Archive for D-Day

D-Day 75 Years On

Posted in Art, History with tags , , , , on June 6, 2019 by telescoper

Today is the 75th anniversary D-Day, the start of the Allied landings on the beaches of Normandy. I thought I’d mark the occasion by posting a slightly edited version of a piece I wrote about 9 years ago about this very famous picture:

This remarkable photograph was taken at 8.32am on 6th June 1944 on “Queen Red” beach, a sector in the centre-left of Sword Area, during the early stages of the D-Day invasion. The precise location is near La Brèche, Hermanville-sur-Mer, Normandy. The shutter clicked just as the beach came under heavy artillery and mortar fire from powerful German divisions inland.

Some time ago I came across a discussion of this image in the Observer. As the article describes, it consists of “a series of tableaux that look like quotations from religious art”. The piece goes on

In the foreground and on the right are sappers of 84 Field Company Royal Engineers. Behind them, heavily laden medical orderlies of 8 Field Ambulance Royal Army Medical Corps (some of whom are treating wounded men) prepare to move off the beach. In the background, men of the 1st Battalion, the Suffolk Regiment and No 4 Army Commando swarm ashore from landing craft.

The sapper in the bottom left, looking directly into the camera, is Jimmy Leisk who was born in Shetland. His strained expression gives the impression that he’s trying to escape from the photograph; through his eyes we get a glimpse of the grim reality of armed conflict. His colleague, turning away from the lens, seems to be calling to the men behind, but the image of his head and upper body links with the more distant figures forming a dramatic arc that pulls you into the centre of the picture before veering off to the right. Each element of this image tells its own story, but apart from one person in the foreground, all the faces are all hidden from view. I’m sure these anonymous figures were all just as frightened as the man in the foreground, but their individual identities are lost as they blend into graphic depiction of the monumental scale of the invasion. It’s a truly wonderful work of art, and a brilliant piece of storytelling, at the same level as an Old Master, but this is made all the more remarkable by the fact that the photographer was risking his life to take this picture.

This photograph, which was taken by Sergeant Jim Mapham of the Army Film and Photography Unit, was described by the US Press as “the greatest picture of the war”.

Jim Mapham was one of seven cameramen of the AFPU who went in on D-Day: Sgt Ian Grant, Sgt Christie, Sgt Norman Clague (killed), Sgt Desmond O’Neill (wounded), Sgt Billie Greenhalgh (wounded) and Sgt George Laws. Their work forms an extraordinary record of the invasion and is still widely used by the media – but rarely credited.

Robert Capa, the famous Hungarian photographer, was also on the beaches that morning, pinned down in the waves by enemy fire. But while he clambered on to a landing craft to get his pictures back to London, Sgt Mapham moved inland with the invasion force…

Jim Mapham survived the D-Day campaign and entered Germany with the army to document the fall of the Third Reich and the horrors of the Belsen concentration camp. He died in 1968. Until today I’d never heard of him. His name should be much more widely celebrated. I understand that the complete set of photographs he took on D-Day can be found in the Imperial War Museum‘s photographic archive.

As a final comment let me add that, contrary to popular myth, the landings at the Sword beaches were by no means a pushover. It’s true that the American forces, especially at Omaha beach, suffered heavier casualties on the actual landings – primarily because they failed to get their tanks and heavy artillery pieces ashore. However, the British troops at Sword were the only ones at any of the five landing areas to encounter strong German Panzer divisions on D-Day.

The main assault force at Sword beach was the British 3rd Infantry Division and its primary objective on the day of the invasion was to capture the city of Caen. As it turned out, the fighting was so heavy that they didn’t manage to take Caen until over a month after D-Day.

In fact it is worth remembering that the Allies failed to achieve any of their goals for D-Day itself: as well as Caen, Carentan, St. Lô, and Bayeux all remained in German hands. Only two of the beaches (Juno and Gold) were linked on the first day, and it wasn’t until 12 June that all five beachheads were connected. The battle to secure and expand the foothold took far longer than anticipated and the success of the operation was by no means the foregone conclusion that some would have you believe.

5th June 23.15 GMT

Posted in Literature, The Universe and Stuff with tags , , , on June 6, 2019 by telescoper

 

Blessent mon cœur
D’une langueur
Monotone.

 

(This excerpt from a poem by Paul Verlaine formed a coded message broadcast to the French resistance by Radio Londres, 5th June 1944 at 23.15 GMT informing them that the Allied invasion of France was imminent. Preceded by extensive airborne operations, the landings on the beaches of Normandy began on the morning of 6th June 1944.)

