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EyesRight Publication
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Fall 2018 PDF Newsletter
Second Annual James Seminaroti Golf Outing Brings in $800 for ALS
Published by VSC of Summit County at November 30, 2018
EyesRight Fall 2018
Following the successful launch of the James C. Seminaroti Benefit Golf Outing in 2017, the second annual event – held Friday, Sept. 21 at the Fox Den Golf Course in Stow – built upon its success by bringing in $800 to support the fight against amyotrophic lateral sclerosis (ALS). All proceeds from the event were donated to the ALS Association Northern Ohio Chapter.
This year's event, hosted jointly by the VSC and American Legion Post 473, drew 32 attendees including members of local veteran organizations and VSC agencies from neighboring counties. Participants included Dennis Gutowski, former executive director of the VSC of Summit County, and Rich McClellan, director of the VSC of Portage County. Also in attendance was Pete Seminaroti, James Seminaroti's son.
"My dad was red, white and blue, through and through," said Pete Seminaroti. "He loved nothing more than his country and helping other veterans who've served it."
Research has shown that veterans from any branch – even those who have served during a time of peace – are at a much higher risk than the general population for developing ALS, a rapidly developing neuromuscular disease that affects thousands of people throughout the U.S.
"ALS is a truly devastating condition that disproportionately affects veterans and their families," said David Burden, project manager, VSC. "The VSC thanks those who came out to support us in our second year fighting against this deadly disease."
An Akron native and World War II veteran, James Seminaroti was actively involved in numerous local veteran organizations, becoming a founder of Fred W. Reese Veterans of Foreign Wars (VFW) post 3310 in Akron and contributing to the American Legion, the Salvation Army and the Military Order of the Cootie of the United States. As a member of The Salvation Army's Akron advisory board for more than 15 years, he organized a program called Operation Concern that aimed to make soldiers' send-offs more pleasant by providing breakfast and free necessity kits.
Seminaroti died Nov. 4, 1981 following his own battle with ALS. His legacy of service lives on in the VSC's annual Veteran of the Year Award, presented each fall to a Summit County veteran who displays selflessness through service.
If you want to donate to the ALS Association Northern Ohio Chapter, visit webnoh.alsa.org.
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How To Create Content That Inspires Action!
In this show, Zef Zan, host of Creative Chat Cafe and cohost Kenya Halliburton www.KenyaHalliburton.com discuss "How to create content that inspires action!"
What type of content inspires action from your audience.
Tips on how to create "call to action" content.
A discussion on Simon Sinek's (a leadership expert) inspiring TED Talk on "How great leaders inspire action."
If you're doing your own marketing, you probably already know that getting your audience to perform an action is crucial to the success of any marketing initiative. So why do we need to discuss content and why is it important?
Sure, you can probably drive your target audience to your content but leading people to it is only step one. Many people get lost in creating content that is not leading people to perform measurable actions. Every piece of content you publish should be crafted with this in the forefront of your mind. If you're not doing this, you're wasting your time.
Imagine going through all the trouble of writing on your blog, and posting on social media sites, but you are not getting the attention nor are your audience taking any action. A good example of this is no one's clicking on your links and they are not buying!
ZEF: Kenya, do you think creating the right content is so much more important than going viral? Why?
KENYA: Going viral is not the only important thing when it comes to content. Having your content go viral is great but whenever you are creating content, the question is, is your content that is going converting into something that you can measure? It not, it is really not serving your business and the people that you serve.
Your content should tie back to some clear goal you have set in your business. You have your "platform" goals that establish leads and sales. Then there are "stair step" goals and these are goals that can lead to the conversion of leads or sales.
ZEF: So what kind of content in your opinion inspires action?
KENYA: First thing you have to do is you have to understand your brand. You must understand who you are and what type of experience do you want people to have? Create content that speaks to the hearts, beliefs, desires, and needs of your audience. You want the messages that come from the heart so that your audience knows what you believe and why you do what you do. There is a lot of noise out there and without this, you will be just another blog, video or sales page of millions being ignored.
Next, you need to know what your goals are. What type of results are you looking to achieve? Leads, sales, thought leadership, awareness. etc, and you need to be able to measure them at some level. Your goal and your ideal customer's needs, plus your resources and skills will determine what form your content takes.
ZEF: True. That when creating content, if we are not creating the content that our audience wants, then why should they be interested in you, right?
KENYA: Yes. With this in mind, you want to create targeted content that provides the most relevant answer to the questions the reader or visitor is asking. This can be found by researching what they are typing in the search engines or by simply understand who your ideal customer is and where they are in the buying process.
What makes content actionable is the "call to action". What are you asking the visitor to do after consuming your content? You need to have a flow and a plan for what happens next. If you don't, all you're left with is a great piece of content and you have lost an opportunity to convert.
ZEF: I agree and in my opinion, we all have to create content so compelling, so inspiring, that the right people respond. Again, if you're not getting your audience to perform measurable actions, you're wasting your time.
"Kenya Halliburton is an online business manager, specializing in digital content creation, content marketing and social media marketing. She is a creative, innovative, outside the box, forward-thinking web choreographer always looking for new opportunities to position my clients for success."
ZEF: Now do you want some tips on how you too can create content that inspire action? Kenya, what do you have for us in your tip box there?
KENYA: Start with understand the "buying cycle of your audience." What questions need to be answered to meet their needs, desires and challenges at each stage? Do this for each of your products and services and you will have all the actionable content you ever need.
Awareness - "Something's not right!", "What's my problem?" Help them understand what their issues are.
Purchase - "I want to feel good about my decision.", "Reinforce my choice." Think about how you buy. Create that experience. Make them feel good about their choice and investment.ZEF: I am a visual person and I relate to images. I know that sometimes what we are creating content online, may they be on our websites, blogs or even via social media, we may have good content but images can create a more powerful emotion that will inspire our audience to take action. What are your thoughts?
KENYA: Anytime you can incorporate images to your content, that will always make people look a little bit closer. It is absolutely a good strategy. You'd want to include images that are compelling enough to drive people back to your content.
ZEF: Here's another important tip that may help you create that content next time and this is a crucial one!
Make sure to track your content. Track those measurable actions that we have been talking about! Newsletter subscribers, social shares, leads, and sales. If you do not track them, you will not know your content's performance! You will not know what works and what doesn't!
Simon Sinek's TED Talk Video Inspires!
ZEF: I love videos and one video that relates to our topic today here is Simon Sinek's TED Talk video titled "How great leaders inspire action." Simon Sinek is a leadership expert. In this TED Talk video, Simon talks about what great leaders need to do to inspire action. This video gave me a different level of inspiration and I was empowered by it. Kenya, have you watched this video? If yes, what one content of this talk that was so powerful that it may have inspired you?
KENYA: "People don't buy what you do, they buy what you believe." What you do proves what you believe.
ZEF: What gave me the goose bumps is, he said, "People don't buy WHAT you do, people buy WHY you do it. The GOAL is not to do business with everybody who needs what you have but the goal is to do business with people who believe what you believe!" That is powerful right there!
This is a must watch video folks! Watch it on TEDTalk.com.


If you are struggling to find a good contractor, you can ask for recommendations. Contact friends and relatives who have recently purchased properties. This will surely help you to gather names of some reliable builders.
Your next step should be viewing work samples. Here, we would like to advise you not to opt for the first project of any builder. Always opt for companies or professionals who can show your samples. Looking at the past projects of the person or company you are looking to rely on will give you the confidence required for investing a big sum.
Don't forget to check certificates and licenses that make a construction a legal construction. You can talk to your lawyer to know what kind of licenses and certificates a real estate contractor must possess. If the contractor fails to show you necessary documents, don't think twice before discarding him.
Whatever might be the area of the property you are looking to purchase the builder should be able to offer you a fixed price quote. You may be looking to buy a commercial property covering an area of 15000 square feet or a 2BHK apartment covering an area of 1000 square feet, you must get a fixed price quote before completing the registration procedure.
If it's a residential property, you should know what kind of neighbors you will be having once you start living in the newly purchased apartment. Top real estate contractors always make sure that people buying apartments from them have a good background. So, it's extremely important that the company you are purchasing the flat from is one of the most trusted builders in the area it is operating in.


