J. Pety and N. Rodríguez-Fernández: Revisiting the theory of interferometric wide-field synthesis<strong>tel</strong>-<strong>00726959</strong>, version 1 - 31 Aug 2012As B is bounded inside the [−d prim , +d prim ]interval,SV up (u s )isa local average, weighted by S up (u s − u ′ s ) B(−u′ s ), of I(u p+u ′ s )around the u p spatial frequency.As expected, we recover Eq. (15) for the ideal case (i.e., infinite,continuous visibility function) because then S up (u s − u ′ s) =δ(u s −u ′ s). A more interesting case arises when the visibility functionis continuously samp<strong>le</strong>d over a limited sky field of view, i.e.,∀u p , S ( u p ,α s)= 1 if |αs |≤θ field /2, (28)∀u p , S ( u p ,α s)= 0 if |αs | >θ field /2. (29)After Fourier transform this gives∀u p , S ( u p , u s)=1d fieldsinc(usd field)· (30)In this case, the local average of the sky brightness Fourier componentshappens on a typical uv sca<strong>le</strong> equal to d field .However,the sinc function is known to decay only slowly. Some observingstrategy (e.g. quickly observing outside the edges of the targetedfield of view to provide a bandguard) could be consideredto apodize the sky-plane dependence of the sampling function,resulting in faster decaying S functions, hence in <strong>le</strong>ss mixing ofthe wide-field spatial frequencies.3.2.2. uv-plane wide-field measurement equationBecause we aim at estimating the Fourier component of I, weintroduce the following change of variab<strong>le</strong>s u ′ ≡ u p + u ′ s anddu ′ = du ′ s ,toderive∫(SV up (u s ) = S up up + u s − u ′) B ( u p − u ′) I ( u ′) du ′ . (31)u ′We then shift-and-average SV(u p , u s ) to build the Fourier transformof a wide-field dirty imageI dirty (u) ≡ 〈 SV 〉 (u) , with u = u p + u s . (32)Substituting the shift-and-average operator by its definition andusing Eq. (31) to replace SV up (u s ), we deriveI dirty (u) =∫∫W ( ) (u p , u−u p S up , u−u ′) B ( u p −u ′) I ( u ′) du p du ′ . (33)u p u ′This uv-plane wide-field measurement equation can be written as∫I dirty (u) = D ( u ′ , u − u ′) I ( u ′) du ′ , (34)u ′if we enforce the following equalityD ( u ′ , u−u ′) ∫≡ W ( ) (u p , u−u p S up , u−u ′) B ( u p −u ′) du p . (35)u pThis is one way to define D, which is convenient though unusual.It is implicit in this definition that we need to make a change ofvariab<strong>le</strong> (u ′′ = u − u ′ )toderiveD ( u ′ , u ′′) ∫≡ W ( ) (u p , u ′ +u ′′ −u p S up , u ′′) B ( u p −u ′) du p . (36)u pIn the following, we use either one or the other definition of D,depending on convenience.3.2.3. InterpretationAppendix A.2 demonstrates that the image and uv-plane widefieldmeasurement equations (Eqs. (23) and(34)) are equiva<strong>le</strong>ntifD ( ) (α p,α s)α p ,α s⊃ D ( )u(u p ,u s) p , u s . (37)The image-plane wide-field measurement equation (Eq. (23))can be written asI dirty (α) = {D α ⋆ I}(α) . (38)Its interpretation is straightforward: the sky brightness distributionis convolved with a dirty beam, D(α ′ , α ′′ ), which varies withthe sky coordinate α ′′ . This raises the question of the rate ofchange of the dirty beam with the sky coordinate. This questionis addressed in Sects. 