 

 

 

 

Captain James Doohan

Posted in History, Television with tags , , , , , on June 6, 2018 by telescoper

The pictures above are photographs of a young Captain James Doohan of the Royal Canadian Artillery.

Doohan was in action on D-Day where he served with exceptional courage and distinction during the assault on Juno beach. He killed two enemy snipers and successfully led his men on foot through a minefield. Doohan was then hit six times by machine gun fire, 4 times in the leg, once in the finger, and once in the chest. The latter round would probably have killed him but for the cigarette case he had in his tunic pocket which deflected the bullet.

In case you haven’t yet realised, after the war was over, James Doohan became an actor, best known for the role of ‘Scotty’ in the TV series Star Trek…

Captain James Doohan was just one of around 160,000 officers and men who took part in the invasion of Normandy that began on 6th June 1944.

Another, not now famous, whose name along with many others, I came across this morning while waiting for my plane, was a George Jones of No 4 Commando who landed at Ouistreham (Sword beach) with the 1st Special Service Brigade around 7.30am on D-Day. Between the beach and Pegasus Bridge, four miles inland, his unit was constantly under fire and all but 80 of his 500 comrades were killed or wounded.

George Jones, James Doohan and countless other brave men like them were fighting to liberate a continent from Nazi tyranny. It is to our shame that so many today who owe their freedom to the sacrifices of an earlier generation are once again marching to the fascist drum.

German Tanks, Traffic Wardens, and the End of the World

Posted in Bad Statistics, The Universe and Stuff with tags , , , , , , , on November 18, 2014 by telescoper

The other day I was looking through some documents relating to the portfolio of courses and modules offered by the Department of Mathematics here at the University of Sussex when I came across a reference to the German Tank Problem. Not knowing what this was I did a google search and  a quite comprehensive wikipedia page on the subject which explains the background rather well.

It seems that during the latter stages of World War 2 the Western Allies made sustained efforts to determine the extent of German tank production, and approached this in two major ways, namely  conventional intelligence gathering and statistical estimation with the latter approach often providing the more accurate and reliable, as was the case in estimation of the production of Panther tanks  just prior to D-Day. The allied command structure had thought the heavy Panzer V (Panther) tanks, with their high velocity, long barreled 75 mm/L70 guns, were uncommon, and would only be encountered in northern France in small numbers.  The US Army was confident that the Sherman tank would perform well against the Panzer III and IV tanks that they expected to meet but would struggle against the Panzer V. Shortly before D-Day, rumoursbegan to circulate that large numbers of Panzer V tanks had been deployed in Normandy.

To ascertain if this were true the Allies attempted to estimate the number of Panzer V  tanks being produced. To do this they used the serial numbers on captured or destroyed tanks. The principal numbers used were gearbox numbers, as these fell in two unbroken sequences; chassis, engine numbers and various other components were also used. The question to be asked is how accurately can one infer the total number of tanks based on a sample of a few serial numbers. So accurate did this analysis prove to be that, in the statistical theory of estimation, the general problem of estimating the maximum of a discrete uniform distribution from sampling without replacement is now known as the German tank problem. I’ll leave the details to the wikipedia discussion, which in my opinion is yet another demonstration of the advantages of a Bayesian approach to this kind of problem.

This problem is a more general version of a problem that I first came across about 30 years ago. I think it was devised in the following form by Steve Gull, but can’t be sure of that.

Imagine you are a visitor in an unfamiliar, but very populous, city. For the sake of argument let’s assume that it is in China. You know that this city is patrolled by traffic wardens, each of whom carries a number on their uniform.  These numbers run consecutively from 1 (smallest) to T (largest) but you don’t know what T is, i.e. how many wardens there are in total. You step out of your hotel and discover traffic warden number 347 sticking a ticket on your car. What is your best estimate of T, the total number of wardens in the city? I hope the similarity to the German Tank Problem is obvious, except in this case it is much simplified by involving just one number rather than a sample.

I gave a short lunchtime talk about this many years ago when I was working at Queen Mary College, in the University of London. Every Friday, over beer and sandwiches, a member of staff or research student would give an informal presentation about their research, or something related to it. I decided to give a talk about bizarre applications of probability in cosmology, and this problem was intended to be my warm-up. I was amazed at the answers I got to this simple question. The majority of the audience denied that one could make any inference at all about T based on a single observation like this, other than that it  must be at least 347.