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A Great and Terrible Beauty (Gemma Doyle Trilogy #1)
ISBN 13: 978-0385732314
Reader Level: Young Adult
A Publishers Weekly Bestseller
A Book Sense Bestseller
BBYA (ALA/YALSA Best Book for Young Adults)
Iowa High School Book Award
Garden State Teen Book Award
Pennsylvania Young Reader's Choice Award
It's 1895, and after the suicide of her mother, 16-year-old Gemma Doyle is shipped off from the life she knows in India to Spence, a proper boarding school in England. Lonely, guilt-ridden, and prone to visions of the future that have an uncomfortable habit of coming true, Gemma's reception there is a chilly one. To make things worse, she's been followed by a mysterious young Indian man, a man sent to watch her. But why? What is her destiny? And what will her entanglement with Spence's most powerful girls—and their foray into the spiritual world—lead to?
A Victorian boarding school story, a Gothic mansion mystery, a gossipy romp about a clique of girlfriends, and a dark other-worldly fantasy–jumble them all together and you have this complicated and unusual first novel…an impressive first book in what should prove to be a fascinating trilogy.
Gripping and suspenseful. Colourful…compelling.
Publishers Weekly, starred review
A Gothic novel touched by modern conceptions of adolescence, shivery with both passion and terror.
An interesting combination of fantasy, light horror, and historical fiction, with a dash of romance thrown in for good measure…Recommend this to fantasy fans who also like Sherlock Holmes or Mary Russell.
Exhilarating and thought-provoking,
There's no doubt the mystical elements, along with a touch of forbidden romance, will draw a large, enthusiastic audience, who will come away wanting more about stubborn, willful Gemma and the strange world whose doors she can open at will.

redpajama_set_name: RedPajamaCommonCrawl


text:
Unity and universalism
Liz Fekete, director of the Institute of Race Relations focused on the links between state racism, hate crime and resistance when she spoke at the Jewish Socialists' Group's first Annual Meeting by Zoom this year. Jewish Socialist magazine has published her speech.
Too much of the focus in antiracist discussion today is on prejudice, interpersonal relations and "hate crimes", with hate divorced from the wider processes that foster racism. Even when we finally come to discuss institutional racism, too often it is misdiagnosed – seen almost as a continuation of prejudice and unconscious bias rather than a structural issue in our society that then legitimises prejudice. The issue that we discuss far too seldom in our movements today is the nature of state racism. Back in the 1970s and '80s, tackling state racism was in our antiracist DNA. Then, we talked about state racism in terms of discriminatory immigration laws and police practices, such as stop and search.
In trying to understand where the turn in our movement came about, I realised that part of the problem went back to Macpherson's definition of institutional racism in the Stephen Lawrence Report. At the time, and for very good reason, we saw Macpherson's recognition of institutional racism as a watershed moment in the history of race relations. The UK was the first country in Europe to recognise the phenomenon of institutional racism. This was a victory for so many antiracist and anti-fascist campaigns, particularly around the death of Black and Brown people in police custody, or on the streets at the hands of racists and the far right, with racist deaths compounded by the police response, as we saw
in the Stephen Lawrence case.
With hindsight, we can recognise that Macpherson's definition of institutional racism had quite a limited scope. Macpherson defined institutional racism as "The collective failure of an organisation to provide an appropriate and professional service to people because of their colour, culture or ethnic origin. It can be seen or detected in processes, attitudes and behaviour which amount to discrimination through unwitting prejudice, ignorance, thoughtlessness and racial stereotyping."
This definition was useful in terms of forcing institutions such as the police to change, as well as, thanks to the Race Relations Amendment Act, placing a positive duty on local authorities and public bodies to collect data and combat discrimination. But MacPherson's definition did not address popular racism in the media, for instance (although he did highlight racial stereotyping) and his wider findings failed to consider whether police operations per se
were racist.
So that definition, important as it was in providing the first step for necessary institutional change, particularly in terms of the evolution of anti-discrimination laws, did not go nearly far enough. It led us to take our eye off the ball when it came to state racism. It also meant that antiracism was being channelled into managerial goals and mechanical change in ways that divorced the antiracist fight from its historical roots in the struggle against colonialism and imperialism. Our fight was becoming disentangled from a more transformative struggle to fundamentally change our political culture and the brutal nature of state power.
From there on, too, the reduction of racism to attitudes, to prejudice, to unconscious bias and to interpersonal racism, became more prevalent. The intimate links between structural racism, state racism and popular culture were being obscured.
The orthodoxy today – looking at racism as prejudice or hate – flattens and personalises racism, and opens the door to very subjective interpretations. But campaigning against structural racism, including state racism, demands a more rigorous, less subjective approach. We can only talk about state racism when we can identify discriminatory practices enshrined in the law that operates with the overt backing of government.
It would be hard to argue that state racism today − much of which comes out of Hostile Environment policies, or the War on Terror, or the way Black youth are criminalised through educational exclusion and police harassment − is targeted at the Jewish community with the overt backing of government. But just because I cannot detect any evidence of state antisemitism in the UK today, that doesn't mean that Jewish communities in other European contexts are not targeted by state racism.
My friend Rafal Pankowski in the Never Again Association in Warsaw identifies state antisemitism directed towards the Jewish community in both Poland and Hungary. In Hungary the law relating to NGOs receiving foreign funding came after an antisemitic campaign focused on George Soros – the antisemitic narrative against Soros has the overt backing of the Hungarian government. The Polish history law that prohibits criticism of Polish complicity in the Holocaust is another example of state targeted antisemitism against the Jewish community. If we are looking at antisemitism in the UK today we should be more critical of the way the Conservative Party, our government and our state are complicit in antisemitism in Poland and Hungary by supporting these overtly antisemitic governments.
How can we begin to build a unified antiracist struggle today where different communities' experiences have become more particular even as they are derived from the same racist roots? How do we have a struggle that doesn't disappear one community's oppression at the expense of another's? In our fights against antisemitism and Islamophobia, for instance, we have been in danger of disappearing the experiences of Black working-class communities who face a race and class experience of police oppression. But until the advent of Black Lives Matter, that particular history of racist oppression has been disappeared and we have done far too little to expose the brutality that Black communities have faced at the hands of the police and the alarming criminalisation (and incarceration) of young black men.
On IRR News on our website, we maintain a regular digest of the most important developments of racism and resistance in terms of asylum, migrant rights, discrimination, policing, and the far right and so on. Just putting stories together over the last few months, on deaths in custody, stop and search, and the use of Tasers, it's absolutely clear that there is a serious escalation of police racism, mostly aimed at multicultural working class neighbourhoods. The policing in black communities has always been derived from the colonial tradition of policing where black communities tend to be subjected to a kind of militarised response, through the use of specialist squads, SPG in the old days, and through mobile violence reduction units today. And obviously the pandemic and the policing of lockdown has handed over so much power to the police in Europe, and indeed globally, in contexts where police have a reputation for harassing and persecuting ethnic minorities. In the European context, too, so much terrible stuff is happening to the Roma communities, Gypsies and Travellers and migrant workers, particularly undocumented workers, in France and Italy.
So when people talk about decolonialising the universities, I remind them about the need to decolonialise the police. This is very urgent because the police colonial tradition kills people.
I explained earlier that Macpherson did not explore the links between state racism and popular racism. This is an area where we can begin to think about how we unite in a broad universal antiracist struggle that doesn't disappear antisemitism even as it makes visible other racisms.
State racism operates in the UK primarily through the Hostile Environment and colonial policing but there is a bifurcated race policy that keeps state racism, the Hostile Environment and militarised policing in place through a divide and rule approach. This bifurcated race policy operates in two ways: firstly by redefining racism as bigotry, hate and prejudice, and therefore disappearing all racisms that derive from the actions of the state; secondly by building in a system of structural denial of racism. In this bifurcated race policy antisemitism is seen as a racism that the Conservative government is concerned about, but at the same time there is structural denial of other forms of racism. This is not good in all sorts of ways, not least in terms of the government's responsibility to maintain good race relations.
We see this institutionalised denial of structural racism in the way that Trevor Phillips was brought in to the Public Health England inquiry, or Munira Mirza or Tony Sewell to shore up the Commission on Racial and Ethnic Disparities. There is now a pattern where the government appoints people to head commissions and inquiries on racism against BME communities who have a track record of denying institutional racism − while at the same time they do not deny that BME communities have certain feelings and perceptions and grievances. But perceptions and grievances are easy to dismiss (they can also be used to prove that BME people have developed a victim mentality which leaves them incapable of pulling themselves up by their bootstraps). Once again we return to racism as something entirely subjective and therefore, in terms of communities labelled "unaspirational", easy to dismiss.
I want to conclude by talking about where there is hope, where there is possibility for progress and how we can build on this. We need to build unity, and this is a challenge. I think the lesson is: we need to be seen in places where we're not expected to be seen. It is quite threatening for those in power when there is a visible presence of young Jewish people on a Black Lives Matter movement protest – or, for instance, as we have seen in the US when young Jews opposed Trump in ways that were rather brave, making it clear they will not be part of the oppression of Black and Latino communities. It is a visual riposte to bifurcated race policies intent on pitching us against each other.
Sadly, as ever, it is the horrible invasive nature of racism that can unite us. When racism is targeted at one community, it normally spills out towards another. So much of popular racism is organised around xeno-racism (racism directed against people perceived as "foreign"). This kind of othering always spills over. We really saw xeno-racism come into being around the war on refugees and migrants. We see the same principles in play when it comes to Muslims, Gypsies and Travellers. While this is not understood by communal leaders (who tend to emphasise the exceptional nature of the racism in the community from which they derive experiences) it is well understood by ordinary people at a community level who always come together under attack. We've seen this in the way that Jewish and Muslim communities have supported each other when mosques or synagogues, are attacked. And in this way, we also see that antisemitism is still very central to a common fight against the far right which again gains legitimacy from state racism, the overt backing of governments for racist laws.
But what is the nature of this antiracism we want to build – is it punitive or is it transformative, does it educate or does it seek to shame and blame? We should not see institutional racism as an amalgamation of individual prejudices. The relationship is actually the other way round, and it is the state and the laws of the land and the popular culture which educate for racism. People are not naturally born prejudiced, naturally born hating. We are educated into hostility and racism. One of the problems we have in the antiracist movement is that we have lost the educational aspect of antiracism. There is too much focus on penalisation and law and order, too much emphasis on punishment, humiliation and shaming (not least on social media) in place of patient and dedicated educational and community-based work.
It may be out of fashion to look at state race policies, but if we do look at the state's race policies it is clear that race relations is seen as something to be managed via communal authorities and that is true across the board. And we have seen in recent years that it is the communal authorities who are most punitive, almost delighting in an inhumane "cancel culture". The kinds of critiques that the Jewish Socialists' Group has mounted against communal authority are also being mounted in the Asian community. We are also part of a much broader fight against communal appropriation of antiracism and for a struggle that transcends the limited politics of identity. We are in a fight for universal values so that we can come together in a broad fight against racism, in all its structured avatars, and by so doing, undermine the state and communal basis of how racism and antiracism are managed.
Although I might have argued that state racism is not directed towards Jews in the UK context today, I am deeply concerned that the government's bifurcated race policy, embraced by Labour and shored up by communal authorities, will leave ordinary people in communities exposed to interracial tensions as well as the racism that kills. There are so many opportunities to build wider communities of resistance. This is already happening in the USA. We urgently need to go back to an approach based on a universal antiracism fight if we are to stop antisemitism, racism and antiracism being used as footballs kicked around by politicians, journalists and other disreputable people in a very manipulative and malevolent way.
Posted: 19 October 2020 | Published in: Jewish Socialist No 74


