the 2k strings from {0, 1}n that are set to zero outside of V is called an (n, k)-zero-fixing source. An ϵ-extractor for (n, k)-zero-fixing sources is a mapping F: {0, 1}n → {0, 1}m, for some m, such that F(X) is ϵ-close in statistical distance to the uniform distribution on {0, 1}m for every (n, k)-zero-fixing source X. Zero-fixing sources were introduced by Cohen and Shinkar in [7] in connection with the previously studied extractors for bit-fixing sources. They constructed, for every μ > 0, an efficiently computable extractor that extracts a positive fraction of entropy, i.e., Ω(k) bits, from (n, k)-zero-fixing sources where k ≥ (log log n)2+μ.In this paper we present two different constructions of extractors for zero-fixing sources that are able to extract a positive fraction of entropy for k substantially smaller than log log n. The first extractor works for k ≥ C log log log n, for some constant C. The second extractor extracts a positive fraction of entropy for k ≥ log(i)n for any fixed i ∈ ℕ, where log(i) denotes i-times iterated logarithm. The fraction of extracted entropy decreases with i. The first extractor is a function computable in polynomial time in n; the second one is computable in polynomial time in n when k ≤ α log log n/log log log n, where α is a positive constant.Our results can also be viewed as lower bounds on some Ramsey-type properties. The main difference between the problems about extractors studied here and the standard Ramsey theory is that we study colorings of all subsets of size up to k while in Ramsey theory the sizes are fixed to k. However it is easy to derive results also for coloring of subsets of sizes equal to k. In Corollary 3.1 of Theorem 5.1 we show that for every l ∈ ℕ there exists β < 1 such that for every k and n, n ≤ expl (k), there exists a 2-coloring of k-tuples of elements of [n], \(\psi :\left({\matrix{{[n]} \cr k \cr}} \right) \to \left\{{- 1,1} \right\}\) such that for every V ⊆ [n], |V| = 2k, we have \(\left| {\sum\nolimits_{X \subseteq V,\left| X \right| = k} {\psi (X)}} \right| \le {\beta ^k}\left({\matrix{{2k} \cr k \cr}} \right)\) (Corollary 3.1 is more general — the number of colors may be more than 2).

Robust adversarial discriminative domain adaptation for real-world cross-domain visual recognition

Deep convolutional networks (CNNs) are able to learn robust representations and empower many computer vision tasks such as object recognition. However, when applying CNNs to industrial visual systems, they usually suffer from domain shift that exists between the training data and testing data. Such shift can be caused by different environment, types of cameras and exteriors of objects, leading to degrading performance and hindering the practical applications of CNNs in real-world visual recognition. To tackle this problem, Adversarial domain adaptation (ADA) reduces such shift by min–max optimization. However, current CNNs with ADA are hard to train due to training instability of adversarial network. In this paper, we propose a unified and easy-to-train domain adaptation framework, namely Attention-based Domain-confused Adversarial Domain ADaptation (AD3). Our method leverages both adversarial and statistical domain alignment, allows flexibility for source and target feature extractors and simultaneously performs feature-level and attention-level alignment. The statistical domain alignment promotes and stabilizes the adversarial domain learning, which reduces the manual work of tuning the hyper-parameters. The experimental results validate that our method performs better adaptation and faster convergence for adversarial domain learning than existing state-of-the-art methods on DIGITS, Office-31 and VisDA domain adaptation benchmarks.

Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data.

Finite element model updating precision depends heavily on sufficient vibration feature extraction. However, adequate amount of sample collection is generally time-consuming in frequency response (FR) model updating. Accurate vibration feature extraction with insufficient data has become a significant challenge in FR model updating. To update the finite element model with a small dataset, a novel approach based on transfer learning is firstly proposed in this paper. A readily available fault diagnosis dataset is selected as ancillary knowledge to train a high-precision mapping from FR data to updating parameters. The proposed transfer learning network is constructed with two branches: source and target domain feature extractor. Considering about the cross-domain feature discrepancy, a domain adaptation method is designed by embedding the extracted features into a shared feature space to train a reliable model updating framework. The proposed method is verified by a simulated satellite example. The comparison results manifest that sample amount dependency has prominently lessened this method and the updated model outperforms the method without transfer learning in accuracy with the small dataset. Furthermore, the updated model is validated through dynamic response out of the training set.