Actually, a single observation like this can lead to a useful inference about T, using Bayes’ theorem. Suppose we have really no idea at all about T before making our observation; we can then adopt a uniform prior probability. Of course there must be an upper limit on T. There can’t be more traffic wardens than there are people, for example. Although China has a large population, the prior probability of there being, say, a billion traffic wardens in a single city must surely be zero. But let us take the prior to be effectively constant. Suppose the actual number of the warden we observe is t. Now we have to assume that we have an equal chance of coming across any one of the T traffic wardens outside our hotel. Each value of t (from 1 to T) is therefore equally likely. I think this is the reason that my astronomers’ lunch audience thought there was no information to be gleaned from an observation of any particular value, i.e. t=347.

Let us simplify this argument further by allowing two alternative “models” for the frequency of Chinese traffic wardens. One has T=1000, and the other (just to be silly) has T=1,000,000. If I find number 347, which of these two alternatives do you think is more likely? Think about the kind of numbers that occupy the range from 1 to T. In the first case, most of the numbers have 3 digits. In the second, most of them have 6. If there were a million traffic wardens in the city, it is quite unlikely you would find a random individual with a number as small as 347. If there were only 1000, then 347 is just a typical number. There are strong grounds for favouring the first model over the second, simply based on the number actually observed. To put it another way, we would be surprised to encounter number 347 if T were actually a million. We would not be surprised if T were 1000.

One can extend this argument to the entire range of possible values of T, and ask a more general question: if I observe traffic warden number t what is the probability I assign to each value of T? The answer is found using Bayes’ theorem. The prior, as I assumed above, is uniform. The likelihood is the probability of the observation given the model. If I assume a value of T, the probability P(t|T) of each value of t (up to and including T) is just 1/T (since each of the wardens is equally likely to be encountered). Bayes’ theorem can then be used to construct a posterior probability of P(T|t). Without going through all the nuts and bolts, I hope you can see that this probability will tail off for large T. Our observation of a (relatively) small value for t should lead us to suspect that T is itself (relatively) small. Indeed it’s a reasonable “best guess” that T=2t. This makes intuitive sense because the observed value of t then lies right in the middle of its range of possibilities.

Before going on, it is worth mentioning one other point about this kind of inference: that it is not at all powerful. Note that the likelihood just varies as 1/T. That of course means that small values are favoured over large ones. But note that this probability is uniform in logarithmic terms. So although T=1000 is more probable than T=1,000,000,  the range between 1000 and 10,000 is roughly as likely as the range between 1,000,000 and 10,000,0000, assuming there is no prior information. So although it tells us something, it doesn’t actually tell us very much. Just like any probabilistic inference, there’s a chance that it is wrong, perhaps very wrong.

Which brings me to an extrapolation of this argument to an argument about the end of the World. Now I don’t mind admitting that as I get older I get more and  more pessimistic about the prospects for humankind’s survival into the distant future. Unless there are major changes in the way this planet is governed, our Earth may indeed become barren and uninhabitable through war or environmental catastrophe. But I do think the future is in our hands, and disaster is, at least in principle, avoidable. In this respect I have to distance myself from a very strange argument that has been circulating among philosophers and physicists for a number of years. It is called Doomsday argument, and it even has a sizeable wikipedia entry, to which I refer you for more details and variations on the basic theme. As far as I am aware, it was first introduced by the mathematical physicist Brandon Carter and subsequently developed and expanded by the philosopher John Leslie (not to be confused with the TV presenter of the same name). It also re-appeared in slightly different guise through a paper in the serious scientific journal Nature by the eminent physicist Richard Gott. Evidently, for some reason, some serious people take it very seriously indeed.

So what can Doomsday possibly have to do with Panzer tanks or traffic wardens? Instead of traffic wardens, we want to estimate N, the number of humans that will ever be born, Following the same logic as in the example above, I assume that I am a “randomly” chosen individual drawn from the sequence of all humans to be born, in past present and future. For the sake of argument, assume I number n in this sequence. The logic I explained above should lead me to conclude that the total number N is not much larger than my number, n. For the sake of argument, assume that I am the one-billionth human to be born, i.e. n=1,000,000,0000.  There should not be many more than a few billion humans ever to be born. At the rate of current population growth, this means that not many more generations of humans remain to be born. Doomsday is nigh.