4.2 and 5.4. Gridding by convolution and regular resamplingWe want to Fourier transform the raw visibilities along the skydimension (α s ) at some constant value in the u p dimension. Theraw data, however, is samp<strong>le</strong>d on an irregular grid in both the uvand sky planes. We need to grid the measured visibilities in boththe uv and the sky planes before Fourier transformation for differentreasons. First, the gridding in the uv plane will hand<strong>le</strong> thevariation in the spatial frequency as the sky is scanned, i.e., thedifficulty and perhaps the impossibility of Fourier-transformingat a comp<strong>le</strong><strong>tel</strong>y constant u p value. Second, the gridding alongthe sky dimension allows the use of Fast Fourier Transforms. Asusual, we grid through convolution and regular resampling.4.1. Convolution4.1.1. DefinitionsWe first define a gridding kernel that depends on both dimensions,G(u,α s ). This gridding kernel can be chosen as the productof two functions, simplifying the following demonstrations:G ( u p ,α s)≡ g(up)γ (αs ) . (39)We then define the samp<strong>le</strong>d visibility function gridded in boththe uv and sky planes asSV ( ) ( )G u p ,α s ≡{G⋆ SV} up ,α s (40)∫∫= g ( u p − u p) ′ ( (γ αs − α ′ s) SV u′p ,α s) ′ du′p dα ′ s. (41)u ′ pα ′ sFinally, when assessing the impact of the gridding on the measurementEq. (34), a new function,Σ ( ) ( )u p ,α s ,α ′′(s ≡ S up ,α s B α′′ )s − α s , (42)and its Fourier transforms naturally appear in the equations.Defining the following Fourier transform relationshipsΣ ( )u p ,α s ,α ′′ α ss ⊃usΣ ( )u p , u s ,α ′′s , (43)andΣ ( u p , u s ,α ′′s) α ′′s⊃u ′′sΣ ( )u p , u s , u ′′s , (44)Page 7 of 21
A&A 517, A12 (2010)<strong>tel</strong>-<strong>00726959</strong>, version 1 - 31 Aug 2012we easily deriveΣ ( ) ( )u p ,α s , u ′′(s = S up ,α s B u′′) s e−i2πu ′′s α s, (45)andΣ ( ) ( )u p , u s , u ′′s = S up , u s + u ′′ (s B u′′) s . (46)Using these notations, we have before gridding,SV ( u p ,α s)=∫α pΣ ( u p ,α s ,α p)I(αp)e−i2πα p u pdα p , (47)andD ( u ′ , u−u ′) ∫= W ( ) (u p , u−u p Σ up , u−u p , u p −u ′) du p . (48)u p4.1.2. Conservation of the wide-field measurement equationAppendix A.3 demonstrates that the wide-field dirty image ishere again the convolution of the sky brightness I by a widefielddirty beam D α or, in the Fourier plane,I G dirty〈SV (u) ≡ G〉 ∫(u) = D G ( u ′ , u − u ′) I ( u ′) du ′ (49)u ′withD G ( u ′ , u−u ′) ∫≡ W ( ) Gu p , u − u ( p Σ u p , u − u p , u ′) du p , (50)u pwhereΣ G ( u p ,α s , u ′) ≡∫∫g ( u p − u p) ′ ( (γ αs − α ′ s) Σ u′p ,α ′ s , u′ p − u′) du ′ p dα′ s . (51)u ′ pα ′ sWe thus have equations that resemb<strong>le</strong> those containing the samplingfunction alone, except for 1) the replacement of the generalizedsampling function Σ by its gridded version Σ G and 2) theway the variab<strong>le</strong>s are linked together both in the gridding of Σ(i.e., Eq. (51)) and in the averaging of Σ G (i.e., Eq. (50)).4.2. Regular resamplingIt is well known that too low a resampling rate in one spaceimplies power aliasing in the conjugate space (see e.g. Bracewell2000; Press et al. 1992). Aliasing must be avoided as much aspossib<strong>le</strong> because it folds power outside the imaged region backinto it. Tab<strong>le</strong> 3 defines the intervals of definition of the differentfunctions we are dealing with (i.e., visibilities, primary beam,dirty image, and dirty beam), as well as the associated samplingrates needed to enforce Nyquist sampling. The boundary valuesof the definition intervals (|u| max and |α| max ) are related to thesampling rates (∂α and ∂u, respectively) through|u| max · ∂α = |α| max · ∂u = 1 , (52)n sampwhere n samp is an integer characterizing the sampling. Nyquistsampling implies n samp = 2. However, slight oversampling (e.g.n samp = 3) is often recommended because the measures sufferfrom errors and the deconvolution is a nonlinear process. In thissection, we examine the properties of the different