Period of Eclipsing Binary EPIC 201458798

In this study we observed and determined the period of the eclipsing binary star EPIC 201458798, finding that its period has not changed since it was last measured and published in the Kepler catalog in 2014. We did this by developing Python code to determine the period using two independent algorithms and partially automating the search for suitable comparison stars. Our system was imaged by the 0.4m Las Cumbres Observatory telescopes in four filters. We analyzed six photometry types returned for each image from the Our Solar Siblings pipeline. Of these photometries, we found source extractor kron (sek) to be the most appropriate type for our 2x2 binned images of EPIC 201458798. Both of the period-finding algorithms we developed gave results that were not statistically different from the Kepler catalog period for the infrared, red, and visual filter images. The blue filter images were statistically different fromthe Kepler catalog period for one of our algorithms but not the other.&nbsp;

Kira: Processing Astronomy Imagery Using Big Data Technology

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, HPC tools are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark—a modern platform for data intensive computing—to parallelize many-task applications. We implement Kira, a flexible and distributed astronomy image processing toolkit, and its Source Extractor (Kira SE) application. Using Kira SE as a case study, we examine the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the Amazon EC2 cloud. By exploiting data locality, Kira SE achieves a 4.1× speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, Kira SE on the Amazon EC2 cloud achieves a 1.8× speedup over the C program on the NERSC Edison supercomputer. A 128-core Amazon EC2 cloud deployment of Kira SE using Spark Streaming can achieve a second-scale latency with a sustained throughput of $\sim$ ∼ 800 MB/s. Our experience with Kira demonstrates that data intensive computing platforms like Apache Spark are a performant alternative for many-task scientific applications.

Semi-supervised center-based discriminative adversarial learning for cross-domain scene-level land-cover classification of aerial images

Supervised scene classification of aerial images is of great importance in land-cover classification. However, annotating the labeled data required for the conventional classifiers and convolutional neural networks (CNNs), costs much manpower and time. Domain adaptation methods can overcome this problem, to some extent, by transferring previously labeled data to the new images, but the classification models trained from the previously labeled data are not discriminative enough for classifying aerial images from other domains because of the data distribution differences caused by the variations in sensors, natural environments, seasons, angles, locations, and so on. In order to solve this problem, we propose a semi-supervised center-based discriminative adversarial learning (SCDAL) framework integrating three parts, namely filtering out easy triplets, proposed hard triplet loss, and the adversarial learning with center loss. In the SCDAL framework, a difficulty measure is proposed to remove easy triplets under the constraint of between-class dissimilarity and intra-class similarity and better distinguish hard triplets. The filtered triplets are then used to train a more discriminative source feature extractor with the proposed hard triplet loss combining the hardest triplet loss and semi-hard triplet loss. Adversarial learning with center loss is also proposed to reduce the feature distribution bias between the source and target feature extractors and increase the discriminative ability of the target feature extractor. The SCDAL framework is tested on two large aerial images as a case study. The experimental results demonstrate that when adequate previously labeled data but limited labeled target data exist, the SCDAL framework is superior to most of the existing domain adaptation methods, with an improvement of at least 3% in overall accuracy. It is also proved that removing easy triplets, proposed hard triplet loss, and the adversarial learning with center loss all help to improve the overall accuracy.

Radio source extraction with ProFound

In the current era of radio astronomy, continuum surveys observe a multitude of objects with complex morphologies and sizes, and are not limited to observing point sources. Typical radio source extraction software generates catalogues by using Gaussian components to form a model of the emission. This may not be well suited to complicated jet structures and extended emission, particularly in the era of interferometers with a high density of short baselines, that are sensitive to extended emission. In this paper, we investigate how the optically-motivated source detection package ProFound (Robotham et al. 2018) may be used to model radio emission of both complicated and point-like radio sources. We use a combination of observations and simulations to investigate how ProFound compares to other source extractor packages used for radio surveys. We find that ProFound can accurately recover both the flux densities of simulated Gaussian sources as well as extended radio galaxies. ProFound can create models that trace the complicated nature of these extended galaxies, which we show is not necessarily the case with other source extraction software. Our work suggests that our knowledge of the emission from extended radio objects may be both over or under-estimated using traditional software. We suggest that ProFound offers a useful alternative to the fitting of Gaussian components for generating catalogues from current and future radio surveys. Furthermore, ProFound's multi-wavelength capabilities will be useful in investigating radio sources in combination with multiwavelength data.