Richard Gott’s version of this argument is logically similar, but is based on timescales rather than numbers. If whatever thing we are considering begins at some time tbegin and ends at a time tend and if we observe it at a “random” time between these two limits, then our best estimate for its future duration is of order how long it has lasted up until now. Gott gives the example of Stonehenge, which was built about 4,000 years ago: we should expect it to last a few thousand years into the future. Actually, Stonehenge is a highly dubious . It hasn’t really survived 4,000 years. It is a ruin, and nobody knows its original form or function. However, the argument goes that if we come across a building put up about twenty years ago, presumably we should think it will come down again (whether by accident or design) in about twenty years time. If I happen to walk past a building just as it is being finished, presumably I should hang around and watch its imminent collapse….

But I’m being facetious.

Following this chain of thought, we would argue that, since humanity has been around a few hundred thousand years, it is expected to last a few hundred thousand years more. Doomsday is not quite as imminent as previously, but in any case humankind is not expected to survive sufficiently long to, say, colonize the Galaxy.

You may reject this type of argument on the grounds that you do not accept my logic in the case of the traffic wardens. If so, I think you are wrong. I would say that if you accept all the assumptions entering into the Doomsday argument then it is an equally valid example of inductive inference. The real issue is whether it is reasonable to apply this argument at all in this particular case. There are a number of related examples that should lead one to suspect that something fishy is going on. Usually the problem can be traced back to the glib assumption that something is “random” when or it is not clearly stated what that is supposed to mean.

There are around sixty million British people on this planet, of whom I am one. In contrast there are 3 billion Chinese. If I follow the same kind of logic as in the examples I gave above, I should be very perplexed by the fact that I am not Chinese. After all, the odds are 50: 1 against me being British, aren’t they?

Of course, I am not at all surprised by the observation of my non-Chineseness. My upbringing gives me access to a great deal of information about my own ancestry, as well as the geographical and political structure of the planet. This data convinces me that I am not a “random” member of the human race. My self-knowledge is conditioning information and it leads to such a strong prior knowledge about my status that the weak inference I described above is irrelevant. Even if there were a million million Chinese and only a hundred British, I have no grounds to be surprised at my own nationality given what else I know about how I got to be here.

This kind of conditioning information can be applied to history, as well as geography. Each individual is generated by its parents. Its parents were generated by their parents, and so on. The genetic trail of these reproductive events connects us to our primitive ancestors in a continuous chain. A well-informed alien geneticist could look at my DNA and categorize me as an “early human”. I simply could not be born later in the story of humankind, even if it does turn out to continue for millennia. Everything about me – my genes, my physiognomy, my outlook, and even the fact that I bothering to spend time discussing this so-called paradox – is contingent on my specific place in human history. Future generations will know so much more about the universe and the risks to their survival that they won’t even discuss this simple argument. Perhaps we just happen to be living at the only epoch in human history in which we know enough about the Universe for the Doomsday argument to make some kind of sense, but too little to resolve it.

To see this in a slightly different light, think again about Gott’s timescale argument. The other day I met an old friend from school days. It was a chance encounter, and I hadn’t seen the person for over 25 years. In that time he had married, and when I met him he was accompanied by a baby daughter called Mary. If we were to take Gott’s argument seriously, this was a random encounter with an entity (Mary) that had existed for less than a year. Should I infer that this entity should probably only endure another year or so? I think not. Again, bare numerological inference is rendered completely irrelevant by the conditioning information I have. I know something about babies. When I see one I realise that it is an individual at the start of its life, and I assume that it has a good chance of surviving into adulthood. Human civilization is a baby civilization. Like any youngster, it has dangers facing it. But is not doomed by the mere fact that it is young,

John Leslie has developed many different variants of the basic Doomsday argument, and I don’t have the time to discuss them all here. There is one particularly bizarre version, however, that I think merits a final word or two because is raises an interesting red herring. It’s called the “Shooting Room”.

Consider the following model for human existence. Souls are called into existence in groups representing each generation. The first generation has ten souls. The next has a hundred, the next after that a thousand, and so on. Each generation is led into a room, at the front of which is a pair of dice. The dice are rolled. If the score is double-six then everyone in the room is shot and it’s the end of humanity. If any other score is shown, everyone survives and is led out of the Shooting Room to be replaced by the next generation, which is ten times larger. The dice are rolled again, with the same rules. You find yourself called into existence and are led into the room along with the rest of your generation. What should you think is going to happen?