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Broken Tomorrow...
Copyright©2017 Michelle Woods
It started in the summer when the heat rose up off the pavement in hazy waves of steam. The air was filled with the sound of children's laughter and the sweet scent of barbeque on the grill. Time seemed to pass slower because the days were long and the runs were short. The club had more than two hundred members at the time, and life was good. No one expected the horror that hovered on the horizon. The first reports blasted across the country and were met with disdain and disbelief. Summer was supposed to be filled with laughter and redneck slip 'n slides, not death and destruction.
Infection, that's what they'd called it in the early days. It was a word used to describe the horror that caused friends to murder friends and strangers to become families. Those were the days when everyone thought there would eventually be a cure, but that was only a pipe dream. It couldn't be stopped and none of the experts expected the infection to get out of hand. They didn't expect it to morph and travel so easily from area to area, they thought they could contain it, but they were wrong.
Now it was a game of survival and the Crimson Blades MC had always been good at surviving, even when shit hit the fan and the world went to hell.
He'd lost count of the number of times that he'd wished they could go back to those long summer days when the world was normal. He would have laughed at anyone who told him he'd miss dealing with club rivalry bullshit or getting harassed by the cops. After two years of living in the shit storm he dealt with on a daily basis, looking back he could definitely say those days seemed idyllic and easy. How had they gone from riding hard and playing even harder, to barely surviving?
Jebidah "Jeb" Blackwell didn't know, but he wanted things to go back to the way they'd been before the infected had started popping up all over the country. As his eyes scanned the sky above him, he let out a long sigh as he leaned back into the support behind him. The porch creaked under his feet and he jerked while glancing around his immediate vicinity with his hand resting on his gun. Realizing it was just the wood settling, he let out a sigh and relaxed. He still couldn't believe that this was his life now. When the first reports started playing on the radio, he'd ignored them. It had seemed like bullshit when they'd talked about people going crazy and attacking each other in the streets.
He wasn't the only one, the whole fucking club had ignored those reports. What did they care, as long as it wasn't affecting their family, or their way of life. Well, Scoot, their very own conspiracy theorist, had paid attention to those stories. Of course, most of the club had thought he was nuts—running around, yelling about preparing for the zombie apocalypse. Nobody had listened, they'd just laughed and teased him about taking his anti-psychotics and went right on living life just like they always had. Jeb glanced over to the wall—made from steel sheets and reinforced with heavy struts—that enclosed the clubhouse and surrounding houses. That wall was thanks to Scoot, and every man in the club was now grateful that he'd been a crazy old coot who'd started collecting the sheets of fifteen-foot metal by liberating them from a construction site about ten miles from the clubhouse just days after those first reports.
Jeb rubbed a hand over his neatly trimmed beard, letting out a groan as he shifted, still thinking of the past. One night after a run, he'd turned the TV on to find an emergency broadcast on every station, even the ones that weren't typically stations for that type of story. Hell, even the sci-fi channel had the broadcast on. It had been eerie and a little disturbing seeing all those stations playing the same thing on a loop. He'd thought that maybe it was some prank one of the boys played on him so he'd walked over to Hobbs place to bitch at him because he was the one most likely behind that shit, being as Hobbs was the resident computer expert.
Jeb had found sixteen of his brothers camped out in Hobbs living room watching the same broadcast with varying degrees of alarm on their faces. That was a freaky fucking sight, because fear was not a part of a bikers' vocabulary. Riding with people allowed you to get to know them, and he'd known that shit was real the moment he'd stepped into Hobbs place because of the expressions on their ugly mugs. Fuck, that night seemed like a lifetime ago. Watching that widescreen TV with seventeen bikers who were all shitting their pants because they couldn't figure out what the fuck to do with the information they were watching.
Jeb dug the pack of cigarettes out of his pants pocket. He shook one out of the pack, realizing it was the last one. Damn, he'd known that he was going to run out, but he'd hoped to make it till they went into town to scavenge. He pulled out his lighter and held it to the end of his cigarette, taking a long drag. He flicked his lighter closed and shoved it back into his pocket along with the empty pack. Looking up at the sky again, he noted that the stars seemed brighter tonight. Every night the sky seemed to be clearer and less polluted, with more stars coming into focus. He'd never thought that cleaner, fresher air would be something he found depressing, but it was. It must be nearing summer because it was hot out and it had been dark for at least an hour.
After taking another long puff, Jeb jumped off the porch and started his nightly perimeter check. Not that he needed to check the perimeter, it was just something useless he did every night, mostly out of boredom. He realized as he walked towards the wall that he'd forgotten his radio back at his cabin. Jeb shrugged it off—he didn't need it inside the walls anyway. He looked up nodding to Kicker, who was positioned on the wall above him, keeping an eye out for trouble. Jeb headed down the south wall checking for any weak spots by shaking the supports, not that any of the steel supports had ever moved. Twisted had been a construction worker before he was part of the club and knew what he was doing when he'd laid out the plan for the wall.
Log on September 7th for the rest of Chapter 1
Also tune in every Tuesday and Thursday
this month to read about Jeb and the world he lives in...


Thank you for visiting my political website.
It is an honor to serve the people of Holyoke as a City Councilor at-Large and as Chair of the Ordinance Committee. Holyoke is changing and the Holyoke community needs to have an active role in shaping the future success of our city.
For the past ten years I have worked hard to ensure that residents and business owners in our city have access to information on the issues we are facing and how individuals can weigh in on the decision-making process. I have been a very diligent representative; researching the issues, pursuing open dialogues with my colleagues on the council and educating the public on how and why I voted in the ways that I have.
I promise to continue to be a voice for positive change in Holyoke. I welcome your feedback and questions. If you have a concern to share or ideas for how we can improve our city, please contact me at rebecca@votelisi.com.


text:
\section{Introduction}

Understanding the properties of stars and the history of star
formation in galaxies remains one of the most fundamental subjects in
astrophysics. The {\it Hubble Space Telescope} ($HST$) provides an
important tool for this endeavor, since it enables the detailed study
of stars and star clusters, not only in the Milky Way and its nearest
neighbors, but in galaxies well beyond the Local Group. The Wide Field
Camera 3 (WFC3) provides a particularly valuable new panchromatic
imaging capability, with spectral coverage from the near-UV to the
near-IR.  This capability is especially useful for studying the
stellar populations in nearby galaxies where individual stars are
resolved.