Large-scale Structure in CHILES Using DisPerSE

Abstract We demonstrate that the Discrete Persistent Source Extractor (DisPerSE) can be used with spectroscopic redshifts to define the cosmic web and its distance to galaxies in small-area deep fields. Here we analyze the use of DisPerSE to identify structure in observational data. We apply DisPerSE to the distribution of galaxies in the Cosmic Evolution Survey (COSMOS) field and find the best parameters to identify filaments. We compile a catalog of 11,500 spectroscopic redshifts from the Galaxy and Mass Assembly (GAMA) G10 data release. We analyze two-dimensional slices, extract filaments, and calculate the distance for each galaxy to its nearest filament. We find that redder and more massive galaxies are closer to filaments. To study the growth of galaxies across cosmic time, and environment, we are carrying out an H i survey covering redshifts of z = 0–0.45, the COSMOS H i Large Extragalactic Survey (CHILES). In addition we present the predicted H i mass fraction as a function of distance to filaments for the spectroscopically known galaxies in CHILES. Lastly we discuss the cold gas morphology of a few individual galaxies and their positions with respect to the cosmic web. The identification of the cosmic web, and the ability of CHILES to study the resolved neutral hydrogen morphologies and kinematics of galaxies, will allow future studies of the properties of neutral hydrogen in different cosmic web environments across the redshift range of z = 0.1–0.45.

The PAU Survey: a forward modeling approach for narrow-band imaging

Weak gravitational lensing is a powerful probe of the dark sector, once measurement systematic errors can be controlled. In [1], a calibration method based on forward modeling, called MCCL, was proposed. This relies on fast image simulations (e.g., UFig [2,3]}) that capture the key features of galaxy populations and measurement effects. The MCCL approach has been used in [4] to determine the redshift distribution of cosmological galaxy samples and, in the process, the authors derived a model for the galaxy population mainly based on broad-band photometry. Here, we test this model by forward modeling the 40 narrow-band photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the same forced photometric pipeline on data and simulations using SOURCE EXTRACTOR [3]. The image simulation scheme performance is assessed at the image and at the catalogues level. We find good statistical agreement for the distribution of pixel values, for the magnitude-size relation and for the inter-band correlations. We also discuss the small residual differences in the magnitude distributions. A principal component analysis is then performed, in order to derive a global comparison of the narrow-band photometry between the data and the simulations. We use a `mixing' matrix to quantify the agreement between the observed and simulated sets of Principal Components (PCs). We find good agreement, especially for the first three most significant PCs. We also compare the coefficients of the PCs decomposition. While there are slight differences for some coefficients, we find that the distributions are consistent. Together, our results show that the galaxy population model derived from broad-band photometry is in good overall agreement with the PAUS data. This offers good prospects for incorporating spectral information to the galaxy model by adjusting it to the PAUS narrow-band data using forward modeling.

Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks

Traditional intelligent fault diagnosis works well when the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, the working conditions can vary between training and testing time. In this paper, we address the issues of intelligent fault diagnosis when the data at training and testing time do not come from the same distribution as a domain adaptation problem using domain adaptive convolutional neural networks (DACNN). Our proposed DACNN consists of three parts: a source feature extractor, a target feature extractor, and a label classifier. We adopt a two-stage training process to obtain strong fault-discriminative and domain-invariant capacity. First, we obtain fault-discriminative features by pre-training the source feature extractor with labeled source training examples to minimize the label classifier error. Then, in the domain adaptive fine-tuning stage, we train the target feature extractor to minimize the squared maximum mean discrepancy between the output of the source and target feature extractor, such that the instances sampled from the source and target domains have similar distributions after the mapping. Furthermore, to enable training efficiency in domain adaptation, the layers between the source and target feature extractors in our DACNN are partially untied during the training stage. Experiments on the bearing and gearbox fault data showed that DACNN can achieve high fault diagnosis precision and recall under different working conditions, outperforming other intelligent fault diagnosis methods. We also demonstrate the ability to visualize the learned features and the networks to better understand the reasons behind the remarkable performance of our proposed model.

PHOTOMETRYPIPELINE: An automated pipeline for calibrated photometry

PHOTOMETRYPIPELINE (PP) is an automated pipeline that produces calibrated photometry from imaging data through image registration, aperture photometry, photometric calibration, and target identification with only minimal human interaction. PP utilizes the widely used Source Extractor software for source identification and aperture photometry; SCAMP is used for image registration. Both image registration and photometric calibration are based on matching field stars with star catalogs, requiring catalog coverage of the respective field. A number of different astrometric and photometric catalogs can be queried online. Relying on a sufficient number of background stars for image registration and photometric calibration, PP is well-suited to analyze data from small to medium-sized telescopes. Calibrated magnitudes obtained by PP are typically accurate within 0.03 mag and astrometric accuracies are of the order of 0.3 arcsec relative to the catalogs used in the registration. The pipeline consists of an open-source software suite written in Python 2.7, can be run on Unix-based systems on a simple desktop machine, and is capable of realtime data analysis. PP has been developed for observations of moving targets, but can be used for analyzing point source observations of any kind.