Leslie’s argument is the following. Each generation not only has more members than the previous one, but also contains more souls than have ever existed to that point. For example, the third generation has 1000 souls; the previous two had 10 and 100 respectively, i.e. 110 altogether. Roughly 90% of all humanity lives in the last generation. Whenever the last generation happens, there bound to be more people in that generation than in all generations up to that point. When you are called into existence you should therefore expect to be in the last generation. You should consequently expect that the dice will show double six and the celestial firing squad will take aim. On the other hand, if you think the dice are fair then each throw is independent of the previous one and a throw of double-six should have a probability of just one in thirty-six. On this basis, you should expect to survive. The odds are against the fatal score.

This apparent paradox seems to suggest that it matters a great deal whether the future is predetermined (your presence in the last generation requires the double-six to fall) or “random” (in which case there is the usual probability of a double-six). Leslie argues that if everything is pre-determined then we’re doomed. If there’s some indeterminism then we might survive. This isn’t really a paradox at all, simply an illustration of the fact that assuming different models gives rise to different probability assignments.

While I am on the subject of the Shooting Room, it is worth drawing a parallel with another classic puzzle of probability theory, the St Petersburg Paradox. This is an old chestnut to do with a purported winning strategy for Roulette. It was first proposed by Nicolas Bernoulli but famously discussed at greatest length by Daniel Bernoulli in the pages of Transactions of the St Petersburg Academy, hence the name.  It works just as well for the case of a simple toss of a coin as for Roulette as in the latter game it involves betting only on red or black rather than on individual numbers.

Imagine you decide to bet such that you win by throwing heads. Your original stake is £1. If you win, the bank pays you at even money (i.e. you get your stake back plus another £1). If you lose, i.e. get tails, your strategy is to play again but bet double. If you win this time you get £4 back but have bet £2+£1=£3 up to that point. If you lose again you bet £8. If you win this time, you get £16 back but have paid in £8+£4+£2+£1=£15 to that point. Clearly, if you carry on the strategy of doubling your previous stake each time you lose, when you do eventually win you will be ahead by £1. It’s a guaranteed winner. Isn’t it?

The answer is yes, as long as you can guarantee that the number of losses you will suffer is finite. But in tosses of a fair coin there is no limit to the number of tails you can throw before getting a head. To get the correct probability of winning you have to allow for all possibilities. So what is your expected stake to win this £1? The answer is the root of the paradox. The probability that you win straight off is ½ (you need to throw a head), and your stake is £1 in this case so the contribution to the expectation is £0.50. The probability that you win on the second go is ¼ (you must lose the first time and win the second so it is ½ times ½) and your stake this time is £2 so this contributes the same £0.50 to the expectation. A moment’s thought tells you that each throw contributes the same amount, £0.50, to the expected stake. We have to add this up over all possibilities, and there are an infinite number of them. The result of summing them all up is therefore infinite. If you don’t believe this just think about how quickly your stake grows after only a few losses: £1, £2, £4, £8, £16, £32, £64, £128, £256, £512, £1024, etc. After only ten losses you are staking over a thousand pounds just to get your pound back. Sure, you can win £1 this way, but you need to expect to stake an infinite amount to guarantee doing so. It is not a very good way to get rich.

The relationship of all this to the Shooting Room is that it is shows it is dangerous to pre-suppose a finite value for a number which in principle could be infinite. If the number of souls that could be called into existence is allowed to be infinite, then any individual as no chance at all of being called into existence in any generation!

Amusing as they are, the thing that makes me most uncomfortable about these Doomsday arguments is that they attempt to determine a probability of an event without any reference to underlying mechanism. For me, a valid argument about Doomsday would have to involve a particular physical cause for the extinction of humanity (e.g. asteroid impact, climate change, nuclear war, etc). Given this physical mechanism one should construct a model within which one can estimate probabilities for the model parameters (such as the rate of occurrence of catastrophic asteroid impacts). Only then can one make a valid inference based on relevant observations and their associated likelihoods. Such calculations may indeed lead to alarming or depressing results. I fear that the greatest risk to our future survival is not from asteroid impact or global warming, where the chances can be estimated with reasonable precision, but self-destructive violence carried out by humans themselves. Science has no way of being able to predict what atrocities people are capable of so we can’t make any reliable estimate of the probability we will self-destruct. But the absence of any specific mechanism in the versions of the Doomsday argument I have discussed robs them of any scientific credibility at all.