A good example of a multi-wavelength survey of nearby galaxies which
employs resolved stars and star clusters is the ACS Nearby Galaxy
Treasury (ANGST) program \citep{dalcanton09}. This study includes
$\sim$65 galaxies out to $\sim$3.5~Mpc, and provides uniform
multi-color ($BVI$) catalogs of tens of thousands of individual stars
in each galaxy. Although the ANGST program provides an excellent survey
for a wide range of studies, it does not provide observations in the
$U$-band since the observations were obtained before WFC3 was
installed on $HST$.  The $U$-band is particularly useful for age-dating
populations of young stars, which is the focus of the current paper.
PHAT (Panchromatic Hubble Andromeda Treasury) is a related study that
does take advantage of the new $U$-band capability on WFC3. While
nowhere as extensive as the Multiple Cycle Treasury Program PHAT
survey, the current study of M83 is complementary in the sense that it
provides similar observations of the resolved stellar component of a
nearby spiral galaxy, and uses quite different analysis techniques, as
will be discussed in Sections 2 and 3.  Future observations of M83
(ID:12513, PI: William Blair) will expand the WFC3 dataset available
for M83 from 2 to 7 fields.

M83 (NGC\,5236), also known as the ``Southern Pinwheel'' galaxy, is a
slightly barred spiral galaxy with a starbursting nucleus located at a
distance of $4.61$\,Mpc, i.e., $(m-M)_{0} = 28.32 \pm 0.13$
\citep{saha06}. The H$\alpha$~ emission can be used to pinpoint regions of
recently formed massive stars along the spiral arms, while red
supergiants can be found throughout the galaxy. The color-magnitude
diagram (CMD) of resolved stars is a powerful diagnostic tool for
understanding the stellar evolution and history of star formation of
galaxies in detail. By comparing stellar evolution models to observed
CMDs, we are able to determine the ages of the stellar populations in
galaxies. However, in active star-forming regions, the stars are often
partially obscured by dust. Applying a single extinction correction
value for the whole galaxy often results in over- or under-estimates
of the ages of individual stars.  If the CMD is the only tool used to
determine the ages, the spatial variations of dust extinction in
galaxies and their effects on the determined ages are not readily
apparent. The additional information available from the color-color
diagram can remedy this problem.  By using techniques developed in
this paper, we can constrain the variations of dust extinction across
M83, and make corrections for individual stars.  We also focus on
spatial variations in the stellar properties throughout the galaxy.

The $HST$\/ WFC3 observations of M83 were performed in August 2009 as
part of the WFC3 Science Oversight Committee (SOC) Early Release
Science (ERS) program (ID:11360, PI: Robert O'Connell). The central
region (3.2\,kpc$\times$3.2\,kpc) of M83 was observed in August, 2009.
A second adjacent field to the NNW was observed in March 2010, and
will be included in future publications. Details of the WFC3 ERS data
calibration and processing are given by \citet{chandar10} and
\citet{windhorst11}.

In this paper we present $UBVI$\/ photometry of resolved stars in M83
and the resulting color-color and color-magnitude diagrams.  We use
$(F336W\!-\!F438W)$ vs. $(F555W\!-\!F814W)$ color-color diagrams to
constrain the variation of dust extinction along the line-of-sight of
each individual star, and correct $F814W$ vs. $(F555W\!-\!F814W)$ CMDs
for the extinction of individual stars to determine their ages. The
high sensitivity and the superb resolving power in the WFC3 F336W-band
plays a key role in allowing us to develop our extinction correction
techniques, and demonstrate the performance of the newly installed
WFC3. This results in improved stellar age estimates, and a better
understanding of the recent star-formation history of M83.

Our investigations are focused on the followings:
\vspace{-0.5cm}
\begin{enumerate}[(1)]
\item Do we see spatial variations of stellar ages in M83? If so, can
  we use these variations to learn more about the evolution of the
  galaxy and what triggers star formation?
\vspace{-0.2cm}
\item How well do various age estimates correlate (i.e., resolved
  stars, integrated light, clusters, number ratio of red-to-blue
  stars, H$\alpha$~\/morphology, stellar surface brightness fluctuations,
  presence of Wolf-Rayet stars)?
\end{enumerate}

This paper is organized as follows.  In \S 2, we describe the
observations, photometric analysis, and the extinction correction
method. CMDs and color-color diagrams of the 50~selected regions are
then used to provide stellar age estimates, as described in \S
3. Comparisons with other age estimates (i.e., integrated light and
star clusters) are also made.  In \S 4, we compare the stellar ages to
a variety of other parameters that correlate with age, including
red-to-blue star ratios, $H\alpha$ morphology, and surface brightness
fluctuations. Section 5 includes a discussion of how these comparisons
can be used as diagnostics, with special attention paid to the
question of what spatial variations can tell us about the star
formation history in M83, and on the question of how star clusters
might dissolve and populate the field.  A summary of our primary
results is provided in \S 6. In Appendix A, we investigate the
locations of sources of Wolf-Rayet stars in our M83 field and discuss
the correlation with the positions of young regions. In Appendix B, we
describe how the spatial resolution affects our measured colors and
magnitudes, and our age estimates of stars in our M83 field.

\section{HST WFC3 Observations}
\subsection{The WFC3/UVIS Data}
The data were obtained in seven broad- and seven narrow-band filters
in the WFC3 UVIS and IR channels. Details are described in
\citet{dopita10} and \citet{chandar10}. In the current paper we use
four broad-band images. The filters and exposure times are F336W (1890
sec), F438W (1880 sec), F555W (1203 sec), and F814W (1203
sec). Although we do not make transformations to the
Johnson-Kron-Cousins $UBVI$\/ system, we will refer to these filters as
$U$, $B$, $V$, and $I$\/ for convenience. Three exposures were taken
in each filter at different dithered positions to remove cosmic rays,
the intrachip gap, and to partially compensate for the undersampled
point-spread function (PSF) of the WFC3/UVIS channel.
The raw data were processed using the {\tt MULTIDRIZZLE} software
\citep{koekemoer02} with an effective pixel size of 0$\farcs$0396,
which corresponds to 0.885 pc per pixel at the distance of 4.61\,Mpc
\citep{saha06}. The resulting multidrizzled images are combined,
cosmic-ray removed, and distortion-corrected.  A color composite of
the F336W, F555W, F814W (broad bands) and F502N, F657N (narrow bands)
images is shown in Figure \ref{fig:m83_reg50}. It covers the nuclear
region of M83, part of its eastern spiral arm, and the inter-arm
region. Stars in the cores of compact star clusters are not resolved
at this resolution, but the brighter stars in the outskirts of the
star clusters and in the field are
generally resolved into individual stars. Details of the effects of
spatial resolution are discussed in Appendix B.  Since we are
interested in spatial variations of stellar ages in M83, we selected
50~regions in the spiral arm and inter-arm areas.
Boxes outlined in blue (very young -- age\,$\lesssim$\,10\,Myr, see \S
3), yellow (young -- 10\,$\lesssim$\,age\,$\lesssim$\,20\,Myr), and red
(intermediate-aged -- age\,$\gtrsim$\,20\,Myr) in Figure
\ref{fig:m83_reg50} show the 50~regions selected for detailed study in
this paper.

\subsection{Photometry and Artificial Star Tests}
Photometric analysis of the WFC3 M83 data was performed on the $U$,
$B$, $V$, and $I$\/ images using the {\tt DoPHOT} package
\citep{schechter93} with modifications made by A.~Saha.  Additional
routines to derive parameters for the kurtosis of the analytic PSFs
used by {\tt DoPHOT}, and for post-processing {\tt DoPHOT} output to
obtain calibrated aperture-corrected photometry, were performed using
customized {\tt IDL} code written by Saha. A more detailed description
of these procedures can be found in \citet{saha10}. The kurtosis terms
were derived by fitting the functional form of the {\tt DoPHOT}
analytic PSF to 20 relatively bright and isolated stars. The remaining
shape parameters ($\sigma_{x}$, $\sigma_{y}$, and $\sigma_{xy}$) are
dynamically optimized within {\tt DoPHOT}.

The difference between the shape parameters for an individual object
and those for a typical star was calculated and used to classify the
object as a star, a galaxy, or a double-star. The shape parameters
were determined separately for each image. The numbers of objects
classified as stars are approximately 20,000 in $U$\,and $B$, 17,000
in $V$, and 15,000 in $I$\,band images, respectively. The calculated
Full Width at Half Maximum (FWHMs) are $U$\,=\,0$\farcs$096 (2.42
pixel), $B$\,=\,0$\farcs$093 (2.34 pixel), $V$\,=\,0$\farcs$091 (2.29
pixel), and $I$\,=\,0$\farcs$113 (2.85 pixel). All magnitudes are in
the WFC3-UVIS VEGAMAG magnitude system, calculated using equation (4)
in \citet{sirianni05} and the latest zeropoint magnitudes:
$U$\,=\,23.46, $B$\,=\,24.98, $V$\,=\,25.81, and $I$\,=\,24.67 mag,
provided by STScI at the $HST$/WFC3
website\footnote{http://www.stsci.edu/hst/wfc3/}.