SEP: Source Extractor as a library

SEP: Source Extractor as a library

All NIRspec Needs is HST/WFC3 Pre-Imaging? The Use of Milky Way Stars in WFC3 Imaging to Register NIRspec MSA Observations

The James Webb Space Telescope (JWST) will be an exquisite new near-infrared observatory with imaging and multi-object spectroscopy through ESA’s NIRspec instrument with its unique Micro-Shutter Array (MSA), allowing for slits to be positioned on astronomical targets by opening specific [Formula: see text]-wide micro shutter doors. To ensure proper Target Acquisition (TA), the on-sky position of the MSA needs to be verified before spectroscopic observations start. An onboard centroiding program registers the position of pre-identified guide stars in a TA image, a short pre-spectroscopy exposure without dispersion (image mode) through the MSA with all shutters open. The outstanding issue is the availability of Galactic stars in the right luminosity range for TA relative to typical high redshift targets. We explore this here using the stars and [Formula: see text] candidate galaxies identified in the source extractor catalogs of Brightest of Reionizing Galaxies survey (BoRG[z8]), a pure-parallel program with Hubble Space Telescope Wide-Field Camera 3. We find that (a) a single WFC3 field contains enough Galactic stars to satisfy the NIRspec astrometry requirement (20 milli-arcseconds), provided its and the NIRspec TA’s are [Formula: see text] AB in WFC3 [Formula: see text], (b) a single WFC3 image can therefore serve as the pre-image if need be, (c) a WFC3 mosaic and accompanying TA image satisfy the astrometry requirement at [Formula: see text] AB mag in WFC3 [Formula: see text], (d) no specific Galactic latitude requires deeper TA imaging due to a lack of Galactic stars, and (e) a depth of [Formula: see text] AB mag in WFC3 [Formula: see text] is needed if a guide star in the same MSA quadrant as a target is required. We take the example of a BoRG identified [Formula: see text] candidate galaxy and require a Galactic star within 20[Formula: see text] of it. In this case, a depth of 25.5 AB in [Formula: see text] is required (with [Formula: see text]97% confidence).

Survival of the impactor during hypervelocity collisions – I. An analogue for low porosity targets

Recent observations of asteroidal surfaces indicate the presence of materials that do not match the bulk lithology of the body. A possible explanation for the presence of these exogenous materials is that they are products of inter-asteroid impacts in the Main Belt, and thus interest has increased in understanding the fate of the projectile during hypervelocity impacts. In order to gain insight into the fate of impactor we have carried out a laboratory programme, covering the velocity range of 0.38 - 3.50 km/s, devoted to measuring the survivability, fragmentation and final state of the impactor. Forsterite olivine and synthetic basalt projectiles were fired onto low porosity (<10%) pure water-ice targets using the University of Kent's Light Gas Gun (LGG). We developed a novel method to identify impactor fragments which were found in ejecta and implanted into the target. We applied astronomical photometry techniques, using the SOURCE EXTRACTOR software, to automatically measure the dimensions of thousands of fragments. This procedure enabled us to estimate the implanted mass on the target body, which was found to be a few percent of the initial mass of the impactor. We calculated an order of magnitude difference in the energy density of catastrophic disruption, Q*, between peridot and basalt projectiles. However, we found very similar behaviour of the size frequency distributions for the hypervelocity shots (>1 km/s). After each shot, we examined the largest peridot fragments with Raman spectroscopy and no melt or alteration in the final state of the projectile was observed.

MEASURING HIGH-PRECISION ASTROMETRY WITH THE INFRARED ARRAY CAMERA ON THE SPITZER SPACE TELESCOPE*

ABSTRACT The Infrared Array Camera (IRAC) on the Spitzer Space Telescope currently offers the greatest potential for high-precision astrometry of faint mid-IR sources across arcminute-scale fields, which would be especially valuable for measuring parallaxes of cold brown dwarfs in the solar neighborhood and proper motions of obscured members of nearby star-forming regions. To more fully realize IRAC's astrometric capabilities, we have sought to minimize the largest sources of uncertainty in astrometry with its 3.6 and 4.5 μm bands. By comparing different routines that estimate stellar positions, we have found that Point Response Function (PRF) fitting with the Spitzer Science Center's Astronomical Point Source Extractor produces both the smallest systematic errors from varying intra-pixel sensitivity and the greatest precision in measurements of positions. In addition, self-calibration has been used to derive new 7th and 8th order distortion corrections for the 3.6 and 4.5 μm arrays of IRAC, respectively. These corrections are suitable for data throughout the mission of Spitzer when a time-dependent scale factor is applied to the corrections. To illustrate the astrometric accuracy that can be achieved by combining PRF fitting with our new distortion corrections, we have applied them to archival data for a nearby star-forming region, arriving at total astrometric errors of ∼20 and 70 mas at signal to noise ratios of 100 and 10, respectively.