There are better grounds for worrying about the future than simple-minded numerology.

 

 

The Fallen Project

Posted in Art, History with tags , , , on June 6, 2014 by telescoper

It’s not known exactly how many people died on D-Day 6th June 1944 when the Normandy landings took place, but a  fairly conservative estimate is about 9000 (including about 3000 French civilians).

In September last year, the beach at Arromanches (code-named Gold ) was the site of a remarkable art installation called The Fallen 9000 during which hundreds of volunteers stencilled images of 9000 fallen soldiers into the sand.

 

o-FALLEN-900

It’s a moving image, not least because the figures were soon to be washed away by the incoming tide. Let’s hope the courage and self-sacrifice of the soldiers who gave their lives that day are not forgotten too. Seventy years on, fascism is apparently once again on the rise in Europe. We should not forget where that road has led in the past.

Lest we forget.

“The Greatest Picture of the War”

Posted in Art with tags , , , , on July 11, 2010 by telescoper

This remarkable photograph was taken at 8.32am on 6th June 1944 on “Queen Red” beach, a sector in the centre-left of  Sword Area, during the early stages of the D-Day invasion. The precise location is near La Brèche, Hermanville-sur-Mer, Normandy. The shutter clicked just as the beach came under heavy artillery and mortar fire from powerful German divisions inland.

I came across a discussion of this image in today’s Observer and decided to post it here, simply because it’s such a great  composition. As the article describes, it consists of “a series of tableaux that look like quotations from religious art”.  The piece goes on

In the foreground and on the right are sappers of 84 Field Company Royal Engineers. Behind them, heavily laden medical orderlies of 8 Field Ambulance Royal Army Medical Corps (some of whom are treating wounded men) prepare to move off the beach. In the background, men of the 1st Battalion, the Suffolk Regiment and No 4 Army Commando swarm ashore from landing craft.

The sapper in the bottom left, looking directly into the camera, looks terrified, and his expression makes it seem like he’s trying to escape from the photograph; through his eyes we get a glimpse of the shocking reality of armed conflict, which is far from the romantic way it’s portrayed in the movies. His colleague, turning away from the lens, seems to be calling to the men behind, but the image of his head and upper body links with the more distant figures forming a dramatic arc that pulls you into the centre of the picture before veering off to the right. Each element of this  image tells its own story, but apart from one person in the foreground, all the faces are all hidden from view. I’m sure these anonymous figures were all just as frightened as the man in the foreground, but their individual identities are lost as they blend into graphic depiction of the monumental scale of the invasion. It’s a truly wonderful work of art, and a brilliant piece of storytelling, at the same level as an Old Master, but this is made all the more remarkable by the fact that the photographer was risking his life to take this picture.

This photograph, which was taken by Sergeant Jim Mapham of the Army Film and Photography Unit, was described by the US Press as “the greatest picture of the war”.

Jim Mapham was one of seven cameramen of the AFPU who went in on D-Day: Sgt Ian Grant, Sgt Christie, Sgt Norman Clague (killed), Sgt Desmond O’Neill (wounded), Sgt Billie Greenhalgh (wounded) and Sgt George Laws. Their work forms an extraordinary record of the invasion and is still widely used by the media – but rarely credited.

Robert Capa, the famous Hungarian photographer, was also on the beaches that morning, pinned down in the waves by enemy fire. But while he clambered on to a landing craft to get his pictures back to London, Sgt Mapham moved inland with the invasion force…

Jim Mapham survived the D-Day campaign and entered Germany with the army to document the fall of the Third Reich and the horrors of the Belsen concentration camp. He died in 1968. Until today I’d never heard of him. His name should be much more widely celebrated. I understand that the complete set of photographs he took on D-Day can be found in the Imperial War Museum‘s photographic archive.

As a final comment let me add that, contrary to popular myth, the landings at the Sword beaches were by no means a pushover. It’s true that the American forces, especially at Omaha beach, suffered heavier casualties on the actual landings – primarily because they failed to get their tanks and heavy artillery pieces ashore. However, the British troops at Sword were the only ones at any of the five landing areas to encounter strong German Panzer divisions on D-Day. The main assault force at Sword beach was the British 3rd Infantry Division and its primary objective on the day of the invasion was to capture the city of Caen. As it turned out, the fighting was so heavy that they didn’t manage to take Caen until almost a month after D-Day.