A multi-wavelength $UBVI$\/ catalog was then constructed by matching
the individual objects in each catalog between these filters.

Interestingly, we found that the extreme image crowding in these
images, combined with the excellent panchromatic sensitivity and
unprecedented spatial resolution provided by the newly installed WFC3,
introduced a new challenge during this matching step, with large
numbers of ``pseudo'' matches occurring if too-large a match radius
was used. We discuss the effects of spatial resolution in Appendix B.

Figure \ref{fig:pseudo} demonstrates this problem by showing the
$F555W$ and color composite image cut-outs near Regions \#\,26 and
\#\,27. In the middle panel of Figure \ref{fig:pseudo}, stars circled
in red are objects with $(V\!-\!I)$\/ colors that are very red
($(V-I)>1.2$~mag), but have $(U\!-\!B)$\/ values that are fairly blue
($-0.4<(U-B)<-1.7$~mag). These can be seen as the spray of points in
the upper right parts of the top right panels of Figures
\ref{fig:allstar_no} and \ref{fig:allstar_cor}, which will be
discussed below. As shown by the postage stamp images in Figure
\ref{fig:pseudo}, about half of these are likely to be blue stars with
very high values of reddening (i.e., the bottom panel and the small
postage stamps on the right show that they are in dusty regions),
while the other half are very close superpositions of at least one red
and one blue star, which we will call ``pseudo'' matches (i.e., the
panels on the left show a strong blue and red gradient across many of
the objects).

We found that using a very small matching radius of 0.5 pixels was
needed to minimize ``pseudo'' matches in our catalog. Earlier attempts
using a matching radius of 3 pixels resulted in $\sim$10~\% pseudo
matches.  Accurate matching also requires precise geometric distortion
corrections in all filters.  However, even when these conditions are
met, there is a finite chance that two different stars will fall
within the same aperture. This is most easily seen by blinking the $U$-
and $I$-band images. While the single stars stay in the same position,
the pseudo match stars move slightly, showing they are two
different stars with different colors.

The final stellar catalog contains $\sim$15,000 objects. We find that
our procedure results in only a few percent of pseudo matches, as will
be discussed in \S 2.4 (i.e., the upper right panel of Figure
\ref{fig:allstar_cor}).

To measure the photometric completeness, we performed artificial star
tests with the same detection and photometry procedure as applied to
the actual stars. We inserted 1000~artificial stars with Gaussian PSFs
and FWHMs appropriate for these stars at random positions into all
four images. The magnitude of the inserted stars varied from 22 to 28
mag in steps of 0.25 mag.  The 50\% completeness levels at 5~$\sigma$
detection thresholds for a typical region are reached at recovery
magnitudes of approximately
24.3, 25.1, 25.3, and 24.8 mag for the $U$, $B$, $V$, and $I$\/
images, respectively. These limits can vary by about a magnitude,
depending on the brightness of the background in a given region.  The
exception is Region \#\,48, which includes the nucleus. The completeness
thresholds are two or more magnitudes brighter in this region due to
the very high background. This region has therefore been excluded from
the rest of the discussion, but is included in Table \ref{tab:50reg}
for reference.

\subsection{CMDs and Color-color Diagrams in M83}
The top panels of Figure \ref{fig:allstar_no} show the $I$~vs. ($V-I$)
CMD ({\it left}) and the $(U\!-\!B)$\, vs. $(V\!-\!I)$\, color-color diagram ({\it
  right}) of resolved stars extracted from the cross-matched catalog.
The CMD shows the presence of young main-sequence (MS) stars,
transition or He-burning ``blue loop'' stars, red giant stars
(hydrogen shell-burning and hydrogen$+$helium shell-burning stars),
and a relatively small number of low-mass red-giant branch (RGB)
stars.  This is because the tip of the low-mass RGB stars is at $I$\,=
24.7 mag \citep{karach07}, which is roughly the same as our
completeness threshold. The ages of these stars range from $\approx$ 1
to $\approx$ 100 Myr. The Padova isochrones \citep{marigo08} ranging
in age from log~$\tau$ (age/yr) = 6.55 (3.5\,Myr) to log~$\tau$
(age/yr) = 8.0 (100 Myr) are included in Figure \ref{fig:allstar_no}
for reference.  We note that the younger isochrones (e.g., 1\,Myr) fall
nearly on top of the 3.5~\,Myr isochrone, and hence are not included in
the diagram.

The arrows in both panels show $A_{V}\!=\!1$~reddening vectors for M83
using the $R_{V}=3.1$ extinction curve of
\citet{cardelli89}. Corrections have been made for foreground Galactic
extinction \citep{schlegel98} using $A_{F336W}=0.361$,
$A_{F438W}=0.290$, $A_{F555W}=0.229$, and $A_{F814W}=0.133$ mag,
respectively.\footnote{NASA/IPAC Extragalactic Database (NED):
  http://ned.ipac.caltech.edu/} In Figure \ref{fig:allstar_no}, we
only plot stars with photometric errors less than 0.25 mag in the
filters used for each diagram. The numbers of objects plotted in the
CMD and the color-color diagram are about 12,000 and 8,500 stars,
respectively. Since approximately 30\%~of the stars are not detected
in one or more of the $U$, $B$, or $I$\/ filters, due to either
reddening or intrinsically red or blue colors, fewer stars are plotted
in the color-color diagram.

We adopt a value of 1.5 times solar metallicity (Z=0.03), the highest
metallicity isochrones available from the Padova database, for M83
based on
\citet{bresolin02}. A test using solar metallicity (Z=0.019)
isochrones showed that the ages for a typical region would be
$\approx$ 2 Myr older if the lower metallicity is adopted.
This agrees with the study by \citet{larsen11}, who found that the
simulated CMDs of young massive clusters in M83 with solar and
super-solar metallicity isochrones would not look much different.
Theoretical isochrones calculated for the WFC3 filters and Z=0.03 (1.5
Z$_{\sun}$) from the Padova database\footnote{CMD version 2.3;
  http://stev.oapd.inaf.it/cgi-bin/cmd} \citep{marigo08} are overlaid
on both panels in Figure \ref{fig:allstar_no}.  The dashed line in
cyan shows the 50\%~completeness level in $I$\/ and $(V\!-\!I)$, using the
completeness threshold numbers from the artificial star test in \S
2.2.

The bottom panels of Figures \ref{fig:allstar_no} and
\ref{fig:allstar_cor} show the histograms of ages and masses for the
same stars plotted in the CMD and color-color diagram. The ages and
masses of stars were estimated by finding the closest match between
the $I$\/ and $(V\!-\!I)$\/ values for each star, using a fine mesh of the
stellar isochrones plotted in the CMD and color-color diagram in
Figures \ref{fig:allstar_no} and \ref{fig:allstar_cor}. More details
about the stellar age-dating from the CMDs and the stellar isochrones
are given in \S 3.1.

\subsection{Extinction Corrections}
Since M83 has an intricate structure of dust lanes associated with
active star forming regions in the spiral arms and thin layers of dust
in the inter-arm area in Figure \ref{fig:m83_reg50}, we cannot use a
single value of internal extinction and apply it to correct for the
extinction of all stars in a given region.  In the color-color diagram
in Figure \ref{fig:allstar_no}, we notice that while the bluest stars
match the model isochrones quite well after we correct for Galactic
foreground extinction, the majority of stars are found redward of the
models, implying a typical extinction of $A_{V}\!\simeq\!0.5$~mag (for
the densest part of the data ``swarm''). As described below, we can
determine the reddening of these stars {\it if} we assume that they
are intrinsically blue stars that belong on the Padova models (i.e.,
with $(V-I) \simeq -0.3$~mag).  A visual inspection supports this
interpretation. Stars with observed values of $(V-I) \approx -0.3$~mag
are found in areas with no obvious dust features surrounding them
while stars with $(V-I) > 0.5$~mag are near dust filaments.