functions todefine their associated sampling rates.Page 8 of 21Tab<strong>le</strong> 3. Interval ranges of definition and associated sampling rates forthe used functions.Functions Intervals SamplingsVisibilities∣∣∣u ∣∣p ≤ dmax ∂u p = 2 d alias /n samp∣∣∣α ∣∣p ≤ θalias /2 ∂α p = θ syn /n samp|u s |≤d prim ∂u s = 2 d image /n samp|α s |≤θ image /2 ∂α s = θ prim /n sampPrimary beam ∣ ∣∣u′ ∣∣s ≤ dprim ∂u ′ s = 2 d alias/n samp∣∣α ′ s∣ ≤ θ alias /2 ∂α ′ s = θ prim/n samp∣∣u ′′s∣ ≤ d prim ∂u ′′s = 2 d alias /n samp∣∣α ′′s∣ ≤ θ alias /2 ∂α ′′s = θ prim /n sampDirty image |u| ≤d max ∂u = 2 d image /n samp|α| ≤θ image /2 ∂α = θ syn /n sampDirty beam |u ′ |≤d max ∂u ′ = 2 d image /n samp|α ′ |≤θ image /2 ∂α ′ = θ syn /n samp|u ′′ |≤d prim ∂u ′′ = 2 d image /n samp|α ′′ |≤θ image /2 ∂α ′′ = θ prim /n samp4.2.1. The α s sampling rate of the visibility functionWhen Fourier transforming the measurement Eq. (1) along theα s axis, we derive the Ekers & Rots Eq. (15). This equation impliesthat V(u p , u s ) is bounded inside the [−d prim , +d prim ]spatialfrequency interval along the u s axis. As a result, the visibilityfunction needs to be regularly resamp<strong>le</strong>d at a rate of only0.5/d prim to satisfy the Nyquist theorem. This was first pointedout by Cornwell (1988). This sampling rate is equal to θ prim /2or∼θ fwhm /2.4. The “usual, wrong” habit of sampling at θ fwhm /2isindeed undersampling with aliasing as a consequence. Mangumet al. (2007) discuss the consequences of undersampling indepthin the framework of sing<strong>le</strong>-dish imaging.4.2.2. The U p sampling rate of the visibility functionNow, the Fourier transform of the measurement Eq. (1) along theu p axis givesV ∼(αp ,α s)= B(αp − α s)I(αp), (53)where( ) α pV αp ∼,α s ⊃ V ( )uu p ,α s . (54)pWe use the tilde sign under V to denote the inverse Fourier transformof V along its first dimension. A well-known Fourier transformproperty implies that B has infinite support because B isbounded. The resampling rate along the u p axis therefore dependson the properties of the product of B(α p −α s ) times I(α p )asa function of α p . Whi<strong>le</strong> no unique answer exists, three facts helpus to find the right sampling rate: 1) B falls off relatively quickly;2) the result depends on the spatial distribution of the sky brightnessand in particular on the dynamic range in brightness neededto accura<strong>tel</strong>y image it; 3) the measure of V(α p , α s ) has a limited∼accuracy owing to thermal noise, phase noise, and other possib<strong>le</strong>systematics (e.g. pointing errors). For simplicity, we quantifythe measurement accuracy by a sing<strong>le</strong> number, namely themaximum instrumental fidelity measured in the image plane asdefined in Pety et al. (2001). There are two cases:1. the maximum instrumental fidelity limits the dynamic rangein brightness. For instance, Pety et al. (2001) showed that the
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Table des matières1 Rapport de sou
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Rapport après soutenanceHabilitati
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Chapitre 2Curriculum vitaetel-00726
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2.7 ANIMATION ET DIFFUSION DE LA CU
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2.8 PARCOURS 131992-1993 ÉCOLE NOR
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Chapitre 3Copyright: IRAM/PdBIIntro
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4.4 LA LUMINOSITY CO PAR MOLÉCULE
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