Our basic approach to correct the colors (and luminosities) of
individual stars for the effects of extinction is to backtrack the
position along the reddening vector for each data point, until we hit
the stellar isochrones in the color-color diagram. What makes this
method work is the fact that all the isochrones are nearly on top of
each other in the range $(U\!-\!B)\!<\!0.0$~mag.  The way we apply this in
practice is to match the observed and predicted values of the
reddening free $Q$ parameter defined by:
\begin{equation}
Q_{UBVI} = (U-B) - \frac{E(U-B)}{E(V-I)} \times (V-I)
\end{equation}
\citep{ga81}. Using observed values of $UBVI$, we
compute the observed $Q_{UBVI}$ values by using the standard slope of
$E(U\!-\!B)/E(V\!-\!I)=0.58$ \citep{ga81,whitmore99}, which
corresponds to $R_V = 3.1$.
The predicted $Q$ values are calculated using the 1.5~Z$_{\odot}$ Padova
isochrones.
A complication is that we cannot simply use the matched $Q$ values to
determine corrected values of $(U\!-\!B)$\/ and $(V\!-\!I)$, since they would,
by definition, fall precisely on the isochrones (i.e., we would be
using circular reasoning). Instead, we solve for the extinction in
$(U\!-\!I)$, which is partly independent and uses the longest wavelength
baseline. We then calculate the extinction values in $UBVI$\/ and the
color excess values in $(U\!-\!B)$\/ and $(V\!-\!I)$.
The averages of the corrected internal extinction and color excess
values by our star-by-star correction method are $A_{F336W}=$ 0.696,
$A_{F438W}=$ 0.559, $A_{F555W}=$ 0.441, $A_{F814W}=$ 0.256 mag, and
$E(U\!-\!B)=$ 0.137 and $E(V\!-\!I)=$ 0.185 mag. This is similar to
the average internal extinction ($A_{F555W}=0.425$ mag) estimated from
the cluster SED fitting by \citet{bastian11}. However, our results
differ from the average extinction ($A_{V}=0.671$\,mag) of 45 clusters
in the nucleus ($\sim20\arcsec$ in diameter) of M83, determined from
$H\alpha/H\beta$ ratios by \citet{harris01}. This is consistent with
the finding of larger extinction in the nuclear region by
\citet{chandar10}.

In the color-color diagram (top right panel) of Figure
\ref{fig:allstar_no}, we note that while most of the data points are
consistent with the standard reddening vector, the stars located in
the green triangle appear to follow a flatter reddening
vector. Whether this is actually due to a different reddening law in
M83 (e.g., as has been suggested for heavily extincted regions such as
\#\,4, \#\,12, and \#\,20), or is some sort of artifact (e.g. due to
photometric uncertainties, unresolved star clusters, or the ``pseudo''
matching problem discussed above) is difficult to determine from our
present dataset.  However, for our specific needs the precise answer
to this question is not critical.  Our approach will be to correct the
data points in the triangle back to a position near the top of the
isochrones, as shown by the intersection of the red dotted line and
the isochrones in Figure \ref{fig:allstar_cor}. This is equivalent to
using a range in $R_{V}$ between 3.1 and 5.7 (the value represented by
the red dotted line).

The upper panels of Figure \ref{fig:allstar_cor} show the CMD and
color-color diagram corrected for the internal extinction in M83. The
arrows in both panels are the reddening vectors.  We do not apply an
extinction correction for stars with $(V-I)\geq1.2$~mag, or above an
extrapolation of the flatter extinction vector from the bluest
possible isochrones (see the red dotted lines in the upper right panel
of Figure \ref{fig:allstar_cor}), since we believe that the colors of
many of these sources are incorrect (i.e., roughly half are likely to
be pseudo matches, as discussed in \S 2.2 and shown in Figure
\ref{fig:pseudo}).  We note that only about 4\,\% of the stars fall in
this part of the diagram. These stars are removed from the subsequent
analysis. If we include these stars, our age estimates typically
increase by about 1\,Myr.

Also, stars below the blue dotted line in Figure \ref{fig:allstar_cor}
are not corrected for extinction, since these are main sequence
turnoff stars (i.e., blue loop or transition stars in general), and
hence, do not have unique $UBVI$ colors for the stars along a given
reddening line, unlike the main sequence stars with $(U\!-\!B)\!<\!0.0$~mag.

Based on the extinction corrected CMD in Figure
\ref{fig:allstar_cor}, we note that: (1) the corrected data now shows
a relatively narrow distribution of points along the left side of the
CMD in agreement with ages of $\sim$3 Myr, and
(2) while the data now aligns with the model isochrones much better,
there is still a fair amount of scatter (especially for $I$~$<$~24),
primarily due to observational uncertainties from the fainter stars.

The bottom panels of Figure \ref{fig:allstar_cor} show the distribution
of ages and masses of stars after we apply our extinction correction
technique.
Based on the automatic method described in \S 3.1, we note that the
luminosity weighted mean ages determined from the corrected colors and
luminosities are significantly younger
($\sim$14~Myr; Figure \ref{fig:allstar_cor}) than the mean ages
determined from the uncorrected colors and luminosities ($\sim$27~Myr;
Figure \ref{fig:allstar_no}), as we expected.

\section {Age-Dating Populations in M83}

In this section we discuss several different methods of age-dating
stellar populations in M83, using both individual stars and star
clusters, and then intercompare the results.
We do not necessarily expect field star and cluster ages to agree,
since the dissolution of clusters may bias the observed cluster
population towards younger ages than the surrounding field stars.

\subsection{Age-Dating the Resolved Stellar Population using Color Magnitude Diagrams}
As shown in Figure \ref{fig:m83_reg50}, we selected 50~regions that
cover spiral arm and inter-arm areas in order to study the recent
star-formation history of M83.
Figures \ref{fig:reg12_7} and \ref{fig:reg29_2} show
cutouts of Regions \#\,12, \#\,7, \#\,29, and \#\,2.\footnote{The
  color-magnitude diagrams and color-color diagrams of all 50 regions
  (similar to Figures 5 and 6) are available on the WFC3 Early Release
  Science Archive website (http://archive.stsci.edu/prepds/wfc3ers/).}
These figures include color-color diagrams of the stars in these
regions, as well as CMDs that are uncorrected (upper) and corrected
(lower) for extinction.  These four regions highlight the range in age
of the dominant stellar population, from very young to intermediate
ages: i.e., Region \#\,12 with very strong H$\alpha$~ emission superposed on
the cluster stars; Region \#\,7 with a large bubble of H$\alpha$~ emission
surrounding the stars and clusters; Region \#\,29 with no H$\alpha$~
emission, but large numbers of bright red and blue stars; Region \#\,2
with no H$\alpha$~ emission and fainter stars.

A cursory glance at the CMDs of these four regions (Figures
\ref{fig:reg12_7} and \ref{fig:reg29_2}) show clear differences. The
primary differences are, as predicted by the isochrones, that younger
regions contain: (1) bluer main sequence stars; (2) larger numbers of
upper main sequence stars; and (3) larger ratios of blue to red
stars.  Our regions do not, however, contain only stars of a single
age.  Even these four regions, which were chosen to include a single
dominant stellar population, contain a mix of young and old stars.  In
this paper, we are primarily interested in the ages of the bright
young stars in these regions, which dominate CMDs in
luminosity-limited samples.

In practice, this focus on the younger population is carried out by
imposing a magnitude cutoff of $M_I=-5.5$~mag (i.e., $I=22.82$
mag which corresponds to the age cutoff of $\sim$60\,Myr in the CMD)
for the stars used to estimate ages (i.e., the magenta dashed line in
Figure \ref{fig:allstar_cor}).  This luminosity limit allows us to
stay above the completeness limit for all but the very reddest stars
(see Figure \ref{fig:allstar_no}), while also focusing on evolved
stars and those on the upper main sequence, which are most sensitive
to the age of a stellar population.  Our primary goal is to obtain
reliable ``relative'' estimates for the 50~regions (rather than
absolute ages), and our magnitude limit is sufficiently deep to
accomplish this goal.

We compare the extinction corrected colors and magnitudes of stars
with the Padova stellar isochrones in two ways to estimate the age of
the dominant stellar population in each region.  First, two of the
authors (HK and BCW) independently estimated ages based on a visual
comparison of the CMDs and isochrones. We focused on features such as
the color of the main sequence stars, the number of stars in the upper
main sequence, and the number ratio of blue to red stars.  The left panel
of Figure \ref{fig:cmdages} shows that the independent, visually
determined age estimates are in good agreement with a 7.9~$\sigma$
(slope/uncertainty of the best linear fit) correlation and a slope
within 1~$\sigma$ of the unity value.
The average ages of these two independent (manual) estimates are listed
in column $Age_{CMD}$$_{man}$ in Table \ref{tab:50reg}.

Next, we estimated the ages automatically, finding the closest match
in both age and mass for each star by comparing the extinction
corrected $I$~magnitude and $(V\!-\!I)$~color with a fine mesh of stellar
isochrones generated from the Padova models (log$\tau$ (age/yr)
ranging from 6.05 to 8.35 in steps of 0.05).  We then calculate a
luminosity-weighted age from the distribution of individual stellar
ages in each region. The result of the automatic age estimate is
listed in column $Age_{CMD}$$_{auto}$ in Table \ref{tab:50reg}.

To assess the uncertainties in our age and mass determinations for
each star, we ran a test for four different cases by adding and
subtracting the photometric errors in the $(V\!-\!I)$~color and $I$-band
magnitude. This moves each object on the CMD to the right
($+(V\!-\!I)_{error}$), left ($-(V\!-\!I)_{error}$), down ($+m_{I_{error}}$),
and up ($-m_{I_{error}}$). We then estimated the ages for these four
cases in the same way as we did to get $Age_{CMD}$$_{auto}$. The
results of this test are summarized in column
$\Delta$$Age_{CMD}$$_{auto}$ in Table \ref{tab:50reg}.
As shown in the $\Delta$$Age_{CMD}$$_{auto}$ column in Table
\ref{tab:50reg}, the variation in the age estimates are typically only
a few Myr (i.e., $\lesssim$10\%).

Figure \ref{fig:age_mass} shows the age and mass histograms of
individual stars in the four regions shown in Figure \ref{fig:reg12_7}
and \ref{fig:reg29_2}.  Luminosity-weighted mean values of the stellar
ages are shown in the upper right of the left panels. The age sequence
of these regions is as expected, from the youngest in Region \#\,12 to
the oldest in Region \#\,2.

One unexpected result was that while Region \#\,2 was selected to be
representative of an older field population, it also contains a fair
number of bright young stars. This suggests that young individual
stars may form in the field in low numbers. Another possibility is
that many of these are ``runaway'' stars, i.e., young stars that have
been dynamically ejected from their birth-sites within young star
clusters due to interactions with other stars.  \citet{whitmore11}
also suggested this as a possible explanation for isolated HII regions
ionized by a single, massive star, i.e., ``Single-Star'' HII (SSHII)
regions (see Figure 3 from Whitmore 2011).  Either
explanation would be interesting, and should be investigated in a
future study.  Figure \ref{fig:reg9} shows a similar ``field'' Region
\#\,9 with primarily old red
giant stars (barely discernible), but which also includes a small
population of isolated young blue stars and four compact clusters
ranging in age from intermediate to old.

The right panel of Figure \ref{fig:cmdages} compares the
$Age_{CMD}$$_{man}$ and $Age_{CMD}$$_{auto}$ estimates for the
different regions.
A unity line is included for comparison, and shows that there is good
agreement (7.6~$\sigma$ correlation) between the two methods which
varies from the unity line by only 1~$\sigma$. We use the objective
luminosity-weighted age estimates ($Age_{CMD}$$_{auto}$) as our
preferred method for estimating ages of the resolved stellar
populations in the remainder of this paper.

\subsection{Comparison With Previous Age Estimates}

\citet{larsen11} recently estimated the ages of two star clusters in
M83 by comparing photometry of individual stars in the outskirts of
the clusters with isochrones.  These clusters are within Regions \#\,3
and \#\,11 here.  \citet{larsen11} used a more sophisticated method,
comparing observed and predicted magnitude distributions for red and
blue stars using Hess diagrams.  They did not however, correct the
magnitudes or colors of their stars for the effects of spatial
variation of extinction, but since these are intermediate age clusters
with minimal internal extinction, this is not a large effect in these
particular cases.  They find log $\tau$ of 7.44 (27.5~Myr) and 7.45
(28.2~Myr) for the clusters in Regions \#\,3 and \#\,11.  We find
similar ages of 21.9~Myr (Region \#\,3) and 26.7~Myr (Region \#\,11)
for the dominant luminous stellar populations, despite the fact that
our photometry includes a significantly larger area around each
cluster. Hence we find good agreement between our age estimates and
those of \citet{larsen11}.

Each method has its own strengths and weaknesses.  Our approach has
the advantage of correcting the photometry of individual stars for the
effects of extinction to improve our age estimates, but the
disadvantage that this requires imaging in more than three optical
bands with a crucial need for the $U$-band observation.
The \citet{larsen11} method does not correct for the effects of
extinction, but allows for a better determination of the star
formation history over a wider age range (i.e., out to $\sim$1\,Gyr).

\subsection{Comparison between \boldmath{$Age_{CMD}$$_{auto}$} and Compact Cluster Ages}

Much of our recent work has involved age-dating star clusters using
the integrated light ($UBVI$\/ and H$\alpha$~ emission) from the cluster and
model spectral energy distribution (SED) fitting. To compare the age
of star clusters in each region to the age determined from individual
stars in each region, we adopt the ages and luminosities of compact
clusters identified in our previous work \citep{chandar10}. Details of
the cluster age-dating method can be found in that paper.

We note that the regions sampled in this study are large (i.e.,
several hundred\,pc$^2$) and may include stars and star clusters
spanning a wide range of ages. This requires making the comparison
between the ages of field stars and star clusters with caution. Only
the largest star forming complexes within spiral galaxies have such
dimensions (e.g., the giant HII region NGC\,604 in M33;
Freedman et al. 2001). More typical regions are much smaller, and hence
would not dominate the entire field. This will tend to weaken our
correlations, especially when comparing resolved stellar ages with
cluster ages.

Using the ages of star clusters determined from the SED fitting, we
compute the luminosity-weighted mean age of each region, as we did for
stars in the same region. The ages of 50~regions are listed in column
$Age_{Cl}$ in Table \ref{tab:50reg}. The upper panel of Figure
\ref{fig:sedages} shows a comparison between the luminosity-weighted
mean age ($Age_{Cl}$) of the compact clusters in a given region and
our stellar age ($Age_{CMD}$$_{auto}$) estimates based on the CMDs of
all the resolved stars in the region (as discussed in \S 3.1). The
correlation between these two ages is fair (5.4~$\sigma$
correlation). We note that while the midpoints for the two methods are
similar, the slope is steeper, as discussed in more details in the
next section.

\subsection{Comparison between \boldmath{$Age_{CMD}$$_{auto}$} and Age Estimates from Integrated Light from the Entire Regions}

We obtain an independent estimate of the age of each region by
measuring the colors of the entire region, and perform a simple SED
fit, in the same way as we did for star clusters
\citep[i.e.,][]{chandar10}.  The lower panel of Figure
\ref{fig:sedages} shows a comparison between our age estimates for the
resolved stellar populations using CMDs ($Age_{CMD}$$_{auto}$) and
from the integrated light ($Age_{Reg}$) in each region.

While there is a fair correlation (4.6~$\sigma$) between the CMD and
integrated photometric age estimates, there is also a fair amount of
scatter and an apparent gap in the region ages in the range 6.8 $<$
log (age/yr) $<$ 7.2. This gap is similar to the well known artifact for
cluster age estimates which is due to the looping of predicted cluster
colors in this age range (see Chandar et al. 2010).  We also note that
the slope of the relation is steeper than the unity vector, with
integrated light ages ranging to much lower ages than the CMD
ages. This is similar to the comparison with $Age_{Cl}$, as discussed
above. This is probably caused by a variety of effects including: 1)
the integrated age estimates take into account H$\alpha$~ emission (which is
very sensitive to massive young stars) while the CMD estimates do not;
2) the 1~Myr isochrones are essentially on top of the 3~Myr
isochrones, making it rare that the minimum age ever gets selected by
the software that does the matching with the isochrones; and 3) the
adoption of a $M_V = -5.5$~mag cutoff removes all stars with ages
$\gtrsim$100 Myr from the $Age_{CMD}$$_{auto}$ estimates,
while the light from older stars is still included in the integrated
light used for the $Age_{Reg}$ estimate (hence, the
$Age_{CMD}$$_{auto}$ estimate can never be $\gtrsim$50~Myr, while the
$Age_{Reg}$ can be older).

This difference in slope in Figure \ref{fig:sedages} highlights the
fact that each age-dating method has its own idiosyncrasies. By
comparing a number of different methods, we begin to understand these
artifacts better, and learn how large the true systematic uncertainties can
be.

\section{Comparison between \boldmath{$Age_{CMD}$$_{auto}$} and Other Parameters that Correlate with Age}

We are now ready to compare our age estimates for the resolved stellar
components in 50~regions to other observables that correlate with
age. These include: (1) number ratio of red-to-blue stas
\citep[e.g.,][]{larsen11}; (2) morphological categories
\citep{whitmore11};
and (3) stellar surface brightness fluctuations \citep{whitmore11}.

\subsection{Comparison between \boldmath{$Age_{CMD}$$_{auto}$} and Number Ratio of Red-to-Blue Stars}
One property that is expected to correlate with age is the number
ratio of red-to-blue stars. At very young age, all stars are blue. As
the population ages, the number of red giant stars (H-shell burning
and H$+$He-shell burning stars) increases. This effect can be seen by
noting that nearly all of the stars in Region \#\,12 (Figure
\ref{fig:reg12_7}) are blue, while there are large numbers of red
stars in Regions \#\,29 and \#\,2 (Figure \ref{fig:reg29_2}).

The number ratio of red-to-blue stars (column Red-to-Blue Ratio in
Table \ref{tab:50reg}) is calculated
by using a criteria that $(V\!-\!I)$\/ be redder than 0.8 mag for red
stars. The top panel of Figure \ref{fig:rb_ha_rms} shows a fairly good
correlation (7.2~$\sigma$) between $Age_{CMD}$$_{auto}$ and
the Red-to-Blue Ratio.

\subsection{Comparison between \boldmath{$Age_{CMD}$$_{auto}$} and Morphological Category}
The middle panel of Figure \ref{fig:rb_ha_rms} shows the correlation
between our automatic CMD age estimates and the morphological
classification for a given region, as defined in \citet{whitmore11}.
Briefly, regions with H$\alpha$~ emission superposed on top of the stellar
component are type 3 (emerging star clusters), regions with small H$\alpha$~
bubbles are category 4a (very young), regions with large H$\alpha$~ bubbles
are category 4b (young) , and regions with no H$\alpha$~ bubbles are category
5 (intermediate age). Based on this criteria, the regions displayed in
Figures \ref{fig:reg12_7} and \ref{fig:reg29_2} are classified as
categories 4a (Region \#\,12), 4b (Region \#\,7), 5a (Region \#\,29), and
5a/5b (Region \#\,2), respectively.
Each region was classified independently by two authors (HK and BCW).
The mean of the two determinations is used in what follows, and is
listed in column ``$H\alpha$ Morphology'' of Table \ref{tab:50reg}.

The middle panel of Figure \ref{fig:rb_ha_rms} shows that stellar CMD
age estimates ($Age_{CMD}$$_{auto}$) and morphological categories
(H$\alpha$~\/morphology) show a fair correlation (5.8~$\sigma$), but
not as good as the strong correlations (9~$\sigma$ for log~$\tau<7$ and
5~$\sigma$ for log~$\tau>7$) found with ages for compact clusters in
\citet{whitmore11}.  This is probably due to the fact that most of the
regions are not dominated by a single age stellar population, making the
morphological classification for an entire region problematic (i.e.,
there is strong H$\alpha$~ emission in part of the region but none in other
parts).

\subsection{Comparison between \boldmath{$Age_{CMD}$$_{auto}$} and Stellar Surface Brightness Fluctuations}
In \citet{whitmore11}, we developed a method to age-date star clusters
based on an observed relation between pixel-to-pixel flux variations
(RMS) within star clusters and their ages. This method relies on the
fact that the young clusters with bright stars have higher
pixel-to-pixel flux variations, while the old clusters with smoother
appearance have small variations.  This is because the brightest stars
in a 100~Myr population have $M_{I} \approx -3$ mag, which is below
our 50\,\% photometric completeness limit ($M_I=-3.6$\,mag) and well
below our magnitude cutoff of $M_{I}=-5.5$\,mag used in our stellar
age-dating method described in \S\,3.1.

The bottom panel of Figure \ref{fig:rb_ha_rms} shows the correlation
between the resolved stellar ages ($Age_{CMD}$$_{auto}$) and the
surface brightness fluctuations (Pixel-to-Pixel RMS) measured in the
50 selected regions. We find little or no correlation (1.1~$\sigma$)
with a relatively large amount of scatter, especially for the younger
clusters. Part of this scatter may be caused by the two-valued nature
of the effect as discussed in \citet{whitmore11}, with a maximum value
of the surface brightness fluctuations around 10~Myr, and lower values
for both smaller and larger ages. Another reason for the scatter is
the fact that many of the regions have mixed populations, rather than
single age populations.

\section{Insights into the Star Formation History in M83 Based on Spatial Variations for 50~regions}
We first examine how the ages for the 50~regions are distributed in
Figure \ref{fig:m83_reg50}.  The boxes in this figure are color-coded
as follows: blue (very young with ages less than about 10 Myr), yellow
(young with ages between about 10 and 20 Myr), and red (intermediate
aged with ages greater than about 20 Myr). As expected, we find that
the very young regions are primarily associated with the spiral arms,
the young ryoungs tend to be ``downstream'' from the spiral structure
(i.e., on the clockwise side away from the dust lanes), while the
intermediate aged regions are found on both sides of the spiral arms
in the interarm regions.

A similar, more detailed analysis is being done by Chandar and Chien
using the cluster ages in M51 (2012, in preparation) and M83 (2012, in
preparation). They compare the age gradients they find with models
developed by \citet{dobbs10}, assuming a variety of different
triggering mechanisms (i.e., spiral arms, bars, stochasticity, and
tidal disturbances).

We can perform a similar experiment here, since we have age estimates
for each individual star.  The three panels in Figure
\ref{fig:pellerin} show the distribution of stars with ages in the
ranges 1--10, 15--35, and 40--100 Myr, respectively. The top panel is
the youngest group of stars, clearly showing that the stars in these
regions are mostly distributed along the active star-forming region
(i.e., associated with strong H$\alpha$~ emission) in the spiral arms.
The stars in the middle panel tend to be found slightly downstream of
the spiral arms, while the older stars are still farther out in the
inter-arm regions, as expected.

These diagrams can also be used to examine whether there is evidence
that most of the young stars form in clusters and clustered regions
and then dissolve into the field. \citet{pellerin07} made a similar
set of diagrams for the galaxy NGC\,1313, which supported this
interpretation.  We find that the young star samples show strong
clustering, while the older star samples are progressively more
uniform.  Hence, these distribution maps of stars in the different age
group support the idea that the stars form in clusters in spiral arms,
and then most of the clusters dissolve, populating the field with
stars \citep{lada03}. Chandar et al. (2012, in preparation) will
examine this subject more quantitatively in the future.

\section{Summary}
Color-magnitude diagrams and color-color diagrams of resolved stars
from the multi-band $HST$/WFC3 ERS observations of M83 have been used to
measure the ages of stellar populations in 50~regions of this well known
face-on
spiral galaxy.  The diagrams show the presence of multiple stellar
features, including recently formed MS, He-burning blue-loop stars,
and shell-burning red giant stars.  Comparisons between our stellar age
estimates, and a wide variety of other age estimators, allow us to
investigate a number of interesting topics.  The primary new results
from this study are as follows:

(1) An innovative new technique using a combination of CMD and
color-color diagrams has been developed in order to correct for the
extinction towards each individual star and to age-date the stellar
population.  The mean extinction values for the 50~regions studied in
this galaxy are 0.696 ($A_{F336W}$), 0.559 ($A_{F438W}$), 0.441
($A_{F555W}$), 0.256 ($A_{F814W}$) mag, respectively.

(2) The various age estimators (stellar, integrated light, clusters
within the region) show fair correlations (i.e., between 4.6~$\sigma$
and 5.4~$\sigma$).  This is expected, since many of the regions have a
mixture of different-aged populations within them, and each technique
uses light from a different subset of the stars (e.g., the stellar age
estimates use only bright stars, while integrated light includes all
the light). A comparison with previous age estimates by
\citet{larsen11} shows a good agreement (see \S 3.2 for details).

(3) Comparisons between the stellar ages and other parameters that are
known to correlate with age show a range from very good correlations
(e.g., with red-to-blue ratios: i.e., 7.2~$\sigma$) and fair
correlations (e.g., H$\alpha$~ morphology with 5.8~$\sigma$) to little or no
correlation
\citep[e.g., pixel-pixel RMS from][] {whitmore11}. This is expected
for reasons similar to the ones discussed above.

(4) The regions with ages younger than 10~Myr are generally
located along the active star-forming regions in the spiral arm.
The intermediate age stars tend to be found ``downstream'' (i.e., on
the opposite side from the dust lane) of the spiral arms, as expected
based on density wave models.  A more detailed comparison with models
with various other triggering mechanisms \citep[e.g., bars, density
  waves, tidal disturbance, and stochasticity; see][]{dobbs10} is
in process by Chandar and Chien for M51 and M83 (2012, in
preparation).

(5) As discussed in the Appendix A, the locations of Wolf-Rayet
sources from \citet{hadfield05} are in broad agreement with the age
estimates discussed in the current paper. The much better spatial
resolution from $HST$\/ shows that many of the Wolf-Rayet ``stars''
from ground-based observations are actually young star clusters.

(6) Effects of spatial resolution on the measured colors, magnitudes,
and the derived ages of stars in our M83 images are described in the
Appendix B. Based on a numerical experiment using a star cluster
NGC\,2108 in the LMC, we found that while individual stars can
occasionally show measurable differences in the colors and magnitudes,
the age estimates for entire regions are only slightly affected.\\

We thank Zolt Levay for making the color images used in Figure
\ref{fig:m83_reg50}. We also thank the anonymous referee for helpful
comments. This project is based on Early Release Science observations
made by the WFC3 Science Oversight Committee. We are grateful to the
Director of the Space Telescope Science Institute for awarding
Director's Discretionary time for this program. Support for program
\#\,11360 was provided by NASA through a grant from Space Telescope
Science Institute, which is operated by the Association of
Universities for Research in Astronomy, Inc., under NASA contract NAS
5-26555.

Facilities: $HST$\,(WFC3)

redpajama_set_name: RedPajamaArXiv


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